• Asian Journal for Public Opinion Research
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• v.2 no.2
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• pp.103-120
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• 2015

Since 1795, 106 districts in Japan have been selected as Important Historic Preservation Districts (Juuyo dentouteki kenzoubutsugun hozon chiku [Juudenken]). The system for protection of cultural properties enables the local government to name a "Preservation District" and allows for the development of a preservation plan based on local ordinances. Moreover, the well-organized, bottom-up networks, which are groups for community development activities on the basis of local participation, play an important role in raising awareness and conducting several preservation projects in their own towns. This study mainly focused on cultural resources management in the local community. The system, which possibly bridged the gap between the local authorities and the community, was revealed. Fifty non-profit groups and active citizens, who were engaged in an advanced stage of community participation in Juudenken, were selected to be interviewed. The results then were analyzed using STAT program. The significant associations were shown by mapping the associations related to the public process of community involvement. Each variable had its own significant meaning and contributed credible indirect association to community involvement. The network mapping indicated that balancing the local economy and technical conservation was important in generating community involvement, which provided a model on how local authorities and communities could articulate and maintain their own cultural resources.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.85-92
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• 2006

In this paper, we study a wireless cellular network design problem. It consists of selecting the location of the base station controllers and mobile service switching centres, selecting their types, designing the network into a tree-topology, and selecting the link types, while considering the location and the demand of base transceiver stations. We propose a constraint programming model and develop a heuristic combining local search and constraint programming techniques to find very good solutions in a reasonable amount of time for this category of problem. Numerical results show that our approach, on average, improves the results from the literature.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.6
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• pp.402-410
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• 1986

The Media Access Control(MAC) Technologies in IEEE 802 Local Area Network(LAN) reference model include CSMA/CD, Token Ring and Token Bus methodes. The channel throughput of LAN can be affected by some parameters such as channel length, transmission rate and packet size, and station numbers. In this paper, the effect of these parameters to channel throughput are analyzed by normalized parameters. And the token ring and token bus method are analyzed by using the normalized parameter, and relatinonship bwtween channel thorughput and parameters is discussed. Finally, results are compared.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.38
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• pp.51-60
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• 1996

There is a general challenge offered to the field of engineering economics by the introduction of advanced technologies. A survey of the existing literature on evaluation of advanced information and manufacturing system indicate that the traditional approaches are inadequate for economic evaluation techniques used in its appraisal, difficulty in evaluating the potential benefits and criteria used to assess management performance. In this paper, an attempt has been made to overcome the above deficiencies by presenting an approach to account for the justification and selection of the Local Area Network(LAN) architectures. This is based on the Analytic Hierarchy Process(AHP) and is capable of taking into account many intangible factors as well. The usefulness of the proposed approach is demonstrated through a case situation and sensitivity analysis. Finally, some research directions for future work are identified.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.8
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• pp.742-747
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• 2004

Outdoors we can easily acquire our accurate location by GPS. However, the GPS signal can't be acquired indoors because of its weak signal power level. Adequate positioning method is demanded for many indoor positioning applications. At present, wireless local area network (WLAN) is widely installed in various areas such as airport, campus, and park. This paper proposes a positioning algorithm using WLAN signal strength to provide the position of the WLAN user indoors. There are two methods for WLAN based positioning, the signal propagation method uses signal strength model over space and the empirical method uses RF power propagation database. The proposed method uses the probability distribution of the power propagation and the maximum likelihood estimation (MLE) algorithm based on power strength DB. Test results show that the proposed method can provide reasonably accurate position information.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05a
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• pp.21-25
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• 2002

Activated sludge processes are widely used in biological wastewater treatment processes. The main motivation of this research is to develop an intelligent control strategy for activated sludge process (ASP). ASP is a complex and nonlinear dynamic system because of the characteristic of wastewater, the change in influent rate, weather conditions, and so on. The mathematical model of ASP also includes uncertainties which are ignored or not considered by process engineer or controller designer. The ASP model based on Matlab/Simulink is designed in this paper. The performance of the model is tested by IWA (International Water Association) and COST (European Cooperation in the filed of Scientific and Technical Research) data that include steady-state results during 14 days. In this paper, fuzzy logic control approach is applied to control the DO (dissolved oxygen) concentration. The fuzzy logic controller that includes two inputs and one output can adjust air flowrate. Also, this paper introduces the remote monitoring and control system that is applied for the CSTR (Continuously Stirred Tank Reactor) wastewater treatment system. The CSTR plant has a local control and the remote monitoring system which is contained communication parts which consist of LAN (Local Area Network) network and CDMA (Code Division Multiple Access) wireless module. Remote control and monitoring systems are constructed in the laboratory.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.37-44
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• 2008

Data stream has the tendency to change in Patterns over time. Also known as concept drift, such problem can reduce the predictive performance of a classification model CVFDT and IOLIN tried to solve the problem of a concept drift through incremental classification model updates. The local changes in patterns. however was revealed to be unable to resolve the problems of local concept drift that occurs by influencing on total classification results. In this paper, we propose adapted IOLIN system that improves system's predictive performance by detecting the local concept drift. The experimental result shows that adaptive IOLIN, the Proposed method, is about 2.8% in accuracy better than IOLIN and about 11.2% in accuracy better than CVFDT.

• Quality Improvement in Health Care
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• v.9 no.1
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• pp.18-32
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• 2002

Background : We developed a model for predicting premature discharge and identifying related factors. Methods : Prediction model was developed by data mining techniques. Basic data were collected from the total discharge data base of a university hospital in Chungnam Province during the period from July 1, 1999 to June 30, 2000. Results : 1. Among 22,873 patients, the number of patients discharged with usual discharge orders were 21,695 or 94.8%. The number of the prematurely discharged patients were 1,178 or 5.2%. 2. The primary reason for unusual discharge was transfer to other hospital. Move to a local hospital closer to their home and burdensome medical expenses were main reasons. 3. Predictability of each model was tested using the top 10 percent of patients with the highest probabilities of premature discharge. The neural network model was chosen as the most appropriate model for predicting prematurely discharged patients. 4. Ten percent of the total number of patients had been selected randomly to test the effectiveness of the neural network model. We have chosen the threshold of the neural network model as 0.7. The number of patients who were expected to discharge prematurely was 312. Among them, 241 had been discharged prematurely (77.2%). Conclusion : Of the several data mining techniques used, the neural network model was the most effective, It can be used to identify and manage the patients who are expected to discharge prematurely.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.11-18
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• 2012

Debris flow deposition model is a model to predict affected areas by debris flow and random walk model (RWM) was used to build the model. Although the model was proved to be effective in the prediction of affected areas, the model has several free parameters decided experimentally. There are several well-known methods to estimate parameters, however, they cannot be applied directly to the debris flow problem due to the small size of training data. In this paper, a modified neural network, called pseudo sample neural network (PSNN), was proposed to overcome the sample size problem. In the training phase, PSNN uses pseudo samples, which are generated using the existing samples. The pseudo samples smooth the solution space and reduce the probability of falling into a local optimum. As a result, PSNN can estimate parameter more robustly than traditional neural networks do. All of these can be proved through the experiments using artificial and real data sets.