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Cross region safety monitoring method of distribution secondary system network under Internet of things

JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2022年第6期43卷 7807-7819页

作者： Lin, Liangcheng Xu, Yonggang Zhang, Yue Kang, Chaoqun Sun, Jian State Grid Cyber Secur Technol Beijing Co Ltd Beijing 102209 Peoples R China State Grid Shanghai Energy Interconnect Res Inst Shanghai Peoples R China State Grid Jiangsu Elect Power Co Ltd Res Inst Nanjing Jiangsu Peoples R China

In order to ensure the safe transmission of the information of the secondary distribution system across the regional network, this paper studies a security monitoring method of the secondary distribution system across the regional network based on the Internet of things technology and the improved fuzzy clustering algorithm. The Internet of things technology is used to collect the information transmission in cross region network of the secondary power distribution system and store it in the database;Combined with the shadow set to improve the basic fuzzy C-means clustering algorithm, the improved fuzzy C-means clustering algorithm is obtained. The cross region information transmission in the clustering database is divided into two categories: security and risk, and the risk information obtained by clustering is divided into four risk types, so as to realize the security monitoring of information transmission in cross region network of secondary power distribution system. The results show that the average monitoring rate of this method can reach 93.93%, the information collection is efficient and accurate, the number of packet losses is low, and the clustering results are stable and reliable, which can ensure the safe information transmission of cross region network of the secondary power distribution system.

关键词： Internet of things technology fuzzy clustering algorithm secondary power distribution system network cross region transmission security monitoring cluster analysis

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A neuro-fuzzy approach for compensating color backlight images

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NEURAL PROCESSING LETTERS 2006年第3期23卷 273-287页

作者： Su, Mu-Chun Yang, Yuan-Shao Lee, Jonathan Chen, Gwo-Dong Natl Cent Univ Dept Comp Sci & Informat Engn Chungli Taiwan

This paper presents a neuro-fuzzy approach for compensating exposure in the case of backlighting, regardless of the position of objects. To achieve the compensation effect, the fuzzy C-means algorithm is first used to extract features from a backlight image. Then these extracted features are presented to a trained artificial immune system based neuro-fuzzy system (AISNFS) to estimate the amount of compensation. Finally, the estimated amount of compensation incorporated with a compensation equation is used to enhance the intensity component of the backlight image to produce a compensated image. Several backlight images were used to test the performance of the algorithm.

关键词： backlight image fuzzy clustering algorithm histogram equalization image enhancement neuro-fuzzy system

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Neural-fuzzy classification for segmentation of remotely sensed images

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1997年第11期45卷 2639-2654页

作者： Chen, SW Chen, CF Chen, MS Cherng, S Fang, CY Chang, KE NATL CENT UNIV CTR SPACE & REMOTE SENSING RESCHUNGLI 32054TAIWAN CHINESE MIL ACAD DEPT ELECT ENGNFENGSHANTAIWAN

An unsupervised classification technique conceptualized in terms of neural and fuzzy disciplines for the segmentation of remotely sensed images is presented, The process consists of three major steps: 1) pattern transformation;2) neural classification;3) fuzzy grouping. In the first step, the multispectral patterns of image pixels are transformed into what we call coarse patterns, In the second step, a delicate classification of pixels is attained by applying an ART neural classifier to the transformed pixel patterns. Since the resultant clusters of pixels are usually too keen to be of practical significance, in the third step, a fuzzy clustering algorithm is invoked to integrate pixel clusters. A function for measuring clustering validity is defined with which the optimal number of classes can be automatically determined by the clustering algorithm, The proposed technique is applied to both synthetic and real images. High classification rates have been achieved far synthetic images. We also feel comfortable with the results of the real images because their spectral variances are even smaller than those of synthetic ones examined.

