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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 802 条记录，以下是301-310 订阅

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Noise Removal in Scattering Media Enviorment Using Peploaraphy and DCLGAN 23

Noise Removal in Scattering Media Enviorment Using Peploarap...

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23rd international conference on Control, Automation and systems, ICCAS 2023

作者： Numata, Riki Kim, Hyun-Woo Ono, Seiya Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Noise removal in scattering media environments such as fog and smoke are an important task in reality. For example, it can be applied in many fields such as searching under turbid water and rescuing in smoke. Therefore, the purpose of this research is to remove the noise under the scattering medium environment and to visualize the object. As a conventional method for removing noise, there is a technique called peplography. Peplograhy enables the denoising of dense fog and reconstructing the image in scattering media environments. However, peplography has a problem that complex noise exists after removing particularly deep noise, and as a result, it becomes difficult to visualize the object. This research aims to solve this problem. We propose a new denoising technology that combines peplography with DCLGAN. DCLGAN is an image transformation algorithm using deep learning and an improved model of CycleGAN which is also used in the field of noise reduction. In this study, we evaluate the conventional method and the proposed method for scattering media images. As the evaluation method, visual evaluation and numerical evaluation using the PSNR index are performed to demonstrate the effectiveness of the proposed method. We believe that the proposed method can be applied in various fields such as object visualization under turbid water and automatic driving technology with fog removed. © 2023 ICROS.

关键词： Smoke

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Enhancement Three-Dimensional Localization of ArUco Marker Under Photon-Starved Conditions 23

Enhancement Three-Dimensional Localization of ArUco Marker U...

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23rd international conference on Control, Automation and systems, ICCAS 2023

作者： Ha, Jin-Ung Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a method to improve the recognition rate of ArUco markers under photon-starved conditions by using preprocessing such as photon counting algorithm and histogram equalization to estimate the exact three-dimensional(3D) location of improved objects. In the conventional ArUco marker recognition technology, the recognition rate of ArUco markers decreases under photon-starved conditions, and the exact 3D location of the object cannot be estimated. In the conventional ArUco marker recognition technology, the photon counting algorithm can be used to estimate the 3D location of objects by recognizing ArUco markers under photon-starved conditions. However, the photon counting algorithm has the problem that the computation speed is slowed down when there are many photons to estimate, and the recognition rate of the ArUco marker is reduced when there are few photons to estimate. To solve these problems of the photon counting algorithm and improve the recognition rate of ArUco markers, we apply histogram equalization to ArUco markers with the photon counting algorithm. To verify the feasibility of the proposed method, we implement simulations using the Open Manipulator-X platform and show performance metrics such as Peak Signal-to-Noise Ratio (PSNR). © 2023 ICROS.

关键词： Photons

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A Novel Reinforcement Learning Framework for Adaptive Routing in Network-on-Chips 23

A Novel Reinforcement Learning Framework for Adaptive Routin...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Changhong Wang, Zicong Dong, Dezun Zhang, Xiaoyun Zhao, Zhenyu National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

Adaptive routing is crucial to the overall performance of network-on-chips (NoCs), and still faces great challenges, especially when emerging applications on many-core architecture exhibit complicated and time-varying traffic patterns. When witnessing most existing heuristic adaptive routing algorithms fail to address multi-objective optimization for complex traffic well, we decide to try and explore a new approach of thinking and extracting insights from network behaviors. Reinforcement learning methods have demonstrated promising opportunity applied to architecture design exploration, however not been well applied on adaptive routing design. We make the first attempt to propose a novel and comprehensive reinforcement learning framework for adaptive routing on NoCs, called RELAR. RELAR is suitable for diversified traffic patterns and resolve multi-objective optimization simultaneously. It is able to effectively isolate endpoint congestion when facing adversary hot-spot and bursty traffic, and achieve dynamic load-balancing and mitigate network congestion when meeting heavy uniform traffic. We utilize state-of-the-art high-performance interconnection benchmark, GPCNeT, as traffic generators to generate rich network congestion workloads and thus enhance online-training efficiency of RELAR. We conduct extensive experiments against state-of-the-art routing algorithms to evaluate our design. The results show that RELAR achieves 14.82% and 9.86% reduction in packet latency on average, and reduces packet latency by up to 34.24% and 16.82% under heavy synthetic traffic workload and high-performance interconnection benchmark, respectively. We also perform cost analysis to validate potential implementation of RELAR on NoCs with low computation, storage and power. © 2021 IEEE.

