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Sökning: WFRF:(Bader Sebastian 1984 )

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1.
  • Adin, Veysi, et al. (författare)
  • Tiny Machine Learning for Damage Classification in Concrete Using Acoustic Emission Signals
  • 2023
  • Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
  • Konferensbidrag (refereegranskat)abstract
    • Acoustic emission (AE) is a widely used non-destructive test method in structural health monitoring applications to identify the damage type in the material. Usually, the analysis of the AE signal is done by using traditional parameter-based methods. Recently, machine learning methods showed promising results for the analysis of AE signals. However, these machine learning models are complex, slow, and consume significant amounts of energy. To address these limitations and to explore the trade-off between model complexity and the classification accuracy, this paper presents a lightweight artificial neural network model to classify damage types in concrete material using raw acoustic emission signals. The model consists of one hidden layer with four neurons and is trained on a public acoustic emission signal dataset. The created model is deployed to several microcontrollers and the performance of the model is evaluated and compared with a state-of-the-art machine learning model. The model achieves 98.4% accuracy on the test data with only 4019 parameters. In terms of evaluation metrics, the proposed tiny machine learning model outperforms previously proposed models 10 to 1000 times. The proposed model thus enables machine learning in real-time structural health monitoring applications. 
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2.
  • Adin, Veysi, et al. (författare)
  • Tiny Machine Learning for Real-Time Postural Stability Analysis
  • 2023
  • Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings. - 9798350323078
  • Konferensbidrag (refereegranskat)abstract
    • Postural sway is a critical measure for evaluating postural control, and its analysis plays a vital role in preventing falls among the elderly. Typically, physiotherapists assess an individual's postural control using tests such as the Berg Balance Scale, Tinetti Test, and time up-and-go test. Sensor-based analysis is available based on devices such as force plates or inertial measurement units. Recently, machine learning methods have demonstrated promising results in the sensor-based analysis of postural control. However, these models are often complex, slow, and energy-intensive. To address these limitations, this study explores the design space of lightweight machine learning models deployable to microcontrollers to assess postural stability. We developed an artificial neural network (ANN) model and compare its performance to that of random forests, gaussian naive bayes, and extra tree classifiers. The models are trained using a sway dataset with varying input sizes and signal-to-noise ratios. The dataset comprises two feature vectors extracted from raw accelerometer data. The developed models are deployed to an ARM Cortex M4-based microcontroller, and their performance is evaluated and compared. We show that the ANN model has 99.03% accuracy, higher noise immunity, and the model performs better with a window size of one second with 590.96 us inference time. 
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3.
  • Aranda, Jesus Javier Lechuga, et al. (författare)
  • A space-coiling resonator for improved energy harvesting in fluid power systems
  • 2019
  • Ingår i: Sensors and Actuators A-Physical. - Elsevier : Elsevier. - 0924-4247 .- 1873-3069. ; 291, s. 58-67
  • Tidskriftsartikel (refereegranskat)abstract
    • Pressure fluctuation energy harvesting devices are promising alternatives to power up wireless sensors in fluid power systems. In past studies, classical Helmholtz resonators have been used to enhance the energy harvesting capabilities of these harvesters. Nevertheless, for fluctuations with frequency components in the range of less than 1000 Hz, the design of compact resonators is difficult, mostly for their poor acoustic gain. This paper introduces a space-coiling resonator fabricated using 3D printing techniques. The proposed resonator can achieve a better acoustic gain bounded by a small bulk volume compared to a classic Helmholtz resonator, improving the energy harvesting capabilities of pressure fluctuation energy harvesters. The resonator is designed and evaluated using finite-element-method techniques and examined experimentally. Three space-coiling-resonators are designed, manufactured and compared to classic Helmholtz resonators for three frequencies: 280 Hz, 480 Hz and 920 Hz. This work displays the possibility of compact, high-performance pressure fluctuation energy harvesters and the advantages of the space-coiling printed resonators to enhance the harvesting performance.
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4.
  • Aranda, Jesus Javier Lechuga, et al. (författare)
  • An Apparatus For The Performance Estimation Of Pressure Fluctuation Energy Harvesters
  • 2018
  • Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 67:11, s. 2705-2713
  • Tidskriftsartikel (refereegranskat)abstract
    • Hydraulic pressure fluctuation energy harvesters are promising alternatives to power up wireless sensor nodes in hydraulic systems. The characterization of these harvesters under dynamic and band-limited pressure signals is imperative for the research and development of novel concepts. To generate and control these signals in a hydraulic medium, a versatile apparatus capable of reproducing pressure signals is proposed. In this paper, a comprehensive discussion of the design considerations for this apparatus and its performance is given. The suggested setup enables the investigation of devices tailored for the harvesting of energy in conventional hydraulic systems. To mimic these systems, static pressures can be tuned up to 300 bar, and the pressure amplitudes with a maximum of 28 Bar at 40 Hz and 0.5 bar at 1000 Hz can be generated. In addition, pressure signals found in commercial hydraulic systems can be reproduced with good accuracy. This apparatus proves to be an accessible, robust, and versatile experimental setup to create environments for the complete performance estimation of pressure fluctuation energy harvesters. 
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5.
  • Aranda, Jesus Javier Lechuga, et al. (författare)
  • Fluid coupling interfaces for hydraulic pressure energy harvesters
  • 2017
  • Ingår i: 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). - : IEEE. - 9781509059980 ; , s. 1556-1562
  • Konferensbidrag (refereegranskat)abstract
    • The need for wireless sensor networks that can run for long times without the need of battery replacement has risen the need for energy harvesters. Industrial environments have plenty of energy sources that can be harvested; pressure fluctuations are a high energy density source that can be harvested using piezoelectric devices. Present devices have introduced flat metallic plates as the main force transmission elements for hydraulic fluctuations energy harvesters. In this paper, we analyze the force transmission efficiency of flat plates when used as the primary fluid coupling interface in hydraulic energy harvesters. Previous work has been focused on the optimization of circuit matching and pressure ripple amplification. In this work, we offer a look into the efficiencies of flat plates in different configurations and pressure loads. The analysis shows that despite the reasonable force transmission efficiency of flat plates in low-pressure environments, the overall efficiency of hydraulic energy harvesters can be improved if instead of flat plates, conventional hydraulic actuators, such as piston cylinders, could be used. 
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6.
  • Aranda, Jesus Javier Lechuga, et al. (författare)
  • Force Transmission Interfaces for Pressure Fluctuation Energy Harvesters
  • 2018
  • Ingår i: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. - : IEEE. - 9781509066841 ; , s. 4230-4235
  • Konferensbidrag (refereegranskat)abstract
    • Wireless sensor nodes in state of the art fluid power systems used in monitoring and maintenance prediction demand long lasting power sources that do not rely on batteries. Energy harvesting is a promising technology that can provide the required energy to power wireless sensors. Pressure fluctuation energy harvesters can be employed in conventional hydraulic systems to convert the acoustic pressure fluctuation to electrical power. Present studies have explored the overall efficiency of these devices while experimentally describing losses in piezoelectric and circuit interfaces, nevertheless there is no study on the fluid to mechanical force transmission efficiency. In this paper we investigate the pressure to force transmission rate of two types of fluid to mechanical interfaces: a flat metal plate and a conventional hydraulic piston. The interfaces are investigated in conditions similar to those found in conventional hydraulic systems. The study shows that flat plate exhibit good force transmission for low pressure applications with a constant rate across frequencies, while exhibiting a decrease in force transmission at higher pressures. On the other hand the piston exhibit a more robust pressure design, with a constant force transmission rate at all pressures but with a dampening of force at higher frequencies. It is shown that small differences in force transmission ratios can have a considerable impact on the power generation.
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7.
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8.
