| 1. |
- Bodell, Victor, et al.
(författare)
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Comparing Machine Learning Estimation of Fuel Consumption of Heavy-Duty Vehicles
- 2021
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Ingår i: World Academy of Science, Engineering and Technology: An International Journal of Science, Engineering and Technology. - 2010-376X .- 2070-3740. ; 15:2, s. 97-101
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Tidskriftsartikel (refereegranskat)abstract
- Fuel consumption (FC) is one of the key factors indetermining expenses of operating a heavy-duty vehicle. A customermay therefore request an estimate of the FC of a desired vehicle.The modular design of heavy-duty vehicles allows their constructionby specifying the building blocks, such as gear box, engine andchassis type. If the combination of building blocks is unprecedented,it is unfeasible to measure the FC, since this would first r equire theconstruction of the vehicle. This paper proposes a machine learningapproach to predict FC. This study uses around 40,000 vehiclesspecific a nd o perational e nvironmental c onditions i nformation, suchas road slopes and driver profiles. A ll v ehicles h ave d iesel enginesand a mileage of more than 20,000 km. The data is used to investigatethe accuracy of machine learning algorithms Linear regression (LR),K-nearest neighbor (KNN) and Artificial n eural n etworks ( ANN) inpredicting fuel consumption for heavy-duty vehicles. Performance ofthe algorithms is evaluated by reporting the prediction error on bothsimulated data and operational measurements. The performance of thealgorithms is compared using nested cross-validation and statisticalhypothesis testing. The statistical evaluation procedure finds thatANNs have the lowest prediction error compared to LR and KNNin estimating fuel consumption on both simulated and operationaldata. The models have a mean relative prediction error of 0.3% onsimulated data, and 4.2% on operational data.
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| 2. |
- Fleyeh, Hasan
(författare)
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Traffic sign recognition without color information
- 2015
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Ingår i: Colour and Visual Computing Symposium (CVCS), 2015. - Borlänge : Högskolan Dalarna. ; , s. 1-6
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Color represents an important attribute in the field of traffic sign recognition. However, when the color of the traffic sign fades or the traffic scene is collected in gray as in the case of Infrared imaging, then color based recognition systems fail. Other problems related to color are simply that different countries use different colors. Even within the European Union, colors of traffic signs are not the same.This paper aims to present a new approach to detect traffic signs without color attributes. It is based a two-stage sliding window which detects traffic signs in the multi-scale image. Histogram of Oriented Gradients (HOG) descriptors are computed as a quality function which are evaluated by two SVM classifier; the coarse and the fine detectors. Different objects detected by the coarse detectors are clustered and a fine search is conducted in the areas where traffic signs are more probable to exist. Experiments conducted to detect traffic signs under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 98% and very low false positive rate. The proposed approach was tested on the Yield traffic signs because it has a simple triangular shape which can be found in many places other than the traffic signs and represent a challenge to the proposed approach.
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| 3. |
- Memedi, Mevludin, 1983-, et al.
(författare)
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A method for measuring Parkinson's disease related temporal irregularity in spiral drawings
- 2016
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Ingår i: 2016 IEEE International Conference on Biomedical and Health Informatics. - New York : Institute of Electrical and Electronics Engineers (IEEE). - 9781509024551 ; , s. 410-413
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Konferensbidrag (refereegranskat)abstract
- The objective of this paper was to develop and evaluate clinimetric properties of a method for measuring Parkinson's disease (PD)-related temporal irregularities using digital spiral analysis. In total, 108 (98 patients in different stages of PD and 10 healthy elderly subjects) performed repeated spiral drawing tasks in their home environments using a touch screen device. A score was developed for representing the amount of temporal irregularity during spiral drawing tasks, using Approximate Entropy (ApEn) technique. In addition, two previously published spiral scoring methods were adapted and their scores were analyzed. The mean temporal irregularity score differed significantly between healthy elderly subjects and advanced PD patients (P<0.005). The ApEn-based method had a better responsiveness and test-retest reliability when compared to the other two methods. In contrast to the other methods, the mean scores of the ApEn-based method improved significantly during a 3 year clinical study, indicating a possible impact of pathological basal ganglia oscillations in temporal control during spiral drawing tasks. In conclusion, the ApEn-based method could be used for differentiating between patients in different stages of PD and healthy subjects. The responsiveness and test-retest reliability were good for the ApEn-based method indicating that this method is useful for measuring upper limb temporal irregularity at a micro-level.
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| 4. |
- Saleh, Roxan, et al.
