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A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks

SENSORS 2022年第4期22卷 1564-1564页

作者： Han, Bing Ran, Feng Li, Jiao Yan, Limin Shen, Huaming Li, Ang Shanghai Univ Dept Mechatron Engn & Automat Shanghai 200444 Peoples R China Shanghai Inst Tech Phys Acad Sci Shanghai 200083 Peoples R China Chinese Acad Sci Key Lab Infrared Syst Detect & Imaging Technol Shanghai 200083 Peoples R China Shanghai Spaceflight Elect Commun Equipment Res I Shanghai 201109 Peoples R China Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China

With the various applications of the Internet of Things, research into wireless sensor networks (WSNs) has become increasingly important. However, because of their limited energy, the communication abilities of the wireless nodes distributed in the WSN are limited. The main task of WSNs is to collect more data from targets in an energy-efficient way, because the battery replacement of large amounts of nodes is a labor-consuming work. Although the life of WSNs can be prolonged through energy-harvesting (EH) technology, it is necessary to design an energy-efficient routing protocol for the energy harvesting-based wireless sensor networks (EH-WSNs) as the nodes would be unavailable in the energy harvesting phase. A certain number of unavailable nodes would cause a coverage hole, thereby affecting the WSN's monitoring function of the target environment. In this paper, an adaptive hierarchical-clustering-based routing protocol for EH-WSNs (HCEH-UC) is proposed to achieve uninterrupted coverage of the target region through the distributed adjustment of the data transmission. Firstly, a hierarchical-clustering-based routing protocol is proposed to balance the energy consumption of nodes. Then, a distributed alternation of working modes is proposed to adaptively control the number of nodes in the energy-harvesting mode, which could lead to uninterrupted target coverage. The simulation experimental results verify that the proposed HCEH-UC protocol can prolong the maximal lifetime coverage of WSNs compared with the conventional routing protocol and achieve uninterrupted target coverage using energy-harvesting technology.

关键词： wireless sensor network energy harvesting hierarchical clustering algorithm environment adaptive routing protocol uninterrupted target coverage

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Reactive Voltage Partitioning Method for the Power Grid With Comprehensive Consideration of Wind Power Fluctuation and Uncertainty

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IEEE ACCESS 2020年 8卷 124514-124525页

作者： Zhang, Xu Chen, Yunlong Wang, Yixian Ding, Ruiting Zheng, Yuchuan Wang, Yi Zha, Xiaobing Cheng, Xueting North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China State Grid Taizhou Power Supply Co Taizhou 318000 Peoples R China State Grid Jiangsu Elect Power Co Ltd Maintenance Div Headquarters Nanjing 211102 Peoples R China State Grid Shanxi Elect Power Co Elect Power Res Inst Taiyuan 030001 Peoples R China

When large-scale wind power is connected to the power grid, the fluctuation and uncertainty in the wind power reduce the stability and accuracy of the grid's reactive voltage division results based on the electrical distance matrix and affect the grid's reactive power regulation. This paper proposes a grid reactive voltage partitioning method that considers the wind power stability and accuracy in a comprehensive manner. The wind power uncertainty and zoning results are characterized by the distribution of wind power forecast error intervals and changes in the zoning result nodes at different moments when the wind power is connected. Regarding volatility, according to the discretization of the probability distribution of the active power output at a certain time based on the wind power prediction, a calculation interval of the wind power output under a single cross-section is formed, and multiple sequential power flow sections within a long time scale are clustered and partitioned by an agglomeration hierarchical clustering method. Finally, an optimal zoning model of reactive voltage is established over a long time scale with the minimum comprehensive stability serving as the objective function. A simulation analysis of the improved IEEE39 node system shows that the partition combination can effectively increase the stability and accuracy of the reactive partitioning.

关键词： Wind power active prediction interval wind power fluctuation wind power uncertainty hierarchical clustering algorithm reactive voltage partitioning

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Changes in the market structure and risk management of Bitcoin and its forked coins

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RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE 2023年 65卷

作者： Kong, Xiaolin Ma, Chaoqun Ren, Yi-Shuai Narayan, Seema Nguyen, Thong Trung Baltas, Konstantinos Hunan Univ Business Sch Changsha Peoples R China Hunan Univ Res Inst Digital Soc & Blockchain Changsha Peoples R China Hunan Univ Ctr Resource & Environm Management Changsha Peoples R China Hunan Univ Sch Publ Adm Changsha Peoples R China Univ Auckland Energy Ctr 12 Grafton Rd Auckland 1010 New Zealand Asia Pacific Appl Econ Assoc Melbourne Australia Univ Econ Ho Chi Minh City Sch Banking Ho Chi Minh City Vietnam Univ Essex Essex Business Sch Colchester England