  • Aranda, Jesus Javier Lechuga (författare)
  • Towards Self-Powered Devices Via Pressure Fluctuation Energy Harvesters
  • 2021
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • The growing interest in the Internet of Things has created a need for wireless sensing systems for industrial and consumer applications. In hydraulic systems, a widely used method of power transmission in industry, wireless condition monitoring can lead to reduced maintenance costs and increase the capacity for sensor deployment. A major problem with the adoption of wireless sensors is the battery dependence of current technologies. Energy harvesting from pressure fluctuations in hydraulic systems can serve as an alternative power supply and enable self-powered devices. Energy harvesting from pressure fluctuations is the process of converting small pressure fluctuations in hydraulic fluid into a regulated energy supply to power low power electronics. Previous studies have shown the feasibility of pressure fluctuation harvesting. However, for the development of self-powered sensor systems, the methods and techniques for converting pressure fluctuations into electrical energy should be further investigated.This thesis explores the methods, limitations, opportunities and trade-offs involved in the development of pressure fluctuation energy harvesters in the context of self-powered wireless devices. The focus is on exploring and characterizing the various mechanisms required to convert pressure fluctuations into electrical energy. In this work, an energy harvesting device consisting of a fluid-to-mechanical interface, an acoustic resonator, a piezoelectric stack, and an interface circuit is proposed and evaluated. Simulations and experimental analysis were used to analyse these different components for excitation relevant to hydraulic motors.The results of this work provide new insights into the development of power supplies for self-powered sensors for hydraulic systems using pressure fluctuation energy harvesters. It is shown that with the introduction of the space coiling resonator for pressure fluctuation amplification and a detailed analysis of the fluid interface and power conditioning circuits, the understanding of the design and optimization of efficient pressure fluctuation energy harvesters is further advanced.
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9.
  • Aranda, Jesus Javier, et al. (författare)
  • Self-powered wireless sensor using a pressure fluctuation energy harvester
  • 2021
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Condition monitoring devices in hydraulic systems that use batteries or require wired infrastructure have drawbacks that affect their installation, maintenance costs, and deployment flexibility. Energy harvesting technologies can serve as an alternative power supply for system loads, eliminating batteries and wiring requirements. Despite the interest in pressure fluctuation energy harvesters, few studies consider end-to-end implementations, especially for cases with lowamplitude pressure fluctuations. This generates a research gap regarding the practical amount of energy available to the load under these conditions, as well as interface circuit requirements and techniques for efficient energy conversion. In this paper, we present a self-powered sensor that integrates an energy harvester and a wireless sensing system. The energy harvester converts pressure fluctuations in hydraulic systems into electrical energy using an acoustic resonator, a piezoelectric stack, and an interface circuit. The prototype wireless sensor consists of an industrial pressure sensor and a low-power Bluetooth System-on-chip that samples and wirelessly transmits pressure data. We present a subsystem analysis and a full system implementation that considers hydraulic systems with pressure fluctuation amplitudes of less than 1 bar and frequencies of less than 300 Hz. The study examines the frequency response of the energy harvester, the performance of the interface circuit, and the advantages of using an active power improvement unit adapted for piezoelectric stacks. We show that the interface circuit used improves the performance of the energy harvester compared to previous similar studies, showing more power generation compared to the standard interface. Experimental measurements show that the self-powered sensor system can start up by harvesting energy from pressure fluctuations with amplitudes starting at 0.2 bar at 200 Hz. It can also sample and transmit sensor data at a rate of 100 Hz at 0.7 bar at 200 Hz. The system is implemented with off-the-shelf circuits. 
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10.
  • Bader, Sebastian, 1984-, et al. (författare)
  • A Comparison of One- and Two-Diode Model Parameters at Indoor Illumination Levels
  • 2020
  • Ingår i: IEEE Access. - 2169-3536. ; 8, s. 172057-172064
  • Tidskriftsartikel (refereegranskat)abstract
    • Indoor photovoltaic (PV) application gains in attraction for low-power electronic systems, which requires accurate methods for performance predictions in indoor environments. Despite this, the knowledge on the performance of commonly used photovoltaic device models and their parameter estimation techniques in these scenarios is very limited. Accurate models are an essential tool for conducting feasibility analyses and component dimensioning for indoor photovoltaic systems. In this paper, we therefore conduct a comparison of the one- and two-diode models with parameters estimated based on two well-known methods. We evaluate the models' performance on datasets of photovoltaic panels intended for indoor use, and illumination conditions to be expected in indoor environments lit by artificial light sources. The results demonstrate that the one-diode model outperforms the two-diode model with respect to the estimation of the overall I-V characteristics. The two-diode model results instead in lower maximum power point errors. Both models show a sensitivity to initial conditions, such as the selection of the diode ideality factor, as well as the curve form of the photovoltaic panel to be modeled, which has not been acknowledged in previous research.
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11.
  • Bader, Sebastian, 1984-, et al. (författare)
  • A concept for remotely reconfigurable solar energy harvesting testbeds
  • 2017
  • Ingår i: Proceedings of IEEE Sensors. - : IEEE. - 9781509010127 ; , s. 837-839
  • Konferensbidrag (refereegranskat)abstract
    • Existing solar energy harvesting systems are typically evaluated with a single configuration. However, results on different harvester configurations are often desired in order to select the appropriate match to specific ambient conditions and application requirements. In this paper, we therefore present a concept for remotely reconfigurable solar energy harvesting testbeds, which allows for multiple harvester configurations to be evaluated with a single system deployment. We demonstrate that such a testbed can be implemented in an efficient manner by utilizing the benefits of wireless sensor networks, resulting in a scalable and flexible system with low power consumption. 
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12.
  • Bader, Sebastian, 1984-, et al. (författare)
  • A tentative model for sustainable pedagogical digital competence development : Exploring networked learning in an educational development project
  • 2022
  • Ingår i: Proceedings for the Thirteenth International Conference on Networked Learning 2022. - Aalborg. ; , s. 1-7
  • Konferensbidrag (refereegranskat)abstract
    • This paper addresses one large university initiative for educational development aimed at further developing educations and teacher competence with a focus on technology-enhanced and lifelong learning. The aim of the paper is to describe and problematize the design of an ongoing project for educational development, Higher Education and Digitalisation (HEaD). It focuses on identifying key components of an educational development project for technology enhanced learning as well as how such a project can be organized to sustain in regular university operations. The article discusses how a project for educational development can create over-time durable infrastructures, organization, policy and motivation for maintaining a continual educational development. In the first phase of the project, a model was developed for how competence development can be conducted sustainably. This model contains two perspectives: (1) an organizational perspective that focuses on the key partners to be involved; and (2) a process perspective that focuses on activities and aims in strategic competence development projects. The tentative model with its two perspectives is described and discussed in this article as a preliminary result. The model includes four identified key entities and their roles in pedagogical digital competence development; academic departments and their faculty, educational developers, infrastructure and IT-department and the pedagogical research unit. Further, a process model based on existing support structures, complemented with activities that can be sustained after the HEaD project ends is presented.
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13.
  • Bader, Sebastian, 1984-, et al. (författare)
  • Distributed Measurement of Light Conditions for Indoor Photovoltaic Applications
  • 2020
  • Ingår i: Proceedings of IEEE Sensors. - 9781728168012
  • Konferensbidrag (refereegranskat)abstract
    • Ambient light measurements and an understanding of light conditions are essential for the accurate estimation of available energy in indoor photovoltaic applications. Light conditions may vary with respect to illumination intensity, duration, and spectral composition. Although the importance of the light spectrum has been documented in laboratory studies, previous distributed measurement methods are limited to intensity as a measure for output power. In this paper, we propose and implement a system for distributed measurement of light conditions that includes spectral information with low overhead. Based on a prototype implementation, we demonstrate that the illumination intensity and spectrum varies considerably over time and space, which confirms the demand for the proposed solution. We, moreover, characterize the energy consumption of the prototype, demonstrating that long-term, unattended characterization of light conditions can be achieved. 
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14.
  • Bader, Sebastian, 1984-, et al. (författare)
  • Durable Solar Energy Harvesting from Limited Ambient Energy Income
  • 2011
  • Ingår i: International Journal on Advances in Networks and Services. - : IARIA. - 1942-2644. ; 4:1&2, s. 66-80
  • Tidskriftsartikel (refereegranskat)abstract
    • Typical wireless sensor network applications inthe domain of environmental monitoring require or profitfrom extended system lifetime. However, restrictions in sensornode resources, especially due to the usage of capacity limitedbatteries, forbid these desired lifetimes to be reached. Asopposed to batteries, energy harvesting from ambient energysources enables for near-perpetual supply of sensor nodes, asthe utilized energy source is inexhaustible. Nevertheless, thesupply from ambient energy sources is rate-limited, whereinthis supply-rate is mainly defined by the system deploymentlocation. On the other hand, the attached sensor node hasa consumption-rate, which has to be supplied to guaranteecontinuous node operation. In this paper, we address thematching of supply-rate and consumption-rate in solar energyharvesting systems at locations with limited insolation. Thefocus lies on the reduction of harvester energy overhead, whichin low-duty cycled system easily reaches similar or higherconsumption levels than the load it supplies. We suggest andpresent two harvester architectures [1], that have their maindesign consideration on simplicity. The individual modulesof the architectures are tested and verified in laboratorymeasurements and we evaluate the fully implemented systemsin an outdoor deployment. Based on the laboratory results,implementation choices for the architecture modules have beenmade. Whereas both harvesting architectures continuouslysupplied the attached load during the deployment period, wewere able to compare their behavior with each other andpresent individual advantages and drawbacks
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15.
