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1.	Ahlgren, Fredrik, 1980-, et al. (författare) Auto Machine Learning for predicting Ship Fuel Consumption 2018 Ingår i: Proceedings of ECOS 2018 - the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. - Guimarães. - 9789729959646 Konferensbidrag (refereegranskat)abstract In recent years, machine learning has evolved in a fast pace as both algorithms and computing power are constantly improving. In this study, a machine learning model for predicting the fuel oil consumption from engine data has been developed for a cruise ship operating in the Baltic Sea. The cruise ship is equipped with legacy volume flow meters and newly installed mass flow meters, as well as an extensive set of logged time series data from the machinery logging system. The model is developed using state-of-the-art Auto Machine Learning tools, which optimises both the model hyper parameters and the model selection by using genetic algorithms. To further increase the model accuracy, a pipeline of different models and pre-processing algorithms is evaluated. An extensive model trained for a certain system can be used for optimisation simulation, as well as online energy efficiency prediction. As the models automatically adapt to noisy sensor data and thus function as a watermark of the machinery system, these algorithms show a potential in predicting ship energy efficiency without installation of additional mass flow meters. All tools used in this study are Open Source tools written in Python and can be applied on board. The study shows great potential for utilising large amounts of already available sensor data for improving the accuracy of the predicted ship energy consumption.
2.	Ahlgren, Fredrik, 1980-, et al. (författare) Energy integration of organic rankine cycle, exhaust gas recirculation and scrubber 2018 Ingår i: Trends and challenges in maritime energy management. - Cham, Switzerland : Springer. - 9783319745756 - 9783319745763 ; , s. 157-168 Bokkapitel (refereegranskat)abstract The vast majority of ships trafficking the oceans are fuelled by residual oil with high content of sulphur, which produces sulphur oxides (SOx) when combusted. Additionally, the high pressures and temperatures in modern diesel engines also produce nitrogen oxides (NOx). These emissions are both a hazard to health and the local environment, and regulations enforced by the International Maritime Organization (IMO) are driving the maritime sector towards the use of either distillate fuels containing less sulphur, or the use of exhaust gas cleaning devices.TwocommontechniquesforremovingSOx andlimitingNOx aretheopen loop wet scrubber and exhaust gas recirculation (EGR). A scrubber and EGR installation reduces the overall efficiency of the system as it needs significant pumping power, which means that the exhaust gases are cleaner but at the expense of higher CO2 emissions. In this paper we propose a method to integrate an exhaust gas cleaning device for both NOx and SOx with an organic Rankine cycle for waste heat recovery, thereby enhancing the system efficiency. We investigate three ORC configurations, integrated with the energy flows from both an existing state-of-the-art EGR system and an additional open loop wet scrubber.
3.	Ahlgren, Fredrik, 1980-, et al. (författare) Predicting dynamic fuel oil consumption on ships with automated machine learning 2019 Ingår i: Innovative Solutions for Energy Transitions. - : Elsevier. ; 158, s. 6126-6131 Konferensbidrag (refereegranskat)abstract This study demonstrates a method for predicting the dynamic fuel consumption on board ships using automated machine learning algorithms, fed only with data for larger time intervals from 12 hours up to 96 hours. The machine learning algorithm trained on dynamic data from shorter time intervals of the engine features together with longer time interval data for the fuel consumption. To give the operator and ship owner real-time energy efficiency statistics, it is essential to be able to predict the dynamic fuel oil consumption. The conventional approach to getting these data is by installing additional mass flow meters, but these come with added cost and complexity. In this study, we propose a machine learning approach using auto machine learning optimisation, with already available data from the machinery logging system.
4.	Ahlgren, Fredrik, 1980- (författare) Reducing ships' fuel consumption and emissions by learning from data 2018 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract In the context of reducing both greenhouse gases and hazardous emissions, the shipping sector faces a major challenge as it is currently responsible for 11% of the transport sector’s anthropogenic greenhouse gas emissions. Even as emissions reductions are needed, the demand for the transport sector rises exponentially every year. This thesis aims to investigate the potential to use ships’ existing internal energy systems more efficiently. The thesis focusses on making existing ships in real operating conditions more efficient based logged machinery data. This dissertation presents results that can make ship more energy efficient by utilising waste heat recovery and machine learning tools. A significant part of this thesis is based on data from a cruise ship in the Baltic Sea, and an extensive analysis of the ship’s internal energy system was made from over a year’s worth of data. The analysis included an exergy analysis, which also considers the usability of each energy flow. In three studies, the feasibility of using the waste heat from the engines was investigated, and the results indicate that significant measures can be undertaken with organic Rankine cycle devices. The organic Rankine cycle was simulated with data from the ship operations and optimised for off-design conditions, both regarding system design and organic fluid selection. The analysis demonstrates that there are considerable differences between the real operation of a ship and what it was initially designed for. In addition, a large two-stroke marine diesel was integrated into a simulation with an organic Rankine cycle, resulting in an energy efficiency improvement of 5%. This thesis also presents new methods of employing machine learning to predict energy consumption. Machine learning algorithms are readily available and free to use, and by using only a small subset of data points from the engines and existing fuel flow meters, the fuel consumption could be predicted with good accuracy. These results demonstrate a potential to improve operational efficiency without installing additional fuel meters. The thesis presents results concerning how data from ships can be used to further analyse and improve their efficiency, by using both add-on technologies for waste heat recovery and machine learning applications.