关键词： Adaptive systems Heuristic algorithms Reinforcement learning computer architecture Traffic control Benchmark testing Routing

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ER-KV: High Performance Hybrid Fault-Tolerant Key-Value Store 23

ER-KV: High Performance Hybrid Fault-Tolerant Key-Value Stor...

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作者： Geng, Yingjie Luo, Jinfei Wang, Gang Liu, Xiaoguang College of Computer Science Tklndst Nankai University Tianjin China

ISBN: (纸本)9781665494571

In-memory Key-Value store (KV-store) has been widely used to meet increasing performance requirements. Such system stores user data in main memory and when a node fails, all data will be lost due to the volatile nature of DRAM. Therefore, it usually takes fault-tolerance mechanisms such as primary-backup replication (PBR) and erasure coding to ensure reliability and availability. PBR achieves high availability and scalability at the cost of high data redundancy. Erasure coding has higher utilization of storage but introduces additional computing and network overhead. This paper presents a high-performance fault-tolerant distributed KV-store, the Erasure-coded Replication KV (ER-KV), which takes advantage of both erasure coding and PBR by applying a two-level fault-tolerant mechanism. To achieve both fast recovery speed and low storage overhead, ER-KV gives priority to PBR for fault tolerance but will turn to erasure coding for data recovery when backup nodes are all offline. When generating parity data, ER-KV leverages erasure coding for large value while applying PBR to small-sized key to avoid frequent encoding and decoding. ER-KV also introduces pipelining into erasure coding to improve system performance, and simultaneously, ER-KV still maintains data consistency. Besides, ER-KV applies persistent memory (PM) to achieve the persistence of redundant data and improve recovery speed. We have applied ER-KV to Redis and the experimental results show that it saves 33% of storage compared with PBR. As for erasure coding, ER-KV has higher recovery efficiency and system availability. And it also brings 15X-80X speedup in data reconstruction when applying PM to backup nodes. © 2021 IEEE.

关键词： Fault tolerance Costs Smart cities System performance Scalability Fault tolerant systems Redundancy

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MPPT techniques application and comparison for photovoltaic panels 23

MPPT techniques application and comparison for photovoltaic ...

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23rd international conference on Control systems and computer Science technologies, CSCS 2021

作者： Dobrea, Marius-Alexandru Arghira, Nicoleta Vasluianu, Mihaela Neculoiu, Giorgian Moldoveanu, Ana-Maria Cristiana University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering Faculty of Hidrotechnics Bucharest Romania

ISBN: (纸本)9781665439398

Photovoltaic (PV) panel modelling and control is very important in renewable energy systems. Due to it variability, PV panel generation power should be maximized for the given climate conditions. This paper considers three Maximum Power Point Tracking (MPPT) algorithms: Perturb Observe, Incremental Conductance and Fuzzy Logic. After a clear description for each algorithm, modelling, testing and validation is conducted on real experimental data for a 250W PV panel. Over one day period, a comparison between the 3 control MPPT algorithms show that Perturb Observe gives the best results, close to Incremental Conductance algorithm application. © 2021 IEEE.

关键词： Fuzzy logic

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A Multiple-Blockchains based Service Monitoring Framework in Edge-Cloud Computing 23

A Multiple-Blockchains based Service Monitoring Framework in...