  • Bader, Sebastian, 1984- (författare)
  • Instrumentation and Measurement Systems : Methods, Applications and Opportunities for Instrumentation and Measurement
  • 2023
  • Ingår i: IEEE Instrumentation & Measurement Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 1094-6969 .- 1941-0123. ; 26:7, s. 28-33
  • Tidskriftsartikel (refereegranskat)abstract
    • Technological development has through time changed and improved the way measurements are being performed. Starting from entirely mechanical designs, today's measurement instruments are electronic, computerized and, in many cases, connected. This has enabled a largely automated collection of physical quantities with high resolution and reliability. The recorded data may be used as the basis for decision making or may be utilized in closed-loop process control.
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16.
  • Bader, Sebastian, 1984-, et al. (författare)
  • Instrumentation and Measurement Systems : The Challenge of Designing Energy Harvesting Sensor Systems
  • 2024
  • Ingår i: IEEE Instrumentation & Measurement Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 1094-6969 .- 1941-0123. ; 27:4, s. 22-28
  • Tidskriftsartikel (refereegranskat)abstract
    • With the advent of low-cost and low-power computation, communication and sensor devices, novel instrumentation and measurement applications have been enabled, such as real-time industrial condition monitoring and fine-grained environmental monitoring. In these application scenarios, a lack of available infrastructures for communication and power supply is a common problem. In industrial applications, for example, the machine to be monitored and the monitoring system itself have significantly different technology lifespans, which requires that the monitoring system be retrofitted to machines that are already in use. In environmental monitoring, measurement systems are deployed as standalone devices in potentially remote areas. Consequently, the more autonomous the sensor system can be in terms of required infrastructure, the better it can match application and business needs.
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17.
  • Bader, Sebastian, 1984-, et al. (författare)
  • One-diode photovoltaic model parameters at indoor illumination levels – A comparison
  • 2019
  • Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 180, s. 707-716
  • Tidskriftsartikel (refereegranskat)abstract
    • Models of photovoltaic devices are used to compare the properties of photovoltaic cells and panels, and to predict their I-V characteristics. To a large extent, modeling methods are based on the one-diode equivalent circuit. Although much research exists on the implementation and evaluation of these methods for typical outdoor conditions, their performance at indoor illumination levels is largely unknown. Consequently, this work performs a systematic study of methods for the parameter extraction of one-diode models under indoor conditions. We selected, reviewed and implemented commonly used methods, and compared their performance at different illumination levels. We have shown that most methods can achieve good accuracies with extracted parameters regardless of the illumination condition, but their accuracies vary significantly when the parameters are scaled to other conditions. We conclude that the physical interpretation of extracted parameters at low illumination is to a large extent questionable, which explains errors based on standard scaling approaches. 
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18.
  • Hamza, K., et al. (författare)
  • Fast Supercapacitor Charging for Electromagnetic Converter Systems by Self Powered Boost Circuit
  • 2023
  • Ingår i: Proceedings. ; , s. 553-558
  • Konferensbidrag (refereegranskat)abstract
    • Electromagnetic energy harvesting (EM) is an interesting method for obtaining energy from vibration sources to power autonomous wireless sensor systems. This is reached with the help of energy management circuits that can be based on voltage multipliers circuits or boost rectifier circuits. These energy management structures aim to raise the voltage of the EM converter to a level that is adequate for the storage element. In this paper, a dual-mode AC/DC circuit for EM is proposed for fast charging of the supercapacitor. The circuit is based on a boost rectifier and a voltage multiplier using discrete elements and without a startup circuit. This structure can work properly with an EM converter generating power in the range of a few µW. The circuit can work under different frequencies and accelerations. In the first step, the proposed circuit store the energy in a supercapacitor in voltage multiplier mode until it reaches 1.9 V. After that, the supercapacitor will supply the ancillary circuit to activate the boost mode based on bidirectional MOSFETs, which reduce the time to charge the supercapacitor. The maximum power consumption of the ancillary circuit is 34 µW. The experimental investigation shows that the system at acceleration ACC = 0.17 g and Freq = 27 Hz is able to charge the supercapacitor by a voltage of 5.41 V in 7 min. The proposed solution helps to enhance the time to charge comparing conventional voltage multiplier structure by 23 min.
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19.
  • Kifle, Yonatan Habteslassie, 1984- (författare)
  • Studies On Design of Near-Field Wireless-Powered Biphase Implantable Stimulators
  • 2022
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Portable and implantable electronics are becoming increasingly important in the healthcare sector. One of the challenges is to guarantee stable systems for longer periods of time. If we consider applications such as electrical nerve stimulation or implanted ion pumps, the requirements for, e.g., levels, duration, etc., vary over time, and there may be a need to be able to remotely reconfigure devices, which in turn extends the life of the implant.  This dissertation studies the efficient healthcare wireless network, wireless power supply, and its use in implantable biomedical systems. The body-area network (BAN) and near-field communication (NFC) are studied. Several Application Specific Integrated Circuits (ASICs) solutions are implemented, manufactured, and characterized. ASICs for portable and implantable sensors and actuators still have high research value. In addition, advances in flexible, implantable inductive coils, along with near-field energy harvesting technology, have driven the development of wireless, implantable devices. The ASICs are used to initiate and generate controlled signals that govern actuators in multiple locations in the body. Electronics specifications may include operations related to tissue-specific absorption rate, stimulation duration or levels to avoid tissue temperature rise, power transmission distance, and controlled current or voltage drivers. In this work, the feasibility of BAN as a healthcare network has been investigated. The functionality of an existing BodyCom communication system was expanded, sensors and actuators are added. The system enables data transfer between several sensor nodes placed on a human body. In BAN, the information is propagated along the skin in a capacitive, electric field. The network was demonstrated with a sensor node (stretchable glove) and implantable ion pump (actuator) for drug delivery. With the stretchable glove, movement patterns could be captured, and ions were delivered from a reservoir in the ion pump.  Furthermore, NFC is studied, and the advantages of NFC compared to BAN are discussed. An ST Microelectronics system was used together with a planar coil developed on a flexible plastic substrate to demonstrate the concept. The efficiency between the primary and secondary coils is measured and characterized. A temperature sensor was chosen as the implantable sensor, and the signal strength at several distances between the primary and secondary inductive coils is characterized.  The next phase of the work focuses on the implementation of ASICs. The first proposed system describes a wirelessly powered peripheral nerve stimulator. The system contains a full-wave rectifier-based energy harvester that operates at 13.56 MHz with the option to select a stimulation current. The stimulation current can be selected in the range of 15 nA up to 1 mA. A reference clock is extracted from the AC input and used to synchronize the data and generate the required control. In addition, a state machine is used to generate the time parameters required for cathodic and anodic nerve stimulation. The design is fabricated in the standard 180 nm CMOS process and is 0.22 mm2 large, excluding an integrated 3.6 nF capacitor. The chip is measured to verify the energy harvester, power cells, and timing control logic with an input amplitude |VAC | = 3 V and a load of 1 kΩ.  Subsequently, a multichannel system was developed that makes it possible to dynamically set the biphase simulation profile. The amplitude modulated data packets transmitted through the inductively coupled interface are demodulated, and the information is extracted. The data stream is then used to generate control signals that activate the desired configuration (channel, stream, time, etc.). 
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20.
