• Structural Engineering and Mechanics
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• 제65권3호
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• pp.233-241
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• 2018

In this paper, the Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO) and Vibrating Particles System (VPS) algorithms and the force method are used for the simultaneous analysis and design of truss structures. The presented technique is applied to the design and analysis of some planer and spatial trusses. An efficient method is introduced using the CBO, ECBO and VPS to design trusses having members of prescribed stress ratios. Finally, the minimum weight design of truss structures is formulated using the CBO, ECBO and VPS algorithms and applied to some benchmark problems from literature. These problems have been designed by using displacement method as analyzer, and here these are solved for the first time using the force method. The accuracy and efficiency of the presented method is examined by comparing the resulting design parameters and structural weight with those of other existing methods.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.681-684
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• 2000

Gradient-type algorithms for adaptive IIR notch filters are very attractive in terms of both performances and computational requirements. Generally, it is quite difficult to assess their performances analytically. There have been several trials to analyze such adaptive algorithms as the sign and the plain gradient algorithms for some types of adaptive IIR notch filters, but many of them still remain unexplored. Furthermore, analysis techniques used in those trials can not be directly applied to different types of adaptive IIR notch filters. This paper presents a detailed performance analysis of the sign algorithm for a well-known adaptive IIR notch filter with constrained poles and zeros, which can not be done by just applying the related existing analysis techniques, and therefore has not been attempted yet. The steady-state estimation error and mean square error (MSE) of the algorithm are derived in closed forms. Stability bounds of the algorithm are also assessed. extensive simulations are conducted to support the analytical findings.

• 센서학회지
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• 제21권5호
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• pp.329-338
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• 2012

This paper presents an effective design method for a gas identification system. The design method adopted the sequential combination between the hybrid genetic algorithms and the TSK fuzzy logic system. First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions. Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively. By these advantages, the proposed identification model demonstrated high accuracy rates for identifying the five different types of gases; it was confirmed throughout the experimental trials.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권1호
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• pp.1-5
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• 2008

Generally the analytical tools of data mining have two learning types which are supervised and unsupervised learning algorithms. Classification and prediction are main analysis tools for supervised learning. In this paper, we perform a comparison study of classification algorithms in data mining. We make comparative studies between popular classification algorithms which are LDA, QDA, kernel method, K-nearest neighbor, naive Bayesian, SVM, and CART. Also, we use almost all classification data sets of UCI machine learning repository for our experiments. According to our results, we are able to select proper algorithms for given classification data sets.

• 한국표면공학회지
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• 제56권6호
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.711-725
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• 2013

A heuristic with multi-byte suffix matching plays an important role in real pattern matching algorithms. By skipping many characters at a time in the process of comparing a given pattern with the text, the pattern matching algorithm based on a heuristic with multi-byte suffix matching shows a faster average search time than algorithms based on deterministic finite automata. Based on various experimental results and simulations, the previous works show that the pattern matching algorithms with multi-byte suffix matching performs well. However, there have been limited studies on the mathematical model for analyzing the performance in a standard manner. In this paper, we propose a new probabilistic model, which evaluates the performance of a heuristic with multi-byte suffix matching in an average-case search. When the theoretical analysis results and experimental results were compared, the proposed probabilistic model was found to be sufficient for evaluating the performance of a heuristic with suffix matching in the real pattern matching algorithms.

• Smart Structures and Systems
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• 제26권4호
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• pp.451-468
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• 2020

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

• 전자통신동향분석
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• 제37권1호
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• pp.63-72
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• 2022

Recently, blockchain has been attracting attention as a high-reliability technology in various fields. However, the Proof-of-Work-based distributed consensus algorithm applied to representative blockchains, such as Bitcoin and Ethereum, has limitations in applications to various industries owing to its excessive resource consumption and performance limitations. To overcome these limitations, various distributed consensus algorithms have appeared, and recently, hybrid distributed consensus algorithms that use two or more consensus algorithms to achieve decentralization and scalability have emerged. This paper introduces the technological trends of the latest high-performance distributed consensus algorithms by analyzing representative hybrid distributed consensus algorithms.

• International Journal of Automotive Technology
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• 제5권4호
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• pp.269-276
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• 2004

There has been considerable activity in recent years in developing timing analysis algorithms for distributed real-time control systems. However, it is difficult for control engineers to analyze the timing behavior of distributed real-time control systems because the algorithms was developed in a software engineer's position and the calculation of the algorithm is very complex. Therefore, there is a need to develop a timing analysis tool, which can handle the calculation complexity of the timing analysis algorithms in order to help control engineers easily analyze or develop the distributed real-time control systems. In this paper, an interactive timing analysis tool, called RAT (Response-time Analysis Tool), is introduced. RAT can perform the schedulability analysis for development of distributed real-time control systems. The schedulability analysis can verify whether all real-time tasks and messages in a system will be completed by their deadlines in the system design phase. Furthermore, from the viewpoint of end-to-end scheduling, RAT can perform the schedulability analysis for series of tasks and messages in a precedence relationship.

• 한국융합학회논문지
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• 제10권11호
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• pp.41-46
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• 2019

최근에 작업 증명(PoW) 블록체인 합의 알고리즘들이 에너지 낭비, 확장성 부족 등의 문제점들이 나타나면서 비잔틴 장애 허용(BFT) 계열 합의 알고리즘들이 주목을 받고 있다. BFT 계열 합의 알고리즘들의 큰 특징 중 하나는 검증자 집합을 형성하여 그 안에서 합의를 이루는 것이다. 본 논문에서는 BFT 계열 합의 알고리즘들 중에서도 알고랜드, 스텔라, 이오스의 검증자 집합 형성 방법들의 확장성, 목표가 설정된 공격 가능 여부, 시빌 공격 가능 여부에 대해서 비교, 분석하였다. 또한 데이터 분석을 통한 각 검증자 형성 방법들의 문제점들을 발견하였고, 해당 합의 알고리즘들은 공통적으로 소수의 권력 있는 노드들이 전체 시스템을 지배하는 중앙화 현상이 나타남을 밝혔다.