5.	Ahlgren, Fredrik, 1980- (författare) Waste heat recovery in a cruise vessel 2016 Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract In three studies of a cruise ship the author has investigated waste heat recovery (WHR)from exhaust gases using an organic Rankine cycle (ORC), and also mapped the energyand exergy flows within the ship. Data were collected from the ship’s machinerysystem for a total extent of one year, and this data were used for simulations andenergy calculations. An off-design analysis was made and an ORC was simulated andoptimised with regards to the ship’s operating conditions. The ORC working fluid wasoptimised in terms for maximum electrical production in the off-design condition. Theoff-design analysis showed that the ship speed and power consumption was far fromits original design. The results indicate that there is a potential for significant savingsby using an organic Rankine cycle for waste heat recovery. The energy and exergyanalysis gave a better understanding of the energy flows and showed that the singlelargest exergy destruction occurs in the ship’s diesel engines.
6.	Ahlgren, Fredrik, 1980-, et al. (författare) Waste Heat Recovery in a Cruise Vessel in the Baltic Sea by Using an Organic Rankine Cycle : A Case Study 2015 Ingår i: ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. - : ASME Press. - 9780791856673 ; , s. 43392-43416 Konferensbidrag (refereegranskat)abstract Maritime transportation is a significant contributor to SOx, NOx and particle matter emissions, even though it has a quite low CO2 impact. New regulations are being enforced in special areas that limit the amount of emissions from the ships. This fact, together with the high fuel prices, is driving the marine industry towards the improvement of the energy efficiency of current ship engines and the reduction of their energy demand. Although more sophisticated and complex engine designs can improve significantly the efficiency of the energy systems in ships, waste heat recovery arises as the most influent technique for the reduction of the energy consumption. In this sense, it is estimated that around 50% of the total energy from the fuel consumed in a ship is wasted and rejected in fluid and exhaust gas streams. The primary heat sources for waste heat recovery are the engine exhaust and the engine coolant. In this work, we present a study on the integration of an organic Rankine cycle (ORC) in an existing ship, for the recovery of the main and auxiliary engines exhaust heat. Experimental data from the operating conditions of the engines on the M/S Birka Stockholm cruise ship were logged during a port-to-port cruise from Stockholm to Mariehamn over a period of time close to one month. The ship has four main engines Wärtsilä 5850 kW for propulsion, and four auxiliary engines 2760 kW used for electrical consumers. A number of six load conditions were identified depending on the vessel speed. The speed range from 12–14 knots was considered as the design condition, as it was present during more than 34% of the time. In this study, the average values of the engines exhaust temperatures and mass flow rates, for each load case, were used as inputs for a model of an ORC. The main parameters of the ORC, including working fluid and turbine configuration, were optimized based on the criteria of maximum net power output and compactness of the installation components. Results from the study showed that an ORC with internal regeneration using benzene would yield the greatest average net power output over the operating time. For this situation, the power production of the ORC would represent about 22% of the total electricity consumption on board. These data confirmed the ORC as a feasible and promising technology for the reduction of fuel consumption and CO2 emissions of existing ships.
7.	Ahlgren, Fredrik, 1980-, et al. (författare) Waste Heat Recovery in a Cruise Vessel in the Baltic Sea by Using an Organic Rankine Cycle : A Case Study 2016 Ingår i: Journal of engineering for gas turbines and power. - : ASME Press. - 0742-4795 .- 1528-8919. ; 138:1 Tidskriftsartikel (refereegranskat)abstract Maritime transportation is a significant contributor to SOx,NOx, and particle matter (PM) emissions, and to a lesser extent, of CO2. Recently, new regulations are being enforced in special geographical areas to limit the amount of emissions from the ships. This fact, together with the high fuel prices, is driving the marine industry toward the improvement of the energy efficiency of ships. Although more sophisticated and complex engine designs can improve significantly of the energy systems on ships, waste heat recovery arises as the most effective technique for the reduction of the energy consump- tion. In this sense, it is estimated that around 50% of the total energy from the fuel con- sumed in a ship is wasted and rejected through liquid and gas streams. The primary heat sources for waste heat recovery are the engine exhaust and coolant. In this work, we present a study on the integration of an organic Rankine cycle (ORC) in an existing ship, for the recovery of the main and auxiliary engines (AE) exhaust heat. Experimental data from the engines on the cruise ship M/S Birka Stockholm were logged during a port-to- port cruise from Stockholm to Mariehamn, over a period of 4 weeks. The ship has four main engines (ME) W€artsil€ a 5850kW for propulsion, and four AE 2760kW which areused for electrical generation. Six engine load conditions were identified depending on the ship’s speed. The speed range from 12 to 14 kn was considered as the design condi- tion for the ORC, as it was present during more than 34% of the time. In this study, the average values of the engines exhaust temperatures and mass flow rates, for each load case, were used as inputs for a model of an ORC. The main parameters of the ORC, including working fluid and turbine configuration, were optimized based on the criteria of maximum net power output and compactness of the installation components. Results from the study showed that an ORC with internal regeneration using benzene as working fluid would yield the greatest average net power output over the operating time. For this situation, the power production of the ORC would represent about 22% of the total elec- tricity consumption on board. These data confirmed the ORC as a feasible and promisingtechnology for the reduction of fuel consumption and CO2 emissions of existing ships.