  • Krug, Silvia, et al. (författare)
  • Suitability of Communication Technologies for Harvester-Powered IoT-Nodes
  • 2019
  • Ingår i: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728112688
  • Konferensbidrag (refereegranskat)abstract
    • The Internet of Things introduces Internet connectivity to things and objects in the physical world and thus enables them to communicate with other nodes via the Internet directly. This enables new applications that for example allow seamless process monitoring and control in industrial environments. One core requirement is that the nodes involved in the network have a long system lifetime, despite limited access to the power grid and potentially difficult propagation conditions. Energy harvesting can provide the required energy for this long lifetime if the node is able to send the data based on the available energy budget. In this paper, we therefore analyze and evaluate which common IoT communication technologies are suitable for nodes powered by energy harvesters. The comparison includes three different constraints from different energy sources and harvesting technologies besides several communication technologies. Besides identifying possible technologies in general, we evaluate the impact of duty-cycling and different data sizes. The results in this paper give a road map for combining energy harvesting technology with IoT communication technology to design industrial sensor nodes. 
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21.
  • Lechuga Aranda, Jesus Javier (författare)
  • Interfaces In Hydraulic Pressure Energy Harvesters
  • 2019
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • The fourth industrial revolution is here and with it a tidal wave of challenges for its prosperous implementation. One of the greatest challenges frustrating the development of the internet of things, and hence the next industrial revolution, is the powering of wireless sensors, as these depend on batteries with a limited lifetime. Recent advances have shown that energy harvesting technologies can be employed to extend the lifetime of batteries and ultimately replace them, thus facilitating the deployment of autonomous self-powered sensors, key components of the internet of things.Energy harvesting is the process of capturing ambient energy and convertingit into electric power. For energy harvesting devices it is crucial that the transduction of energy is as efficient as possible, meaning that the methods for capturing, interfacing and converting the ambient energy should be understood and characterized for every application. This thesis investigates the harvesting of the energy found in pressure fluctuations in hydraulic systems, a widely used power transmission system used in the industry and consumer applications; the focus is on the fluid interface and energy focusing methods.In summary, the contributions in this thesis show that the methods for converting pressure fluctuations in hydraulic systems to electrical power depend on the hydraulic system environment, in essence, the static pressure and the frequency of the pressure fluctuations. The results can serve as a starting point in the research, design, and development of hydraulic pressure energy harvesters.
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22.
  • Ma, Xinyu, et al. (författare)
  • A Scalable, Data-driven Approach for Power Estimation of Photovoltaic Devices under Indoor Conditions
  • 2019
  • Ingår i: ENSsys'19 Proceedings of the 7th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems. - New York, USA : ACM Digital Library. - 9781450370103 ; , s. 29-34
  • Konferensbidrag (refereegranskat)abstract
    • For the output power estimation of photovoltaic devices in indoor applications, models are needed that perform accurately at the low illumination levels encountered. As a robust and scalable solution, we propose a data-driven modeling method, spanning an interpolated surface between two reference I-V curves. The proposed approach is evaluated based on experimental data of two exemplar PV panels at indoor illumination levels. The results are compared to two common parameter extraction methods for the one-diode circuit model. This investigation demonstrates that the proposed surface model has a high performance under all test conditions, whereas the reference models show a performance dependency on the PV panel type. It can be concluded that the surface model is a competitive alternative for output power estimations at indoor illumination levels, removing many of the uncertainties of traditionally used physical parameter extraction and scaling methods.
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23.
  • Ma, Xinyu, et al. (författare)
  • Characterization of Indoor Light Conditions by Light Source Classification
  • 2017
  • Ingår i: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; 17:12, s. 3884-3891
  • Tidskriftsartikel (refereegranskat)abstract
    • The characterization of light conditions plays an important role in the estimation of available energy levels to ambient light energy harvesting systems. Indoor light conditions are commonly described by illuminance levels. The same illuminance levels, however, can be generated by different light source types, which radiate different spectral components. This means that based on their spectral response, solar panels can produce different output powers even though identical illuminance levels are observed. We propose a method to distinguish these conditions based on limited spectral information. Using low-cost sensors, spectral characteristics of the light condition can be acquired and used to classify the underlying light source type, which allows for a more accurate estimation of the solar panel response. The method was evaluated experimentally for a number of common indoor light sources and under different conditions. Evaluation results have shown that the method can be used to distinguish the light sources under test with very high classification accuracy. Moreover, the method can be used accurately in situations with limited interference. This makes it a low-cost alternative to the characterization of light conditions using spectrometers, the use of which is infeasible in spatially distributed characterization applications.
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24.
  • Ma, Xinyu, et al. (författare)
  • Estimating Harvestable Energy in Time-Varying Indoor Light Conditions
  • 2020
  • Ingår i: ENSsys 2020 - Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450381291 ; , s. 71-76
  • Konferensbidrag (refereegranskat)abstract
    • Ambient light energy harvesting is a cost-effective and mature approach for supplying low-power sensor systems with power in many indoor applications. Although the spectral information of a light source is known to influence the efficiency and output power of a photovoltaic cell, the spectrum of the ambient illumination is due to measurement complexity often neglected when characterizing light conditions for power estimation purposes. In this paper we evaluate the influence of considering spectral information on the energy estimation accuracy. We create a dataset of varying light conditions in a typical indoor environment based on eight locations. For each location, we compare the energy estimation accuracy with and without spectral considerations. The results of this investigation demonstrate that a spectrum-based method leads to significant performance improvements in cases where the light condition is not defined by a single light source.
  •  
25.
  • Ma, Xinyu, et al. (författare)
  • On the Performance of the Two-Diode Model for Photovoltaic Cells under Indoor Artificial Lighting
  • 2021
  • Ingår i: IEEE Access. - 2169-3536. ; 9, s. 1350-1361
  • Tidskriftsartikel (refereegranskat)abstract
    • Models of photovoltaic devices are an important tool for the estimation of their I-V characteristics. These characteristics, in turn, can be used to optimize production, compare devices, or predict the output power under different illumination conditions. Equivalent circuit models are the most common model types utilized. Although these models and the estimation of their parameters are thoroughly investigated, little is known about their performance under indoor illumination conditions. This, however, is essential for applications where photovoltaic devices are used indoors, such as for PV-powered sensors, wearables or Internet of Things devices. In this paper, a comprehensive and quantitative study of parameter estimation methods for the two-diode model is conducted, focusing particularly on the performance at indoor illumination levels. We reviewed and implemented a set of six common parameter estimation methods, and evaluate the performance of the estimated parameters on a typical photovoltaic module utilized in indoor scenarios. The results of this investigation demonstrate that there is a large performance variation between different parameter estimation methods, and that many methods have difficulties to estimate accurate parameters at low illumination conditions. Moreover, the majority of methods result in physically infeasible parameters, at least under some of the evaluated conditions. When applying physically motivated parameter scaling methods to these parameters, large estimation errors are observed, which limits the model’s applicability for power estimation purposes. 
  •  
26.
  • Ma, Xinyu (författare)
  • Power Estimation for Indoor Light Energy Harvesting
  • 2021
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • The growing popularity of indoor light energy harvesting for wireless sensor systems and low-power electronics has created a demand for systematic power estimation methods for different lighting conditions. Although existing research has recognized the critical role played by the spectral information on the output power of a photovoltaic cell, power estimation methods have rarely considered it. The vast majority of studies on the power estimation method in the past few years have focused on the conventional diode model, and even though scaling the parameters to other light conditions seems plausible, it is sometimes problematic to interpret the physical meanings of some parameters from theory. Therefore, a systematic investigation of the light condition characterization and PV cell modeling is fundamental to appropriately estimate the available light energy of an indoor environment. The power estimation method proposed in this thesis takes both spectral and intensity information into account and provides a data-driven approach to solve the scaling problem. We use low-cost sensors to measure spectral information and select an appropriate device model based on the classification of the light source. The evaluation results for both lab and real-world light conditions show that the proposed method achieves sufficient accuracy. This study provides new insights into the indoor light energy harvesting system design and makes a contribution to research on available energy estimation of the ambient environment.
  •  
27.
