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BM-RCGL: Benchmarking Approach for Localization of Reliability-Critical Gates in Combinational Logic Blocks

IEEE TRANSACTIONS ON COMPUTERS 2022年第5期71卷 1063-1076页

作者： Xiao, Jie Shi, Zhanhui Yang, Xuhua Lou, Jungang Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 311000 Peoples R China Huzhou Univ Zhejiang Prov Key Lab Smart Management & Applicat Huzhou 313000 Peoples R China

Accurate and effective localization of reliability-critical gates (RCGs) is one of the important prerequisites for low-cost circuit fault tolerance in the early stages of circuit design. This article introduces an accurate and effective approach for localizing RCGs in combinational logic blocks through a benchmarking technique. In the proposed approach, uniform non-Bernoulli sequences are used to produce a set of input vectors for driving circuits. A full-period linear congruential algorithm is employed to generate a sequence that provides the sampled order for the RCGs to be analyzed. This ensures that each gate in the circuit is treated as fairly as possible. To accelerate the localization process, an input-vector-based pruning technique combined with a counting method is also introduced to identify the specified number of RCGs. Then, the criticality of gate reliability for each RCG is measured through benchmarking. A clustering algorithm carries out the convergence checking for the proposed approach. The performance of the proposed approach was evaluated in terms of accuracy, stability, and time-space overhead by various simulations on 74-series circuits and ISCAS-85 benchmark circuits. The results show that its accuracy is close to that of the Monte Carlo model and its stability is better than that of other approximate methods. Moreover, compared with approximate methods, the time overhead of our approach is advantageous in the presence of similar memory overheads.

关键词： Logic gates Reliability Integrated circuit reliability clustering algorithms Approximation algorithms Location awareness Integrated circuit modeling Full-period linear congruential algorithm pruning technique benchmarking approach clustering algorithm reliability-critical gates

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SAR Change Detection algorithm Combined with FFDNet Spatial Denoising

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Journal of Environmental & Earth Sciences 2023年第2期5卷 88-101页

作者： Yuqing Wu Qing Xu Zheng Zhang Jingzhen Ma Tianming Zhao Xinming Zhu Institute of Geospatial Information Information Engineering UniversityZhengzhouHenan450001China

Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extraction of change *** order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map,this paper proposes a method that combines the popular deep neural network with the clustering ***:Firstly,the SAR image with speckle noise was constructed,and the FFDNet architecture was used to retrain the SAR image,and the network parameters with better effect on speckle noise suppression were *** the log ratio operator is generated by using the reconstructed image output from the ***,K-means and FCM clustering algorithms are used to analyze the difference images,and the binary map of change detection results is ***:The experimental results have high detection accuracy on Bern and Sulzberger’s real data,which proves the effectiveness of the method.

关键词： SAR change detection Image noise reduction FFDNet Difference diagram clustering algorithm

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FIBFT: An Improved Byzantine Consensus Mechanism for Edge Computing

FIBFT: An Improved Byzantine Consensus Mechanism for Edge Co...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Gao, Ningjie Huo, Ru Wang, Shuo Huang, Tao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (纸本)9781665491228

Blockchain has been widely used to solve data privacy and security issues in edge computing scenarios. However, the blockchain based on edge computing still has some performance problems, such as insufficient scalability, difficulty in balancing security and edge device power consumption, and inability to simultaneously meet low latency, high throughput, high security and privacy issues, etc. In order to solve these problems, this paper proposes a generally improved Byzantine consensus mechanism based on the K-medoids clustering algorithm - FIBFT. Considering the different performance characteristics of each node in the network, the node's state is first abstracted into a multi-dimensional state space containing eigenvalues, and then the nodes are divided into subnets by the efficient K-medoids clustering algorithm. Each subnet uses a Byzantine consensus mechanism based on arbitration for consensus and data interaction, and the consensus data could be exchanged between the subnets without interfering with the consensus process. The research results show that FIBFT has better scalability and throughput while ensuring high security compared with the traditional Byzantine consensus algorithm.

关键词： Blockchain Edge Computing clustering algorithm Byzantine Consensus Mechanism

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PREDICTION OF CRUDE OIL VISCOSITY FOR PIPELINE OPERATION SAFETY USING OPTIMIZED GRNN MODEL

PREDICTION OF CRUDE OIL VISCOSITY FOR PIPELINE OPERATION SAF...

