• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1173-1192
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• 2015

The Support Vector Data Description (SVDD) has achieved great success in anomaly detection, directly finding the optimal ball with a minimal radius and center, which contains most of the target data. The SVDD has some limited classification capability, because the hyper-sphere, even in feature space, can express only a limited region of the target class. This paper presents an anomaly detection algorithm for mitigating the limitations of the conventional SVDD by finding the minimum volume enclosing ellipsoid in the feature space. To evaluate the performance of the proposed approach, we tested it with intrusion detection applications. Experimental results show the prominence of the proposed approach for anomaly detection compared with the standard SVDD.

• The Journal of Information Systems
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• v.21 no.3
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• pp.45-70
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• 2012

Based on the usage levels of target costing systems(TCS) and information technology (IT) infrastructure, this study aims to develop a framework useful for classifying four types of knowledge management(KM) strategies in manufacturing firms: process-oriented, product-oriented, mixed and negative. We adopted a multi-methodological approach by mixing both qualitative and quantitative methods. Before developing a framework, through a case study of the H Motor Company in Korea, this paper investigated and showed the functions of TCS in the management of tacit knowledge. The results from the case study indicated that with the use of TCS, a firm can create, transfer, and share diverse kinds of tacit knowledge among employees for the facilitation of process innovation. We also empirically confirmed the four types of KM strategies, and demonstrated the characteristics(i.e., size, total sales, age, and knowledge intensity) of the organizations adopting each strategy.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.167-172
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• 2014

In this paper we propose a waveform design algorithm that localizes the maximum output power in the target direction. We extend existing monostatic radar optimal waveform design schemes to bistatic multiple-input multiple-output (MIMO) radar systems. The algorithm simultaneously calculates the direction of departure (DoD) and the direction of arrival (DoA) using a two-dimensional multiple signal classification (MUSIC) method, and successfully localizes the maximum transmitted power to the target locations by exploiting the calculated DoD. The simulation results confirm the performance of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.29-33
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• 1996

A categorization for cluster is necessary when an unsupervised classfication is used for remote sensing image classification. It is desirable that this method is performed automatically, because manual categorization is a highly time consuming process. In this paper, several automatic determination methods were proposed and evaluated. They are four methods. a) maximum number method : which assigns the tharget cluster to the category which occupies the largest area of that cluster b) maximum percentage method : which assigns the target cluster to the category which shows the maximum percentage within the category in that cluster. c) minmun distance method : which assigns the target cluster to the category having minmum distance with that cluster d) element ratio matching method : which assigns local regions to the category having the most similar element ratio of that region From the results of the experiments, it was certified that the result of minimum distance method was almost the same as the result made by a human operator.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.370-373
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• 2002

This paper proposes a new measurement system determine the localization and the type of object which use only three ultrasonic sensors, one the transmitter, one the receiver and one transduce doing both transmitter and receiver. this system can classifies the type and determines the pose of the target object. it used the method of Pseudoamplitude Scan. So it significantly simple the sensing system and reduce the signal processing time so that the working environment can be recognized in real time.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.

• Journal of the Optical Society of Korea
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• v.12 no.2
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• pp.88-92
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• 2008

This paper discusses a photon-counting linear discriminant analysis (LDA) with computational integral imaging (II). The computational II method reconstructs three-dimensional (3D) objects on the reconstruction planes located at arbitrary depth-levels. A maximum likelihood estimation (MLE) can be used to estimate the Poisson parameters of photon counts in the reconstruction space. The photon-counting LDA combined with the computational II method is developed in order to classify partially occluded objects with photon-limited images. Unknown targets are classified with the estimated Poisson parameters while reconstructed irradiance images are trained. It is shown that a low number of photons are sufficient to classify occluded objects with the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.334-341
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• 1998

This paper proposes an algorithm to obtain the time-to-contact between an observer and a target and surface orientation of the target. These two physical elements are computed from the image deformation of a known shape, which is extracted by supervised classification of detected line segments based on MAP and Mahalanobis distance. The proposed algorithm was applied to the natural outdoor traffic scene and would contribute to the development for a collision avoidance system.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.263-272
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• 2021

At a time when securing driving safety is the most important in the development and commercialization of autonomous vehicles, AI and big data-based algorithms are being studied to enhance and optimize the recognition and detection performance of various static and dynamic vehicles. However, there are many research cases to recognize it as the same vehicle by utilizing the unique advantages of radar and cameras, but they do not use deep learning image processing technology or detect only short distances as the same target due to radar performance problems. Radars can recognize vehicles without errors in situations such as night and fog, but it is not accurate even if the type of object is determined through RCS values, so accurate classification of the object through images such as cameras is required. Therefore, we propose a fusion-based vehicle recognition method that configures data sets that can be collected by radar device and camera device, calculates errors in the data sets, and recognizes them as the same target.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.595-598
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• 2020

IoU (Intersection over Union) is the most commonly used index in target detection. The core requirement of target detection is what is in the image and where. Based on these two problems, classification training and positional regression training are needed. However, in the process of position regression, the most commonly used method is to obtain the IoU of the predicted bounding box and ground-truth bounding box. Calculating bounding box regression losses should take into account three important geometric measures, namely the overlap area, the distance, and the aspect ratio. Although GIoU (Generalized Intersection over Union) improves the calculation function of image overlap degree, it still can't represent the distance and aspect ratio of the graph well. As a result of technological progress, Bounding-Box is no longer represented by coordinates x,y,w and h of four positions. Therefore, the IoU can be further optimized with the center point and aspect ratio of Bounding-Box.