• 경영과정보연구
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• 제14권
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• pp.51-65
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• 2004

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we Propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the ISODATA used traditionally in this field since the objective function is changed. We show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1317-1341
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• 2021

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.

• 한국컴퓨터정보학회논문지
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• 제21권1호
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• pp.9-16
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• 2016

In this paper, we present a performance evaluation agent based on fuzzy cluster analysis and validity measures. The proposed agent is consists of three modules, fuzzy cluster analyzer, performance evaluation measures, and feature ranking algorithm for feature selection step in CAD system. Feature selection is an important step commonly used to create more accurate system to help human experts. Through this agent, we get the feature ranking on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. Also we design a CAD system incorporating the agent and apply five different feature combinations to the system. Experimental results proposed approach has higher classification accuracy and shows the feasibility as a diagnosis supporting tool.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• Asian-Australasian Journal of Animal Sciences
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• 제17권8호
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• pp.1047-1052
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• 2004

The genomes of Chinese native chicken populations were screened using microsatellites as molecular markers. A total of, 528 individuals comprisede12 Chinese native chicken populations were typed for 7 microsatellite markers covering 5 linkage groups and genetic variations and genetic distances were also determined. In the 7 microsatellite loci, the number of alleles ranged from 2 to 7 per locus and the mean number of alleles was 4.6 per locus. By using fuzzy cluster, 12 Chinese native chicken populations were divided into three clusters. The first cluster comprised Taihe Silkies, Henan Game Chicken, Langshan Chicken, Dagu Chicken, Xiaoshan Chicken, Beijing Fatty Chicken and Luyuan Chicken. The second cluster included Chahua Chicken, Tibetan Chicken, Xianju Chicken and Baier Chicken. Gushi Chicken formed a separate cluster and demonstrated a long distance when comparing with other chicken populations.

• 제어로봇시스템학회논문지
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• 제11권2호
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• pp.109-116
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• 2005

In this paper, we investigate the visual and quantitative analysis at the same time with an electronic tongue(e-tongue) system using an array of ISE(ion-selective electrode). We apply the FCM(fuzzy c-means) algorithm combined with PCA(principal component analysis), which can be reduced multi-dimensional data to third-dimensional data, to classify data patterns detected by E-Tongue system. The proposed technique can be designed to solve the cluster centers and membership grade of patterns combined with the output results obtained by PCA method. According to the proposed technique, the membership grade of unknown pattern, which does not shown previously can be determined and analyzed visually. Conclusionally, the relationship between the standard patterns and unknown pattern can be easily analyzed. Throughout the experimental trials, the proposed technique has been confirmed using developed E-Tongue system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.781-786
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• 1991

This paper introduces a new methodology for multisensor data fusion. The method makes use of fuzzy techniques and possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable. We propose a simple sensor fuzzy modeling method which can be used for cluster validity analysis. As a result, the feasibility of these multisensor data fusion modules is demonstrated by computer simulation applicable to the problem of object identification.

• 대한지리학회지
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• 제30권3호
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• pp.296-310
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• 1995

본 연구는 퍼지이론을 공간분석에 적용하기 위한 이론적인 배경을 고찰하고, 퍼지 분류법의 특성에 대해 살펴본 것이다. 이를 위해 필자는 공간정보의 모호성에 대해 살펴보 고, 퍼지공간분석의 전제를 설정한 다음 퍼지분류법을 소개하였다. 그리고 퍼지분류법의 특 성을 명확히 하기 위해 경상남도 읍급이상 도시의 산업별 고용비율을 대상으로 퍼지분류를 행한 후, 퍼지분류와 전통적인 군집분석의 결과를 비교하였다. 그 결과, 공간정보의 모호성 은 구체성의 부족, 인간행태, 인내치문제, 분류기준의 부족 등에 의해 발생하는데 기존의 공 간분석기법으로는 공간의 모호성을 반영할 수 없으므로 퍼지기법을 도입한 퍼지공간분석의 필요성이 있음을 확인하였다. 퍼지분류법 중, 퍼지이산분류는 계산절차는 상대적으로 간단하 나 분류결과가 집단간의 점이성을 고려하지 못하며, 퍼지중첩분류는 분류집단간의 점이성은 고려하나 분류결과가 지나치게 많아 적절한 분류수준을 선택하기 어렵고 결과해석이 상대적 으로 난해하다는 문제점이 있음이 밝혀졌다, 또 경남의 도시기능분류는 분류기법에 따라 다 르게 이루어졌지만 창원, 울산, 마산, 진해, 김해, 양산, 웅상, 장승포, 신현으로 구성된 제조 업 군집과 단독군집 충무의 존재가 세 가지 분류 모두에서 공통적으로 확인되었다.

• Asian-Australasian Journal of Animal Sciences
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• 제14권5호
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• pp.603-607
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• 2001

This paper is based on the 36 wild Japanese quails which migrated to and settled in the Weishan Lake area. The gene frequency of 10 loci encoding the enzymes in viscera and muscle was detected. After collecting the same data about 20 quail colonies in China and other countries, it clusters the 21 quail populations by fuzzy cluster analysis. The study indicates that the wild Japanese quail in the Weishan Lake area is closer to domestic quail for phylogenetic system than wild Japanese quails in Japanese Islands. The paper supports the thesis that the quail domestication area should be further studied.

• Structural Monitoring and Maintenance
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• 제6권3호
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• pp.173-190
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• 2019

Steel-confined reinforced concrete (SCRC) columns feature highly complex and invisible mechanisms that make damage evaluation and pattern recognition difficult. In the present article, the prevailing acoustic emission (AE) technique was applied to monitor and evaluate the damage process of steel-confined RC columns in a quasi-static test. AE energy-based indicators, such as index of damage and relax ratio, were proposed to trace the damage progress and quantitatively evaluate the damage state. The fuzzy C-means algorithm successfully discriminated the AE data of different patterns, validity analysis guaranteed cluster accuracy, and principal component analysis simplified the datasets. A detailed statistical investigation on typical AE features was conducted to relate the clustered AE signals to micro mechanisms and the observed damage patterns, and differences between steel-confined and unconfined RC columns were compared and illustrated.