• ETRI Journal
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• 제34권1호
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• pp.130-133
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• 2012

Recently, several noise-robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.

• Journal of Information Processing Systems
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• 제7권4호
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.1-8
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• 2021

The subject of navigation has drawn a large interest in the last few years. Navigation problem (or path planning) finds the path between two points, source location and destination location. In smart cities, solving navigation problem is essential to all residents and visitors of such cities to guide them to move easily between locations. Also, the navigation problem is very important in case of moving robots that move around the city or part of it to get some certain tasks done such as delivering packages, delivering food, etc. In either case, solution to the navigation is essential. The core to navigation systems is the navigation algorithms they employ. Navigation algorithms can be classified into navigation algorithms that depend on maps and navigation without the use of maps. The map contains all available routes and its directions. In this proposal, we consider the first class. In this paper, we are interested in getting path planning solutions very fast. In doing so, we employ a parallel platform, Reconfigurable mesh (R-Mesh), to compute the path from source location to destination location. R-Mesh is a parallel platform that has very fast solutions to many problems and can be deployed in moving vehicles and moving robots. This paper presents two algorithms for path planning. The first assumes maps with linear streets. The second considers maps with branching streets. In both algorithms, the quality of the path is evaluated in terms of the length of the path and the number of turns in the path.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.230-236
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• 2022

Objectives: The primary goal of this article is to compare the multiple algorithms used for deadlock handling methods and then outline the common method in deadlock handling methods. Methods: The article methodology begins with introducing a literature review studying different algorithms used in deadlock detection and many algorithms for deadlocks prevented, recovered, and avoided. Discussion and analysis of the literature review were done to classify and compare the studied algorithms. Findings: The results showed that the deadlock detection method solves the deadlock. As soon as the real-time deadlock detection algorithm is identified and indicated, it performs better than the non-real-time deadlock detection algorithm. Our novelty the statistics that we get from the percentages of reviewing outcomes that show the most effective rate of 47% is in deadlock prevention. Then deadlock detection and recovery with 28% finally, a rate of 25% for deadlock avoidance.

• 한국정보과학회논문지:데이타베이스
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• 제28권3호
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• pp.279-300
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• 2001

지식탐사 프로세스의 핵심적인 역할을 담당하는 데이터마이닝 단계에서는 여러 가지 목적에 따라 알고리즘을 선택하여 사용한다. 최근 통계, 비즈니스, 전자 상거래, 의학, 생물학 등의 분야에서 데이터마이닝 기술아 적극적으로 활용되고 있으며, 이를 위해 다양한 알고리즘들이 계속해서 연구.개발되고 있다. 그러나 시간이 지나면 이들 중 각 분야 별로 우수한 응용성을 보이는 알고리즘이나 방대한 양의 데이터를 다루는데 있어 좋은 성능을 보이는 몇몇 알고리즘만이 남게 될 것이며 또한 앞으로는 이러한 알고리즘들만을 선별하여 집중 연구할 필요가 있다. 따라서 본 논문에서는 데이터마이닝에 널리 사용되고 활발한 연구가 진행중인 알고리즘들 중에서 연관규칙(association rule), 클러스터링(clustering), 신경망(neural network), 결정트리(decision tree), 유전자 알고리즘(genetic algorithm), 베이지안 네트워크(bayesian network), 메모리 기반 추론(memory-based reasoning)등 7가지 카테고리에 속하는 알고리즘들을 선정하여 분류.분석하였다. 우선 각 알고리즘의 계통과 특성들을 분석하였고 이를 토대로 비교.분석을 위한 14가지의 분류 기준을 제시하였다. 이러한 분류 기준에 근거하여 세부 알고리즘들을 분석해 보고 비교 가능한 일부 알고리즘은 여러 특징과 성능을 중심으로 각각 최상의 알고리즘을 도출해 보았다. 본 연구 결과는 데이터마이닝 분야의 흔재된 알고리즘들을 분류.분석함으로써 마이닝 기술 적용시 사용자에게 알고리즘 선택의 지표를 제시할 수 있을 것이다.