  • Ma, Xinyu, et al. (författare)
  • Power Estimation for Indoor Light Energy Harvesting Systems
  • 2020
  • Ingår i: IEEE Transactions on Instrumentation and Measurement. - 0018-9456 .- 1557-9662. ; 69:10, s. 7513-7521
  • Tidskriftsartikel (refereegranskat)abstract
    • The growing interest in indoor light energy harvesting for wireless sensor systems and low-power electronics has created a demand for systematic design methods that optimize the system implementation and component choices for different lighting scenarios. Although the spectrum of light is known to influence the efficiency and output power of a photovoltaic cell, existing power estimation methods neglect the spectrum. By contrast, the power estimation method proposed in this paper takes spectral and intensity information into account. It uses low-cost sensors to measure spectral information and select an appropriate device model based on the classification of the light source. The method is evaluated under different light conditions, including individual light sources, mixed artificial light sources, and mixtures of artificial light and sunlight. The results demonstrate that the proposed implementation selects a reasonable model in most cases, including mixed light source conditions. Using light source specific models for photovoltaic panels, the resulting estimation error is low and has clear advantages over methods neglecting spectral information.
  •  
28.
  • Martinez Rau, Luciano, et al. (författare)
  • On-Device Feeding Behavior Analysis of Grazing Cattle
  • 2024
  • Ingår i: IEEE Transactions on Instrumentation and Measurement. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9456 .- 1557-9662. ; 73
  • Tidskriftsartikel (refereegranskat)abstract
    • Precision livestock farming (PLF) leverages cutting-edge technologies and data-driven solutions to enhance the efficiency of livestock production, its associated management, and its welfare. Continuous monitoring of the masticatory sound of cattle allows the estimation of dry-matter intake, classification of jaw movements (JMs), and recognition of grazing and rumination bouts. Over the past two decades, algorithms for analyzing feeding sounds have seen improvements in performance and computational requirements. Nevertheless, in some cases, these algorithms have been implemented on resource-constrained electronic devices, limiting their functionality to one specific task: either classifying JMs or recognizing feeding activities (such as grazing and rumination). In this work, we present an acoustic monitoring system that comprehensively analyzes grazing cattle's feeding behavior at multiple scales. This embedded system classifies different types of JMs, identifies feeding activities, and provides predictor variables for estimating dry-matter intake. Results are transmitted remotely to a base station using long-range communication (LoRa). Two variants of the system have been deployed on a Raspberry Pi Pico board, based on a low-power ARM Cortex-M0+ microcontroller. Both firmware versions make use of direct access memory, sleep mode, and clock-gating techniques to minimize energy consumption. In laboratory experiments, the first deployment consumes 20.1 mW and achieves an F1-score of 87.3% for the classification of JMs and 87.0% for feeding activities. The second deployment consumes 19.1 mW and reaches an F1-score of 84.1% for JMs and 83.5% for feeding activities. The modular design of the proposed embedded monitoring system facilitates integration with energy-harvesting power sources for autonomous operation in field conditions.
  •  
29.
  • Martinez Rau, Luciano, et al. (författare)
  • Real-Time Acoustic Monitoring of Foraging Behavior of Grazing Cattle Using Low-Power Embedded Devices
  • 2023
  • Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings. - 9798350323078
  • Konferensbidrag (refereegranskat)abstract
    • Precision livestock farming allows farmers to optimize herd management while significantly reducing labor needs. Individualized monitoring of cattle feeding behavior offers valuable data to assess animal performance and provides valuable insights into animal welfare. Current acoustic foraging activity recognizers achieve high recognition rates operating on computers. However, their implementations on portable embedded systems (for use on farms) need further investigation. This work presents two embedded deployments of a state-of-the-art foraging activity recognizer on a low-power ARM Cortex-M0+ microcontroller. The parameters of the algorithm were optimized to reduce power consumption. The embedded algorithm processes masticatory sounds in real-time and uses machine-learning techniques to identify grazing, rumination and other activities. The overall classification performance of the two embedded deployments achieves an 84% and 89% balanced accuracy with a mean power consumption of 1.8 mW and 12.7 mW, respectively. These results will allow this deployment to be integrated into a self-powered acoustic sensor with wireless communication to operate autonomously on cattle. 
  •  
30.
  • Mozelius, Peter, Docent, 1959-, et al. (författare)
  • Educational development - Challenges, opportunities, tools and techniques
  • 2022
  • Ingår i: Proceedings of the 21st European Conference on e-Learning - ECEL 2022. - Reading, UK : ACI Academic Conferences International. - 9781914587559 ; , s. 264-271
  • Konferensbidrag (refereegranskat)abstract
    • As pointed out by many researchers, the ongoing pandemic has been a catalyst for educational development. With the increasing need for reskilling and lifelong learning, the current model of technology-enhanced learning needs updating, and so does also the university programmes for bachelor's and master's students. This study is based on an online brainstorming session and submitted development plans in the HEaD (Higher Education and Digitalisation) project, a five-year initiative for technology-enhanced educational development. HEaD is a development project aimed at supporting university teachers to work with research and development in the field of technology-enhanced and lifelong learning. As the research strategy, an action research approach was used, with the purpose of improving the educational process where authors also have the roles of teachers and facilitators. The aim of the study is to describe and discuss pilot project members' perceptions of challenges, opportunities, tools and techniques in higher education development. Data gathered from workshop discussion summaries and project plans were thematically analysed. Ideas from the workshop sessions were written down and saved with the use of the digital notice board Padlet. Results from the thematic analysis have been grouped into the four predefined categories of challenges, opportunities, tools and techniques. Findings show that course participants and project members have interesting ideas that have the potential to reinforce the current educational model at the university. Several tools and techniques that could support synchronous as well as asynchronous online learning will be tested and evaluated. Both the workshop summaries and the project plans show a high degree of creativity, but on the other hand, the method descriptions were scarce and would need improvement. The conclusion is that the project has had a good start if seen as development, but that there is a need for improvement and more input to achieve the intended core idea of research and development
  •  
31.
  • Muthumala, Uditha, et al. (författare)
  • Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according to the damage mechanism they represent. These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it. However, it is currently unknown which of these approaches is preferred. With the goal of model deployment on resource-constrained embedded Internet of Things (IoT) systems, this work evaluates and compares both approaches in terms of classification accuracy, memory requirement, processing time, and energy consumption. To accomplish this, features are extracted and carefully selected, neural network models are designed and optimized for each input data scenario, and the models are deployed on a low-power IoT node. The comparative analysis reveals that all models can achieve high classification accuracies of over 99\%, but that embedded feature extraction is computationally expensive. Consequently, models utilizing the raw AE signal as input have the fastest processing speed and thus the lowest energy consumption, which comes at the cost of a larger memory requirement.
  •  
32.
  • Phan, Tra Nguyen, et al. (författare)
  • Design optimization and comparison of cylindrical electromagnetic vibration energy harvesters
  • 2021
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:23
  • Tidskriftsartikel (refereegranskat)abstract
    • Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power. 
  •  
33.
  • Phan, Tra, et al. (författare)
  • Performance of an electromagnetic energy harvester with linear and nonlinear springs under real vibrations
  • 2020
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:19
  • Tidskriftsartikel (refereegranskat)abstract
    • The introduction of nonlinearities into energy harvesting in order to improve the performance of linear harvesters has attracted a lot of research attention recently. The potential benefits of nonlinear harvesters have been evaluated under sinusoidal or random excitation. In this paper, the performances of electromagnetic energy harvesters with linear and nonlinear springs are investigated under real vibration data. Compared to previous studies, the parameters of linear and nonlinear harvesters used in this paper are more realistic and fair for comparison since they are extracted from existing devices and restricted to similar sizes and configurations. The simulation results showed that the nonlinear harvester did not generate higher power levels than its linear counterpart regardless of the excitation category. Additionally, the effects of nonlinearities were only available under a high level of acceleration. The paper also points out some design concerns when harvesters are subjected to real vibrations. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
  •  
34.
  • Phan, Tra, et al. (författare)
  • Towards Automated Design Optimization of Electromagnetic Energy Harvesting Transducers
  • 2022
  • Ingår i: SenSys '22. - New York, NY, USA : ACM Digital Library. - 9781450398862 ; , s. 871-877
  • Konferensbidrag (refereegranskat)abstract
    • A new scheme for the automated design and optimization of electromagnetic energy harvesters is presented. The proposed method aims to deal with the limitations of current design techniques and to improve the efficiency of the design process. Most of the current design approaches require significant user experience and knowledge to make informed design decisions, which means that a large amount of time is needed to obtain the optimized design. Additionally, the design solution is suitable only for a specific application, which makes it difficult to adopt it to other application demands. In the proposed method, the development cycle has been sped up significantly by minimizing human efforts and utilizing a generic model for the design process. The method is based on a generic template based on specific resources and requirements, which is processed by a black-box optimization algorithm to come up with a number of promising configuration suggestions. The method's effectiveness and its autonomous operation are demonstrated based on the design optimization of an electromagnetic pick-up unit for vibration energy harvesters using a Halbach array.
