• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.39-47
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• 2006

The evaluation of MDx family hash functions such as MD5 is difficult because the design background or a generalized model is unknown. In this paper, an approximated model is proposed to generalize hash functions. The diffusion of a input difference is tested by an approximated model for MD5. The results show that MD5 does not provide perfect diffusion, so MD5 is weak against some attacks. We propose a multiple chaining pattern which provides perfect diffusion in approximated model of hash function without extra calculation or memory. And We show the probability of differential characteristics of our proposal.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.7-16
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• 2008

There are unique aspects of truck vehicle movements compared with the personal travel in trip chaining. This paper reports an analysis on the truck vehicle trip chaining which intercity/metropolitan/intraregional trips are classified. Data collected from the travel dairy survey is used the truck trip-chaining analysis. The pattern of trip chaining classes is classified by the GIS mapping based on orgin-destination trip information. The physical index and efficiency index for each trip diary is used to the truck vehicle activity. Truck trips lengths and time differs from its truck type, service type and travel patterns. It is shown that the efficiency of the truck trip chaining depends on vehicle types and its delivery patterns. There are many other topics for research on trip chaining modeling such as the classification of trip chain, time use and mode choice by trip chaining.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3010-3022
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• 1997

Expert system is one of AI area which was came out at the end of 19705s. It simulates the human's way of thinking to give solutions of Problem in many applications. Most expert system consists of many components such as inference engine, knowledge base, and so on. Especially the performance of expert system depends on the control of enfficiency of inference engine. Inference engine has to get features; tirst, if possible to minimize restrictions when the knowledge base is constructed second, it has to serve various kinds of inferencing methods. In this paper, we design and implement the inference engine which is able to support the general functions to knowledge domain and inferencing method. For the purpose, forward chaining, backward chaining, and direct chaining was employed as an inferencing method in order to be able to be used by user request selectively. Also we not on1y selected production system which makes one ease staradization and modulation to obtain knowledges in target domain, but also constructed knowledge base by means of Extended Clause Bit Metrics (ECBM). Finally, the performance evaluation of inference engine between Rete pattern matching and ECBM has been done.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.714-717
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• 1996

This paper presents a fault detection and diagnosis methodologies based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model(WSM) is used to generate those candidates. The weight is determined from dynamic simulation. Using WSMs, the methodology can generate the cause candidates and rank them according to the probability. Secondly, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.520-525
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• 1997

This paper presents a fault detection and diagnosis methodology based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. In the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model is used to generate those candidates. The weight is determined from dynamic simulation. Using WSM, the methodology can generate the cause candidates and rank them according to the probability. Second, the fault propagation trends identified from the partial or complete sequence of measurements are compared with the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies, and the results showed satisfactory diagnostic resolution.

• Journal of Korean Society of Transportation
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• v.30 no.3
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• pp.107-118
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• 2012

The purpose of the study is to analyze the travel characteristics of freight trucks in metropolitan areas, focusing on activity generation, destination choice, and trip chaining behaviors. The results showed that the number of service companies at departure areas has a primary influence on the activity generation pattern and destination choice behavior of trucks in metropolitan areas. The number of trips within a trip chain is largest, in case where the prevailing industry in destination areas is wholesale or retail and the shipment item is food or beverage. These results imply that for the reasonable estimation of truck travel demand both the trip chaining behaviors and the industrial compositions in departure and destination areas should be separately considered for each type of commodity.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.195-200
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• 2002

ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

• Journal of Bio-Environment Control
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• v.12 no.1
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• pp.30-37
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• 2003

An expert system was developed for the stress diagnosis of cucumber and tomato using FoxPro. The principle points in building the system were integration with Korean, effective processing of mass information, and easy access for non-experts such as farmers. The method of inferencing was forward chaining based on pattern matching. Knowledge base was expressed with IF∼THEN rules and was expressed in the form of tree. Also, the expert system was designed so that additions and modifications of all information could easily be performed on windows. The results tested by farmers with the developed system showed that the expert system was reliable for the practical use. It was expected the expert system could be directly applied to the stress diagnosis of other vegetable plants by modifying only data bases.