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作者： Fu, Hang Wang, Puwei Li, Haoran Zhan, Ying Chen, Jinchuan Du, Xiaoyong Guizhou University of Finance and Economics Guiyang City550025 China School of Information Renmin University of China China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing100872 China

ISBN: (纸本)9781665494571

In the edge-cloud computing, the applications usually are delivered as services, each of which runs independently and can cooperate to construct the complicated applications. However, it is difficult to monitor the services in the decentralized edge-cloud environment since there usually does not exist the central authorization to guarantee trustiness of the service quality data collected. To address the trust issue, the blockchain techniques have been introduced to the service monitoring. Considering the services usually are distributed on the edge nodes, if we deploy only a blockchain on the edge nodes near to the services, it may have to support both the service registration and monitoring because of the loose-coupling principle. That would influence the performance, even would crash the edge nodes. In this paper, we propose a multiple-blockchains based framework, including a registration blockchain that focuses on the service registration and discovery, and a set of monitoring blockchains that focus on the service invocation and monitoring. The registration blockchain is deployed on the cloud nodes, and the monitoring blockchains are deployed on the edge nodes near to the services for collecting efficiently and precisely the service quality data. Based on the cross-chain technology, we design a cross-chain service discovery between the monitoring blockchain and the registration blockchain for finding the services the requesters need. Finally, we implement the registration and monitoring blockchains based on the blockchain platform Cosmos and conduct a set of experiments to show the effectiveness of the proposed framework. © 2021 IEEE.

关键词： Authorization Cloud computing Smart cities computer crashes Blockchains Monitoring

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A Novel DGA Oil Interpretation Approach Based on Combined Techniques 23

A Novel DGA Oil Interpretation Approach Based on Combined Te...

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23rd international Middle East Power systems conference, MEPCON 2022

作者： Badawi, Mohamed Ibrahim, Shimaa A. El-Faraskoury, Adel Mansour, Diaa-Eldin A. Ward, Sayed A. Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Egypt Egyptian Electricity Holding Company Extra High Voltage Research Centre School of Electronics Communications and Computer Engineering Egypt-Japan University of Science and Technology Electrical Power Engineering Department Alexandria Egypt Tanta University Faculty of Engineering Electrical Power and Machines Engineering Department Tanta Egypt Delta University for Science and Technology Faculty of Engineering Department of Electrical Engineering Cairo11629 Egypt

ISBN: (纸本)9781665463638

One of the most popular chemical tests for power transformer incipient faults detection is the dissolved gas analysis (DGA). This study presents a new DGA approach for transformer fault interpretation with increased accuracy. The suggested approach is constructed based on the combination of the outputs of the two recently developed DGA techniques;1) conditional probability and 2) artificial neural network, with two scenarios. These scenarios based on the diagnosis of the same techniques;artificial neural network (ANN) and Conditional probability techniques but with different procedures. This presented approach improved the overall accuracy compared with the existing DGA techniques. The positive feature of the proposed approach is proven by using 240 datasets received from the Egyptian Electricity Transmission Company (EETC) with known faults. The proposed approach concluded that the integration between different DGA techniques with various fault accuracy improves the overall accuracy of fault detection. The suggested fault diagnosis approach, with the two scenarios, exhibited higher accuracy compared to both ANN and conditional probability technique. Also, 42 samples have been used as testing samples for validating the proposed DGA approach, which result in its superiority compared to ANN and conditional probability technique. © 2022 IEEE.

关键词： Power transformers

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Bhyve - JSON file format snapshot 23

Bhyve - JSON file format snapshot

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23rd international conference on Control systems and computer Science technologies, CSCS 2021

作者： Mihalache, Ionut Carabas, Mihai Mihai, Darius Mihailescu, Maria-Elena Tapus, Nicolae University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665439398

Bhyve, the BSD hypervisor, uses multiple files for the snapshot mechanism, one of them in particular is a binary file which makes the saved states difficult to analyze, making the debug process problematic. A simple debug process would help with the implementation of the the needed functionalities that would allow the hypervisor to reach a mature stage. At the same time the save/restore process as a whole considers that the data is saved and restored in the same order which may cause a vulnerability. © 2021 IEEE.

