• Journal of the military operations research society of Korea
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• v.24 no.2
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• pp.130-145
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• 1998

This paper is concerned with the optimization of a system reliability. Two kinds of the reliability model for optimal allocation of parallel redundancy are considered. The algorithm for solving the optimal redundancy problem is proposed by the use of dynamic programming(DP) method. The problem is approached with a standard DP formulation and the DP algorithm is applied to the model and then the optimal solution is found by the backtracking method. The method is applicable to the models having no constraints or having a cost constraint subject to a specified minimum requirement of the system reliability. A consequence of this study is that the developed computer program package are implemental for the optimization of the system reliability.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.1
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• pp.173-182
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• 1988

A program for drawing of penetrating lines was developed in personal computer. PROLOG, a language of Artificial Intelligence, was used and a data structure using relational data base was designed. An algorithm for finding the penetrating lines in the real space was developed. The program can be applied at any types of penetrating problems like curve-surface, surface-surface, curve-object, surface-object, object-object, etc. In developing the program, the following results were obtained. (1) Relational data base built in PROLOG and the function of backtracking are helpful in Computer Graphics. (2) In spite of increasing the number of edges, assigning direction to the edges makes it possible to represent the polygon meshes as the non ordered sets of directional half edges. (3) Topologicaly the penetrating lines of a polygon can be represented as the edge-pairs in the edge list of the polygon,

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.685-692
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• 2012

B frame bi-directional predictions and the DIRECT mode coding of the H.264 video compression standard necessitate a complex mode decision process, resulting in a long computation time. To make H.264 feasible, this paper proposes an image backtrack-based fast (IBFD) algorithm and evaluates the performances of two promising fast algorithms (i.e., AFDM and IBFD). Evaluation results show that an image backtrack-based fast (IBFD) algorithm can determine DIRECT mode macroblocks with 13% higher accuracy, as compared with the AFDM. Furthermore, IBFD is shown to reduce the motion estimation time of B frames by up to 23% with a negligible quality degradation.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.290-292
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• 2003

인터넷의 발달은 지리적인 문제들로 인하여 시간의 소비를 가져왔던 문제들을 해결시켜주었다. 지리적인 문제의 해결은 더더욱 모든 일에 대한 인터넷의 의존도롤 높여갔지만, 1969년도에 생겨난 인터넷은 조금씩 구조적인 문제들을 드러내고, 이러한 구조적인 문제들은 해커들로 하여금 사이버공간에서의 범죄를 일으키는 데 이용되고 있다. 다른 해킹들보다 최근 몇 년간 그 수위를 높여가고 있는 분산 서비스 거부 공격은 불특정다수의 인터넷 사용자들에게 네트워크 사용 또는 서비스 사용에 심각한 영향을 미친다는 점에서 그 공격기법에 대한 대응방안 모색이 시급한 실정이다. 따라서 본 논문은 효율적인 네트워크 자원 사용을 저해하는 분산 서비스 거부 공격의 근원지를 탐색하여 차단하는 메커니즘을 제안한다.

• IE interfaces
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• v.6 no.2
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• pp.117-131
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• 1993

A job sequencing model is developed and its computer system is tested for processing cold-rolled coils in Tandem Cold Mills(TCM) at the Pohang Iron and Steel Company. Given coils waiting to be processed, this system generates a sequence of jobs satisfying operational constraints for the TCM process. We formulate the problem as a constraint satisfaction problem and employ the backtracking technique combined with looking ahead features in order to generate a feasible solution within a reasonable time. Our system is implemented in C language on 80486-based IBM PC. Some tests based on the real data show that our system is adequate with respect to search time and that it consistantly generates a good feasible solution.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.206-213
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• 2003

Application of existing decision tree algorithms for Internet retail customer classification is apt to construct a bushy tree due to imprecise source data. Even excessive analysis may not guarantee the effectiveness of the business although the results are derived from fully detailed segments. Thus, it is necessary to determine the appropriate number of segments with a certain level of abstraction. In this study, we developed a stopping rule that considers the total amount of information gained while generating a rule tree. In addition to forwarding from root to intermediate nodes with a certain level of abstraction, the decision tree is investigated by the backtracking pruning method with misclassification loss information.

• Language and Information
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• v.6 no.2
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• pp.105-128
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• 2002

Morphologically annotated corpora are the basis for many tasks of computational linguistics. Most current approaches use statistically driven methods of morphological analysis, that provide just POS-tags. While this is sufficient for some applications, a rule-based full morphological analysis also yielding lemmatization and segmentation is needed for many others. This work thus aims at 〔1〕 introducing a rule-based Korean morphological analyzer called Kormoran based on the principle of linearity that prohibits any combination of left-to-right or right-to-left analysis or backtracking and then at 〔2〕 showing how it on be used as a POS-tagger by adopting an ordinary technique of preprocessing and also by filtering out irrelevant morpho-syntactic information in analyzed feature structures. It is shown that, besides providing a basis for subsequent syntactic or semantic processing, full morphological analyzers like Kormoran have the greater power of resolving ambiguities than simple POS-taggers. The focus of our present analysis is on Korean text.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.94-101
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• 2019

Building a stable knowledge base is an important issue in the application of knowledge engineering. In this paper, we present an algorithm for detecting and locating discrepancies in the line of the reasoning process especially when discrepancies occur on belief values. This includes backtracking the rule firing from a goal node of the rule network. Retracting a belief function allows the current belief state to move back to another belief state without the rule firing. It also gives an estimate, called contribution measure, of how much the rule has an impact on the current belief state. Examining the measure leads the expert to locate the possible cause of problem in the rule. For non-monotonic reasoning, the belief retraction method moves the belief state back to the previous state. A tracing algorithm is presented to identify and locate the cause of problem. This also gives repair suggestions for rule refinement.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.410-421
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• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.803-816
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• 2023

Explainable artificial intelligence is a method that explains how a complex model (e.g., a deep neural network) yields its output from a given input. Recently, graph-type data have been widely used in various fields, and diverse graph neural networks (GNNs) have been developed for graph-type data. However, methods to explain the behavior of GNNs have not been studied much, and only a limited understanding of GNNs is currently available. Therefore, in this paper, we propose an explanation method for node classification using graph convolutional networks (GCNs), which is a representative type of GNN. The proposed method finds out which features of each node have the greatest influence on the classification of that node using GCN. The proposed method identifies influential features by backtracking the layers of the GCN from the output layer to the input layer using the gradients. The experimental results on both synthetic and real datasets demonstrate that the proposed explanation method accurately identifies the features of each node that have the greatest influence on its classification.