• 대한산업공학회지
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• 제30권4호
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• pp.285-293
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• 2004

In this study, a machine grouping problem for the formation of manufacturing cells is considered. We constructed the problem as minimizing manufacturing leadtime consisting of parts' processing, moving, and waiting time. Specifically, the main objective of the defined problem is established as minimizing inter-cell traffic in order to minimize the part's moving time. In addition, to reduce the waiting time of parts, the load balance among cells is implicitly included as constraints. Since this problem is well known as NP-complete and cannot be solved in polynomial time, a genetic algorithm is implemented to obtain solutions. Also, a local optimization algorithm is applied in order to improve the solution by the genetic algorithm. Several experiments show that the suggested algorithms guarantee near optimal solutions in a few seconds.

• 전기학회논문지
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• 제61권8호
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• pp.1164-1171
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• 2012

Recently, machine olfactory systems as an artificial substitute of the human olfactory system are being studied actively because they can scent dangerous gases and identify the type of gases in contamination areas instead of the human. In this paper, we present an effective design method for the gas identification system. Even though dimensionality reduction is the very important part, in pattern analysis, We handled effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, we constructed the hierarchical rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Journal of applied mathematics & informatics
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• 제40권5_6호
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• pp.1151-1165
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• 2022

Key drive of information quarrying is to digest liked information starting possible information. With the colossal amount of realities kept in documents, information bases, and stores, in the medical care area, it's inexorably significant, assuming excessive, arising compelling resources aimed at examination besides comprehension like information on behalf of the withdrawal of gen that might assistance in independent direction. Classification is method in information mining; it's characterized as per private, passing on item toward a specific course established happening it is likeness toward past instances of different substances trendy the data collection. In pre-owned recycled four Classification algorithm that incorporate Multi-Layer perception, KSTAR, Bayesian Network and PART to fabricate the grouping replicas arranged the malaria data collection and analyze the replicas, degree their exhibition through Waikato Environment for Knowledge Analysis introduced to Java Development Kit 8, then utilizations outfit's technique trendy promoting presentation of the arrangement methodology. The outcome perceived that Bayesian Network return most elevated exactness of 50.05% when working on followed by Multi-Layer perception, with 49.9% when helping is half, then, at that point, Kstar with precision of 49.44%, 49.5% when supporting individually and PART have lesser precision of 48.1% when helping, The exploration recommended that Bayesian Network is awesome toward remain utilized on Malaria data collection in our sanatoriums.

• 정보처리학회논문지B
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• 제11B권4호
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• pp.485-490
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• 2004

자조의 표현에서 군집화는 주어진 데이터를 서로 유사한 개체들끼리 몇 개의 집단으로 묶는 작업을 수행한다. 군집화의 유사도 결정 측도는 맡은 연구들에서 매우 다양한 것들이 사용되었다. 하지만 군집화 결과의 성능 측정에 대한 객관적인 기준 설정이 어렵기 때문에 군집화 결과에 대한 해석은 매우 주관적이고, 애매한 경우가 많다. 퍼지 군집화는 이러한 주관적인 군집화 문제에 있어서 객관성 있는 군집 결정 방안을 제시하여 준다. 각 개체들이 특정 군집에 속하게 될 퍼지 멤버 함수값을 원소로 하는 유사도 행렬을 통하여 군집화를 수행한다. 본 논문에서는 차원 축소기법의 하나인 주성분 분석과 강력한 통계적 학습 이론인 베이지안 학습을 결합한 군집화 모형을 제안하여, 객관적인 퍼지 군집화를 수행하였다. 제안 알고리즘의 성능 평가를 위하여 UCI Machine Loaming Repository의 Iris와 Glass Identification 데이터를 이용한 실험 결과를 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.