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ASME Pressure Vessels and Piping Conference (PVP) on Re-Energizing the Pressure Vessels and Piping Community

作者： Zhang, Jianming Wang, Xiaolin Shi, Lei Cui, Kaiyan Chang, Liang Sun, Qianyao Zhao, Yatong Sinopec Dalian Res Inst Petr & Petrochem Co Ltd Dalian Liaoning Peoples R China Engn Technol Res Co Ltd Beijing Peoples R China Dalian Intelligent Oil & Gas Storage & Transporta Dalian Peoples R China Safety Engn Res Ctr Dalian Peoples R China

ISBN: (纸本)9780791887455

In the whole process of crude oil storage and transportation, the obtaining of the viscosity of crude oil timely and accurately is very important for the safe operation of crude oil pipelines. This paper proposes a new method to predict crude oil viscosity based on Generalized Recurrent Neural Network (GRNN), Particle Swarm Optimization (PSO) and K-Means clustering algorithm, which are different from the normal measurement in the laboratory. Feature selection, focusing on obtaining most closely related components to the viscosity of crude oil at 50 degrees C from the content of Kantian residual carbon, sulfur, nitrogen, water, salt, acid value, ash, colloid, asphaltene, wax, Fe, Ni, Cu and V, is introduced by using the Maximal Information Coefficient (MIC) and Sequential Floating Forward Selection (SFFS) algorithm. The density and pour point, which are easily available, are determined as the final input of the GRNN by physical property correlation analysis to replace the most related components obtained. For most practical engineering problems that only have a small data set with partial poor samples, the K-Means clustering algorithm is introduced in the model to analyze the distribution characteristics of the samples and avoid the impact on the prediction results caused by poor data set. It effectively makes model evaluation more convincing. A Generalized Recurrent Neural Network ( GRNN) with the parameter optimization by Particle Swarm Optimization (PSO) is constructed as prediction model. The final results show that the optimized GRNN prediction model in this paper has a good performance on prediction accuracy and stability at the viscosity of crude oil.

关键词： Feature selection clustering algorithm PSO GRNN Viscosity prediction

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Application of an improved clustering approach on GPS height time series at CMONOC stations in Southwestern China

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EARTH PLANETS AND SPACE 2021年第1期73卷 1-15页

作者： Wu, Shuguang Li, Zhao Li, Houpu Nie, Guigen Liu, Jingnan He, Yuefan Naval Univ Engn Dept Control Engn Wuhan Peoples R China Wuhan Univ GNSS Res Ctr Wuhan Peoples R China

The hydrological, geological and meteorological conditions in southwestern China are relatively complex, so that the land surface deformation presents various features. Using 58 Crustal Movement Observation Network of China (CMONOC) stations across four provinces in Southwestern China, we adopt an improved clustering algorithm to classify 49 stations into 12 clusters with different similarity levels. Our results show that the average annual signals of GPS stations within each cluster have strong consistency, while obvious differences exist among the 12 clusters, indicating that clustering algorithm helps to describe surface deformation features more accurately in regions with complex conditions. We then combine other earth observation techniques, such as the Gravity Recovery and Climate Experiment (GRACE) satellite datasets and surface loading models (SLM), and observe that GPS, GRACE and SLM have strong correlation in their monthly displacement series at GPS stations. After excluding non-clustered stations according to our previous clustering results, the correlation coefficients of GPS/GRACE and GPS/SLM are enhanced. Also, the RMS reduction rates of GPS coordinate time series have been improved after deducting displacements obtained from GRACE and SLM, thus the clustering algorithm proves to be effective in improving the consistency of three techniques in joint detection of surface deformation. Moreover, the vertical displacements of four riverside GPS stations in the Three Gorges Reservoir (TGR) area show significant negative correlation with water level of TGR, hence we conclude that the Three Gorges Dam (TGD) may directly affect the consistency of GPS annual signals of its upstream and downstream GPS stations.

关键词： clustering algorithm CMONOC stations GPS height time series Land surface deformation GRACE SLM

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clustering Approach for the Efficient Solution of Multiscale Stochastic Programming Problems: Application to Energy Hub Design and Operation under Uncertainty

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PROCESSES 2023年第4期11卷

作者： Alkatheri, Mohammed Alhameli, Falah Betancourt-Torcat, Alberto Almansoori, Ali Elkamel, Ali Univ Waterloo Dept Chem Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada Khalifa Univ Sci & Technol Dept Chem Engn Sas Al Nakhl CampusPOB 2533 Abu Dhabi U Arab Emirates

The management of the supply chain for enterprise-wide operations generally consists of strategic, tactical, and operational decision stages dependent on one another and affecting various time scales. Their integration usually leads to multiscale models that are computationally intractable. The design and operation of energy hubs faces similar challenges. Renewable energies are challenging to model due to the high level of intermittency and uncertainty. The multiscale (i.e., planning and scheduling) energy hub systems that incorporate renewable energy resources become more challenging to model due to an integration of the multiscale and high level of intermittency associated with renewable energy. In this work, a mixed-integer programming (MILP) superstructure is proposed for clustering shape-based time series data featuring multiple attributes using a multi-objective optimization approach. Additionally, a data-driven statistical method is used to represent the intermittent behavior of uncertain renewable energy data. According to these methods, the design and operation of an energy hub with hydrogen storage was reformulated following a two-stage stochastic modeling technique. The main outcomes of this study are formulating a stochastic energy hub optimization model which comprehensively considers the design and operation planning, energy storage system, and uncertainties of DRERs, and proposing an efficient size reduction approach for large-sized multiple attributes demand data. The case study results show that normal clustering is closer to the optimal case (full scale model) compared with sequence clustering. In addition, there is an improvement in the objective function value using the stochastic approach instead of the deterministic. The present clustering algorithm features many unique characteristics that gives it advantages over other clustering approach and the straightforward statistical approach used to represent intermittent energy, and it can be easily

关键词： clustering algorithm multiscale supply chain computational complexity energy hub

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Extraction and Design Application of Modeling Factors for Peking Opera Faces 24

Extraction and Design Application of Modeling Factors for Pe...