关键词： computer science Virtual machine monitors Control systems

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Secure Skyline Groups Queries on Encrypted Data on Cloud Platform 23

Secure Skyline Groups Queries on Encrypted Data on Cloud Pla...

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作者： Teng, Yiping Sun, Yue Shi, Zhan Jiang, Dongyue Zhao, Liang Fan, Chunlong School of Computer Shenyang Aerospace University Liaoning Large-scale Distributed System Laboratory Shenyang China

ISBN: (纸本)9781665494571

As an important category of skyline queries, the skyline groups query has drawn growing interest from the academic communities. For taking the benefit of cost savings, data owners are motivated to outsource the data storage and query processing to the public cloud to offload the massive data management. However, direct outsourcing may raise severe privacy issues. In this paper, we define and study the secure skyline groups query problem, and propose the secure skyline groups query methods. To address this problem, we first propose the Basic Secure Skyline Groups Query (BSSGQ) method to find the skyline groups on encrypted data. In BSSGQ method, candidate groups and their aggregate tuples are generated under encryption. To calculate group dominance relations, we propose a secure group dominance protocol to facilitate the dominance computation between groups. With the dominance relations, the skyline groups w.r.t the query group can be obtained, and finally the query results of the skyline groups can be securely returned without being learnt by the cloud servers. To address the efficiency problem in BSSGQ method, we further propose a Dynamic-programming-based Secure Skyline Groups Query (DSSGQ) method to effectively avoid generating the candidate groups. In DSSGQ method, the encrypted data are equivalently mapped based on the query group as the preprocessing to achieve a dynamic skyline groups query. Then, DSSGQ method dynamically constructs and calculates the skyline sub-groups in ciphertext, and the final skyline groups can be combined with the obtained skyline sub-groups. Thorough analysis shows the security and complexity of the proposed methods, and extensive experimental results on real datasets and synthetic datasets illustrate the performance of our proposed methods. © 2021 IEEE.

关键词： Cloud computing Privacy Protocols Smart cities Query processing Memory Complexity theory

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A Lightweight VK-Net Based on Motion Profiles for Hazardous Driving Scenario Identification 23

A Lightweight VK-Net Based on Motion Profiles for Hazardous ...

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作者： Gao, Zhen Xu, Jingning Zheng, Jiang Yu Fan, Hongfei Yu, Rongjie Zong, Jiaqi Li, Xinyi School of Software Engineering Tongji University Shanghai China Indiana University-Purdue University Department of Computer Science Indianapolis United States Jiangsu China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education Shanghai China College of Transportation Engineering Tongji University Shanghai China

ISBN: (纸本)9781665494571

Hazardous driving scenarios are critical for the development and testing of highly autonomous vehicles. Recent studies have proposed new approaches for extracting hazardous scenarios from driving videos, which greatly improve the detection accuracy compared with traditional approaches that extract information from structured kinematic data. However, direct processing on driving videos is costly, and the videos need to be uploaded and processed in a centralized manner. In this study, we propose a novel approach for reducing the cost of video analysis while maintaining high accuracy in hazardous scenario identification. Firstly, the videos are significantly compressed into motion profile images that contain motion trajectories of objects. Secondly, a lightweight CNN with attention mechanism has been designed to extract risky trajectory features of the conflicting traffic objects. Furthermore, data augmentation has been applied to increase the generalizability of the model. The proposed approach achieves significantly lightweight network size, and meanwhile, experimental results have demonstrated that the approach achieves high accuracy with sensitivity 0.95, specificity 0.85 and AUC 0.95, which outperforms state-of-the-art hazardous scenario identification approaches. The efficient execution of identification implies that it is suitable to be distributed and deployed at each vehicle, and consequently, videos could be processed via edge computing, which would improve the diversity of hazardous scenarios and the efficiency of scenario collection for further research work. © 2021 IEEE.

关键词： Sensitivity Image coding Smart cities Kinematics Feature extraction Trajectory Data mining

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