  •  
35.
  • Rusu, Cristina, et al. (författare)
  • Challenges for Miniaturised Energy Harvesting Sensor Systems
  • 2018
  • Ingår i: 2018 10th International Conference on Advanced Infocomm Technology (ICAIT). - : Institute of Electrical and Electronics Engineers Inc.. - 9781538679364 ; , s. 214-217
  • Konferensbidrag (refereegranskat)
  •  
36.
  • Tran, Thanh, et al. (författare)
  • An artificial neural network-based system for detecting machine failures using a tiny sound dataset : A case study
  • 2022
  • Ingår i: Proceedings - 2022 IEEE International Symposium on Multimedia, ISM 2022. - : IEEE conference proceedings. - 9781665471725 ; , s. 163-168
  • Konferensbidrag (refereegranskat)abstract
    • In an effort to advocate the research for a deep learning-based machine failure detection system, we present a case study of our proposed system based on a tiny sound dataset. Our case study investigates a variational autoencoder (VAE) for augmenting a small drill sound dataset from Valmet AB. A Valmet dataset contains 134 sounds that have been divided into two categories: "Anomaly"and "Normal"recorded from a drilling machine in Valmet AB, a company in Sundsvall, Sweden that supplies equipment and processes for the production of biofuels. Using deep learning models to detect failure drills on such a small sound dataset is typically unsuccessful. We employed a VAE to increase the number of sounds in the tiny dataset by synthesizing new sounds from original sounds. The augmented dataset was created by combining these synthesized sounds with the original sounds. We used a high-pass filter with a passband frequency of 1000 Hz and a low-pass filter with a passband frequency of 22 000 Hz to pre-process sounds in the augmented dataset before transforming them to Mel spectrograms. The pre-trained 2D-CNN Alexnet was then trained using these Mel spectrograms. When compared to using the original tiny sound dataset to train pre-trained Alexnet, using the augmented sound dataset enhanced the CNN model's classification results by 6.62%(94.12% when trained on the augmented dataset versus 87.5% when trained on the original dataset). For reproducing and deploying the proposed method, an open-source repository is available at https://gitfront.io/r/user-1913886/MKyfLWwTPm87/Paper5/ 
  •  
37.
  • Tran, Thanh, et al. (författare)
  • Denoising Induction Motor Sounds Using an Autoencoder
  • 2023
  • Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE. ; , s. 01-06
  • Konferensbidrag (refereegranskat)abstract
    • Denoising sound is essential for improving signal quality in various applications such as speech processing, sound event classification, and machine failure detection systems. This paper proposes an autoencoder method to remove two types of noise, Gaussian white noise, and environmental noise from water flow, from induction motor sounds. The method is trained and evaluated on a dataset of 246 sounds from the Machinery Fault Database (MAFAULDA). The denoising effectiveness is measured using the mean square error (MSE), which indicates that both noise types can be significantly reduced with the proposed method. The MSE is below or equal to 0.15 for normal operation sounds and misalignment sounds. This improvement in signal quality can facilitate further processing, such as induction motor operation classification. Overall, this work presents a promising approach for denoising machine sounds using an autoencoder, with potential for application in other industrial settings.
  •  
38.
  • Tran, Thanh (författare)
  • Drill Failure Detection based on Sound using Artificial Intelligence
  • 2021
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • In industry, it is crucial to be able to detect damage or abnormal behavior in machines. A machine's downtime can be minimized by detecting and repairing faulty components of the machine as early as possible. It is, however, economically inefficient and labor-intensive to detect machine fault sounds manual. In comparison with manual machine failure detection, automatic failure detection systems can reduce operating and personnel costs.  Although prior research has identified many methods to detect failures in drill machines using vibration or sound signals, this field still remains many challenges. Most previous research using machine learning techniques has been based on features that are extracted manually from the raw sound signals and classified using conventional classifiers (SVM, Gaussian mixture model, etc.). However, manual extraction and selection of features may be tedious for researchers, and their choices may be biased because it is difficult to identify which features are good and contain an essential description of sounds for classification. Recent studies have used LSTM, end-to-end 1D CNN, and 2D CNN as classifiers for classification, but these have limited accuracy for machine failure detection. Besides, machine failure occurs very rarely in the data. Moreover, the sounds in the real-world dataset have complex waveforms and usually are a combination of noise and sound presented at the same time.Given that drill failure detection is essential to apply in the industry to detect failures in machines, I felt compelled to propose a system that can detect anomalies in the drill machine effectively, especially for a small dataset. This thesis proposed modern artificial intelligence methods for the detection of drill failures using drill sounds provided by Valmet AB. Instead of using raw sound signals, the image representations of sound signals (Mel spectrograms and log-Mel spectrograms) were used as the input of my proposed models. For feature extraction, I proposed using deep learning 2-D convolutional neural networks (2D-CNN) to extract features from image representations of sound signals. To classify three classes in the dataset from Valmet AB (anomalous sounds, normal sounds, and irrelevant sounds), I proposed either using conventional machine learning classifiers (KNN, SVM, and linear discriminant) or a recurrent neural network (long short-term memory). For using conventional machine learning methods as classifiers, pre-trained VGG19 was used to extract features and neighborhood component analysis (NCA) as the feature selection. For using long short-term memory (LSTM), a small 2D-CNN was proposed to extract features and used an attention layer after LSTM to focus on the anomaly of the sound when the drill changes from normal to the broken state. Thus, my findings will allow readers to detect anomalies in drill machines better and develop a more cost-effective system that can be conducted well on a small dataset.There is always background noise and acoustic noise in sounds, which affect the accuracy of the classification system. My hypothesis was that noise suppression methods would improve the sound classification application's accuracy. The result of my research is a sound separation method using short-time Fourier transform (STFT) frames with overlapped content. Unlike traditional STFT conversion, in which every sound is converted into one image, a different approach is taken. In contrast, splitting the signal into many STFT frames can improve the accuracy of model prediction by increasing the variability of the data. Images of these frames separated into clean and noisy ones are saved as images, and subsequently fed into a pre-trained CNN for classification. This enables the classifier to become robust to noise. The FSDNoisy18k dataset is chosen in order to demonstrate the efficiency of the proposed method. In experiments using the proposed approach, 94.14 percent of 21 classes were classified successfully, including 20 classes of sound events and a noisy class.
  •  
39.
  • Tran, Thanh (författare)
  • Enhancing Machine Failure Detection with Artificial Intelligence and sound Analysis
  • 2023
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • The detection of damage or abnormal behavior in machines is critical in industry, as it allows faulty components to be detected and repaired as early as possible, reducing downtime and minimizing operating and personnel costs. However, manual detection of machine fault sounds is economically inefficient and labor-intensive. While prior research has identified various methods to detect failures in drill machines using vibration or sound signals, there remain significant challenges. Most previous research in this field has used manual feature extraction and selection, which can be tedious and biased. Recent studies have used LSTM, end-to-end 1D CNN, and 2D CNN as classifiers, but these have limited accuracy for machine failure detection. Additionally, machine failure is rare in the data, and sounds in the real-world dataset have complex waveforms that are a combination of noise and sound.To address these challenges, this thesis proposes modern artificial intelligence methods for the detection of drill failures using image representations of sound signals (Mel spectrograms and log-Mel spectrograms) and 2-D convolutional neural networks (2D-CNN) for feature extraction. The proposed models use conventional machine learning classifiers (KNN, SVM, and linear discriminant) or a recurrent neural network (long short-term memory) to classify three classes in the dataset (anomalous sounds, normal sounds, and irrelevant sounds). For using conventional machine learning methods as classifiers, pre-trained VGG19 is used to extract features, and neighborhood component analysis (NCA) is used for feature selection. For using LSTM, a small 2D-CNN is proposed to extract features, and an attention layer after LSTM focuses on the anomaly of the sound when the drill changes from normal to the broken state. The findings allow for better anomaly detection in drill machines and the development of a more cost-effective system that can be applied to a small dataset.Additionally, I also present a case study that advocates for the use of deep learning-based machine failure detection systems. We focus on a small drill sound dataset from Valmet AB, a company that supplies equipment and processes for biofuel production. The dataset consists of 134 sounds that have been categorized as "Anomaly" and "Normal" recorded from a drilling machine. However, using deep learning models for detecting failure drills on such a small sound dataset is typically unsuccessful. To address this problem, we propose using a variational autoencoder (VAE) to augment the small dataset. We generated new sounds by synthesizing them from the original sounds in the dataset using the VAE. The augmented dataset was then pre-processed using a high-pass filter with a passband frequency of 1000 Hz and a low-pass filter with a passband frequency of 22,000 Hz, before being transformed into Mel spectrograms. We trained a pre-trained 2D-CNN Alexnet using these Mel spectrograms. We found that using the augmented dataset enhanced the classification results of the CNN model by 6.62% compared to using the original dataset (94.12% when trained on the augmented dataset versus 87.5% when trained on the original dataset). Our study demonstrates the effectiveness of using a VAE to augment a small sound dataset for training deep learning models for machine failure detection.Background noise and acoustic noise in sounds can affect the accuracy of the classification system. To improve the sound classification application's accuracy, a sound separation method using short-time Fourier transform (STFT) frames with overlapped content is proposed. Unlike traditional STFT conversion, in which every sound is converted into one image, the signal is split into many STFT frames, improving the accuracy of model prediction by increasing the variability of the data. Images of these frames are separated into clean and noisy ones and subsequently fed into a pre-trained CNN for classification, making the classifier robust to noise. The efficiency of the proposed method is demonstrated using the FSDNoisy18k dataset, where 94.14 percent of 21 classes were classified successfully, including 20 classes of sound events and a noisy class.
