| 1. |
- Carpatorea, Iulian, 1982-, et al.
(författare)
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Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers
- 2017
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Ingår i: 2017 Intelligent Systems Conference (IntelliSys). - : IEEE. - 9781509064359 - 9781509064366 ; , s. 476-481
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Konferensbidrag (refereegranskat)abstract
- Improving the performance of systems is a goal pursued in all areas and vehicles are no exception. In places like Europe, where the majority of goods are transported over land, it is imperative for fleet operators to have the best efficiency, which results in efforts to improve all aspects of truck operations. We focus on drivers and their performance with respect to fuel consumption. Some of relevant factors are not accounted for inavailable naturalistic data, since it is not feasible to measure them. An alternative is to set up experiments to investigate driver performance but these are expensive and the results are not always conclusive. For example, drivers are usually aware of the experiment’s parameters and adapt their behavior.This paper proposes a method that addresses some of the challenges related to categorizing driver performance with respect to fuel consumption in a naturalistic environment. We use expert knowledge to transform the data and explore the resulting structure in a new space. We also show that the regions found in APPES provide useful information related to fuel consumption. The connection between APPES patterns and fuel consumption can be used to, for example, cluster drivers in groups that correspond to high or low performance. © 2017 IEEE
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| 2. |
- Carpatorea, Iulian, 1982-, et al.
(författare)
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Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data
- 2016
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Ingår i: Proceedings. - Los Alamitos, CA : IEEE Computer Society. - 9781509061662 ; , s. 1067-1072
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Konferensbidrag (refereegranskat)abstract
- Fuel used by heavy duty trucks is a major cost for logistics companies, and therefore improvements in this area are highly desired. Many of the factors that influence fuel consumption, such as the road type, vehicle configuration or external environment, are difficult to influence. One of the most under-explored ways to lower the costs is training and incentivizing drivers. However, today it is difficult to measure driver performance in a comprehensive way outside of controlled, experimental setting.This paper proposes a machine learning methodology for quantifying and qualifying driver performance, with respect to fuel consumption, that is suitable for naturalistic driving situations. The approach is a knowledge-based feature extraction technique, constructing a normalizing fuel consumption value denoted Fuel under Predefined Conditions (FPC), which captures the effect of factors that are relevant but are not measured directly.The FPC, together with information available from truck sensors, is then compared against the actual fuel used on a given road segment, quantifying the effects associated with driver behavior or other variables of interest. We show that raw fuel consumption is a biased measure of driver performance, being heavily influenced by other factors such as high load or adversary weather conditions, and that using FPC leads to more accurate results. In this paper we also show evaluation the proposed method using large-scale, real-world, naturalistic database of heavy-duty vehicle operation.
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| 3. |
- Fan, Yuantao, 1989-, et al.
(författare)
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Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
- 2015
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Ingår i: Procedia Computer Science. - Amsterdam : Elsevier. - 1877-0509. ; 53, s. 447-456
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Tidskriftsartikel (refereegranskat)abstract
- Managing the maintenance of a commercial vehicle fleet is an attractive application domain of ubiquitous knowledge discovery. Cost effective methods for predictive maintenance are progressively demanded in the automotive industry. The traditional diagnostic paradigm that requires human experts to define models is not scalable to today's vehicles with hundreds of computing units and thousands of control and sensor signals streaming through the on-board controller area network. A more autonomous approach must be developed. In this paper we evaluate the performance of the COSMO approach for automatic detection of air pressure related faults on a fleet of city buses. The method is both generic and robust. Histograms of a single pressure signal are collected and compared across the fleet and deviations are matched against workshop maintenance and repair records. It is shown that the method can detect several of the cases when compressors fail on the road, well before the failure. The work is based on data from a three year long field study involving 19 buses operating in and around a city on the west coast of Sweden. © The Authors. Published by Elsevier B.V.
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| 4. |
- Fan, Yuantao, 1989-, et al.
