• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2001년도 학술논문발표회
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• pp.99-117
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• 2001

In the past, The selection of individual method of construction was done by head of construction site or an experienced person very frequently. By doing this, The wrong selection of construction method without exact adjudication of construction site situation lead to increasing of cost and extension of construction term. Finally it will effect all over the construction process. Especially, In case of Underground construction in the beginning, there are a lot of a variable factor and it also effect on the entire construction process and it need rely careful process. The purpose of this study is to present the best suitable methodology fer selection of construction method by considering potential risk of construction method and variables together with external condition for Underground construction. The purpose of this study is to select the most suitable construction method by analysing potential conditions(Construction site situation and Client. Request in designing) To do this, We prepared arrangement rule to arrangement conditions for construction method. And then make Checklist the analyzing construction method. Though above process, To expect the risk of individual construction method using above risk checklist and using Analytic Hierarchy Process among Multiple-Criteria Decision Making, the professional opinions is to be adapted. By doing this, It can lead and select the most suitable considering method considering the data which get from risk density test.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 대한전기학회논문지
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• 제41권8호
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• pp.831-840
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• 1992

This paper presents an efficient short-term generation scheduling method using a rule-based expert/consulting system approach to assist electric energy system operators and planners. The expert system approach is applied to improve the Dynamic Programming(DP) based generation scheduling algorithm. In the selection procedure of the feasible combinations of generating units at each stage, automatic consulting on the manipulation of several constraints such as the minimum up time, the minimum down time and the maximum running time constraints of generating units will be performed by the expert/consulting system. In order to maximize the solution feasibility, the aforementioned constraints are controlled by a rule-based expert system, that is, instead of imposing penalty cost to those constraint violated combinations, which sometimes may become the very reason of no existing solution, several constraints will be manipulated within their flexibilities using the rules and facts that are established by domain experts. In this paper, for the purpose of implementing the consulting of several constraints during the dynamic process of generation scheduling, an expert system named STGSCS is developed. As a building tool of the expert system, C Language Integrated Production System(CLIPS) is used. The effectiveness of the proposed algorithm has been demonstrated by applying it to a model electric energy system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• IEIE Transactions on Smart Processing and Computing
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• 제2권5호
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• pp.266-276
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• 2013

LSB-based steganography is a simple and well known information hiding technique. In most LSB based techniques, a secret message is embedded into a specific position of LSB in the cover pixels. On the other hand, the main threat of LSB-based steganography is steganalysis. This paper proposes an asynchronous-cellular-automata(ACA)-based steganographic method, where secret bits are embedded into the selected position inside the cover pixel by ACA rule 51 and a secret key. As a result, it is very difficult for malicious users to retrieve a secret message from a cover image without knowing the secret key, even if the extraction algorithm is known. In addition, another layer of security is provided by almost random (rule-based) selection of a cover pixel for embedding using ACA and a different secret key. Finally, the experimental results show that the proposed method can be secured against the well-known steganalysis RS-attack.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.107-110
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• 전자공학회논문지B
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• 제31B권5호
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• pp.149-161
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• 1994

The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.

• Journal of the Korean Data and Information Science Society
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• 제8권1호
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• pp.9-20
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• 1997

밀도함수를 추정하는 방법에 있어서 퓨리에(Fourier) 급수기법과 핵(kernel) 기법, 스플라인(spline)평활기법들이 많은 통계학자들의 관심의 대상이 되어 왔다. 이 연구는 확률밀도함수의 추정에 있어서 전통적으로 각각 독립적으로 사용하여 왔던 정진규칙(stopping rule)과 승수규칙(selection multiplier)을 조합하여 퓨리에 급수기법을 이용한 새로운 추정기법을 연구하였다. 모의 실험을 통해 제시된 추정기법이 기존의 연구기법들보다 다소 우월 하다는 결론을 얻었다.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1999년도 춘계학술대회 발표논문집
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• pp.145-153
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• 1999

We consider a Direct Input Output Manufacturing System(DIOMS) which has a munber of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval (S/R) machine handles parts placed on pallets for the machine centers located at either one or both sides of the As/Rs. This report studies the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the S/R machine, machine center-based part type selection rule, and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this report, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• 한국시뮬레이션학회논문지
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• 제14권1호
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• pp.9-18
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• 2005

Attempts to attack hosts in the network have become diverse, due to crackers developments of new creative attacking methods. Under these circumstances the role of intrusion detection system as a security system component gets considerably importance. Therefore, in this paper, we have suggested multiple intrusion detection system based on the contract net protocol which provides the communication among multiple agents. In this architecture, fuzzy rule based system has been applied for agent selection among agents competing for being activated. The simulation models are designed and implemented based on DEVS formalism which is theoretically well grounded means of expressing discrete event simulation models.