8.	Baldi, Francesco, 1986, et al. (författare) Energy and exergy analysis of a cruise ship 2015 Ingår i: Proceedings of ECOS 2015 - the 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. - Pau : Pau University. - 9782955553909 Konferensbidrag (refereegranskat)abstract The shipping sector is today facing numerous challenges. Fuel prices are expected to increase in the medium-long term, and a sharp turn in environmental regulations will require several companies to switch to more expensive distillate fuels. In this context, passenger ships represent a small but increasing share of the industry. The complexity of the energy system of a ship where the energy required by propulsion is no longer the trivial main contributor to the whole energy use thus makes this kind of ship of particular interest for the analysis of how energy is converted from its original form to its final use on board.To illustrate this, we performed an analysis of the energy and exergy flow rates of a cruise ship sailing in the Baltic Sea based on a combination of available measurements from ship operations and of mechanistic knowledge of the system. The energy analysis allows identifying propulsion as the main energy user (41% of the total) followed by heat (34%) and electric power (25%) generation; the exergy analysis allowed instead identifying the main inefficiencies of the system: exergy is primarily destroyed in all processes involving combustion (88% of the exergy destruction is generated in the Diesel engines and in the oil-fired boilers) and in the sea water cooler (5.4%); the main exergy losses happen instead in the exhaust gas, mostly from the main engines (67% of total losses) and particularly from those not equipped with heat recovery devices.The improved understanding which derives from the results of the energy and exergy analysis can be used as a guidance to identify where improvements of the systems should be directed.
9.	Baldi, Francesco, 1986, et al. (författare) Energy and exergy analysis of a cruise ship 2018 Ingår i: Energies. - Basel, Switzerland : MDPI. - 1996-1073. ; 11:10, s. 1-41 Tidskriftsartikel (refereegranskat)abstract In recent years, the International Maritime Organization agreed on aiming to reduce shipping’s greenhouse gas emissions by 50% with respect to 2009 levels. Meanwhile, cruise ship tourism is growing at a fast pace, making the challenge of achieving this goal even harder. The complexity of the energy system of these ships makes them of particular interest from an energy systems perspective. To illustrate this, we analyzed the energy and exergy flow rates of a cruise ship sailing in the Baltic Sea based on measurements from one year of the ship’s operations. The energy analysis allows identifying propulsion as the main energy user (46% of the total) followed by heat (27%) and electric power (27%) generation; the exergy analysis allowed instead identifying the main inefficiencies of the system: while exergy is primarily destroyed in all processes involving combustion (76% of the total), the other main causes of exergy destruction are the turbochargers, the heat recovery steam generators, the steam heaters, the preheater in the accommodation heating systems, the sea water coolers, and the electric generators; the main exergy losses take place in the exhaust gas of the engines not equipped with heat recovery devices. The application of clustering of the ship’s operations based on the concept of typical operational days suggests that the use of five typical days provides a good approximation of the yearly ship’s operations and can hence be used for the design and optimization of the energy systems of the ship.
10.	Baldi, Francesco, 1986, et al. (författare) Optimal load allocation of complex ship power plants 2016 Ingår i: Energy Conversion and Management. - : Elsevier BV. - 0196-8904 .- 1879-2227. ; 124, s. 344-356 Tidskriftsartikel (refereegranskat)abstract In a world with increased pressure on reducing fuel consumption and carbon dioxide emissions, thecruise industry is growing in size and impact. In this context, further effort is required for improvingthe energy efficiency of cruise ship energy systems.In this paper, we propose a generic method for modelling the power plant of an isolated system withmechanical, electric and thermal power demands and for the optimal load allocation of the different componentsthat are able to fulfil the demand.The optimisation problem is presented in the form of a mixed integer linear programming (MINLP)problem, where the number of engines and/or boilers running is represented by the integer variables,while their respective load is represented by the non-integer variables. The individual components aremodelled using a combination of first-principle models and polynomial regressions, thus making thesystem nonlinear.The proposed method is applied to the load-allocation problem of a cruise ship sailing in the Baltic Sea,and used to compare the existing power plant with a hybrid propulsion plant. The results show thebenefits brought by using the proposing method, which allow estimating the performance of the hybridsystem (for which the load allocation is a non-trivial problem) while also including the contribution ofthe heat demand. This allows showing that, based on a reference round voyage, up to 3% savings couldbe achieved by installing the proposed system, compared to the existing one, and that a NPV of11 kUSD could be achieved already 5 years after the installation of the system.