Inconsistency of consensus results in blockchain forks, which create a new financial risk. After filtering out Bitcoin's linear, nonlinear, and lag impacts on forked coins, this study employs a bottom-up hierarchical clustering algorithm to examine the logarithmic return series for Bitcoin and its 14 forked coins from 2018 to 2021. The results indicate that the market for forked coins can be divided into three clusters: SegWit-supported forked coins, mature forked coins, and the latest forked coins. Bitcoin and the mature forked coins form a cluster, and its performance is superior to others. Although Bitcoin's return significantly affects that of its forked coins, it does not affect the market structure. Furthermore, this study provides references for risk aversion among investors in forked coins and presents macro-level information for cryptocurrency market authorities.

关键词： Bitcoin Forked coin Market structure Financial risk hierarchical clustering algorithm Blockchain

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Weakly supervised retinal vessel segmentation algorithm without groundtruth

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ELECTRONICS LETTERS 2020年第23期56卷 1235-+页

作者： Lu, Zheng Chen, Dali Xue, Dingyu Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

In the current image processing field, medical image segmentation needs a lot of groundtruths, and the process of making these groundtruths is time-consuming and laborious. Thus, a novel retinal vessel segmentation algorithm without groundtruth is proposed in this Letter. The hierarchical clustering algorithm is first used to binary classify vessel and non-vessel pixels. Then classification results based on DRIVE databases are used as pseudo groundtruths to train the neural networks and transfer learning is considered for subsequent processing. Next the trained network is used as the feature extraction tool, by calculating and comparing the difference of image features between the target domain data (DRIVE database) and the source domain data (STARE, CHASE DB1, and HRF databases) extracted from the network. The data required for training is expanded based on semi-supervised clustering in this image feature space, finally the deep neural network is further fine-tuned. Experiments on the publicly available fundus image dataset DRIVE demonstrate that the proposed method outperforms many other state-of-the-art weakly supervised and unsupervised methods.

关键词： unsupervised methods nonvessel pixels transfer learning image feature space supervised retinal vessel segmentation algorithm biomedical optical imaging image features learning (artificial intelligence) blood vessels pattern clustering image enhancement feature extraction neural nets subsequent processing target domain data fundus image dataset hierarchical clustering algorithm source domain data binary classify vessel eye medical image segmentation deep neural network semisupervised clustering feature extraction tool image segmentation pseudogroundtruths medical image processing novel retinal vessel segmentation algorithm DRIVE database

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Dynamic switching control of buck converters using unsupervised machine learning methods

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JOURNAL OF ENGINEERING-JOE 2020年第12期2020卷 1155-1164页

作者： Abegaz, Brook W. Loyola Univ Chicago Dept Engn Sci 1032 W Sheridan Rd Chicago IL 60660 USA

This study presents the implementation of a new unsupervised machine learning based system called a buck converter controller using unsupervised machine learning (ABCML) to control the operation of a type of switching voltage regulators, commonly called buck converters. The system uses the voltage and current state variables of buck converters to evaluate four switching-based clustering algorithms, namely a Gaussian mixture model, a self-organising mapping (SOM), a k-means clustering and a hierarchical clustering algorithm. Step response results of the controller implementation with the buck converter show that the SOM controller improves the performance of the voltage regulator system by 99.77% in terms of voltage overshoot and by 63.24% in terms of fall time, whereas the hierarchical clustering algorithm improves the settling time of the output voltage by 3.91%. This finding of optimal machine learning implementation approaches and their comparison could be used to improve the stability and the performance of switching voltage regulation systems which are widely used in electronic systems of today.

