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APPROXIMATE OPTIMAL AUTOSAR SOFTWARE COMPONENTS DEPLOYING APPROACH FOR AUTOMOTIVE E/E SYSTEM

INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2017年第6期18卷 1109-1119页

作者： Ran, Zheng Yan, Hua Zhang, Huimin Li, Yun Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China

The AUTOSAR has been developed as the worldwide standard for automotive E/E software systems, making the electronic components of different suppliers to be employed universally. However, as the number of component-based applications in modern automotive embedded systems grows rapidly and the hardware topology becomes increasingly complex, deploying such large number of components in automotive distributed system in manual way is over-dependent on experience of engineers which in turn is time consuming. Furthermore, the resource limitation and scheduling analysis make the problems more complex for developers to find out an approximate optimal deploying approach in system integration. In this paper, we propose a novel method to deploy the AUTOSAR components onto ECUs with the following features. First, a clustering algorithm is designed for deploying components automatically within relatively low time complexity. Second, a fitness function is designed to balance the ECUs load. The goal of our approach is to minimize the communication cost over all the runnable entities while meeting all corresponding timing constraints and balancing all the ECUs load. The experiment results show that our approach is efficient and has well performance by comparing with other existing methods in specific and synthetic data set.

关键词： AUTOSAR Component deploying Communication network Load balance clustering algorithm

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A Robust clustering algorithm for Interval Data

A Robust Clustering Algorithm for Interval Data

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IEEE International Conference on Fuzzy Systems

作者： Miin-Shen Yang Hsien-Chun Kuo Wen-Liang Hung Department of Applied Mathematics Chung Yung Christian University Graduate Institute of Computer Science National Hsinchu University of Education

ISBN: (纸本)9781467315074

In this paper we propose a robust clustering algorithm for interval data. The proposed method is based on similarity measure that is not necessary to specify a cluster number and initials. Several numerical examples demonstrate the effectiveness of the proposed robust clustering algorithm. We then apply this algorithm to the real data set with cities temperature interval data. The proposed clustering algorithm actually presents its robustness.

关键词： clustering algorithm Interval data Robustness clustering algorithms Robustness observational data

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A SPA-BASED K-MEANS clustering algorithm FOR THE REMOTE SENSING INFORMATION EXTRACTION

A SPA-BASED K-MEANS CLUSTERING ALGORITHM FOR THE REMOTE SENS...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Xiangjian Xie Junsan Zhao Hongbo Li Wanqiang Zhang Lei Yuan Faculty of Land and Resource Engineering Kunming University of Science and Technology Faculty of Construction Engineering Kunming University of Science and Technology

ISBN: (纸本)9781467311601

Set Pair Analysis (SPA) is a new methodology to describe and process uncertainty system, which has been applied in many fields recently. In this paper, a new approach to remote sensing information extraction, the SPA-based k-means clustering algorithm (SPAKM), has been proposed based on the principle of SPA. The basic ideals and steps of SPAKM are discussed. The proposed algorithm can overcome the limitation of K-means clustering algorithm to certain extent. Finally, cluster analysis experiments of LANDSAT TM image have been made. The results show that the improved K-means clustering algorithm is superior to K-means in classification accuracy of land cover classes of mixed pixels.

关键词： K-means clustering algorithm Set Pair Analysis IDC connection degree Remote sensing image

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The LIFE DYNAMAP project: Towards a procedure for dynamic noise mapping in urban areas

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APPLIED ACOUSTICS 2017年 124卷 52-60页

作者： Zambon, Giovanni Benocci, Roberto Bisceglie, Alessandro Roman, H. Eduardo Bellucci, Patrizia Univ Milano Bicocca Dept Earth & Environm Sci Piazza Sci 1 I-20126 Milan Italy Univ Milano Bicocca Dept Phys Piazza Sci 3 I-20126 Milan Italy ANAS SpA Ctr Sperimentale Str Via Stn Cesano 311 I-00123 Cesano Di Roma RM Italy

Noise maps are considered a powerful tool for determining the population exposure to environmental noise. To make the process of updating noise maps easier, more cost effective and more frequent, there is a need for integrated systems that combine real-time measurement and processing to assess the acoustic impact of noise sources. To this end, a dedicated project, named Dynamic Acoustic Mapping (DYNAMAP), has been proposed and co-financed in the framework of the Financial Instrument for the Environment (LIFE) 2013 program with the aim of developing a dynamic noise mapping system capable of detecting and representing in real-time the acoustic impact of road infrastructures. Noise maps are updated by scaling the noise levels of pre-calculated noise maps as functions of the differences observed between measured and calculated original grid data. The total map is updated by energetic summation of single source levels from updated noise maps. Given the large number of roads present in Milan city, obtaining the dynamics acoustics map of this city requires application of a statistical approach where the roads having similar flow conditions and thus similar noise trends are grouped (clustered) together. In order to obtain these groups (clusters), an extensive measurement campaign was executed. The maps obtained using this method can be associated with an error that will depend on the chosen integration time of noise levels. Results show that two statistical clusters differentiated by rush hour traffic flow are sufficient and better for categorization than the road types provided by Italian road regulation. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Traffic noise emission Urban noise mapping Dynamic map clustering algorithm Continuous monitoring Statistical analysis

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Anomaly Detection for Civil Aviation Pilots Using Step-Sensors

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IEEE ACCESS 2017年 5卷 11236-11243页

作者： Yuan, Weiwei Zhou, Li Guan, Donghai Han, Guangjie Shu, Lei Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China Hohai Univ Dept Informat & Commun Syst Changzhou 213022 Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China Guangdong Univ Petrochem Technol Guangdong Petrochem Equipment Fault Diag Key Lab Maoming 525000 Peoples R China Corroborat Innovat Ctr Novel Software Technol & I Nanjing 210093 Jiangsu Peoples R China

To enhance the safety and efficiency of civil aviation, special attentions should be paid to pilot's physical and mental health. Existing works used video monitoring and social network mining to find the potential anomalies in pilot's daily life. However, video monitoring suffers from the privacy problems and social network mining is computational complex. To solve the problems of existing works, we propose a novel pilot anomaly detection method using step-sensors. The key idea of this method is that the pilots step information reflects their daily behaviors, and it is also influenced by the behaviors of the pilots social networks;if a pilot step number is extremely different from his historical step numbers or the step numbers of his social networks, this would probably be an anomaly. We, therefore, use the step-sensor to collect pilots step information and use the cluster method to detect anomalies. Experiments are held on 65 pilot candidates, which are divided into two social groups. We collect their step information during 50 days. Using our proposed anomaly detection method, outliers can be successfully detected for further analysis. Our method is also free of privacy problem and is highly efficient.

关键词： Pilot anomaly detection step-sensors clustering algorithm civil aviation

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Online semi-supervised multi-channel time series classifier based on growing neural gas

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NEURAL COMPUTING & APPLICATIONS 2017年第11期28卷 3491-3505页

作者： Nooralishahi, Parham Seera, Manjeevan Loo, Chu Kiong Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Swinburne Univ Technol Fac Engn Comp & Sci Sarawak Campus Kuching Sarawak Malaysia

Challenges in time series classification has attracted attention in the past decade. Although large amounts of labeled data are assumed to be available, in reality, labeled data might be scarce to find in many domains. In this paper, we propose an online semi-supervised multi-channel classifier for time series based on growing neural gas (GNG) learning scheme. The method is able to handle multi-channel time series with variation in dimensions and it introduces a label prediction strategy to minimize misclassification. It measures the similarity of input instance and learned templates using weighted multi-channel dynamic time warping technique and learns the topology of input data space specified for each class using the GNG learning algorithm. Comprehensive evaluation is conducted using various datasets, such as gesture recognition, human activity recognition, and human daily-life activity recognition. Experimental results demonstrate good classification results, with indication that the proposed approach requires only a handful of labeled instances to construct an accurate classification model.

关键词： clustering algorithm Dynamic time warping Growing neural gas Label prediction Multi-channel time series Topology formation

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A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems

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JOURNAL OF MEDICAL SYSTEMS 2018年第12期42卷 255-255页

作者： Yang, Zhichao Rafiei, Mohammad H. Hall, Alexis Thomas, Caroline Midtlien, Hali A. Hasselbach, Alexander Adeli, Hojjat Gauthier, Lynne V. Univ Massachusetts Coll Informat & Comp Sci Amherst MA 01003 USA Ohio State Univ Dept Phys Med & Rehabil Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Ohio State Univ Dept Neurol Columbus OH 43210 USA Ohio State Univ Dept Neurosci Columbus OH 43210 USA Univ Massachusetts Dept Phys Therapy Lowell MA 01854 USA

Virtual rehabilitation yields outcomes that are at least as good as traditional care for improving upper limb function and the capacity to carry out activities of daily living. Due to the advent of low-cost gaming systems and patient preference for game-based therapies, video game technology will likely be increasingly utilized in physical therapy practice in the coming years. Gaming systems that incorporate low-cost motion capture technology often generate large datasets of therapeutic movements performed over the course of rehabilitation. An infrastructure has yet to be established, however, to enable efficient processing of large quantities of movement data that are collected outside of a controlled laboratory setting. In this paper, a methodology is presented for extracting and evaluating therapeutic movements from game-based rehabilitation that occurs in uncontrolled and unmonitored settings. By overcoming these challenges, meaningful kinematic analysis of rehabilitation trajectory within an individual becomes feasible. Moreover, this methodological approach provides a vehicle for analyzing large datasets generated in uncontrolled clinical settings to enable better predictions of rehabilitation potential and dose-response relationships for personalized medicine.

关键词： Stroke Telerehabilitation Serious games Kinect Motion capture Motor rehabilitation Hemiparesis Signal processing clustering algorithm Probability density function

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Missing value estimation for microarray data through cluster analysis

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KNOWLEDGE AND INFORMATION SYSTEMS 2017年第3期52卷 709-750页

作者： Pati, Soumen Kumar Das, Asit Kumar St Thomas Coll Engn & Technol Dept Informat Technol Kolkata India Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur Howrah India

Microarray datasets with missing values need to impute accurately before analyzing diseases. The proposed method first discretizes the samples and temporarily assigns a value in missing position of a gene by the mean value of all samples in the same class. The frequencies of each gene value in both types of samples for all genes are calculated separately and if the maximum frequency occurs for same expression value in both types, then the whole gene is entered into a subset;otherwise, each portion of the gene of respective sample type (i.e., normal or disease) is entered into two separate subsets. Thus, for each gene expression value, maximum three different clusters of genes are formed. Each gene subset is further partitioned into a stable number of clusters using proposed splitting and merging clustering algorithm that overcomes the weakness of Euclidian distance metric used in high-dimensional space. Finally, similarity between a gene with missing values and centroids of the clusters are measured and the missing values are estimated by corresponding expression values of a centroid having maximum similarity. The method is compared with various statistical, cluster-based and regression-based methods with respect to statistical and biological metrics using microarray datasets to measure its effectiveness.

关键词： DNA Microarray data High-dimensional space clustering algorithm Similarity measurement Missing value imputation

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Two-layer obstacle collision avoidance with machine learning for more energy-efficient unmanned aircraft trajectories

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ROBOTICS AND AUTONOMOUS SYSTEMS 2017年 98卷 158-173页

作者： Choi, Youngjun Jimenez, Hernando Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA Georgia Inst Technol Aerosp Syst Design Lab Atlanta GA 30332 USA

This paper proposes a new two-layer obstacle avoidance algorithm that allows an unmanned aircraft system to avoid multiple obstacles with minimal effort. The algorithm includes a global-path optimization that identifies the number of obstacles resulting from a clustering technique based on obstacle information from an airborne sensor, and specifies a potential threat. A local-path trajectory optimization employs a model predictive control structure based on a multi-phase optimal trajectory resulting from approximated dynamics, vehicle constraints, and the result of the global-path optimization. Numerical flight simulations are conducted with a conventional one-layer obstacle avoidance algorithm and the two-layer obstacle avoidance algorithm. The results of the numerical simulation show that the proposed two-layer optimal obstacle avoidance algorithm generates more energy-efficient avoidance trajectories when an unmanned aircraft meets multiple obstacles. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Obstacle avoidance Optimal trajectory clustering algorithm Model predictive control UAV Path-planning

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Graph analysis of structural brain networks in Alzheimer's disease: beyond small world properties

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BRAIN STRUCTURE & FUNCTION 2017年第2期222卷 923-942页

作者： John, Majnu Ikuta, Toshikazu Ferbinteanu, Janina North Shore LIJ Hlth Syst Feinstein Inst Med Res Manhasset NY USA North Shore LIJ Hlth Syst Zucker Hillside Hosp Psychiat Res Glen Oaks NY USA Hofstra Univ Dept Math Hempstead NY 11550 USA Univ Mississippi Dept Commun Sci & Disorders Oxford MS USA SUNY Hlth Sci Ctr Dept Physiol & Pharmacol Brooklyn NY 11203 USA SUNY Hlth Sci Ctr Dept Neurol Brooklyn NY 11203 USA

Changes in brain connectivity in patients with early Alzheimer's disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations. In this study, we constructed structural brain networks out of 87 regions using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm. At the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed that small worldness diminished with AD only in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. The second sub-network, which did not present significant AD-induced modifications of 'classical' small world parameters, nonetheless showed a trend towards an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Beyond small world properties, complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks. These results show that neurodegenerative processes impact volumetric network

关键词： Degenerative processes Complex networks clustering algorithm Small world properties Entropy Complexity Alzheimer's disease

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