• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.65-75
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• 2004

It is notorious that PIN(Personal Identification Number) is used widely for user verification and authentication in networked environment. But, when the user Identification and password are exposed by hacking, we can be damaged monetary damage as well as invasion of privacy. In this paper, we adopt face recognition-based authentication which have nothing to worry what the ID and password will be exposed. Also, we suggest the remote authentication and verification system by considering not only 2-Tier system but also 3-Tier system getting be distributed. In this research, we analyze the face feature data using the SVM(Support Vector Machine) and PCA(Principle Component Analysis), and implement artificial intelligence face recognition module in distributed environment which increase the authentication speed and heightens accuracy by utilizing artificial intelligence techniques.

• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.77-91
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• 2004

This research compares the use of accounting data versus market data in the prediction of bankruptcy. Comparison is made through neural networks so that prediction accuracy is model-independent. Results of this study indicate that both market and accounting data provide useful information on corporate bankruptcies. Interestingly, using market and accounting information together can achieve substantial gain in prediction accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3488-3500
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• 2020

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years, the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1027-1028
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• 2008

Most of the urban transit operation company in Korea have a passive surveillance system to monitor the status of the passengers and facilities in the urban transit service area. The surveillance system is based on CCTV, closed circuit television, and several sensors, such as a fire sensor. However, this system has some limitations to prevent and cope with the emergency quickly. So the urban transit operation companies have plans to be change their surveillance system to be active. The active surveillance system has an intelligent function to detect the event predefined by managers automatically. To construct the active surveillance system, there are a standard concept design and a function analysis. In this paper, we propose the classification of the functions of the active surveillance system for urban transit. We divide the functions into five parts, ordinary monitoring, safety monitoring, environment monitoring, administration support, and record management. And we describe the systems related to the every functions to clarify the classified functions.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.327-335
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• 2006

Case Based Reasoning (CBR) has come to be considered as an appropriate technique for diagnosis, prognosis and prescription in medicine. However, canventional CBR has a limitation in that it cannot incorporate asymmetric misclassification cast. It assumes that the cast of type1 error and type2 error are the same, so it cannot be modified according ta the error cast of each type. This problem provides major disincentive to apply conventional CBR ta many real world cases that have different casts associated with different types of error. Medical diagnosis is an important example. In this paper we suggest the new knowledge extraction technique called Cast-Sensitive Case Based Reasoning (CSCBR) that can incorporate unequal misclassification cast. The main idea involves a dynamic adaptation of the optimal classification boundary paint and the number of neighbors that minimize the tatol misclassification cast according ta the error casts. Our technique uses a genetic algorithm (GA) for finding these two feature vectors of CSCBR. We apply this new method ta diabetes datasets and compare the results with those of the cast-sensitive methods, C5.0 and CART. The results of this paper shaw that the proposed technique outperforms other methods and overcomes the limitation of conventional CBR.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.196-204
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• 2017

Behavior of intelligent video surveillance system is recognized by analyzing the pattern of the object of interest by using the frame information of video inputted from the camera and analyzes the behavior. Detection of object's certain behaviors in the crowd has become a critical problem because in the event of terror strikes. Recognition of object's certain behaviors is an important but difficult problem in the area of computer vision. As the realization of big data utilizing machine learning, data mining techniques, the amount of video through the CCTV, Smart-phone and Drone's video has increased dramatically. In this paper, we propose a multiple-sliding window method to recognize the cumulative change as one piece in order to improve the accuracy of the recognition. The experimental results demonstrated the method was robust and efficient learning units in the classification of certain behaviors.

• The Journal of Society for e-Business Studies
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• v.10 no.3
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• pp.85-110
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• 2005

OWL-S has a major leadership in the field of Web Service discovery and is being actively studied in LARKS and METEOR-S projects. These researches do not consider all components of OWL-S standards, and it is needed to enhance their discovery algorithms. In this paper, we propose matching algorithms based on process information such as process structure matching, service classification matching and business pattern matching algorithms. We also improve the QoS matching algorithm of METEOR-S project. Finally, we integrate these two kinds of matching algorithms as accommodate users preferences.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.637-643
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• 2016

Due to the popularity of artificial intelligent, medical image processing using artificial neural network is increasingly attracting the attention of academic and industry researches. Deep learning with a convolutional neural network has been proved to very effective representation of images. However, the training process requires high performance H/W platform. Thus, the realtime learning of a large number of high dimensional samples within low-power devices is a challenging problem. In this paper, we attempt to establish this possibility by presenting a realtime neural network method on Raspberry pi using online sequential extreme learning machine. Our experiments on high-dimensional dataset show that the proposed method records an almost real-time execution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• YAKHAK HOEJI
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• v.42 no.6
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• pp.627-633
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• 1998

Impurity profiling analysis of methamphetamine seized in Korea was investigated for the evidential and intelligent purpose. Samples were extracted with ethylacetate which contai ns internal standard of dioctylsebacate under basic condition and extracts were analyzed by GC-FID. Ephedrine, chloroephedrine & 1,2-dimethyl-3-phenylaziridine were identified impurities in illicit methamphetamine by GC-MS. These impurities revealed that most of abused methamphetamine in Korea were synthesized from ephedrine as a starting material. For the classification of samples. firstly, 24 impurity peaks were selected after inspection of every peak in 50 samples as the specific markers of impurities. Secondly, corresponding peak retention time and area ratio to the internal standard were calculated and database was created with values of 24 peaks by in-house program. Finally, cluster analysis was attempted with the resultant profiles using the STAR plot, which was based on the Euclidian distance for evaluating similarity among samples. A total of 76 samples were divided into 8 different groups within 90% statistical similarity and inter-batch samples showed similar impurity patterns by this procedure. In conclusion, the analysis of impurities is a suitable index for estimation the common or different origin of methamphetamine sample.