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International Technical Conference o Circuits/systems, Computers and Communications (ITC-CSCC)

作者： Yusei Suzuki Masaya Kai Kazutoshi Sakakibara Ryo Takano Takuya Matsumoto Masaki Nakamura Dept. of Information Systems Engineering Toyama Prefectural University Toyama Japan Graduate School of Engineering Toyama Prefectural University Toyama Japan Dept. of Data Science Toyama Prefectural University Toyama Japan

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

In metal recycling plants, equipment problems orig-inating from materials are not infrequent when recycling various types of input materials. Appropriate scheduling is required while taking such uncertainties into account. In this study, the recycling process is modeled as a mixed integer programming problem and then as a two-stage stochastic programming problem that takes into account the uncertainty caused by troubles and the overtime caused by delays. Since the two-stage stochastic programming model becomes more difficult to derive the optimal solution as the problem size increases, a sampling-based solution method is used to reduce computation time by applying the integer variable values of the solution obtained by optimization in a small number of scenarios as constants to a model that includes all scenarios. The effectiveness of the model is examined through numerical experiments based on actual factory operations.

关键词： Integer programming Uncertainty Computational modeling Stochastic processes Metals Programming Production facilities

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CorcPUM: Efficient Processing Using Cross-Point Memory via Cooperative Row-Column Access Pipelining and Adaptive Timing Optimization in Subarrays 23

CorcPUM: Efficient Processing Using Cross-Point Memory via C...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Chengning Wang Dan Feng Wei Tong Jingning Liu Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China (School of Computer Science and Technology Huazhong University of Science and Technology) Wuhan China

ISBN: (纸本)9798350323481

Emerging cross-point memory can in-situ perform vector-matrix multiplication (VMM) for energy-efficient scientific computation. However, parasitic-capacitance-induced row charging and discharging latency is a major performance bottleneck of subarray VMM. We propose a memory-timing-compliant bulk VMM processing-using-memory design with row access and column access co-optimization from rethinking of read access commands and μ-op timing. We propose row-level-parallelism-adaptive timing termination mechanism to reduce tail latency of tRCD and tRP by exploiting row nonlinear charging and bulk-interleaved row-column-cooperative VMM access mechanism to reduce tRAS and overlap CL without increasing column ADC precision. Evaluations show that our design can achieve 5.03× performance speedup compared with an aggressive baseline.

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Predicting Partial Discharges of Transformers: Decision Support System for Factory Acceptance Test

Predicting Partial Discharges of Transformers: Decision Supp...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Gigerl, B. Zhao, Y. Raminger, J. Bakakeu, J. Kern, R. Thalmann, S. Siemens Energy Department of Process Digitalization Weiz Austria Siemens Energy Department of Manufacturing Technology Nürnberg Germany Siemens Energy Department of Test Field Weiz Austria Alteryx Department of Sales Engineering München Germany Institute of Interactive Systems and Data Science Technical University of Graz Graz Austria Institute of Operations and Information Systems University of Graz Graz Austria

ISBN: (纸本)9798350323153

Partial discharges, mainly caused by an insufficient drying processes or different types of contamination, reduce the lifetime of a transformer, and thus lead to expensive rework costs. Herein, a decision support system for partial discharges of transformers occurring in oven and filling processes is introduced. Based on machine learning (ML), partial discharge results are evaluated in dependency of various manufacturing parameters and an automated prediction tool to guide the production with preventive actions is developed. The required data, obtained from sensors and manufacturing sources, is used to train supervised learning algorithms that aim to predict and classify partial discharges. To achieve adequate accuracy and reliability, multiple ML and data mining techniques are applied, including feature engineering, clustering, and final evaluation of performance by cost factors. The evaluation results show that the introduced ML models effectively detect and classify early test failures in oven and filling processes, resulting in a successful identification of key factors and consequently to more efficient action derivation. Overall, the potential of decision support systems as a valuable tool in the field of transformers is emphasized. © 2023 IEEE.

关键词： Decision Support System Machine Learning Partial Discharges Predictive Quality Control

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Dynamic Partition Strategy of Warehouse Area Based on Workload Balance 3

Dynamic Partition Strategy of Warehouse Area Based on Worklo...

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3rd International Conference on Internet of Things and Smart City, IoTSC 2023

作者： Zhan, Jianjun Liu, Huihong Yang, Guoli Gao, Zhenyu Fang, Zihao Lv, Zhaolin College of Systems Engineering National University of Defense Technology Hunan China National Science Library Chinese Academy of Sciences Beijing China Department of Library Information and Archives Management School of Economics and Management University of Chinese Academy of Sciences Beijing China Department of Intelligent Supply Chain JD Logistics Beijing China Department of Big Data Intelligence Advanced Institute of Big Data Beijing China

ISBN: (纸本)9781510666375

In order to improve the efficiency of warehouse outbound delivery and avoid local congestion of AGVs, this paper considers both strategies of AGVs path optimization and tasks balancing in the warehouse. In this paper, the A * algorithm is improved. Different estimation functions are used in different areas to more accurately depict the distance relationship between each point pair, and efficiently calculate the shortest feasible path from each storage location to each picking station. At the same time, the outbound tasks are planned reasonably. By establishing a 0-1 integer programming model and using Gurobi to solve it, tasks are allocated regularly to each picking station in the warehouse to maintain the workload balance among the picking areas. The experimental results show that the above strategies reduce the maximum working time of each picking area from 1793t seconds to 1511t seconds, and effectively reduce the total mileage of AGVs and the probability of AGV local congestion. © 2023 SPIE.