关键词： adaptive representation ART neural classifier fuzzy clustering algorithm histogram-based nonuniform coarse coding measure of performance

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Enhancing scene perception using a multispectral fusion of visible-near-infrared image pair

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IET IMAGE PROCESSING 2019年第13期13卷 2467-2479页

作者： Kumar, Wahengbam Kanan Nongmeikapam, Kishorjit Singh, Aheibam Dinamani North Eastern Reg Inst Sci & Technol Dept Elect & Commun Engn Nirjuli Arunachal Prade India Indian Inst Informat Technol Dept Comp Sci & Engn Heingang Manipur India

In this study, a method for fusing of visible (or standard RGB) with near-infrared (NIR) image pair for enhancing a hazy image is proposed. Better image enhancement in terms of contrast, sharpness, increased perception is realised by combining the components from both the spectra. While there are a number of applications that use NIR images, very few combine RGB and NIR information of the same scene taken from two separate imaging devices. Although NIR images are greyscaled in nature, they have intrinsic properties desirable in both colour and visible imagery of a number of scenes. Instances are increased the contrast between sky and clouds;shadowed and non-shadowed regions;and increased optical depth. These features are in most cases missed by a standard RGB camera and therefore, a fusion of such RGB-NIR image pairs is highly beneficial. The scheme for fusing these image pairs is realised by using a novel, fuzzy clustering algorithm along with wavelet transform so that not only contrast and sharpness are enhanced, but also the chromaticity of the original RGB image is retained. The proposed technique is well demonstrated for various outdoor scenes, and better results are obtained when compared against some of the state-of-the-art techniques proposed recently.

关键词： image fusion wavelet transforms image enhancement infrared imaging pattern clustering image colour analysis cameras fuzzy systems image enhancement visible imagery standard RGB camera RGB-NIR image pairs scene perception enhancement multispectral visible-near-infrared image pair fusion near-infrared image fusion NIR image fusion hazy image enhancement fuzzy clustering algorithm wavelet transform

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fuzzy Entropy clustering Approach to Evaluate the Reliability of Emergency Logistics System

Fuzzy Entropy Clustering Approach to Evaluate the Reliabilit...

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2012 International Conference on Future Energy, Environment, and Materials(FEEM 2012)

作者： Bengang Gong Xiang Chen Chaozhong Hu School of Management Engineering Anhui Polytechnic University

Because of burstiness, weak economy, time-uncertain of emergency logistics system, the paper constructs the operation model and its evaluation index system for emergency logistics system. In view of the imperfection of subjective determining index weight method in traditional evaluation, the index weight coefficients are determined by information entropy method of combining subjective judgment with objective evaluation. Then the fuzzy clustering and information entropy to determine the weights are used into emergency logistics system reliability evaluation. Finally the paper obtains a specific solution steps based on an example.

关键词： Emergency logistics system Reliability evaluation fuzzy clustering algorithm Information entropy method

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Evaluation of the Effect of Changes in Climatic Variables at Different Levels on the Rate of Potential Evapotranspiration in Arid and Semi-Arid Regions

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PURE AND APPLIED GEOPHYSICS 2023年第1期180卷 421-437页

作者： Zarei, Abdol Rassoul Mahmoudi, Mohammad Reza Fasa Univ Coll Agr Sci Dept Range & Watershed Management Nat Engn Fasa Iran Fasa Univ Coll Sci Dept Stat Fasa Iran

Potential evapotranspiration (PET) is an effective variable in the hydrological cycle which has a determinative role in the amount of water consumption, especially in agricultural sectors. Considering the effect of climatic variables and their changes on the rate of PET, in this research, the effects of changes in six climatic variables on the rate of PET were evaluated at seven levels, namely -20%, -10%, -5%, observed or actual data, +5%, +10%, and +20%. In this study, five stations were used from March 21, 1988, to January 20, 2019, on the daily and seasonal time scales. The fuzzy clustering algorithm (FCA) was applied to evaluate the effect of the changes in climatic variables on the PET. The results indicated that the sensitivity of PET to the increasing changes in the mean daily maximum and minimum temperature, the mean daily maximum and minimum relative humidity, and the mean daily wind speed variables were more than the decreasing changes. Our analysis indicates, on daily and seasonal time scales, PET was most sensitive to changes in the maximum temperature. A 20% increase in maximum temperature corresponds to a 10% increase in the rate of PET. Also, PET was least sensitive to changes in the minimum temperature. A 20% increase in minimum temperature corresponds to a 0.5% increase in the rate of PET. The results showed that the effect of change in maximum temperature and minimum temperature variables on the PET values in warmer seasons such as summer and spring was more than in colder seasons such as winter and autumn.