(författare)
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An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs
- 2022
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Ingår i: Applied Sciences. - : MDPI AG. - 2076-3417. ; 12:5
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Tidskriftsartikel (refereegranskat)abstract
- The road traffic signs in Sweden have no inventory system and it is unknown when a sign has reached the end of its service life and needs to be replaced. As a result, the road authorities do not have a systematic maintenance program for road traffic signs, and many signs which are not in compliance with the minimum retroreflectivity performance requirements are still found on the roads. Therefore, it is very important to find an inexpensive, safe, easy, and highly accurate method to judge the retroreflectivity performance of road signs. This will enable maintenance staff to determine the retroreflectivity of road signs without requiring measuring instruments for retroreflectivity or colors performance. As a first step toward the above goal, this paper aims to identify factors affecting the retroreflectivity of road signs. Two different datasets were used, namely, the VTI dataset from Sweden and NMF dataset from Denmark. After testing different models, two logarithmic regression models were found to be the best-fitting models, with R2 values of 0.50 and 0.95 for the VTI and NMF datasets, respectively. The first model identified the age, direction, GPS positions, color, and class of road signs as significant predictors, while the second model used age, color, and the class of road signs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 5. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems
- 2016
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Konferensbidrag (refereegranskat)abstract
- This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.
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| 6. |
- Aghanavesi, Somayeh, 1981-
(författare)
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Sensor-based knowledge- and data-driven methods : A case of Parkinson’s disease motor symptoms quantification
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The overall aim of this thesis was to develop and evaluate new knowledge- and data-driven methods for supporting treatment and providing information for better assessment of Parkinson’s disease (PD).PD is complex and progressive. There is a large amount of inter- and intravariability in motor symptoms of patients with PD (PwPD). The current evaluation of motor symptoms that are done at clinics by using clinical rating scales is limited and provides only part of the health status of PwPD. An accurate and clinically approved assessment of PD is required using frequent evaluation of symptoms.To investigate the problem areas, the thesis adopted the microdata analysis approach including the stages of data collection, data processing, data analysis, and data interpretation. Sensor systems including smartphone and tri-axial motion sensors were used to collect data from advanced PwPD experimenting with repeated tests during a day. The experiments were rated by clinical experts. The data from sensors and the clinical evaluations were processed and used in subsequent analysis.The first three papers in this thesis report the results from the investigation, verification, and development of knowledge- and data-driven methods for quantifying the dexterity in PD. The smartphone-based data collected from spiral drawing and alternate tapping tests were used for the analysis. The results from the development of a smartphone-based data-driven method can be used for measuring treatment-related changes in PwPD. Results from investigation and verification of an approximate entropy-based method showed good responsiveness and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity.The next two papers, report the results from the investigation and development of motion sensor-based knowledge- and data-driven methods for quantification of the motor states in PD. The motion data were collected from experiments such as leg agility, walking, and rapid alternating movements of hands. High convergence validity resulted from using motion sensors during leg agility tests. The results of the fusion of sensor data gathered during multiple motor tests were promising and led to valid, reliable and responsive objective measures of PD motor symptoms.Results in the last paper investigating the feasibility of using the Dynamic Time-Warping method for assessment of PD motor states showed it is feasible to use this method for extracting features to be used in automatic scoring of PD motor states.The findings from the knowledge- and data-driven methodology in this thesis can be used in the development of systems for follow up of the effects of treatment and individualized treatments in PD.
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| 7. |
- Aghanavesi, Somayeh, 1981-
(författare)
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Smartphone-based Parkinson’s disease symptom assessment
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS).At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms.Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication.The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure.
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| 8. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A fuzzy rule-based decision support system for Duodopa treatment in Parkinson
- 2006
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Ingår i: 23rd annual workshop of the Swedish Artificial Intelligence Society. - Umeå.
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Konferensbidrag (refereegranskat)abstract
- A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.
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| 9. |
- Al-Dulaimy, Auday, et al.
(författare)
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Fault Tolerance in Cloud Manufacturing : An Overview
- 2023
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Ingår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. - : Springer Science and Business Media Deutschland GmbH. - 9783031318900 ; , s. 89-101
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Konferensbidrag (refereegranskat)abstract
- Utilizing edge and cloud computing to empower the profitability of manufacturing is drastically increasing in modern industries. As a result of that, several challenges have raised over the years that essentially require urgent attention. Among these, coping with different faults in edge and cloud computing and recovering from permanent and temporary faults became prominent issues to be solved. In this paper, we focus on the challenges of applying fault tolerance techniques on edge and cloud computing in the context of manufacturing and we investigate the current state of the proposed approaches by categorizing them into several groups. Moreover, we identify critical gaps in the research domain as open research directions. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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| 10. |
- Carling, Kenneth, 1967-, et al.
(författare)
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On deploying eCOmpass : a decision support tool for environmentally friendly retail locations
- 2024
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Much focus in the joint retailing and transportation domain has been on the transition to e-tailing and the reformation of supply-chain logistics. However, traditional retailing, where consumers visit stores for shopping, dominates and will continue to do so for the foreseeable future. Retailers continuously expand, contract, and reconfigure their store network for strategic reasons. This paper reports on a project aiming to facilitate the incorporation of environmental consequences into the retailer’s reconfiguration decision process. It describes the design and deployment process of eCOmpass, an online decision support tool that enables retailers to estimate the change in transportation-related CO2 emissions caused by a reconfiguration of their store network. This description encompasses the judgmental choices of data acquisition, optimization technology, and user interface.
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