6,467 research outputs found

    Data mining for vehicle telemetry

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    This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height

    Data mining for vehicle telemetry

    Get PDF
    This paper presents a data mining methodology for driving condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labelling problems: Road Type (A, B, C and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, namely, signal selection, feature extraction, and feature selection. The selection methods used include Principal Components Analysis (PCA) and Mutual Information (MI), which are used to determine the relevance and redundancy of extracted features, and are performed in various combinations. Finally, as there is an inherent bias towards certain road and carriageway labellings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension heigh

    Investigating the feasibility of vehicle telemetry data as a means of predicting driver workload

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    Driving is a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to use vehicle telemetry data as a performance measure for workload. In this paper, we present the Warwick-JLR Driver Monitoring Dataset (DMD) and analyse it to investigate the feasibility of using vehicle telemetry data for determining the driver workload. We perform a statistical analysis of subjective ratings, physiological data, and vehicle telemetry data collected during a track study. A data mining methodology is then presented to build predictive models using this data, for the driver workload monitoring problem

    Spacecraft attitude detection system by stellar reference Patent

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    Attitude detection system using stellar references for three-axis control and spin stabilized spacecraf

    Application of an AIS to the problem of through life health management of remotely piloted aircraft

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    The operation of RPAS includes a cognitive problem for the operators(Pilots, maintainers, ,managers, and the wider organization) to effectively maintain their situational awareness of the aircraft and predict its health state. This has a large impact on their ability to successfully identify faults and manage systems during operations. To overcome these system deficiencies an asset health management system that integrates more cognitive abilities to aid situational awareness could prove beneficial. This paper outlines an artificial immune system (AIS) approach that could meet these challenges and an experimental method within which to evaluate it

    Nasa to launch second pegasus meteoroid satellite

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    Second Pegasus meteoroid satellite launching by NAS

    Evaluation of high density polyethylene plastic bag performance towards edge and point stresses using taguchi method

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    Plastic bag are widely used due to it is low cost and convenience for packaging items. The problem with the strength of the plastic bag tends to tear easily and perforated. This study aims to validate the simulation results of High Density Polyethylene (HDPE) plastic towards HDPE plastic bags manufactured in UTHM and thus to evaluate the performance of plastic bag towards mass, edge and point stresses. The tensile test simulation was conducted using Solidworks 2017 to validate the HDPE plastic material properties by comparing the tensile test performed according to ASTM D882-18. The real life application was conducted to validate the simulation result by comparing plastic film’s displacement with different mass applied. Taguchi Method was used to arrange the edge and point stress test parameter with varied angle, mass, length and distance between the loads. The result showed that the error percentage for all loads was lower than 10.00 % for simulation compared to experimental tensile test. It also showed that error percentage was less than 5.00 % by comparing real life application and simulation results for displacement of plastic film. For mass stress test, the loads with 5.0 kg square base has the highest stress acted on the plastic film’s surface which is 22.399 MPa. For edge stress test, sample D with 1.0 kg, 20 mm of edge’s length and 20 ° of edge’s angle have highest maximum stress and displacement acted on plastic film’s surface which are 34.086 MPa and 84.94 mm respectively. For point stress test, sample G with 1.0 kg, 10 ° of angle and 30 mm of the distance between the point load have highest maximum stress and displacement acted on surface of plastic film which are 50.676 MPa and 63.64 mm accordingly. Both sample D and G were perforated since the maximum stress acted was exceed the tensile strength of HDPE plastic which is 28.4 MPa. The validation of HDPE plastic towards HDPE plastic bag manufactured in UTHM was proven from the result obtained. The plastic bag’s performance towards mass, edge and point stresses was successfully evaluated by using Finite Element Analysis and Taguchi Method
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