11.	Baldi, Francesco, 1986, et al. (författare) The application of process integration to the optimisation of cruise ship energy systems: A case study 2016 Ingår i: ECOS 2016 - Proceedings of the 29th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems. - 9789616980159 Konferensbidrag (refereegranskat)abstract In recent years, the shipping industry has faced an increasing number of challenges in terms of fluctuating fuel prices, stricter environmental regulations, and concerns about global warming. In this situation, passenger volumes on cruise ships have increased from around 4 million to 13 million from 1990 to 2008 and keep growing today. A small cruise ship can emit about 85 tons of CO2 per day, and require around 27 tons of fuel per day. To keep up with market demand, while reducing their impact on the environment, cruise ships will need to improve their energy efficiency. Most previous research in marine technology relates to energy efficiency focused on propulsion, which for most ship types constitutes the largest energy demand. On cruise ships, however, auxiliary heat and electric power also have a significant importance. For this reason, we focus in this paper on the heat demand and its integration with available sources of waste heat on board. In this study, the principles of process integration are applied to the energy system of a cruise ship operating in the Baltic Sea. The heat sources (waste heat from the main and auxiliary engines in form of exhaust gas, cylinder cooling, charge air cooling, and lubricating oil cooling) and sinks (HVAC, hot water, fuel heating) are evaluated based on one year of operational data and used to generate four operating conditions that best represent ship operations. Applying the pinch analysis to the system revealed that the theoretical potential for heat integration on board could potentially allow the reduction of the external heat demand by between 35% and 85% depending on the investigated case. A technoeconomic optimisation allowed the identification of the most economically viable heat exchanger network designs: two in the “retrofit” scenario and one in the “design” scenario, with a reduction of 13-33%, 15-27% and 46-56% of the external heat demand, respectively. Given the high amount of heat being available after the process integration, we also analysed the potential for the installation of a steam turbine for the recovery of the energy available in the exhaust gas, which resulted in up to 900 kW of power being available for on board electric power demand.
12.	Dalipi, Fisnik, Senior lecturer, et al. (författare) Sentiment Analysis of Students’ Feedback in MOOCs : A Systematic Literature Review 2021 Ingår i: Frontiers in Artificial Intelligence. - : Frontiers Media S.A.. - 2624-8212. ; 4 Forskningsöversikt (refereegranskat)abstract In recent years, sentiment analysis (SA) has gained popularity among researchers in various domains, including the education domain. Particularly, sentiment analysis can be applied to review the course comments in massive open online courses (MOOCs), which could enable instructors to easily evaluate their courses. This article is a systematic literature review on the use of sentiment analysis for evaluating students’ feedback in MOOCs, exploring works published between January 1, 2015, and March 4, 2021. To the best of our knowledge, this systematic review is the first of its kind. We have applied a stepwise PRISMA framework to guide our search process, by searching for studies in six electronic research databases (ACM, IEEE, ScienceDirect, Springer, Scopus, and Web of Science). Our review identified 40 relevant articles out of 440 that were initially found at the first stage. From the reviewed literature, we found that the research has revolved around six areas: MOOC content evaluation, feedback contradiction detection, SA effectiveness, SA through social network posts, understanding course performance and dropouts, and MOOC design model evaluation. In the end, some recommendations are provided and areas for future research directions are identified.
13.	Hedayati, Soudabeh, et al. (författare) MapReduce scheduling algorithms in Hadoop : a systematic study 2023 Ingår i: Journal of Cloud Computing. - : Springer. - 2192-113X. ; 12 Tidskriftsartikel (refereegranskat)abstract Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.
14.	Katerina, Zdravkova, et al. (författare) Integration of Large Language Models into Higher Education : A Perspective from Learners 2024 Ingår i: 2<em>023 International Symposium on Computers in Education (SIIE)</em>, Setúbal, Portugal, 2023. - : IEEE. - 9798350329315 - 9798350329322 Konferensbidrag (refereegranskat)abstract Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail.