(författare)
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Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
- 2015
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Ingår i: Frontiers in Artificial Intelligence and Applications. - Amsterdam : IOS Press. - 0922-6389 .- 1879-8314. ; 278, s. 58-67
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Tidskriftsartikel (refereegranskat)abstract
- In the automotive industry, cost effective methods for predictive maintenance are increasingly in demand. The traditional approach for developing diagnostic methods on commercial vehicles is heavily based on knowledge of human experts, and thus it does not scale well to modern vehicles with many components and subsystems. In previous work we have presented a generic self-organising approach called COSMO that can detect, in an unsupervised manner, many different faults. In a study based on a commercial fleet of 19 buses operating in Kungsbacka, we have been able to predict, for example, fifty percent of the compressors that break down on the road, in many cases weeks before the failure.In this paper we compare those results with a state of the art approach currently used in the industry, and we investigate how features suggested by experts for detecting compressor failures can be incorporated into the COSMO method. We perform several experiments, using both real and synthetic data, to identify issues that need to be considered to improve the accuracy. The final results show that the COSMO method outperforms the expert method.
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| 5. |
- Fan, Yuantao, 1989-, et al.
(författare)
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Predicting Air Compressor Failures with Echo State Networks
- 2016
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Ingår i: PHME 2016. - : Prognostics and Health Management Society. - 9781936263219 ; , s. 568-578
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Konferensbidrag (refereegranskat)abstract
- Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the driver, but they can also be exploited to detect faults and predict failures. The traditional diagnostics paradigm, which relies heavily on human expert knowledge, scales poorly with the increasing amounts of data generated by highly digitised systems. The next generation of equipment monitoring and maintenance prediction solutions will therefore require a different approach, where systems can build up knowledge (semi-)autonomously and learn over the lifetime of the equipment.A key feature in such systems is the ability to capture and encode characteristics of signals, or groups of signals, on-board vehicles using different models. Methods that do this robustly and reliably can be used to describe and compare the operation of the vehicle to previous time periods or to other similar vehicles. In this paper two models for doing this, for a single signal, are presented and compared on a case of on-road failures caused by air compressor faults in city buses. One approach is based on histograms and the other is based on echo state networks. It is shown that both methods are sensitive to the expected changes in the signal's characteristics and work well on simulated data. However, the histogram model, despite being simpler, handles the deviations in real data better than the echo state network.
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| 6. |
- Khandelwal, Siddhartha, 1987-
(författare)
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Gait Event Detection in the Real World
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Healthy gait requires a balance between various neuro-physiological systems and is considered an important indicator of a subject's physical and cognitive health status. As such, health-related applications would immensely benefit by performing long-term or continuous monitoring of subjects' gait in their natural environment and everyday lives. In contrast to stationary sensors such as motion capture systems and force plates, inertial sensors provide a good alternative for such gait analysis applications as they are miniature, cheap, mobile and can be easily integrated into wearable systems.This thesis focuses on improving overall gait analysis using inertial sensors by providing a methodology for detecting gait events in real-world settings. Although the experimental protocols for such analysis have been restricted to only highly-controlled lab-like indoor settings; this thesis presents a new gait database that consists of data from gait activities carried out in both, indoor and outdoor environments. The thesis shows how domain knowledge about gait could be formulated and utilized to develop methods that are robust and can tackle real-world challenges. It also shows how the proposed approach can be generalized to estimate gait events from multiple body locations. Another aspect of this thesis is to demonstrate that the traditionally used temporal error metrics are not enough for presenting the overall performance of gait event detection methods. The thesis introduces how non-parametric tests can be used to complement them and provide a better overview.The results of comparing the proposed methodology to state-of-the-art methods showed that the approach of incorporating domain knowledge into the time-frequency analysis of the signal was robust across different real-world scenarios and outperformed other methods, especially for the scenario involving variable gait speeds in outdoor settings. The methodology was also benchmarked on publicly available gait databases yielding good performance for estimating events from different body locations. To conclude, this thesis presents a road map for the development of gait analysis systems in real-world settings.
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| 7. |
- Manasa, Justen, et al.