  •  
40.
  • Xiao, H., et al. (författare)
  • Hydraulic Pressure Ripple Energy Harvesting : Structures, Materials, and Applications
  • 2022
  • Ingår i: Advanced Energy Materials. - : Wiley. - 1614-6832 .- 1614-6840. ; 12:9
  • Forskningsöversikt (refereegranskat)abstract
    • The need for wireless condition monitoring and control of hydraulic systems in an autonomous and battery-free manner is attracting increasing attention in an effort to provide improved sensing functionality, monitoring of system health, and to avoid catastrophic failures. The potential to harvest energy from hydraulic pressure ripples and noise is particularly attractive since they inherently have a high energy intensity, which is associated with the hydraulic mean pressure and flow rate. This paper presents a comprehensive overview of the state of the art in hydraulic pressure energy harvesting, which includes the fundamentals of pressure ripples in hydraulic systems, the choice of electroactive materials and device structures, and the influence of the fluid–mechanical interface. In addition, novel approaches for improving the harvested energy and potential applications for the technology are discussed, and future research directions are proposed and outlined. 
  •  
41.
  • Xu, Ye, et al. (författare)
  • A Survey on Variable Reluctance Energy Harvesters in Low-Speed Rotating Applications
  • 2018
  • Ingår i: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; 18:8, s. 3426-3435
  • Tidskriftsartikel (refereegranskat)abstract
    • Energy harvesting converts ambient energy to electrical energy that can be used to power, for example, sensors and sensor systems. Variable reluctance energy harvesting is a suitable candidate for the conversion of rotary kinetic motion, an energy form commonly found in industrial applications. The implementation of a variable reluctance energy harvester, however, has a significant effect on its performance and is not well studied. In this paper, we therefore conduct a survey on different structures of variable reluctance energy harvesters. Six existing structures, previously used in variable reluctance sensors, are presented and analyzed according to their approaches for magnetic flux change improvement. Together with a newly proposed structure, these structures are evaluated based on a finite element analysis, and their results are compared. It is demonstrated that the choice of structure considerably affects the power output of the harvester and is dependent on the improvement approaches the structure utilizes. The newly proposed structure outperforms all existing structures with respect to power output and power density, which comes at a cost of higher parasitic torque generation. A 53-fold power improvement over the reference and an 1.2-fold power improvement over the next best structure is observed. As a result, applications of variable reluctance energy harvesting become viable even at low angular velocities.
  •  
42.
  • Xu, Ye, et al. (författare)
  • Design, modeling and optimization of an m-shaped variable reluctance energy harvester for rotating applications
  • 2019
  • Ingår i: Energy Conversion and Management. - : Elsevier BV. - 0196-8904 .- 1879-2227. ; 195, s. 1280-1294
  • Tidskriftsartikel (refereegranskat)abstract
    • The variable reluctance principle can be used to convert rotational kinetic energy into electrical energy, creating a Variable Reluctance Energy Harvester (VREH) based on electromagnetic induction. This can be used to implement self-sustaining wireless sensors in rotating applications. In this paper, we present and investigate a novel design of a VREH with high volumetric power density that targets low-speed rotating applications. The design uses an m-shaped pole-piece and two opposing magnets. We theoretically analyze key design parameters that influence the VREH’s output power, and relate these parameters to geometrical design factors of the proposed structure. Key design factors include the coil height, the permanent magnet height and the tooth height. A method based on numerical simulations is introduced, enabling to determine the optimal geometrical dimensions of the proposed structure under given size-constraints. The results demonstrate that the method leads to optimal structure configurations, which has been evaluated for different cases and is verified experimentally. Good agreement between numerical simulations and experiments are reported with deviations in output power estimation below 3%. The optimized m-shaped VREH, moreover, provides output power levels sufficient for wireless sensor operation, even in low-speed rotating applications.
  •  
43.
  • Xu, Ye, et al. (författare)
  • Energy-autonomous On-rotor RPM Sensor Using Variable Reluctance Energy Harvesting
  • 2019
  • Ingår i: 2019 IEEE 8th International Workshop on Advances in Sensors and Interfaces (IWASI). - : IEEE. - 9781728105574 - 9781728105581 ; , s. 175-180
  • Konferensbidrag (refereegranskat)abstract
    • Energy-autonomous wireless sensor systems have the potential to enable condition monitoring without the need for a wired electrical infrastructure or capacity-limited batteries. In this paper, a robust and low-cost energy-autonomous wireless rotational speed sensor is presented, which harvests energy from the rotary motion of its host using the variable reluctance principle. A microelectromechanical system (MEMS) gyroscope is utilized for angular velocity measurements, and a Bluetooth Low Energy System-on-Chip (SoC) transmits the acquired samples wirelessly. An analysis on the individual subsystems is performed, investigating the output of the energy transducer, the required energy by the load, and energy losses in the whole system. The results of simulations and experimental measurements on a prototype implementation show that the system achieves energy-autonomous operation with sample rates between 1 to 50 Hz already at 10 to 40 rotations per minute. Detailed investigations of the system modules identify the power management having the largest potential for further improvements.
  •  
44.
  • Xu, Ye (författare)
  • Rotational Electromagnetic Energy Harvesting Through Variable Reluctance
  • 2022
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Rotating components are found in a majority of modern industrial applications.As key parts for machinery operations, rotating components need tobe monitored in order to detect and prevent machine failures. This requiresvarious sensor devices, which are electronic systems that detect and respondto physical quantities obtained from rotating components or their surroundingenvironments.With the rapid development of semiconductor technology, sensor deviceshave low power consumption, enabling energy harvesting to remove the dependenceon battery or wired power solutions and thus leading to self-poweredsensing applications. The kinetic energy of rotating components provides aubiquitous and stable energy source that can be exploited, resulting in rotationalenergy harvesting as a promising solution to produce electrical powerfor sensor devices.The research in this thesis focuses on the rotational energy harvesting bymeans of variable reluctance (VR) principle. In the literature, despite VR energyharvesting being a suitable candidate for the conversion of rotary kineticmotion, a comprehensive study on this energy harvesting system is still lacking.Moreover, low rotational speeds lead to a low level of extracted energyand negative mechanical effects on the rotary host which makes the deploymentof a VR energy harvesting to achieve a self-powered sensing applicationin rotating environment challenging, requiring a closer investigation onthe design and implementation. Based on theoretical analyses and numericalsimulations, combined with experimental validations, this research expandson the study of VR energy harvesting by exploring various structural designs,introducing a systematical optimization, demonstrating a sensing application,and investigating different circuits for AC/DC energy conversion to minimizethe circuit losses. The results of this research provide a guideline for enhancingthe performance of VR energy harvesting in low-speed rotational applications,which expands the research field on energy harvesting for realizingself-powered wireless sensing systems used in rotating environments.