关键词： learning (artificial intelligence) pattern clustering self-organising feature maps voltage regulators Gaussian processes unsupervised learning switching systems (control) voltage control power convertors control engineering computing dynamic switching control buck converters unsupervised machine learning voltage regulators switching-based clustering algorithms Gaussian mixture model hierarchical clustering algorithm controller implementation buck converter show SOM controller voltage regulator system voltage overshoot output voltage optimal machine voltage regulation systems electronic systems

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Passenger counting in minivan-taxis using crowd-sourcing and hierarchical clustering

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SCIENTIFIC AFRICAN 2021年 13卷

作者： Adjaidoo, Theresa-Samuelle Akowuah, Emmanuel Kofi Obeng, Daniel Atuah Dzisi, Emmanuel Ackaah, Williams Kwame Nkrumah Univ Sci & Technol Dept Comp Engn Kumasi Ghana Kwame Nkrumah Univ Sci & Technol Reg Transport Res & Educ Ctr Dept Comp Engn Kumasi TRECK Kumasi Ghana Kwame Nkrumah Univ Sci & Technol Reg Transport Res & Educ Ctr Dept Civil Engn Kumasi TRECK Kumasi Ghana CSIR Bldg & Rd Res Inst Kumasi Ghana

A novel method for estimating the passenger densities of minivan taxis popularly known as Trotros in Ghana is proposed. A smartphone is used to collect time, location and velocity data from groups of passengers travelling in parts of the Kumasi Metropolitan Assembly, Ghana. Passengers are clustered by four different combinations of their location, time and average acceleration data using the agglomerative hierarchical clustering algorithm. A classification method was then used to externally validate the results by comparing the cluster labels to an initial class labelling which had been assigned during data collection called the group code. The count of the group code represented the estimated number of passengers aboard the vehicle. Results from the various clustering combinations performed indicated that using the time and location variables only for classification gave the highest accuracy of about 89.2% as compared to the other combinations. The proposed method of counting passengers in moving vehicles is particularly useful in the Ghanaian context due to the fact that trotros do not have to be retro-fitted with expensive devices for data collection and thus can be implemented without financially burdening the privately-owned trotro industry. Also, counting passengers in trotros adds to the growing pool of trotro research data which is beneficial for improvements in the trotro industry and also for future research. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： intelligent transport Passenger counting Minivan taxi Trotro Crowdsourcing hierarchical clustering algorithm Ghana

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Optimization of time-of-use electricity tariffs for multi-type consumers using price-based demand response and consumer classification model

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Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2025年第1期47卷

作者： Yuhua Lv Yuqian Fang Power Dispatching and Control Center Huizhou Electric Power Supply Bureau of Guangdong Power Grid Corporation Huizhou China

ABSTRACTExisting researches predominantly focus on optimizing time-of-use tariffs for single-type consumers, overlooking the fact that an actual distribution system comprises multi-type consumers with distinct power consumption behaviors and preferences. To better cater to the diverse needs of different consumer types, this study proposes an optimization method for multi-type consumers of time-of-use tariffs using price-based demand response and a consumer classification model. Firstly, a joint classification model for multi-type consumers is constructed based on convolutional neural network autoencoder and hierarchical clustering algorithm. Then, by applying the constructed model to historical power consumption data, consumers are classified into several groups. Secondly, a fuzzy heuristic algorithm is introduced to optimize time-of-use tariffs for these consumer groups. Through fuzzy reasoning, the optimal tariffs during different time periods are determined for each group using the fuzzy heuristic algorithm. The effectiveness and adaptability of the proposed method is verified through an application example involving time-of-use tariff optimization for an actual distribution network. Simulation results demonstrated that the maximum power demands for consumers powered by the power company reduced by 6%~9%. The load rate for the studied power company increased from 0.77 to 0.81 ~ 0.84. In addition, compared with two latest methods, when the proposed method was used for ToU tariff optimization, reduction rate of the maximum power demand was improved by 0.75%~2.05% and 0.98%~2.52%, respectively. Load rate for the power company was improved by 0.01 ~ 0.03 and 0.02 ~ 0.03.

关键词： Consumer classification convolutional neural network autoencoder demand response hierarchical clustering algorithm time-of-use tariff optimization

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IBRIDIA: A hybrid solution for processing big logistics data

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 97卷 792-804页

作者： AlShaer, Mohammed Taher, Yehia Haque, Rafiqul Hacid, Mohand-Said Dbouk, Mohamed Univ Versailles St Quentin En Yvelines Versailles France Univ Claude Bernard Lyon 1 Lyon France Lebanese Univ Beirut Lebanon Cognitus Paris France

