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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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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 1046页

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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Design and Implementation of a Multi-layered Employee Profile System for Power Enterprises Based on LSTM and Mini-batch K-means 5

Design and Implementation of a Multi-layered Employee Profil...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Wang, Ziheng Li, Xiaozhen Meng, Yu Zhang, Qian State Grid Information and Communication Industry Group Co. LTD. Beijing China

ISBN: (纸本)9798350309034

Abstract-To facilitate more efficient and convenient online office work, major enterprises have launched mobile portal applications, integrating various office micro-applications to provide employees with a unified entry point. However, employees in different positions have varying application demands and there is a lack of a personalized recommendation mechanism. Accurately profiling employees has thus become a research focus for many scholars. In response to this issue, this paper proposes a multi-layered employee profile system for power enterprises. We designed a data collection model suitable for processing massive logs, and used LSTM and mini-batch K-means algorithms to cluster employees at two levels: application usage and provincial distribution. Employee labels were established through statistical characteristics and professional analysis, and a classification model was trained for employee profiling. To verify the effectiveness of the system proposed in this paper, real data was collected to conduct experiments. The results were compared with traditional clustering algorithms and other outstanding profile algorithms from previous studies. The findings demonstrate that the profile system proposed in this paper achieves superior performance in profiling employees of power enterprises compared to other algorithms. © 2023 IEEE.

关键词： Classification algorithm clustering algorithm LSTM Mini-batch K-means User Profiling

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An improved K-prototype-based cluster algorithm for mixed educational data mining

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2022年第5期15卷 448-456页

作者： Wang, Yuan Yang, Liping Wu, Jun Chen, Shaomiao Beijing Univ Chem Technol Coll Humanities & Law Beijing 100029 Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China

Educational data mining is one of the important ways to improve the quality of education. clustering is a classic data mining method. It is often applied to single-attribute data. Educational data which contains multiple types of attributes. This brings great challenges to the dissimilarity measurement of clustering algorithms. In this paper, we first collect a large amount of student education data with 18 different types of attributes. Then, based on the K-prototype algorithm framework, we improve the dissimilarity measurement method so that it can be applied to the mixed attribute characteristics of education data. Finally, a Kruskal-Wallis test is used to determine the significance of clustering properties. Experimental results show that the proposed method can obtain higher quality clustering than the state-of-the-art clustering methods.

关键词： clustering algorithm educational data mining EDM mixed attribute learning analysis campus big data

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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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Intelligent Pricing and Replenishment Strategy for Vegetables Based on ARIMA Prediction and BP Neural Networks

Intelligent Pricing and Replenishment Strategy for Vegetable...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Yang, Quyixiao Liu, Enyu Qiao, Yue Dalian University of Technology Dalian China

ISBN: (纸本)9798350309614

With the improvement of people's living standard, people pay more and more attention to nutritional balance, which brings huge commercial profits, because vegetable goods can only be sold on the same day, taking into account the special characteristics of vegetable goods, the use of past sales data for data mining, to help store managers to make the best decision from the single product, category, replenishment volume, pricing. Firstly, using the idea of clustering, the single items are clustered, and Spearman correlation analysis model is established for correlation analysis, then, the weekly sales volume, sales unit price, wholesale price are counted, and the cost-plus pricing relationship of each category is back-calculated daily through the cost-plus pricing formula, and the total amount of replenishment of the vegetable category for a week and pricing strategy are predicted using the BP neural network prediction model forecasting, and the total amount of replenishment of the vegetable category for a week is predicted. Finally, the ARIMA time series prediction model is built to solve the pricing reference strategy for a single item on a single day and determine the replenishment quantity of a single item on a single day by considering the unit price of the vegetable category, the wholesale unit price, and the net profit from the sale of the vegetable category. The proposed model has excellent forecasting ability and the results are evaluated more objectively. © 2023 IEEE.

关键词： BP Neural Network Prediction Models clustering algorithm Intelligent Decision Making for Goods Polynomial Regression Spearman Correlation

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An improved method for coherent structure identification based on mutual K-nearest neighbors

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JOURNAL OF TURBULENCE 2022年第11-12期23卷 655-673页

作者： Wei, Zeming Zhang, Jiazhong Jia, Ruidong Gao, Jingsheng China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian Peoples R China Tokushima Univ Adv Technol & Sci Tokushima Japan Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China

