• Korean Chemical Engineering Research
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• 제52권5호
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• pp.603-612
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• 2014

대규모 산업 단지 내에는 다양한 네트워크가 형성되어 있다. 각각의 네트워크들은 네트워크를 구성하는 요소들이 필요로 하는 물질의 생산 및 수송을 통하여 물질의 수요를 충족시킨다. 네트워크 자체적으로 직접 생산을 통하여 각 공장들이 필요로 하는 물질의 수요를 충족시키기도 하며 수요량의 변화나 경제적 요소들로 인하여 네트워크 외부에서 필요로 하는 물질을 구매하여 네트워크 내에서 수송하기도 한다. 특히나 유틸리티 네트워크와 수소 네트워크는 대규모 산업 단지의 대표적인 네트워크들이며 이러한 네트워크들의 비용적 절감 및 네트워크 구성의 최적화와 관련된 많은 연구들이 수행되어 왔다. 하지만 두 네트워크를 연결하여 통합된 네트워크 모델을 구축하여 최적화를 진행한 연구는 진행되어 오지 않았다. 본 논문에서는 유틸리티 네트워크에서 발생되는 여분의 스팀을 수증기 메탄 개질 공정의 원료로 사용하여 수소를 생산한 후, 생산된 수소를 수소 네트워크에 주입하여 수소 네트워크의 수소 수요량을 충족시키는 모델을 개발하였다. 제시된 모델은 유틸리티 네트워크의 유틸리티 수요량과 수소 네트워크의 수소 수요량을 모두 충족시키면서 통합된 네트워크 모델의 최적 설계 및 네트워크 구성도를 결정할 수 있게 하고, 요구되는 전체 비용을 계산 가능하게 한다. 본 연구에서 제시한 모델의 타당성을 평가하기 위하여 국내 최대 규모의 대규모 석유 화학 산업단지를 가지고 있는 여수 석유 화학 단지를 대상으로 사례를 적용해 보았으며 이 사례 연구를 통하여 얻은 결과는 기존의 유틸리티 네트워크와 수소 네트워크를 개별적으로 연구한 결과와 비교하여 더 최적의 결정을 제시할 것이다.

• 한국경영과학회지
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• 제38권1호
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• pp.139-152
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• 2013

The purpose of this study is to claim the validity of tuning the architecture of neural network models for multi-class classification. A neural network model for multi-class classification is basically constructed by building a series of neural network models for binary classification. Building a neural network model, we are required to set the values of parameters such as number of hidden nodes and weight decay parameter in advance, which draws special attention as the performance of the model can be quite different by the values of the parameters. For better performance of the model, it is absolutely necessary to have a prior process of tuning the parameters every time the neural network model is built. Nonetheless, previous studies have not mentioned the necessity of the tuning process or proved its validity. In this study, we claim that we should tune the parameters every time we build the neural network model for multi-class classification. Through empirical analysis using wine data, we show that the performance of the model with the tuned parameters is superior to those of untuned models.

• 한국지능시스템학회논문지
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• 제15권4호
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• pp.437-442
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• 2005

하천에서의 홍수유출 예측은 하천의 치수적인 측면에서도 중요하다. 본 논문에서는 신경회로망 모형을 이용해서 개발된 홍수유출 예측 시스템의 적용성을 검토하였다. 입력층에는 강우자료와 홍수량 자료를 출력층에는 홍수유출량이 예측되도록 구성하였다. 홍수유출 예측 시스템 구성시 예측모형 선정을 위해 신경회로망 모형과 상태공간 모형을 이용하여 홍수시 실시간 하천유출량 예측을 수행하였다. 두 모형의 예측결과 비교시 신경회로망 모형이 실시간 홍수량 예측에 적합한 모형으로 선정되었다. 신경회로망 모형은 Web 상에서 사용이 가능하게 변환하여 홍수유출 예측시스템의 기본모형으로 개발하였다.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• 대한토목학회논문집
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• 제39권1호
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• pp.63-71
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• 2019

Herman과 Prigogine에 의해 제안된 Two-fluid Model은 네트워크에서 거시교통류를 분석하는데 유용하다. Two-fluid Model은 정지차량 비율과 네트워크 평균속도의 관계를 통해 네트워크를 분석하는 것으로, 주로 신호 혹은 비신호교차로가 다수 존재하는 도시부 교통망에 적용되어왔다. 일반적으로 네트워크의 교통수요가 증가함에 따라 네트워크 내 평균주행속도와 평균통행속도 모두 감소하며, 네트워크 내 정지차량의 비율과 정체로 인한 저속차량의 비율도 증가한다. 본 연구는 정체상황을 고려한 Two-fluid Model을 제안하였다. 정체상황을 구분하는 임계속도와 정체상황이 네트워크에 미치는 가중치는 평균 제곱근 편차(RMSE)를 최소화하도록 값을 정산하여 적용하였다. 서울시 네트워크의 임계속도는 약 34 kph로 나타났으며, 정체상황이 네트워크에 미치는 영향의 가중치는 약 0.61로 나타났다. 본 연구에서 제안한 Model은 기존 Model에 비하여 $R^2$가 0.78에서 0.99로 크게 증가하였으며, 파라미터의 값은 큰 차이를 보이지 않았다. 이는 제안한 Model이 네트워크 및 신호운영 평가에 사용될 가능성이 있음을 의미한다.

• 환경영향평가
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• 제14권2호
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• pp.55-62
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• 2005

This study applied the Neural Network and Fuzzy theory to show water-purity control and preventive measure in water quality forecasting of the future river. This study picked out NAJU and HAMPYUNG as the subject of investigation and used monthly the water quality and the outflow data of KWANGJU2, NAJU, YOUNGSANNPO and HAMPYUNG from 1995 to 1999 to forecast BOD, COD, T-N, T-P water density. The datum from 1995 to 1999 are used for study and that of 2000 are used for verification. To develop model of water quality forecasting, firstly, this research formed Neural Network model and divided Neural Network model into two case - the case of considering lag and not considering. And this study selected optimal Neural Network model through changing the number of hidden layer based on input layer(n) from n to 3n. Through forecasting result, the case without considering lag showed more precise simulated result. Accordingly, this study intended to compare, analyse that Fuzzy model using the method without considering lag with Neural Network model. As a result, this study found that the model without considering lag in Neural Network Network shows the most excellent outcome. Thus this study examined a forecasting accuracy, analyzed result and verified propriety through appling the method of water quality forecasting using Neural Network and Fuzzy Algorithms to the actual case.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2003년도 추계학술발표회
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• pp.543-547
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• 2003

We applied the modified invasion percolation (MIP) model to the migration of DNAPL within a two-dimensional random fracture network. The MIP model was verified against laboratory experiments, which was conducted using a two-dimensional random fracture network model. The results showed that the MIP needs modification. To remove TCE trapped in a random fracture network, the density-surfactant-motivated removal method was applied and found very effective to remove TCE from dead-end fractures.

• 한국환경과학회지
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• 제9권6호
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• pp.455-462
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• 2000

This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.

• 전기학회논문지
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• 제62권10호
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• pp.1451-1457
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• 2013

Recently, new concept of energy systems such as microgrid, smart grid, supergrid, and energy network has been introducing. In this paper, the concept of the centralized micro-energy network, which is an energy community of a building group without district heating system, is introduced. In addition, a mathematical model for optimal operation of the micro-energy network as a main function of an energy management system (EMS) for the micro-energy network is proposed. In order to show the validation, the proposed model is tested through the simulation and analyzed.