15.	Landälv, Ludvig, 1982-, et al. (författare) Phase evolution of radio frequency magnetron sputtered Cr-rich (Cr,Zr)(2)O-3 coatings studied by in situ synchrotron X-ray diffraction during annealing in air or vacuum 2019 Ingår i: Journal of Materials Research. - : CAMBRIDGE UNIV PRESS. - 0884-2914 .- 2044-5326. ; 34:22, s. 3735-3746 Tidskriftsartikel (refereegranskat)abstract The phase evolution of reactive radio frequency (RF) magnetron sputtered Cr0.28Zr0.10O0.61 coatings has been studied by in situ synchrotron X-ray diffraction during annealing under air atmosphere and vacuum. The annealing in vacuum shows t-ZrO2 formation starting at similar to 750-800 degrees C, followed by decomposition of the alpha-Cr2O3 structure in conjunction with bcc-Cr formation, starting at similar to 950 degrees C. The resulting coating after annealing to 1140 degrees C is a mixture of t-ZrO2, m-ZrO2, and bcc-Cr. The air-annealed sample shows t-ZrO2 formation starting at similar to 750 degrees C. The resulting coating after annealing to 975 degrees C is a mixture of t-ZrO2 and alpha-Cr2O3 (with dissolved Zr). The microstructure coarsened slightly during annealing, but the mechanical properties are maintained, with no detectable bcc-Cr formation. A larger t-ZrO2 fraction compared with alpha-Cr2O3 is observed in the vacuum-annealed coating compared with the air-annealed coating at 975 degrees C. The results indicate that the studied pseudo-binary oxide is more stable in air atmosphere than in vacuum.
16.	Maleki, Neda, et al. (författare) DeltaBin : An Efficient Binary Data Format for Low Power IoT Devices 2023 Ingår i: <em>2023 International Conference on Computer, Information and Telecommunication Systems (CITS), Genoa, Italy, 2023</em>. - Genoa, Italy : IEEE Press. - 9798350336108 - 9798350336092 Konferensbidrag (refereegranskat)abstract The Internet of Things (IoT) notion is quickly influencing t he architectures of data-driven systems d ue to the ever-increasing rapid technological progress in all sectors. The IoT involves the collection and exchange of data from a large number of interconnected devices or sensors. The collected data is structured and transmitted in a variety of different data formats such as JSON, CBOR, BSON, or simply a binary format. The data format used by an IoT device can have a significant i mpact on t he efficiency of its data transmission. In general, using a more compact and efficient data format can help to reduce t he amount of data that needs to be transmitted, which can improve the overall speed and performance of the device. For example, using a binary data format rather than a text-based format can often result in smaller data sizes and faster transmission times. Similarly, using a binary format in a more compressed form can further help to reduce the size of the data being transmitted, which can further improve the efficiency of the transmission. In this paper, we propose Delta Binary (i.e., DeltaBin) to reduce the binary data format by transmitting only changed data. We assess DeltaBin using a real IoT deployment scenario.
17.	Maleki, Neda, et al. (författare) DynaSens : Dynamic Scheduling for IoT Devices Sustainability 2022 Ingår i: 2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 20222022. - : IEEE. - 9781665485982 Konferensbidrag (refereegranskat)abstract The Internet of Things (IoT) have shown numerous potential applications that can enhance our quality of life. IoT is becoming a core technology to bring smart homes, smart cities, and smart industries into reality. However, with potential benefits comes a challenge of sustainability, and one major concern is to minimize energy consumption. In a citywide area, managing the operation of such large-scale IoT networking is one of the complex tasks. One of the ways is to utilize dynamic sensing scheduling where the IoT device goes to the sleep mode and prevents unnecessary data transmission. In this paper, we propose a dynamic sensing (DynaSens) algorithm for an IoT-based waste management system. This algorithm helps to reduce the waste bin overflowing, thus, provides better sanitation, and it is also helpful in reducing the fuel cost of waste collection vehicles. Our work utilizes measured values such as current consumption, LiDAR measurement time, and LoRa transmission time as the input data for the simulation experiment to evaluate energy consumption. We also assessed DynaSens using a real dataset obtained from a recycling house. We use Pycom LoPy4 micro-controller as a development board. For a number of garbage-thrown scenarios, DynaSens enables longer battery longevity by reducing the repeated execution of the same tasks. Â© 2022 IEEE.
18.	Maleki, Neda, et al. (författare) Unraveling Energy Consumption Patterns : Insights Through Data Analysis and Predictive Modeling 2023 Ingår i: 15th International Conference on Applied Energy. Konferensbidrag (refereegranskat)abstract Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society’s energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R2) value of 0.91 compared to XGBoost value of 0.87.