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45.
  • Xu, Ye, et al. (författare)
  • Self-powered RPM Sensor using a Single-Anchor Variable Reluctance Energy Harvester with Pendulum Effects
  • 2023
  • Ingår i: ENSsys '23: Proceedings of the 11th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems. - Istanbul Turkiye : Association for Computing Machinery (ACM). - 9798400704383 ; , s. 72-78
  • Konferensbidrag (refereegranskat)abstract
    • The feasibility of energy harvesting as a viable alternative for powering low-energy electronics has been demonstrated through advancements in transduction mechanisms. Energy harvesters incorporating counterweights have gained attention in rotational energy harvesting to develop single-anchored devices with flexible placement and easy installation. In this work, a three-phase variable reluctance energy harvester (VREH) with low torque ripple is combined with a counterweight to facilitate a single-anchored design, specifically targeting low rotational speed applications. The energy harvester is integrated with a low-power sensor system to enable energy-neutral operation. We present the design, implementation, and evaluation of an on-rotor RPM sensor system powered by the single-anchored three-phase VREH. Experimental evaluations on a laboratory test bench demonstrate the system performance under varying conditions, with the ability to supply the sensor system at low speeds achieving, for example, a 3.5 Hz sample rate at a low speed of 3 rpm. Evaluations of the system illustrate that pendulum effects induced by the interaction of the cogging torque and the gravitational torque improve the output power of the harvester under low-speed conditions. This promises for the proposed design to be suitable to power wireless sensors for industrial condition monitoring, providing a flexible solution for energy-neutral sensor systems with reduced installation complexity.
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46.
  • Xu, Ye, et al. (författare)
  • System Implementation Trade-Offs for Low-Speed Rotational Variable Reluctance Energy Harvesters
  • 2021
  • Ingår i: Sensors. - Basel, Switzerland : MDPI. - 1424-8220. ; 21:18
  • Tidskriftsartikel (refereegranskat)abstract
    • Low-power energy harvesting has been demonstrated as a feasible alternative for the power supply of next-generation smart sensors and IoT end devices. In many cases, the output of kinetic energy harvesters is an alternating current (AC) requiring rectification in order to supply the electronic load. The rectifier design and selection can have a considerable influence on the energy harvesting system performance in terms of extracted output power and conversion losses. This paper presents a quantitative comparison of three passive rectifiers in a low-power, low-voltage electromagnetic energy harvesting sub-system, namely the full-wave bridge rectifier (FWR), the voltage doubler (VD), and the negative voltage converter rectifier (NVC). Based on a variable reluctance energy harvesting system, we investigate each of the rectifiers with respect to their performance and their effect on the overall energy extraction. We conduct experiments under the conditions of a low-speed rotational energy harvesting application with rotational speeds of 5rpm–20rpm, and verify the experiments in an end-to-end energy harvesting evaluation. Two performance metrics—power conversion efficiency (PCE) and power extraction efficiency (PEE)—are obtained from the measurements to evaluate the performance of the system implementation adopting each of the rectifiers. The results show that the FWR with PEEs of 20 % at 5 rpm to 40 % at 20 rpm has a low performance in comparison to the VD (40–60 %) and NVC (20–70 %) rectifiers. The VD-based interface circuit demonstrates the best performance under low rotational speeds, whereas the NVC outperforms the VD at higher speeds (>18 rpm). Finally, the end-to-end system evaluation is conducted with a self-powered rpm sensing system, which demonstrates an improved performance with the VD rectifier implementation reaching the system’s maximum sampling rate (40 Hz) at a rotational speed of approximately 15.5 rpm. 
  •  
47.
  • Xu, Ye, et al. (författare)
  • Three-phase variable reluctance energy harvesting
  • 2022
  • Ingår i: Energy Conversion and Management. - : Elsevier BV. - 2590-1745. ; 14
  • Forskningsöversikt (refereegranskat)abstract
    • A variable reluctance energy harvester (VREH) based on electromagnetic induction is developed for generating electrical energy from low-speed rotary motion. The challenge of a VREH at low rotational speeds is not only the low output power, but also the torque ripple that the harvester generates. Cogging torque, the major contribution to this torque ripple, is an inherent characteristic of VREH and is caused by its geometric features. Cogging torque produces acoustic noise and mechanical vibration for a drive system into which the VREH is embedded. This issue is of particular importance at low speeds and with light loads. In this paper, we use an m-shaped VREH as an example to propose a three-phase design in order to reduce the cogging torque but maintain a high output power at low speeds of 5 rpm to 20 rpm. Three identical m-shaped pickup units in a proper arrangement generate high amounts of electrical energy in three phases, but result in a lower torque ripple. Ten prototypes based on the proposed design were fabricated and tested, and their performance were in good agreement with the simulation results. By using the three pickup units in an optimized arrangement, the VREH enhances the energy harvesting performance in comparison to three single pickup units. At the same time, the torque ripple is reduced to one fifth of that produced by a single pickup unit. This demonstrates the strong potential of the three-phase VREH for implementations of self-powered wireless sensing systems in terms of energy output and mechanical effects on the rotary host. 
  •  
48.
  • Ying, Zhang, et al. (författare)
  • Theoretical modeling and experimental verification of rotational variable reluctance energy harvesters
  • 2021
  • Ingår i: Energy Conversion and Management. - Amsterdam, Netherlands : Elsevier. - 0196-8904 .- 1879-2227. ; 233
  • Tidskriftsartikel (refereegranskat)abstract
    • Energy harvesting has great potential for powering low-power wireless sensor nodes by converting environmental energies into the electricity. It can be widely used for real-time online industrial monitoring. Among different transducers, the variable reluctance energy harvester (VREH) has attracted much attention due to the great performance for the low-speed rotations. However, there is a lack of precise models for performance prediction. In this paper, a new modeling method for VREH is proposed to predict the output voltage. A combined Substituting Angle - Magnetic Field Division modeling method is presented to accurately model the magnetic permeance of the air–gap for the VREH. Then, the magnetic flux change in the magnetic circuit is derived to calculate the voltage response of the coil. The numerical and experimental results of voltage responses verify the effectiveness of proposed model with the maximum error of 4%. The influence of some key factors on voltage response is investigated, including the thickness of air–gap and tooth height. Moreover, power analysis demonstrates that the output power increases from 5.06 mW to 46.7 mW with the rotational speed from 100 rpm to 300 rpm.
  •  
49.
  • Zelenika, S., et al. (författare)
  • Energy harvesting technologies for structural health monitoring of airplane components—a review
  • 2020
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:22
  • Tidskriftsartikel (refereegranskat)abstract
    • With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.
  •  
50.
  • Zhang, Y., et al. (författare)
  • A comprehensive review on self-powered smart bearings
  • 2023
  • Ingår i: Renewable & sustainable energy reviews. - : Elsevier. - 1364-0321 .- 1879-0690. ; 183
  • Forskningsöversikt (refereegranskat)abstract
    • Recently, with the development of industrial informatization and intellectualization, the development of smart bearings has attracted a lot of significant attention in an attempt to reduce maintenance costs and increase reliability by providing online health condition monitoring. As a result of advances in miniaturization and low power consumption of wireless sensor nodes (WSNs), self-powered technologies have been considered as a promising method to achieve autonomous WSNs in smart bearings. Although the self-powered technology has received considerable achievements, there are less reviews covering the development of self-powered structures towards smart bearing and providing potential guidelines for the future development. To bridge the gap, this paper presents a comprehensive state-of-the-art review and guidelines on self-powered methods to create smart bearings, including outlining the underlying theory, modeling methods, methodologies and technologies. The topology of a self-powered smart bearing is clarified, and the mechanisms and benefits of piezoelectricity, electromagnetism, triboelectricity, thermoelectricity and wireless power transfer for powering WSNs in smart bearing are discussed. To improve the applicability of self-powered smart bearing in a range of working conditions, the design methodologies and technologies of a variety of transducers are reviewed to provide guidelines for performance enhancement. Finally, the future challenges and perspectives are proposed for outlining potential research directions and opportunities in future self-powered smart bearing systems, including the impact on bearing performance, engineering implementation, reliability, power management and storage. 
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