12,097 research outputs found

    What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

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    Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

    A safe and energy efficient robotic system for industrial automatic tests on domestic appliances: Problem statement and proof of concept

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    In this paper, the design and the development of a robotic platform conceived to perform accelerated life tests on a newly manufactured domestic appliances is presented. The proposed system aims at improving the safety of human operators that share the workspace with the robotic platform which is a common scenario of test laboratories. A deep learning algorithm is used for the human detection and pose estimation, while the integration between a conventional motion planning algorithm with a fast 3D collision checker has been implemented as a global planner plugin for the ROS navigation stack. With the twofold objective of improving safety and saving energy in the battery-powered mobile manipulator used in this project, the problem of minimizing the overall kinetic energy is addressed through a properly designed task priority controller, in which the manipulator inertia matrix is used to weight the joint speeds while satisfying multiple robotic tasks according to a hierarchy designed to interact with the appliances while preserving the safety of the human operators. Simulations are carried out to evaluate the overall control architecture and preliminary results indicate the effectiveness of the developed system in the test laboratory floors

    Magnetic Modelling of Synchronous Reluctance and Internal Permanent Magnet Motors Using Radial Basis Function Networks

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    The general trend toward more intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. Among the candidates, pure reluctance and anisotropic permanent magnet motors are gaining popularity, despite their complex structure. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. This paper focuses on the relations between currents and flux linkages, which are obtained through innovative radial basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The theoretical foundations of the radial basis function networks, the design hints, and a commented series of experimental results on a real laboratory prototype are included in this paper. The simple structure of the neural network fits for implementation on standard drives. The online training and tracking will be the next steps in field programmable gate array based control systems

    Deep learning for video game playing

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    In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

    Multimodal Content Analysis for Effective Advertisements on YouTube

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    The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users. In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements. We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement. The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users. Our proposed framework employs the signal processing technique of cross modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation. Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness. We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201

    GIS Application to Support Land Administration Services in Ghana: Institutional Factors and Software Developments

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    In June 1999, the Ghanaian Government launched a new land policy document that sought to address some fundamental problems associated with land administration and management in the country. The document identified the weak land administration system as a particular problem and recommended the introduction of computer-aided information systems in the ‘lands sector’. In 2001, the Government made further proposals to prepare and implement a Land Administration Programme (LAP) to provide a better platform for evolving an efficient land administration that would translate the ‘National Land Policy’ into action. Thus, an up-to-date land information system (LIS), supporting efficient management of land records, is to be constructed, which provides a context for the research reported in this paper. We document two aspects of our research on the adoption of GIS by the Lands Commission Secretariat (LCS) which form part of a pilot project in GIS diffusion. Part one of the paper mainly outlines the empirical results arising from fieldwork undertaken during 2001 to determine the information and GIS requirements of the LCS in relation to their routine administrative processes and to identify the critical factors that are required to ensure that any new GIS applications are successfully embraced. Part two explains the prototype software system developed using ArcView 3.2 and Access that provides the LCS with a means to automate some of the routine administrative tasks that they are required to fulfil. The software has been modified and upgraded following an initial evaluation by LCS employees also conducted as part of the fieldwork in Accra

    IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management

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    Curbing road accidents has always been one of the utmost priorities in every country. In Malaysia, Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents has increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is driver’s behaviours. However, drivers’ behaviour was challenging to regulate by the enforcement team or fleet operators, especially heavy vehicles. We proposed adopting the Internet of Things (IoT) and its’ emerging technologies to monitor and alert driver’s behavioural and driving patterns in reducing road accidents. In this work, we proposed a lane tracking and iris detection algorithm to monitor and alert the driver’s behaviour when the vehicle sways away from the lane and the driver feeling drowsy, respectively. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as an intelligent transportation system in the vehicle. We implemented face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring, respectively. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioural activities in real-time through the user access portal. We validated it successfully on Malaysian roads.  The outcome of this pilot project benefits the safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioural in the future

    IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management

    Get PDF
    Curbing road accidents has always been one of the utmost priorities in every country. In Malaysia, Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents has increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is driver’s behaviours. However, drivers’ behaviour was challenging to regulate by the enforcement team or fleet operators, especially heavy vehicles. We proposed adopting the Internet of Things (IoT) and its’ emerging technologies to monitor and alert driver’s behavioural and driving patterns in reducing road accidents. In this work, we proposed a lane tracking and iris detection algorithm to monitor and alert the driver’s behaviour when the vehicle sways away from the lane and the driver feeling drowsy, respectively. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as an intelligent transportation system in the vehicle. We implemented face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring, respectively. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioural activities in real-time through the user access portal. We validated it successfully on Malaysian roads.  The outcome of this pilot project benefits the safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioural in the future
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