19.	Manzoni, Pietro, et al. (författare) Crowdsourcing Through TinyML as aWay to Engage End-Users in IoT Solutions 2023. - 1 Ingår i: Mobile Crowdsourcing. - Switzerland : Springer. - 9783031323973 - 9783031323966 ; , s. 359-387 Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract This book offers the latest research results in recent development on the principles, techniques and applications in mobile crowdsourcing. It presents state-of-the-art content and provides an in-depth overview of the basic background in this related field. Crowdsourcing involves a large crowd of participants working together to contribute or produce goods and services for the society. The early 21st century applications of crowdsourcing can be called crowdsourcing 1.0, which includes businesses using crowdsourcing to accomplish various tasks, such as the ability to offload peak demand, access cheap labor, generate better results in a timely matter, and reach a wider array of talent outside the organization. Mobile crowdsensing can be described as an extension of crowdsourcing to the mobile network to combine the idea of crowdsourcing with the sensing capacity of mobile devices. As a promising paradigm for completing complex sensing and computation tasks, mobile crowdsensing serves the vital purpose of exploiting the ubiquitous smart devices carried by mobile users to make conscious or unconscious collaboration through mobile networks. Considering that we are in the era of mobile internet, mobile crowdsensing is developing rapidly and has great advantages in deployment and maintenance, sensing range and granularity, reusability, and other aspects. Due to the benefits of using mobile crowdsensing, many emergent applications are now available for individuals, business enterprises, and governments. In addition, many new techniques have been developed and are being adopted.
20.	Mohammadian, Mehrdad, et al. (författare) Persis : A Persian Font Recognition Pipeline Using Convolutional Neural Networks 2022 Ingår i: <em>2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)</em>, Mashhad, Iran, Islamic Republic of. - : IEEE. ; , s. 196-204 Konferensbidrag (refereegranskat)abstract What happens if we see a suitable font for our design work but we do not know its name? Visual Font Recognition (VFR) systems are used to identify the font typeface in an image. These systems can assist graphic designers in identifying fonts used in images. A VFR system also aids in improving the speed and accuracy of Optical Character Recognition (OCR) systems. In this paper, we proposed the first publicly available datasets in the field of Persian font recognition and employed Convolutional Neural Networks (CNN) to address the Persian font recognition problem. The results show that the proposed pipeline obtained 78.0% top-1 accuracy on our new datasets, 89.1% in the IDPL-PFOD dataset, and 94.5% in the KAFD dataset. Furthermore, the average time spent in the entire pipeline for one sample of our proposed datasets is 0.54 and 0.017 seconds for CPU and GPU, respectively. We conclude that CNN methods can be used to recognize Persian fonts without the need for additional pre-processing steps such as feature extraction, binarization, normalization, etc.
21.	Mondejar, Maria E., et al. (författare) Quasi-steady state simulation of an organic Rankine cycle for waste heat recovery in a passenger vessel 2017 Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 185:Special Issue Part 2, s. 1324-1335 Tidskriftsartikel (refereegranskat)abstract In this work we present the quasi-steady state simulation of a regenerative organic Rankine cycle (ORC)integrated in a passenger vessel, over a standard round trip. The study case is the M/S Birka Stockholmcruise ship, which covers a daily route between Stockholm (Sweden) and Mariehamn (Finland).Experimental data of the exhaust gas temperatures, engine loads, and electricity demand on board werelogged over a period of four weeks. These data where used as inputs for a simulation model of an ORC forwaste heat recovery of the exhaust gases. A quasi-steady state simulation was carried out on an offdesignmodel, based on optimized design conditions, to estimate the average net power production ofthe ship over a round trip. The maximum net power production of the ORC during the round trip wasestimated to supply approximately 22% of the total power demand on board. The results showed apotential for ORC as a solution for the maritime transport sector to accomplish the new and morerestrictive regulations on emissions, and to reduce the total fuel consumption.
22.	Mondejar, Maria, et al. (författare) Study of the on-route operation of a waste heat recovery system in a passenger vessel 2015 Ingår i: Energy Procedia. - : Elsevier BV. - 1876-6102. ; 75, s. 1646-1653, s. 1646-1653 Tidskriftsartikel (refereegranskat)abstract Waste heat recovery systems for power generation are gaining interest among the marine transport sector as a solution to accomplish the upcoming more restrictive regulations on emissions, and to reduce the total fuel consumption. In this paper we evaluate how a waste heat recovery system based on a regenerative organic Rankine cycle (rORC) could improve the performance of a passenger vessel. The case study is based on the M/S Birka Stockholm cruise ship, which covers a daily route between Stockholm (Sweden) and Mariehamn (Finland). Experimental data on exhaust gas temperatures, fuel consumption and electricity demand on board were logged for a period of four weeks. Based on the results of a fluid and configuration optimization performed in a previous work, an off-design model of a rORC working with benzene was used to estimate the net power production of the rORC at the different load conditions during a port-to-port trip of the vessel. The power generation curve of the rORC over time was compared to that of the electricity demand of the ship. Results showed that the rORC could provide up to 16 % of the total power demand. However, this value should be corrected if the auxiliary engines load is reduced as a consequence of the partial coverage of the electricity demand by the ORC.