(författare)
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Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors
- 2017
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Ingår i: Scientific Reports. - London : Nature Publishing Group. - 2045-2322. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Several groups have proposed that genotypic determinants in gag and the gp41 cytoplasmic domain (gp41-CD) reduce protease inhibitor (PI) susceptibility without PI-resistance mutations in protease. However, no gag and gp41-CD mutations definitively responsible for reduced PI susceptibility have been identified in individuals with virological failure (VF) while receiving a boosted PI (PI/r)-containing regimen. To identify gag and gp41 mutations under selective PI pressure, we sequenced gag and/or gp41 in 61 individuals with VF on a PI/r (n = 40) or NNRTI (n = 20) containing regimen. We quantified nonsynonymous and synonymous changes in both genes and identified sites exhibiting signal for directional or diversifying selection. We also used published gag and gp41 polymorphism data to highlight mutations displaying a high selection index, defined as changing from a conserved to an uncommon amino acid. Many amino acid mutations developed in gag and in gp41-CD in both the PI- and NNRTI-treated groups. However, in neither gene, were there discernable differences between the two groups in overall numbers of mutations, mutations displaying evidence of diversifying or directional selection, or mutations with a high selection index. If gag and/or gp41 encode PI-resistance mutations, they may not be confined to consistent mutations at a few sites. © 2017 The Author(s).
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| 8. |
- Prytz, Rune, 1980-, et al.
(författare)
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Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data
- 2015
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Ingår i: Engineering applications of artificial intelligence. - Oxford : Pergamon Press. - 0952-1976 .- 1873-6769. ; 41, s. 139-150
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Tidskriftsartikel (refereegranskat)abstract
- Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain. © 2015 Elsevier Ltd.
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| 9. |
- Rögnvaldsson, Thorsteinn, 1963-, et al.
(författare)
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Self-monitoring for maintenance of vehicle fleets
- 2018
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Ingår i: Data mining and knowledge discovery. - New York : Springer-Verlag New York. - 1384-5810 .- 1573-756X. ; 32:2, s. 344-384
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Tidskriftsartikel (refereegranskat)abstract
- An approach for intelligent monitoring of mobile cyberphysical systems is described, based on consensus among distributed self-organised agents. Its usefulness is experimentally demonstrated over a long-time case study in an example domain: a fleet of city buses. The proposed solution combines several techniques, allowing for life-long learning under computational and communication constraints. The presented work is a step towards autonomous knowledge discovery in a domain where data volumes are increasing, the complexity of systems is growing, and dedicating human experts to build fault detection and diagnostic models for all possible faults is not economically viable. The embedded, self-organised agents operate on-board the cyberphysical systems, modelling their states and communicating them wirelessly to a back-office application. Those models are subsequently compared against each other to find systems which deviate from the consensus. In this way the group (e.g. a fleet of vehicles) is used to provide a standard, or to describe normal behaviour, together with its expected variability under particular operating conditions. The intention is to detect faults without the need for human experts to anticipate them beforehand. This can be used to build up a knowledge base that accumulates over the life-time of the systems. The approach is demonstrated using data collected during regular operation of a city bus fleet over the period of almost four years. © 2017 The Author(s)
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| 10. |
- Rögnvaldsson, Thorsteinn, 1963-, et al.
(författare)
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State of the art prediction of HIV-1 protease cleavage sites
- 2015
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Ingår i: Bioinformatics. - Oxford : Oxford University Press. - 1367-4803 .- 1367-4811. ; 31:8, s. 1204-1210
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Understanding the substrate specificity of HIV-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved.Results: The linear support vector machine with orthogonal encod-ing is shown to be the best predictor for HIV-1 protease cleavage. It is considerably better than current publicly available predictor ser-vices. It is also found that schemes using physicochemical proper-ties do not improve over the standard orthogonal encoding scheme. Some issues with the currently available data are discussed.Availability: The data sets used, which are the most important part, are available at the UCI Machine Learning Repository. The tools used are all standard and easily available. © 2014 The Author.
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