• Journal of Information Technology Services
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• v.9 no.4
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• pp.109-121
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• 2010

Most organizations adopt their own performance measurement systems. Those organizations select performance measures to meet their goals. Organizations can give only limited description of what performance measures are. Kaplan and Norton suggest that the Balanced Scorecard (BSC) to complement the conventional performance measures. The BSC can provide management system with a comprehensive strategic vision and integrates non-financial measures with financial measures. The BSC is widely used for measuring corporate performance. This paper investigates how the BSC-based performance measures can be applied to Learning City. The Learning City's performance measures and strategy map on the basis of the BSC are suggested in this research. This paper adopt the AR(assurance region)-DEA model which could limit the range of weight on performance measures to prevent each viewpoint of BSC from having unlimited elasticity. The proposed model is based on CCR model including a property of unit invariance to use the data without normalization process.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.2
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• pp.102-106
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• 2003

This paper describes a new pattern classifier neural network to extract the feature from a letter. The proposed pattern classifier is based on relative distance, which is measure between an input datum and the center of cluster group. So, the proposed classifier neural network is called relative neural network(RNN). According to definitions of the distance and the learning rule, the structure of RNN is designed and the pseudo code of the algorithm is described. In feature extraction of letter, RNN, in spite of deletion of learning rate, resulted in the identical performance with those of winner-take-all(WTA), and self-organizing-map(SOM) neural network. Thus, it is shown that RNN is suitable to extract the feature of a letter.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.766-768
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• 2001

정보통신의 질적 양적 팽창과 더불어 컴퓨터 시스템에 대한 침입 또한 증가하고 있다. 침입탐지시스템은 이를 해결하기 위한 대표적인 수단으로, 최근 관련된 연구의 방향이 오용탐지 기법에서 비정상 행위탐지 기법으로 옮겨가고 있는 상황이다. HMM(Hiddem Markov Model)은 비정상행위탐지 기법에 사용되어 다양한 척도(measure)에 대한 정상행위를 효과적으로 모델링할 수 있는 방법이다. 다양한 척도의 결과값들로부터 침입을 판정하는 방법에 대한 연구는 미흡하다. 본 논문에서는 SOM(self organizing map)을 통해 축약된 데이터를 HMM으로 모델링한 비정상행위기반 침입탐지 시스템의 성능을 향상시키기 위해 퍼지 침입판정 방법을 제시한다. 실험결과 척도에 따른 결과들의 기계적 결합보다 향상된 결과를 얻었으며, 퍼지 관련 파라메터의 개선을 통해 더욱 좋은 효과를 기대할 수 있었다.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.601-604
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• 2008

Acceleration analysis system transfers DSS into acceleration value utilizing Quantera that receives the measurement from the accelerometers installed in the whole nation. When earthquake occurs, the system gives accleration values in certain locations in a map where the accelerometers are installed. And it suggests a measure to fix the problems related to abnormal operations of accelerometers.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.10
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• pp.932-939
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• 2012

Recently, data centers are being established because IT and telecommunication industries are growing. The data centers have to install emergency generators to prevent unexpected shutdown of the electrical power supply. When the data centers are located in the densely populated urban area, the operating noise of the emergency generators can be a cause of people's complaints. In this case, it is necessary to establish effective countermeasure by noise & vibration specialists. To achieve this, noise reduction measure using noise measurement data and 3D noise analysis method have been employed in this study.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.238-244
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• 2021

Image mosaicking is a common and useful technique to visualize a global map by stitching a large number of local images obtained from visual surveys in underwater environments. In particular, visual inspection of underwater structures using underwater robots can be a potential application for image mosaicking. Feature-based pairwise image registration is a commonly employed process in most image mosaicking algorithms to estimate visual odometry information between compared images. However, visual features are not always uniformly distributed on the surface of underwater structures, and thus the performance of image registration can vary significantly, which results in unnecessary computations in image matching for poor-conditioned image pairs. This study proposes a pairwise registrability measure to select informative image pairs and to improve the overall computational efficiency of underwater image mosaicking algorithms. The validity and effectiveness of the image mosaicking algorithm considering the pairwise registrability are demonstrated using an experimental dataset obtained with a full-scale ship in a real sea environment.

• ETRI Journal
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• v.44 no.6
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• pp.977-990
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• 2022

This study proposes a novel method for identifying the primary conspirators involved in terrorist activities. To map the information related to terrorist activities, we gathered information from different sources of real cases involving terrorist attacks. We extracted useful information from available sources and then mapped them in the form of terrorist networks, and this mapping provided us with insights in these networks. Furthermore, we came up with a novel centrality measure for identifying the primary conspirators of a terrorist attack. Because the leaders of terrorist attacks usually direct conspirators to conduct terrorist activities, we designed a novel algorithm that can identify such leaders. This algorithm can identify terrorist attack leaders even if they have less connectivity in networks. We tested the effectiveness of the proposed algorithms on four real-world datasets and conducted an experimental evaluation, and the proposed algorithms could correctly identify the primary conspirators and leaders of the attacks in the four cases. To summarize, this work may provide information support for security agencies and can be helpful during the trials of the cases related to terrorist attacks.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.38.1-38.1
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• 2019

The formation and evolution of galaxies is known to be fundamentally linked to the local environment in which they reside. In the highest-density cluster environments, galaxies tend to be more massive, have lower star formation rates and dust content, and a higher fraction have elliptical morphologies. The stellar populations of these cluster galaxies are older implying that they formed the bulk of their stars much earlier and have since evolved passively. Quantifying the specific environmental factors that contribute to shaping cluster galaxies over the Hubble time and measuring their early evolution can only be accomplished by directly tracing the galaxy growth in young clusters and forming porto-clusters. In this talk, I will present a novel technique designed to map out the total dust obscured star formation relative to where existing stars lie. I will demonstrate that this technique can be used 1) to determine if/where/when the activity is heightened or suppressed in dense cluster environment; 2) to measure the total mass and spatial distribution of stellar populations; and 3) to better inform theoretical models. Our ongoing work to extend this analysis out to protoclusters (z~2-4) will be discussed.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.111-129
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• 2013

This study analyzed the process of multimedia services and indexed the core quality characteristics through the tools employed in 'Define' and 'Measure' steps of the 6 Sigma DMAIC methodology. To achieve the goal, as a case study of 'S' University Central Library, first, brainstorming technique was used to tap the librarians, second, interview with the half-time student workers, third, user's opinion on homepage was analyzed and lastly, the interview with the users. This study also drew some potential causal factors which would likely to affect the multimedia service using Process Map and C&E Diagram. Those factors were prioritized using X-Y matrix and Pareto Chart. This study revealed 14 factors affecting multimedia services: 'presence/absence of the persons who are in charge of selection', 'cycle of selection materials', 'presence/absence of seperated multimedia room', 'materials arrangement method', 'notification of popular materials', 'notification of documents arrivals', 'methods of noticing new arrivals', 'methods of ordering materials', 'cycle of checking order status', 'types of persons requesting materials', 'methods of requesting materials', 'checking of the status of the returned materials', 'methods of noticing materials in housed', and 'methods of ways to return overdue materials'.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.861-866
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• 2007

A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.