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3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Zheng, Cuixian Liu, Jiali Hubei University of Technology Wuhan China

ISBN: (纸本)9798400716942

Peking Opera is a cherished intangible cultural heritage in China. However, face painting, a vital component of this art form, has not received the attention it deserves. This paper aims to share the beauty of Peking Opera face painting with the public;and to explore innovative ways of blending traditional and modern culture while preserving this heritage. To achieve this goal, the paper uses Peking Opera face modeling as an example. It draws on both historical and physical materials to carefully select typical samples;and extracts the spectral and color factors from these samples using a clustering algorithm. These factors are then applied to the face design, resulting in a stunning integration of traditional Peking Opera culture and modern design. © 2024 ACM.

关键词： clustering algorithm Color factor Peking opera faces Spectral factor

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Data Stream clustering: An In-depth Empirical Study

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Proceedings of the ACM on Management of Data 2023年第2期1卷 1-26页

作者： Xin Wang Zhengru Wang Zhenyu Wu Shuhao Zhang Xuanhua Shi Li Lu Ohio State University Columbus OH USA Nvidia Shanghai China University of Manchester Manchester United Kingdom Singapore University of Technology and Design Singapore Singapore Huazhong University of Science and Technology Wuhan China Sichuan University Chengdu China

Data Stream clustering (DSC) plays an important role in mining continuous and unlabeled data streams in real-world applications. Over the last decades, numerous DSC algorithms have been proposed with promising clustering accuracy and efficiency. Despite the significant differences among existing DSC algorithms, they are commonly built around four key design aspects: summarizing data structure, window model, outlier detection mechanism, and offline refinement strategy. However, there is a lack of empirical studies on these key design aspects in the same codebase using real-world workloads with distinct characteristics. As a result, it is difficult for researchers to improve upon the state-of-the-art. In this paper, we conduct such a study of DSC on its four key design aspects. We implemented state-of-the-art variants of all of these design choices in an open-sourced platform from scratch and evaluated them using both real-world and synthetic workloads. Our analysis identifies the fundamental issues and trade-offs of each design choice in terms of both accuracy and efficiency. We even find that combining flexible design choices led to the development of a new algorithm called Benne, which can be tuned to achieve either better accuracy or better efficiency compared to the state-of-the-art.

关键词： clustering algorithm data stream empirical study

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Effectively Filtering Images for Better Multi-modal Knowledge Graph 6th

Effectively Filtering Images for Better Multi-modal Knowledg...

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM)

作者： Peng, Huang Xu, Hao Tang, Jiuyang Wu, Jibing Huang, Hongbin Natl Univ Def Technol Lab Big Data & Decis Changsha 410000 Peoples R China

ISBN: (纸本)9789819913534;9789819913541

The existing multi-modal knowledge graph construction techniques have become mature for processing text modal data, but lack effective processing methods for other modal data such as visual modal data. Therefore, the focus of multi-modal knowledge graph construction lies in image and image and text fusion processing. At present, the construction of multi-modal knowledge graph often does not filter the image quality, and there are noises and similar repetitive images in the image set. To solve this problem, this paper studies the quality control and screening of images in the construction process of multi-modal knowledge graph, and proposes an image refining framework of multi-modal knowledge graph, which is divided into three modules. The final experiment proves that this framework can provide higher quality images for multi-modal knowledge graphs, and in the benchmark task of multi-modal entity alignment, the effect of entity alignment based on the multi-modal knowledge graphs constructed in this paper has been improved compared with previous models.

关键词： Multi-modal knowledge graph clustering algorithm Image-text matching Entity alignment

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clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

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DRONES 2023年第11期7卷

作者： Xiao, Peng Li, Ni Xie, Feng Ni, Haihong Zhang, Min Wang, Ban Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Xian Aircraft Intelligent Recognit & Control Key L Xian 710072 Peoples R China Aviat Ind Corp China Chengdu Aircraft Design & Res Inst Chengdu 610091 Peoples R China

Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous UAVs can significantly amplify computational complexity, posing challenges to solving the multi-region coverage path-planning problem. Consequently, this study studies a clustering-based method to tackle the multi-region coverage path-planning problem of heterogeneous UAVs. First, the constraints necessary during the planning process are analyzed, and a planning formula based on an integer linear programming model is established. Subsequently, this problem is decomposed into regional allocation and visiting order optimization subproblems. This study proposes a novel clustering algorithm that utilizes centroid iteration and spatiotemporal similarity to allocate regions and adopts the nearest-to-end policy to optimize the visiting order. Additionally, a distance-based bilateral shortest-selection strategy is proposed to generate region-scanning trajectories, which serve as trajectory references for real flight. Simulation results in this study prove the effective performance of the proposed clustering algorithm and region-scanning strategy.

关键词： heterogeneous UAVs multi-region coverage-path planning regional allocation clustering algorithm

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