• Proceedings of the KIEE Conference
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• summer
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• pp.1429-1431
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• 2004

경량전철의 무인운전을 위한 무선제어 신호 시스템 중에서 열차 제어정보 처리를 위한 무선데이터 전송시스템을 개발하였다. 개발된 시스템은 각각 중앙운용시스템, 지상무선중계시스템 및 개별차량운용시스템으로 구성된다. 무선데이터 네트웍은 2.4GHz 대역의 확산스펙트럼 방식의 주파수 호핑 라디오 모뎀을 사용하여 전용망을 구성하였으며 1초 이내에 무선망 내에 위치하는 모든 무선데이터 전송시스템과 정보를 교환하도록 설계되었다. 현재 실험실 테스트를 완료하고 새로이 건설된 전용 시험선에서 그 유효성을 테스트 중에 있다.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.3
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• pp.206-210
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• 2015

The most important thing for NOVEC gas fire extinguishing equipment is to release NOVEC gas, which contained in the extinguishing container, to the safety section by the time appointed. For this matter, it is significant to decide arrangement and size of the proper piping equipment. This study has developed the design methodology of NOVEC gas fire extinguishing equipment in use of pipe network analysis techniques. Based on the design methodology, each design coefficient is chosen. It is found that the calculated result, which is 6.498 seconds, has been counted within the 10 seconds limit, which is fairly satisfied with extinguishing releasing time based on the developed methodology. At that time, the pressure loss is 21.09bar.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.280-291
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• 2023

Purpose: As fire accidents happen at the production and storage sites of superabsorbent polymers for convenience of daily life, an experimental study was conducted to secure basic data to establish practical preventive measures against them. Method: The sample container (20cm width × 20cm length) was made into a rectangular cuboid with the heights of 3cm, 5cm, 7cm, and 14cm, respectively, to allow access to the infinite flat plane. The front and back of the container were covered with a 300-mesh stainless steel mesh for one-dimensional heat transfer. The sample container was placed in the center of the thermostatic bath, which was heated to a predetermined temperature by setting the thermostat program in advance, and it was determined to be 'ignited' when the central temperature of the sample rose by more than 20℃ above the set temperature, and "unignited" when it was maintained at an approximate value of the set temperature. Result: The critical autoignition temperature was calculated to be 217.5℃ when the height of the sample container was 3 cm, 212.5℃ when it was 5 cm, 202.5℃ when it was 7cm, and 187.5℃ when it was 14cm. The ignition induction time to reach the maximum temperature was 34hours for 3cm, 76hours for 5cm, 143hours for 7cm, and 318hours for 14cm. Conclusion: ① As the size of the container increased, the autoignition temperature decreased and the induction time to reach the maximum temperature increased. ② An apparent activation energy was calculated to be 44.92kcal/mol, with a correlation of 96.93%.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1943-1944
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• 2006

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1949-1950
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• 2006

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Proceedings of the Korea Association of Information Systems Conference
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• 1997.10b
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• pp.39-61
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• 1997

지역정보화를 통한 효과적인 정보서비스를 위해서는 지역정보통신망의 구축에 앞 서 지역에서 제공되어야 할 정보서비스를 사용자의 입장에서 검토하고 사용자가 원하는 정 보를, 사용자가 쉽게 적응할 수 있는 형태로, 가장 효율적으로 제공할 수 있는 정보제공자 가 제공할 수 있도록 계획을 추진하여야 한다. 이를 위해서는 지역정보통신망에서 제공될 정보 서비스에 대한 기초적인 조사연구가 이루어져야 한다. 현재 우리 나라에서 지역정보화 의 사례나 정책, 제도적 측면의 연구, 또한 기술적 측면의 연구는 활발히 이루어지고 있는 편이나 지역정보화를 통해 제공되는 서비스 또는 지역정보통신망에서 제공될 정보서비스에 대한 연구는 별로 없다. 본 연구에서는 지역정보통신망에서 제공될 수 있는 최종사용자 중 심의 정보서비스에 대한 조사를 통해 정보서비스의 목록을 작성하고, 정보서비스의 분류기 준을 마련해 보고자 한다. 이러한 정보서비스의 분류들은 지역정보통신망에서 제공하고 있 거나 미래에 제공하여야 할 정보서비스들을 이해하고 이들 가운데 우선적으로 제공해야 할 서비스를 합리적으로 선정하는데 도움을 줄 수 있을 것이다. 뿐만 아니라 지역사회의 제한 된 정보관련 자원의 효율적 활용을 기할 수 있으며, 나아가 지역정보통신망의 성공적인 구 축을 돕고 지역민들의 정보생활수준의 균형있는 발전에 공헌할 것이다.

• The Journal of Information Systems
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• v.6 no.1
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• pp.181-221
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• 1997

In order to provide effective information services in a province, we should first select vendors who enable to meet end-users' needs and develop information services for the sake of end-users. Cases, policies, technological and legislative issues in information services have been researched well. But no research has been done on potential information services in the future and their classification. In this study, based on literature survey, future information services are gathered, described, and classified with respect to the needs of end-users, and finally a framework for the classification of information services is developed. This framework can be used as a criterion to select, with a priority, information services to be provided in province through the information super highway. The framework will contribute to accomplishing the effective use of information resources in the province, and eventually balancing the level of information utilization between provinces.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.890-893
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• 1999

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are demonstrate the effectiveness of the Proposed learning using the DNP.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1138-1144
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• 2008

To study the feasibility of applying wireless communication technology to the control of train for the effective control of train and for the reduction of cost and time to construct the necessary infra structure, we investigate into the application of the existing commercial WCDMA network to CBTC (communication-based train control) to grasp the obstacles and propose the solutions to circumvent them. The obstacles can be categorized into the hand-off problem, the interference problem near the stations, and the problem of radio shadow areas. We propose, as solutions, the cell overlap method and multi-terminal approach for the hand-off problem, the cell sectoring method for the interference problem, and establishment of new base stations along the railroad both to provide the wireless train control and communication service to the customers on the train which was otherwise impossible because of the shadowing effect.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.123-131
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• 2021

The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance.