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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Shitong Shen Jian Cao Yinyue Yang Yameng Guo Shanghai Jiao Tong University Shanghai China Shangdong Container Terminal Branch Shanghai International Port (Group) Co.Ltd Shanghai China

ISBN: (纸本)9781665426565

Compared with the air transportation and land transportation, water transportation has many advantages such as larger loading capacity, lower unit transportation cost, lower construction investment and so on. What’s more, water transportation has played an important role in the economical development of China, especially in the aspect of international trade. Therefore, the improvement in the efficiency of water transportation will be of great significance. In this paper, we designed a system to predict the containers’ entering time distribution of a given voyage at a specific port by using machine learning algorithms and statistical methods. Using Shanghai Yangshan Port phase IV automated terminal’s data, we perform some experiments, and the result shows that our system can provide valid predictions.

关键词： international trade Machine learning algorithms Costs Statistical analysis Loading Land transportation Containers

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Twin Delayed Multi-Agent Deep Deterministic Policy Gradient

Twin Delayed Multi-Agent Deep Deterministic Policy Gradient

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Mengying Zhan Jinchao Chen Chenglie Du Yuxin Duan Northwestern Polytechnical University Xi’an China Beijing Simulation Center Science and Technology on Special System Simulation Laboratory Beijing China

ISBN: (纸本)9781665426565

Recently, reinforcement learning has made remarkable achievements in the fields of natural science, engineering, medicine and operational research. Reinforcement learning addresses sequence problems and considers long-term returns. This long-term view of reinforcement learning is critical to find the optimal solution of many problems. The existing multi- agent reinforcement learning algorithms have the problem of overestimation in estimating the Q value. Unfortunately, there have not been many studies on overestimation of agent reinforcement learning, which will affect the learning efficiency of reinforcement learning. Based on the traditional multi-agent reinforcement learning algorithm, this paper improves the actor network and critic network, optimizes the overestimation of Q value and adopts the update delayed method to make the actor training more stable. In order to test the effectiveness of the algorithm structure, the modified method is compared with the traditional MADDPG, DDPG and DQN methods in the simulation environment.

关键词： Training conferences Reinforcement learning Delays informatics Convergence

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Image Stitching Algorithm Based on Region Division for Underwater Dam Crack Image

Image Stitching Algorithm Based on Region Division for Under...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Yuanbo Huang Zhuo Zhang Xiaolong Xu The College of IoT Engineering Hohai University Changzhou China

ISBN: (纸本)9781665426565

The surface crack of underwater dam is one of the important indexes to evaluate the normal operation of the dam. Complete crack image is an important means to improve the accuracy of evaluation. In view of the limitations of traditional Algorithms in underwater crack image stitching, we propose an underwater dam surface crack image stitching algorithm based on region division(ISA-RD). First of all, an image enhancement algorithm aiming at increasing the number of feature points is used. Secondly, we simplify the process of feature point selection and matching by relying on the features of multiple regions in the local crack image, and improve the matching accuracy by mining the close relationship between the matching of feature points and different regions. Finally, the high matching feature point pairs are used for image fusion. We take the crack image of the real scene as the research object. Compared with the classical image stitching algorithm, the feature point matching algorithm proposed in this paper improves the accuracy of feature point matching. Obviously, the image quality after stitching is improved.

关键词： Image quality Dams Transforms Feature extraction Indexes Surface cracks informatics

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A Computer-Aided Recognition Method of Heart Rate Deflection Point

A Computer-Aided Recognition Method of Heart Rate Deflection...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Shenglan Wang Junhui Li Mingying Lan Li Gao Xiaolin Gao School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China China Institute of Sport Science Beijing China

ISBN: (纸本)9781665426565

Lactate threshold or gas exchange threshold is commonly used to guide exercise intensity, but direct measurement of these two are never easy for general population. Among all physiological indicators, heart rate is very easy to obtain. And the heart rate deflection point is consistent with the lactate threshold during incremental exercise. However, previous studies suffer from expertise or a priori information requirement, computation inefficiency, lack of cohort diversity, etc. Based on prior knowledge, this contribution proposes a computer-aided methods to automatically identity heart rate intersection points by sections, and further optimization. As result, among 200 healthy college student volunteers, only 8 subjects fall beyond the 95% confidence interval in residual analysis. Therefore, a self-consistent, economic, noninvasive method to estimate the lactate threshold with heart rate data only is demonstrated.

关键词： Heart rate Economics conferences Sociology Physiology Statistics informatics

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Spatial Data Infrastructure Integrated with Geospatial Artificial Intelligence: A Systematic Literature Review 8

Spatial Data Infrastructure Integrated with Geospatial Artif...

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8th international conference on ICT for Smart Society, ICISS 2021

作者： Nugroho, Yudha Setya Supangkat, Suhono Harso Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781665416979

In the era of industrial revolution 4.0, artificial intelligence has became a driver in the high technology sustainability. It is important to acceleration of development planning and decision making for stakeholder based on region zone, point of society and distribution an object with system coordinates. The linkage of information and communications technology (ICT) with spatial data, located in the use of data sharing technology, architecture and data dissemination. The availability of spatial data is increasingly numerous and varied requires a high computing technology that users can access and use spatial data from various sources of spatial data providers. Therefore, the Spatial Data Infrastructure (SDI) was built to facilitate access and exchange of spatial data from various sources of spatial data providers, both government and private, which doing an important role in conducted system and platform interoperability as spatial services. The government of Indonesia has made regulation for national spatial data infrastructure (NSDI) in Law Of Presidential Number is 27 of 2014 and and strengthened by onemap policy. The advantages using geospatial artificial intelligence (GeoAI) can reduce duplication and analysis spatial processing of big data spatial. The systematic literature review process contain of regulation, method for harvesting spatial data in SDI and several GeoAI model research from 2016 to this year. The conclusion in this paper is find possibility ellaborate the artificial intelligence technology with spatial data infrastructure development from past studies in other country. © 2021 ieee.

关键词： Artificial intelligence

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Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds

Enhanced Particle Swarm Optimization for Workflow Scheduling...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Chang Lu Dayu Feng Jie Zhu Haiping Huang Nanjing University of Posts & Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

ISBN: (纸本)9781665426565

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow’s tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow’s structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm’s searching ability and finding better solutions.

关键词： Schedules Costs Processor scheduling NP-hard problem Parallel processing Scheduling Proposals

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On future development of autonomous systems: A report of the plenary panel at ieee ICAS'21

On future development of autonomous systems: A report of the...

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2021 ieee international conference on Autonomous Systems, ICAS 2021

作者： Wang, Yingxu Pitas, Ioannis Plataniotis, Konstantinos N. Regazzoni, Carlo S. Sadler, Brian M. Roy-Chowdhury, Amit Hou, Ming Mohammadi, Arash Marcenaro, Lucio Atashzar, Farokh Alzahir, Saif University of Calgary AB Canada Greece University of Toronto ON Canada University of Genova Italy US Army Research Laboratory United States University of California RiversideCA United States Defence Research and Development Canada Toronto Canada Univ. of Concordia MontrealQC Canada New York University NY United States Univ. of Concordia MontrealQC Canada

ISBN: (纸本)9781728172897

Autonomous Systems (AS) are perceived as the most advanced intelligent systems evolved from those of reflexive, imperative, and adaptive intelligence. A plenary panel on 'Future Development of Autonomous Systems' is organized at the inaugural ieee ICAS'21. This paper reports the panel discussions about the-state-of-the-art and paradigms of AS, the basic research on theoretical foundations and mathematical means of AS, and challenges to the future development of AS. As an emerging and increasingly demanded field, AS provide an unprecedented approach to contemporary intelligent industries including deep machine learning, highly intelligent robotics, cognitive computers, general AI technologies, and industrial applications enabled by transdisciplinary advances in intelligence science, system science, brain science, cognitive science, robotics, computational intelligence, and intelligent mathematics. © 2021 ieee.