23.	Musaddiq, Arslan, et al. (författare) Industry-Academia Cooperation : Applied IoT Research for SMEs in South-East Sweden 2023 Ingår i: Internet of Things. GIoTS 2022. - Cham : Springer. - 9783031209352 - 9783031209369 ; , s. 397-410 Konferensbidrag (refereegranskat)abstract This paper presents the activities of the Applied IoT Lab at the Department of Computer Science and Media Technology, Linnaeus University (LNU), Kalmar, Sweden. The lab is actively engaged in IoT-based educational programs, including a series of workshops and pilot cases. The lab is funded by the European Union and two Swedish counties – Kalmar and Kronoberg. The workshops and pilot cases are part of the research project named IoT Lab for Small and Medium-sized Enterprises (SMEs). One of the lab’s main objectives is to strengthen and support local companies with IoT. The project IoT Lab for SMEs also aims to spread knowledge and inspire the local community about the possibilities of using IoT technologies by organizing open lab days, in-depth lectures, and seminars. This paper introduces Applied IoT Lab at LNU, its educational programs, and industry-academic cooperation, including workshops and a number of ongoing pilot cases.
24.	Musaddiq, Arslan, et al. (författare) Integrating Object Detection and Wide Area Network Infrastructure for Sustainable Ferry Operation 2023 Ingår i: <em>2023 IEEE International Conference on Imaging Systems and Techniques (IST)</em>, Copenhagen, Denmark. - : IEEE. - 9798350330830 - 9798350330847 Konferensbidrag (refereegranskat)abstract Low-Power Wide-Area Network (LPWAN) technologies offer new opportunities for data collection, transmission, and decision-making optimization. Similarly, a wide range of use cases of computer vision and object detection algorithms can be found across different industries. This paper presents a case study focusing on the utilization of LPWAN infrastructure, specifically the Helium network, coupled with computer vision and object detection algorithms, to optimize passenger ferry operation. The passenger ferry called M/S Dessi operates between Kalmar and Färjestaden in Sweden during the summer season. By implementing an Edge-computing solution, real-time data collection and communication are achieved, enabling accurate measurement of passenger flow. This approach is superior to traditional methods of collecting passenger data, such as manual counting or CCTV surveillance. Real-time passenger data is invaluable for traffic planning, crowd prediction, revenue enhancement, and speed and fuel optimization. The utilization of the Helium network ensures reliable and long-distance data transmission, extending the system’s applicability to multiple ferries and distant locations. The proposed approach can be utilized to integrate passenger ferries that operate in close proximity to urban areas into society’s digital transformation efforts. This study highlights the potential of LPWAN, computer vision, and object detection in enhancing passenger ferry operations, contributing to enhanced efficiency and sustainability.
25.	Musaddiq, Arslan, et al. (författare) Internet of Things for Wetland Conservation using Helium Network : Experience and Analysis 2022 Ingår i: 12th International Conference on the Internet of Things, IoT 2022, Delft 7 - 10 November 2022. - New York, NY, USA : ACM Digital Library. - 9781450396653 ; , s. 143-146 Konferensbidrag (refereegranskat)abstract The Internet of Things (IoT), as a new paradigm of connected things or objects to the Internet, allows us to monitor the environment by collecting data in a wide spatial and temporal window. Especially the utilization of IoT has increased significantly since the development of the Long Range Wide Area Network (LoRaWAN). However, deploying LoRa gateways, maintaining network infrastructure, operational cost, and quality of service are challenging. Helium has emerged as one of the largest networks in terms of coverage for IoT devices to solve such problems. Helium is decentralized, cryptocurrency incentives-based network infrastructure replacing traditional service providers. However, due to network incentives, currently, it contains more hotspots compared to active users. This paper presents our experience and analysis of deploying IoT devices for real-world applications using the Helium network. We present experiences from the IoT device’s deployment for wetland conservation in southern Sweden.
26.	Musaddiq, Arslan, et al. (författare) Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments : Theoretical Perspective and Challenges 2023 Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:19 Tidskriftsartikel (refereegranskat)abstract Internet of Things (IoT) devices are increasingly popular due to their wide array of application domains. In IoT networks, sensor nodes are often connected in the form of a mesh topology and deployed in large numbers. Managing these resource-constrained small devices is complex and can lead to high system costs. A number of standardized protocols have been developed to handle the operation of these devices. For example, in the network layer, these small devices cannot run traditional routing mechanisms that require large computing powers and overheads. Instead, routing protocols specifically designed for IoT devices, such as the routing protocol for low-power and lossy networks, provide a more suitable and simple routing mechanism. However, they incur high overheads as the network expands. Meanwhile, reinforcement learning (RL) has proven to be one of the most effective solutions for decision making. RL holds significant potential for its application in IoT device’s communication-related decision making, with the goal of improving performance. In this paper, we explore RL’s potential in IoT devices and discuss a theoretical framework in the context of network layers to stimulate further research. The open issues and challenges are analyzed and discussed in the context of RL and IoT networks for further study.