The clustering algorithm based on mutual K-nearest neighbors (MKNN) is presented to identify coherent structures in complicated fluid flows, in order to analyze the mass mixing and transport. First, both trajectory similarity and spatial proximity are used to describe and measure the coherence between particles. These two identification criteria are frame-invariant since they are derived from the relative distances of particles. Then, the concept of mutual K-nearest neighbors is introduced further, and particles with the same cluster label are identified as coherent structures after the initialization and merging process of clusters, while incoherent regions consist of incoherent particles, which cannot form a mutual K-nearest neighbors relationship with other particles. Finally, the MKNN-based clustering algorithm is applied to three examples, realizing the identification and tracking of coherent structures. The identification results show that the MKNN-based clustering algorithm is robust to parameter K, and a higher threshold lambda of cluster quantity will be helpful to identify the finer structures in flows. Moreover, spatial proximity performs better in vortex identification, and trajectory similarity is more suitable for elongated structures (jets) identification. Importantly, the method presented analyzes the evolutions of vortices in detail, including the generation, stretching, and merging processes. In summary, the MKNN-based clustering algorithm takes particle trajectories as input data, analyzes the evolution of relative distances between particles quantitatively, and carries out clustering analysis on particles according to trajectory similarity and spatial proximity. The combination of the MKNN-based clustering algorithm and frame-invariant identification criteria shows great potential in coherent structure identification of complicated fluid flows.

关键词： Mutual K-nearest neighbors clustering algorithm coherent structure vortex identification

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Decentralized Multi-Target Tracking for Netted Radar Systems with Non-Overlapping Field of View

Decentralized Multi-Target Tracking for Netted Radar Systems...

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IEEE Radar Conference (RadarConf)

作者： Peng, Cong Mao, Haiyi Liu, Yue Chai, Lei Yi, Wei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781665436694

In this paper, a robust and high-accuracy decentralized fusion strategy is proposed for multi-target tracking (MTT) in netted radar systems with non-overlapping field of view (FoV). Each radar in the network runs a local Probability Hypothetical Density (PHD) filter with the decentralized consensus protocol to reduce communication bandwidth and eliminate information inconsistency among nodes. In the above process, the most critical core is an effective fusion strategy. Our proposed method adopts the geometric covariance intersection (GCI) rule to improve fusion accuracy. However, the standard GCI fusion is not suitable for the netted radar systems with non-overlapping FoV because it only focuses on the targets within the intersection of radar FoVs. Consider that, we extend the weights in GCI fusion to be a set of state-dependent weights instead of scalars to perform GCI fusion in a more robust manner. Furthermore, the radar FoVs are always unknown and time-varying in practical scenarios. Towards addressing this case, we combine a clustering algorithm based on highest posterior density to maintain a good fusion performance. The Gaussian mixture implementation of the proposed method is provided. Numerical simulations are designed to verify the effectiveness of the proposed method.

关键词： decentralized multi-target tracking field of view consensus protocol state-dependent generalized covariance intersection fusion clustering algorithm

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RETRACTED: Audio-Visual Automatic Speech Recognition Towards Education for Disabilities (Retracted Article)

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JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS 2023年第9期53卷 3581-3594页

作者： Debnath, Saswati Roy, Pinki Namasudra, Suyel Crespo, Ruben Gonzalez Alliance Univ Dept Comp Sci & Engn Bangalore Karnataka India Natl Inst Technol Dept Comp Sci & Engn Silchar Assam India Natl Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Univ Int La Rioja Logrono Spain

Education is a fundamental right that enriches everyone's life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition.

关键词： AV-ASR LBP-TOP GLCM MFCC clustering algorithm Supervised learning

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Weld Defect Location Method of U-Shaped Crane Boom Based on Helical Guided Waves 10th

Weld Defect Location Method of U-Shaped Crane Boom Based on ...

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10th European Workshop on Structural Health Monitoring (EWSHM)

作者： Lu, Zhaojing Liu, Zenghua Jiang, Wenshuo Chen, Honglei Beijing Univ Technol Fac Mat & Mfg Beijing 100124 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China

ISBN: (纸本)9783031073229;9783031073212

The boom is the key load-bearing component of the crane, and its health seriously threatens the service performance of the crane. In order to ensure the safe production of cranes, nondestructive testing (NDT) and structural health monitoring (SHM) of boom become more and more urgent and important. In this paper, the intelligent defect location algorithm based on helical guided waves is applied to the weld defect detection of U-shaped boom. Intelligent defect location algorithm is an imaging algorithm that combines ellipse imaging principle, evolutionary algorithm and K-means clustering algorithm. This paper verifies the effectiveness of this algorithm for weld defect location of U-shaped boom through experimental research. Firstly, the propagation regularity of helical guided waves in the U-shaped boom structure is studied. In addition, the elliptical imaging algorithm is used to image and analyze the weld defects in the U-shaped boom, and the location of the defects is preliminarily determined. The defect location analysis of weld defects is carried out by using the intelligent defect location algorithm. By comparing the imaging results of the two methods, it is found that the intelligent defect location algorithm has higher resolution and can effectively improve the defect location accuracy. This algorithm provides a tool for health monitoring of special-shaped structures based on guided waves.

关键词： Structural health monitoring U-shaped crane boom Helical guided waves Evolutionary algorithm clustering algorithm

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