关键词： Cognitive systems

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A Comparison of Wearable Sensor Configuration Methods for Human Activity Recognition Using CNN

A Comparison of Wearable Sensor Configuration Methods for Hu...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Lina Tong Qianzhi Lin Chuanlei Qin Liang Peng School of Mechanical Electronic & Information Engineering China University of Mining and Technology-Beijng Beijing China State Key Laboratory of Management and Controlfor Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426565

The number and location configuration methods of wearable sensors for human activity recognition (HAR) are analytically discussed. Based on the publicly available Daily and Sports Activities data set, a convolutional neural network (CNN) was built to recognize 19 kinds of daily and sports activities, and then the model was optimized for better performance. The results of numerous comparative experiments show that deep learning-based HAR is better than machine learning-based HAR in terms of accuracy, and its improvement in accuracy is not directly related to the increase of sensor quantity. Due to its strong capability of feature extraction, deep learning extracts not only activity-related features but also individual differences, therefore, the location with less individual randomness should be selected according to practical engineering. Moreover, the results are also influenced by the limb symmetry in the data set. Finally, the feasibility of achieving higher accuracy with fewer sensors is proved.

关键词： Deep learning Interference Activity recognition Feature extraction Data models Convolutional neural networks informatics

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ieee Access Special Section: Intelligent Data Sensing, Collection, and Dissemination in Mobile computing

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ieee ACCESS 2020年 8卷 222248-222254页

作者： Liu, Xuxun Liu, Anfeng Tadrous, John He, Ligang Ji, Bo Zheng, Zhongming South China Univ Technol Guangzhou 510641 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Gonzaga Univ Elect & Comp Engn Spokane WA 99258 USA Univ Warwick Coventry CV4 7AL W Midlands England Temple Univ CIS Dept Tokyo 1540004 Japan Temple Univ Ctr Networked Comp CNC Tokyo 1540004 Japan Temple Univ Coll Sci & Technol Dept Comp & Informat Sci Tokyo 1540004 Japan Ericsson Canada Inc Ottawa ON K2K 2V6 Canada

Data are an important parts of building applications and providing services to users. With the development of modern technology, the requirements for data quantity, quality, and processing capabilities have become more stringent. Sufficient high-quality data are the foundation for most mobile applications. It is important to collect enough data to effectively build your application. In addition to ensure the amount of data collected, the quality of the data collected is also important. Otherwise, a large amount of data redundancy will occur, which will cause trouble for data processing. At the same time, large amounts of data also mean the need for data processing capabilities. Traditional processing methods cannot meet the growing demand for data processing. Mobile computing is a promising solution to meet the growing computing needs of various mobile applications. The advancement and application of mobile computing require more intelligent ways to sense and collect data, which constantly poses new challenges to researchers.

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Human Action Recognition Based on STDMI-HOG and STjoint Feature

Human Action Recognition Based on STDMI-HOG and STjoint Feat...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Qianhan WX Qian Huan Xing LL Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University School of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9781665426565

More and more attention has been focused on the human action recognition domain. The existing methods are mostly based on single-mode data. However, single-mode data lacks adequate information. So, it is necessary to propose methods based on multimode data. In this paper, we extract two kinds of features from depth videos and skeleton sequences, named STDMI-HOG and STjoint feature respectively. STDMI-HOG is extracted from a new depth feature map Spatial-Temporal Depth Motion Image by Histogram of Oriented Gradient. STjoint feature is extracted from skeleton sequences by ST-GCN extractor. Then two kinds of features are connected to make up a one-dimensional vector. Finally, SVM classifies the actions according to the feature vector. To evaluate the performance, several experiments are conducted on two public datasets: the MSR Action3D dataset and the UTD-MHAD dataset. The accuracy of our method on two datasets is compared with the existing methods, and the experiments prove the outperformance of our method.

关键词： Support vector machines Histograms conferences Feature extraction Skeleton informatics Videos

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