• ETRI Journal
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• 제25권3호
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• pp.156-170
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• 2003

Gamut compression algorithms have traditionally been defined functionally and then tested with deductive methods, e.g., psychophysical experiments. Our study offers an alternative, an inductive method, in which observers judge image colors to represent the original images more accurately. We developed a computer-controlled interactive tool that modifies the color appearance of pictorial images displayed on a monitor. In experiments, observers used the tool to alter color pixels according to the region of color space to which they belonged. We created three different gamut compression algorithms based on the observer experimental data. Observer groups evaluated the performance of the newly-developed algorithms, existing gamut compression algorithms, and an image based on the average observers' results from experiments in this study. The study of gamut extension is unlike the study of gamut compression in that it mainly deals with the degree of image pleasantness as judged by observers. The results of the gamut extension experiments in this study not only make available worthwhile data but also suggest a methodology for using the observer experimental tool for future gamut extension research.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.109-118
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• 2023

Energy awareness is an essential design flaw in wireless sensor network. Clustering is the most highly regarded energy-efficient technique that offers various benefits such as energy efficiency and network lifetime. Clusters create hierarchical WSNs that introduce the efficient use of limited sensor node resources and thus enhance the life of the network. The goal of this paper is to provide an analysis of the various energy efficient clustering algorithms. Analysis is based on the energy efficiency and network lifetime. This review paper provides an analysis of different energy-efficient clustering algorithms for WSNs.

• 한국과학교육학회지
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• 제7권2호
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• pp.37-43
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• 1987

Computer in one of the most tremendous achievements of the modern scientific technique. Not only in government, business, research and education but in our daily life. computers are widely utilized to assist in solving various problems. With increasing frequency, it is recognized that a right understanding of the computer is necessary: naturally, this recognition places a great emphasis on the computer education. In Korea computer is chosen either as an optional subject or as a kind of group activity in many schools. It is the purpose of this study to compare and analyze the internal sorting algorithms which are used frequently in data processing. and to present the results of program analysis. which will make it possible to choose the appropriate sorting algorithm for each data processing. Generally the algorithms are coded in a language appropriate for structured programming. like PASCAL: however, here the algorithms are expressed in BASIC which is widely used with the personal computers so that the students and the teachers may understand them easily.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.129-134
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• 2023

The greatest challenge of this century is the protection of stored and transmitted data over the network. This paper provides a new hybrid algorithm designed based on combination algorithms, in the proposed algorithm combined with Hill and the Advanced Encryption Standard Algorithms, to increase the efficiency of color image encryption and increase the sensitivity of the key to protect the RGB image from Keyes attackers. The proposed algorithm has proven its efficiency in encryption of color images with high security and countering attacks. The strength and efficiency of combination the Hill Chipper and Advanced Encryption Standard Algorithms tested by statical analysis for RGB images histogram and correlation of RGB images before and after encryption using hill cipher and proposed algorithm and also analysis of the secret key and key space to protect the RGB image from Brute force attack. The result of combining Hill and Advanced Encryption Standard Algorithm achieved the ability to cope statistically

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2004년도 하계학술대회
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• pp.236-241
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• 2004

문제해결을 위한 절차로 정의되는 알고리즘은 교육적으로 많은 가치를 갖는다. 이 알고리즘은 지필 알고리즘과 컴퓨터 알고리즘으로 나된다. 수학교과에서는 이미 컴퓨터를 이용한 지필 알고리즘 지도에 관한 연구가 활발하게 이루어지고 있다. 반면 컴퓨터 교과에서는 알고리즘 지도를 위한 연구가 미진한 실정이다. 따라서 컴퓨터 교과에서 알고리즘을 효과적으로 지도하기 위한 방향 정립이 필요하다. 이에 알고리즘을 분석한 후 컴퓨터 교과에서 알고리즘교육의 방향을 제안하고자 한다.