27.	Palma, Francis, et al. (författare) Investigating the Linguistic Design Quality of Public, Partner, and Private REST APIs 2022 Ingår i: Proceedings - 2022 IEEE International Conference on Services Computing, SCC 2022. - : IEEE. - 9781665481465 ; , s. 20-30 Konferensbidrag (refereegranskat)abstract Application Programming Interfaces (APIs) define how Web services, middle-wares, frameworks, and libraries communicate with their clients. An API that conforms to REpresentational State Transfer (REST) design principles is known as REST API. At present, it is an industry-standard for interaction among Web services. There exist mainly three categories of APIs: public, partner, and private. Public APIs are designed for external consumers, whereas partner APIs are designed aiming at organizational partners. In contrast, private APIs are designed solely for internal use. The API quality matters regardless of their category and intended consumers. To assess the (linguistic) design of APIs, researchers defined linguistic patterns (i.e., best API design practices) and linguistic antipatterns (i.e., poor API design practices.) APIs that follow linguistic patterns are easy to understand, use, and maintain. In this study, we analyze and compare the design quality of public, partner, and private APIs. More specifically, we made a large survey by analyzing and performing the detection of nine linguistic patterns and their corresponding antipatterns on more than 2,500 end-points from 37 APIs. Our results suggest that (1) public, partner, and private APIs lack quality linguistic design, (2) among the three API categories, private APIs lack linguistic design the most, and (3) end-points are amorphous, contextless, and non-descriptive in partner APIs. End-points have contextless design and poor documentation regardless of the API categories.
28.	Pena, Blanca, et al. (författare) A Review on Applications of Machine Learning in Shipping Sustainability 2020 Ingår i: SNAME Maritime Convention 2020 – A Virtual Event 29 September- 2 October. - : Society of Naval Architects and Marine Engineers (SNAME). Konferensbidrag (refereegranskat)abstract The shipping industry faces a significant challenge as it needs to significantly lower the amounts of Green House Gas emissions at the same time as it is expected to meet the rising demand. Traditionally, optimising the fuel consumption for ships is done during the ship design stage and through operating it in a better way, for example, with more energy-efficient machinery, optimising the speed or route. During the last decade, the area of machine learning has evolved significantly, and these methods are applicable in many more fields than before. The field of ship efficiency improvement by using Machine Learning methods is significantly progressing due to the available volumes of data from online measuring, experiments and computations. This amount of data has made machine learning a powerful tool that has been successfully used to extract information and intricate patterns that can be translated into attractive ship energy savings. This article presents an overview of machine learning, current developments, and emerging opportunities for ship efficiency. This article covers the fundamentals of Machine Learning and discusses the methodologies available for ship efficiency optimisation. Besides, this article reveals the potentials of this promising technology and future challenges.
29.	Xie, Xianwei, et al. (författare) Fuel Consumption Prediction Models Based on Machine Learning and Mathematical Methods 2023 Ingår i: Journal of Marine Science and Engineering. - : MDPI. - 2077-1312. ; 11:4 Tidskriftsartikel (refereegranskat)abstract An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermore, we use the predicted correlation between fuel consumption rates and speed under simulated conditions for model performance validation. We also discuss applying the data-cleaning method in the preprocessing of the black-box model. The results demonstrate that this method is feasible and can support the performance of the fuel consumption model in a broad and dense distribution of noise data in data collected from real ships. We improved the error to 4% of the white-box model and the R22 to 0.9977 and 0.9922 of the XGBoost and RF models, respectively. After applying the Kwon cleaning method, the value of R22 also can reach 0.9954, which can provide decision support for the operation of shipping companies.
30.	Zapico, Jorge Luis, et al. (författare) Insect biodiversity in agriculture using IoT : opportunities and needs for further research 2022 Ingår i: IEEE Global Communications Conference, 7-11 December 2021, Madrid, Spain. - : IEEE. - 9781665423908 - 9781665423915 ; , s. 1-5 Konferensbidrag (refereegranskat)abstract Recent research points out an alarming decline in insect biodiversity and biomass. Changing agriculture practices, together with climate change, are a main driver behind this decline. Biodiversity interventions in agriculture can therefore play an important role for insect conservation. Validating the impact of such interventions is limited by expertise and labor intensive methods, and there is a growing number of projects exploring how IoT could help. For instance using remote sensors to capture insect images and sound fingerprints non-intrusively, and machine learning models to automatically classify the obser- vation in different taxa. This article will: (a) explore recent advances in Internet of Things, Edge ML and LPWAN technologies and their application for monitoring insect biodiversity; (b) discuss opportunities, needs and ideas for validating the impact of biodiversity inter- ventions in agriculture using these technologies; and (c) outline future research opportunities.

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