• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.4
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• pp.745-750
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• 2009

The goal of this study is to examine on reasoning and search for construction of diagnosis ontology as a knowledge base of diagnosis expert system in oriental medicine. Expert system is a field of artificial intelligence. It is a system to acquire information with diverse reasoning methods after putting expert's knowledge in computer systematically. A typical model of expert system consists of knowledge base and reasoning & explanatory structure offering conclusion with the knowledge. To apply ontology as knowledge base to expert system practically, consideration on reasoning and search should be together. Therefore, this study compared and examined reasoning, search with diagnosis process in oriental medicine. Reasoning is divided into Rule-based reasoning and Case-based reasoning. The former is divided into Forward chaining and Backward chaining. Because of characteristics of diagnosis, sometimes Forward chaining or backward chaining are required. Therefore, there are a lot of cases that Hybrid chaining is effective. Case-based reasoning is a method to settle a problem in the present by comparing with the past cases. Therefore, it is suitable to diagnosis fields with abundant cases. Search is sorted into Breadth-first search, Depth-first search and Best-first search, which have respectively merits and demerits. To construct diagnosis ontology to be applied to practical expert system, reasoning and search to reflect diagnosis process and characteristics should be considered.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.460-465
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• 2009

This paper presents a new algorithm that includes a mechanism to avoid local solutions in a motion vector detection method that uses the steepest descent method. Two different implementations of the algorithm are demonstrated using two major search methods for tree structures, depth first search and breadth first search. Furthermore, it is shown that by avoiding local solutions, both of these implementations are able to obtain smaller prediction errors compared to conventional motion vector detection methods using the steepest descent method, and are able to perform motion vector detection within an arbitrary upper limit on the number of computations. The effects that differences in the search order have on the effectiveness of avoiding local solutions are also presented.

• East Asian Journal of Business Economics (EAJBE)
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• v.6 no.4
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• pp.13-24
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• 2018

Purpose - This study investigates the moderation effect of internal factor, a firms size, on the external knowledge sourcing strategy and its effectiveness in generating radical innovation. We incorporate concepts of breadth and depth as two measures to gauge the degree of openness in firms external search Research design and methodology - The dependent variable in the regression model is the percentage of innovative sales and therefore, Tobit regression is employed for estimating significant factors affecting on the ratio of first-to-market by breadth and depth in external knowledge, internationalization, and size. Results - The results show that the external knowledge, in terms of both breadth and depth, has a positive relationship with radical innovation. However internationalization as external knowledge resources is not statistically accepted. Firm size has moderating effect on innovation negatively only in case of using external knowledge resources to a high degree. Conclusions - Firms obtain external information mostly from customers, competitors, and suppliers etc. empirical knowledge in terms of scope and intensity is an important contributor to innovation. And intensity use of external knowledge and information resources can work in favor of smaller firms rather than larger ones. Internationalization seems to have little effect on innovation but it requires further researches with clear criteria and more data.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.1093-1102
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• 2003

The dynamic nature of the World Wide Web challenges Search engines to find relevant and recent pages. Obtaining important pages rapidly can be very useful when a crawler cannot visit the entire Web in a reasonable amount of time. In this paper we study way spiders that should visit the URLs in order to obtain more “important” pages first. We define and apply several metrics, ranking formula for improving crawling results. The comparison between our result and Breadth-first Search (BFS) method shows the efficiency of our experiment system.

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.71-77
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• 2004

While doing games, units have to find goal And according to algorism, there is great difference in time and distance. In this paper the researcher compared and described characteristics of each of the improved algorism and A* algorism by giving depth-first search, breadth-first search and distance value and then argued algorism. In addition. by actually calculating the presumed value in A* a1gorism, the researcher finds the most improved value. Finally, by means of comparison between A* algorism and other one, the researcher verified its excellence and did simple path-finding using A* algorism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1845-1865
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• 2019

Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithm based on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of the WRE-CVNE are demonstrated.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.49-55
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• 2017

Robots, games and life applications have been developed while computer areas are developed. Moreover, various applications have been utilized for various users including the early childhood. Recently, smart phones have been dramatically used by various users including early childhood. Many applications need to find a path from a starting point to destinations. For example, without using real maps, users can find the direct paths for the destinations in realtime. Specifically, path exploration in game programs is so important to have accurate results. Nowadays, with these techniques, diverse applications for educations of early childhood have been developed. To deal with the functions, necessity of efficient path search programs with high accuracy becomes much higher. In this paper, we design and develop a friendly maze program for early childhood based on a path searching algorithm. Basically, the path of lineal distance from a starting location to destination is considered. Moreover, weight values are calculated by considering heuristic weighted h(x). In our approach, A* algorithm searches the path considering weight values. Moreover, we utilize depth first search approach instead of breadth first search in order to reduce the search space. so it is proper to use A* algorithm in finding efficient paths although it is not optimized paths.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.9
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• pp.684-692
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• 1989

An expert system which is a part of artificial intelligence is developed for controlling violated voltages. Control equipments such as shunt capacitors, inductors, transformer tap changers and generator voltages are utilized. A breadth-first search method is used. A sensitivity tree is suggested to minimize the number of control devices. If the voltage condition program should be utilized to efficiently solve the problem. The expert system uses PROLOG and for the sub-program C language is used. This expert system, when applied to an 8 bus power system, shows satisfactory results.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.58-60
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• 2003

원숭이와 바나나 문제는 논리적 추론에 의한 문제 해결 과정을 설명하기 위해 사용되는 대표적 예제이다. 본 논문에서는 전통적인 접근 방식과는 달리, 이 문제를 그래프 탈색의 그것으로 이해한 후 DNA 컴퓨팅에 근거한 너비 우선 탐색(breadth-first search, BFS)을 통해 해들을 발견하고자 한다. 그 결과, 최적해 (optimal solution)를 포함한 최소 4개 이상의 다양한 해들이 실제 DNA 생화학 실험을 통해 확인되었다.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.119-132
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• 2007

In molecular biology, approximate subsequence search is one of the most important operations. In this paper, we propose an accurate and efficient method for approximate subsequence search in large DNA databases. The proposed method basically adopts a binary trie as its primary structure and stores all the window subsequences extracted from a DNA sequence. For approximate subsequence search, it traverses the binary trie in a breadth-first fashion and retrieves all the matched subsequences from the traversed path within the trie by a dynamic programming technique. However, the proposed method stores only window subsequences of the pre-determined length, and thus suffers from large post-processing time in case of long query sequences. To overcome this problem, we divide a query sequence into shorter pieces, perform searching for those subsequences, and then merge their results. To verify the superiority of the proposed method, we conducted performance evaluation via a series of experiments. The results reveal that the proposed method, which requires smaller storage space, achieves 4 to 17 times improvement in performance over the suffix tree based method. Even when the length of a query sequence is large, our method is more than an order of magnitude faster than the suffix tree based method and the Smith-Waterman algorithm.