关键词： fuzzy clustering algorithm climatic variables potential evapotranspiration FAO Penman-Monteith Fars Province

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Modification of rock mass rating system using soft computing techniques

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ENGINEERING WITH COMPUTERS 2019年第4期35卷 1333-1357页

作者： Rad, Hima Nikafshan Jalali, Zakaria Tabari Univ Babol Coll Comp Sci Babol Sar Iran Shahid Bahonar Univ Kerman Dept Min Engn Higher Educ Complex Zarand Kerman Iran

Classification systems such as rock mass rating (RMR) are used to evaluate rock mass quality. This paper intended to evaluate RMR based on a fuzzy clustering algorithm to improve linguistic and empirical criteria for the RMR classification system. In the proposed algorithm, membership functions were first extracted for each RMR parameter based on the questionnaires filled out by experts. RMR clustering algorithm was determined by considering the percent importance of each parameter in the RMR classification system. In all implementation stages of the proposed algorithm, no empirical judgment was made in determining the classification classes in the RMR system. According to the obtained results, the proposed algorithm is a powerful tool to modify the rock mass rating system and can be generalized for future research.

关键词： RMR system based on continuous rating Multi-objective optimization Genetic algorithm fuzzy clustering algorithm

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Applying data mining for online CRM marketing strategy: An empirical case of coffee shop industry in Taiwan

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BRITISH FOOD JOURNAL 2018年第3期120卷 665-675页

作者： Chiang, Wen-Yu Aletheia Univ Taipei Taiwan

Purpose The purpose of this paper is to propose a data mining approach for mining valuable markets for online customer relationship management (CRM) marketing strategy. The industry of coffee shops in Taiwan is employed as an empirical case study in this research. Design/methodology/approach Via a proposed data mining approach, the study used fuzzy clustering algorithm and Apriori algorithm to analyze customers for obtaining more marketing and purchasing knowledge of online CRM systems. Findings The research found three hard markets and one fuzzy market. Furthermore, the study discovered two association rules and two fuzzy association rules. Originality/value However, industry of coffee shops has been always a fast-growing and competitive business around the world. Thus, marketing strategy is important for this industry. The results and the proposed data mining approach of this research can be used in the industry of coffee shop or other retailers for their online CRM marketing systems.

关键词： fuzzy association rules Apriori algorithm Coffee shops Data mining approach fuzzy clustering algorithm Online CRM marketing systems

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fuzzy IF-THEN RULE GENERATION BASED ON NEURAL NETWORK AND clustering ALGOAITHM TECHNIQUES

FUZZY IF-THEN RULE GENERATION BASED ON NEURAL NETWORK AND CL...

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2002年IEEE十区计算机、通信、控制和电机工程会议

作者： Masaharu MIZUMOTO Faculty of Information Science and Technology Osaka Electro-Communication University Osaka 572-8530 Japan School of Mathematics University of South Australia SA 5095 Australia

This paper proposes a self-tuning methodfor fuzzy if-then rule generation based on neuralnetwork and clustering algorithm techniques. In thetuning approach, the initial parameters of fuzzy rulesare roughly designed by using a fuzzy clusteringalgorithm, and then fuzzy rules under fuzzysingleton-type reasoning are tuned by using aneuro-fuzzy learning algorithm. By this approach,the learning time can be reduced and the generatedfuzzy rules are reasonable and suitable for theidentified system. Finally, identifying a nonlinearfunction shows the efficiency of the tuningapproach.

关键词： fuzzy rule generation Neural network learning fuzzy clustering algorithm

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Evaluation method for groundwater pollution treatment process based on fuzzy analytic hierarchy process

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2024年第4-6期27卷 444-460页

作者： Deng, Mingming Hunan Tech Coll Railway High Speed Coll Humanity Hengyang 421002 Hunan Peoples R China

In order to scientifically evaluate groundwater pollution treatment processes, a fuzzy analytic hierarchy process evaluation method for groundwater pollution treatment processes is proposed. Firstly, select the initial evaluation indicators and use fuzzy clustering algorithm to cluster them to eliminate redundant indicators;then, a scientific evaluation index system for groundwater pollution treatment process is constructed, and the fuzzy analytic hierarchy process is used to calculate the weight values of each evaluation index. A fuzzy mathematical comprehensive evaluation model is constructed, and finally, the evaluation of groundwater pollution treatment process is completed based on the calculation results of index weight and the fuzzy membership matrix. The experimental results show that this method can accurately evaluate different groundwater pollution treatment processes, and the application effect of the upflow anaerobic sludge bed process is the best. After its treatment, the pollution degree in the study area has decreased by more than 60%.

关键词： fuzzy analytic hierarchy process FAHP groundwater pollution treatment fuzzy clustering algorithm evaluation index system weight value

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