Internet of Things (IoT) is leading to a paradigm shift within the logistics industry. Logistics services providers use sensor technologies such as GPS or telemetry to track and manage their shipment processes. Additionally, they use external data that contain critical information about events such as traffic, accidents, and natural disasters. Correlating data from different sensors and social media and performing analysis in real-time provide opportunities to predict events and prevent unexpected delivery delay at run-time. However, collecting and processing data from heterogeneous sources foster problems due to the variety and velocity of data. In addition, processing data in real-time is heavily challenging that it cannot be dealt with using conventional logistics information systems. In this paper, we present a hybrid framework for processing massive volume of data in batch style and real-time. Our framework is built upon Johnson's hierarchical clustering (HCL) algorithm which produces a dendrogram that represents different clusters of data objects. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Realtime processing clustering Big data Internet of Things Logistics hierarchical clustering algorithm

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Network- and distance-based methods in bioregionalization processes at regional scale: An application to the terrestrial mammals of Iran

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JOURNAL OF BIOGEOGRAPHY 2019年第11期46卷 2433-2443页

作者： Yusefi, Gholam Hosein Safi, Kamran Brito, Jose Carlos Univ Porto CIBIO InBIO Ctr Invest Biodiversidade & Recursos Genet P-4485661 Vairao Portugal Univ Porto Fac Ciencias Dept Biol Porto Portugal Mohltban Soc Tehran Iran Max Planck Inst Anim Behav Dept Migrat Radolfzell am Bodensee Germany Univ Konstanz Dept Biol Constance Germany

Aim In recent years, novel approaches have been proposed to improve current bioregionalization methods, but these have not been thoroughly compared. We assessed the applicability of the recently developed network-based clustering method (Infomap algorithm) in bioregionalization analysis at regional spatial scales and compared the results with commonly used distance-based methods (hierarchical clustering algorithm). We also identified climate regions by using a model-based cluster analysis (Gaussian algorithm). Finally, we quantified the representation of climate regions and bioregions in current protected areas (PAs). Location Iran. Taxa Terrestrial mammals. Methods To define bioregions we used the Infomap algorithm and distance-based clustering methods based on species distribution data (over 14,000 occurrence records for 188 species). The Infomap algorithm was applied using the interactive web application "INFOMAP BIOREGIONS" and the distance-based clustering was based on unweighted pair-group method using arithmetic averages (UPGMA). To identify climate regions we used principal components analysis and a model-based cluster analysis both based on 15 climatic variables as well as a terrain ruggedness index. Results The Infomap algorithm detected nine biogeographical units: seven bioregions and two transition zones. The distance-based method suggested five bioregions. The identified bioregions differed between methods with some consistent spatial patterns across methods. Temperature and precipitation explained 85.8% of the environmental variation. Eight climate regions were identified. In general, climate variation and bioregional patterns are currently poorly represented in PAs (<25% coverage). Main conclusions The network-based method allowed identifying bioregions at regional scale and was apparently more sensitive than the conventional distance-based method. The detection of transition zones by the Infomap algorithm was an advantage, and stressed the fact that

关键词： beta diversity biogeographical regionalization climate regions hierarchical clustering algorithm Infomap Bioregions map equation Palaearctic Saharo-Arabian transition zone

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Time series data intelligent clustering algorithm for landslide displacement prediction

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第4期35卷 4131-4140页

作者： Han, Liu Shang, Tao Shu, Jisen Chowdhury, Ahmed Jalal Khan China Univ Min & Technol Sch Mines Min Engn Xuzhou 221116 Jiangsu Peoples R China Int Islamic Univ Malaysia Dept Marine Sci Kulliyyah Sci Jalan Sultan Ahmad Shah Kuantan Pahang Malaysia

The traditional time series data clustering for landslide displacement prediction is based on Euclidean distance measure. The time series data is clustered by distance calculation of two vectors. The correlation between components is not considered. The multiple components with single feature will interfere with the clustering results, and the accuracy of clustering results is greatly reduced. To address this problem, an intelligent clustering algorithm for time series data in landslide displacement prediction based on nonlinear dynamic time bending is proposed in this paper. By reconstructing the phase space of the landslide displacement time series, the phase space transposed matrix is obtained as the time series reconstruction matrix. After embedding dimension processing, the time series of landslide displacement is predicted by SVM data mining model. Dynamic time warping calculation is based on the correlation of time series sequence and the components. The local optimal solution is obtained by recursive search, and the whole curve path is obtained. clustering calculation of time series data set is carried out by using hierarchical clustering algorithm according to bending path. The intelligent clustering results of time series data in landslide displacement prediction is obtained. Experimental results show that the proposed algorithm has better clustering effect and higher clustering accuracy.

关键词： Landslide displacement time series data intelligent clustering nonlinear dynamic time bending hierarchical clustering algorithm

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