关键词： Warehouses

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Segment-Adaptive Spread Spectrum Audio Watermarking Using NSGA-Ⅱ

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Chinese Journal of Electronics 2021年第4期30卷 727-735页

作者： SU Zhaopin ZHANG Guofu ZHOU Xianxian LI Wangwang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology

In the field of robust audio watermarking,how to seek a good trade-off between robustness and imperceptibility is challenging. The existing studies use the same embedding parameter for each part of the audio signal, which ignores that different parts may have different requirements for embedding parameters. In this work, the constraints on imperceptibility are first ***, we present a segment multi-objective optimization model of the scaling parameter under the constrained Signal-to-noise ratio(SNR) in Spread spectrum(SS)audio watermarking. Additionally, we adopt the Nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) to solve the proposed model. Finally, we compare our algorithm(called SS-SNR-NSGA-Ⅱ) with the existing methods. The experimental results show that the proposed SS-SNRNSGA-Ⅱ not only provides flexible choices for different application demands but also achieves more and better trade-offs between imperceptibility and robustness.

关键词： Spread spectrum audio watermarking Scaling parameter Segment Constrained SNR NSGA-Ⅱ

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Joint Sensing, Computation, and DAI Resource Management for Integrated Non-Terrestrial and Terrestrial Networks

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IEEE Network 2024年

作者： Gong, Yongkang Liu, Xiaonan Yao, Haipeng Xiong, Zehui Cheng, Xiuzhen Niyato, Dusit Shandong University School of Computer Science and Technology China University of Aberdeen School of Natural and Computing Science United Kingdom Beijing University of Posts and Telecommunications School of Information and Communication Engineering China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Nanyang Technological University College of Computing and Data Science China

Integrated Non-Terrestrial and Terrestrial Networks (NTN-TN) have been emerging as a promising architecture to facilitate state sensing, computation migration, and privacy protection for terrestrial users, where both low, medium and geostationary orbit satellites collaboratively support various tasks and allocate resources effectively. Nevertheless, an integration of sensing, computation, and artificial intelligence (AI) in dynamic NTN-TNs has certain challenges in achieving high-efficient task offloading and guaranteeing robust privacy protection. To address these issues, we introduce an intelligent NTN-TN framework, focusing on sensing, computation migration, and privacy protection. Furthermore, we develop a versatile and scalable experimental platform for NTN-TNs to validate related network functions. Simulation results show that the proposed framework reduces network latency compared with advanced benchmarks. Finally, we present potential research directions and innovative solutions in future NTN-TNs. © 2024 IEEE.

关键词： Differential privacy

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Deep Reinforcement Learning algorithms for Low Latency Edge Computing systems

Deep Reinforcement Learning algorithms for Low Latency Edge ...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： K. Kumaran E. Sasikala Department of Information Technology SRM Institute of Science and Technology Easwari Engineering College Chennai India Department of Data Science & Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India

Nowadays, due to the increase of technological development in smart devices, more computational capabilities are needed with better performance. The maximization of the cloud computing resources used in mobile networks and Internet of Things makes good results. The data processing along with delay in the communication network will be easily handled by edge computing technology. The edge server brought as much as close to the end devices to avoid the frequent cloud access. In edge servers, the processing of the computational task is time consuming, which causes latency. The inference of deep learning along with reinforcement models makes the better resource allocation and task scheduling for complex systems. In this work, deep reinforcement models like Q learning, Double Q learning, Deep Q network, Double Deep Q learning has been compared. The developed Deep Reinforcement learning model has been deployed in the edge-based system. As a result, it is inferred that Double Deep Q learning is having the ability to make better decisions in resource allocation by maximizing the gains and rewards. The simulation result shows that Double Deep Q learning performs better with decreased latency and increases the performance of the edge systems.

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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Modelling Air Pollution and Traffic Congestion Problem Through Mobile Application 1st

Modelling Air Pollution and Traffic Congestion Problem Throu...

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1st International Conference on Deep sciences for Computing and Communications, IconDeepCom 2022

作者： Amudha, S. Shobana, J. Satheesh Kumar, M. Chitra, P. Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Information Technology K.L.N. College of Engineering Tamil Nadu Madurai India Department of Electrical and Electronics Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031276217

As we know, air pollution is one of the major problems in the metropolis. Due to this a lot of consequences have to be faced by all people. One of the main reasons for this are the vehicles. Most of the time people use their vehicles unnecessarily without any certain reasons. Due to the same reason another problem of traffic congestion arises. And this can be reduced if people become a bit more serious about it. They should prefer using public transport rather than personal vehicles for travelling large distances. By this the number of vehicles on the road will be turned down and it might solve the above two problems directly. So, the main approach is based on this theory itself to reduce the number of transports on the road. The solution is an application which runs on phone and keep track of the fuel used by the vehicle. A certain fuel limit shall be set based on vehicle type, its fuel consumption and other constraints. Once the limit got exceeded, fuel price shall differ and automated calculation would get you actual fuel amount. This aims at reducing the unnecessary usage of vehicles. There is a CO2 emission model in an application which can predict the amount of CO2 produced by the vehicle. The objective of the proposed work is to control the usage of vehicles based on the fuel used by a particular vehicle for a period of time. As the module has been tested with ML model for mileage prediction and CO2 emission with different algorithms and got the best accuracy of around 85%. There will be no hardware in this work, if any hardware implemented all over India then that prototype model will produce good impact in the vehicle fuel usage prediction and CO2 emissions. Hardware based projects using IOT devices, VTRS etc. is something which is very hard to implement in India as CCTV are still not installed and used properly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic congestion

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