• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.357-360
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. In this research, we tried the back-propagation algorithm of artificial neural network to find an optimum solution in the cooling system design of injection mold. The cooling system optimization problem that was once solved by a response surface method with 4 design variables was solved by applying the back-propagation algorithm, resulting in a solution with a sufficient accuracy. Furthermore the number of training points was much reduced by applying the fractional factorial design without losing solution accuracy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.527-530
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• 2004

This paper presents an optimization formulation for resource scheduling in Critical Resource Diagramming (CRD) of production scheduling networks. A CRD network schedules units of resources against points of needs in a production network rather than the conventional approach of scheduling tasks against resource availability. This resource scheduling approach provides more effective tracking of utilization of production resources as they are assigned or 'moved' from one point of need to another. Using CRD, criticality indices can be developed for resource types in a way similar to the criticality of activities in Critical Path Method (CPM). In our supply chain model, upstreams may choose either normal operation or expedited operation in resource scheduling. Their decisions affect downstream's resource scheduling. The suggested optimization formulation models resources as CRD elements in a production two-stage supply to minimize the total operation cost

• ETRI Journal
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• v.41 no.6
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• pp.782-796
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• 2019

This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large-scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi-Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.260-269
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• 2020

The Steam Assisted Gravity Drainage (SAGD) process is an enhanced method to extract oil from bitumen which involves surface and central process facilities. This paper describes the Central Process Facilities (CPF) of SAGD and proposes several retrofit plans to the Heat Exchanger Network (HEN). In this approach, the process integration scheme is applied to estimate the energy saving in HENs, and various cases are modeled in favor of a commercial simulator. Throughout this work, a minimum approach temperature of 10℃ is assumed. The results reveal that, due to the HEN optimization using process integration, the heating and cooling duties can be reduced to 29.68MW and 1.886MW, respectively. Compared with the Husky case, all cases considered in this study indicate a potential reduction of at least 6% in total cost, including investment and operation costs.

• ETRI Journal
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• v.40 no.6
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• pp.714-725
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• 2018

Destination-assisted jamming (DAJ) is usually used to protect confidential information against untrusted relays and eavesdroppers in wireless networks. In this paper, a DAJ-based untrusted relay network with multiple antennas installed is presented. To increase the secrecy, a joint optimization of beamforming and power allocation at the source and destination is studied. A matched-filter precoder is introduced to maximize the cooperative jamming signal by directing cooperative jamming signals toward untrusted relays. Then, based on generalized singular-value decomposition (GSVD), a novel transmitted precoder for confidential signals is devised to align the signal into the subspace corresponding to the confidential transmission channel. To decouple the precoder design and optimal power allocation, an iterative algorithm is proposed to jointly optimize the above parameters. Numerical results validate the effectiveness of the proposed scheme. Compared with other schemes, the proposed scheme shows significant improvement in terms of security performance.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.80-83
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• 2021

The article analyzes and shows the rapid development information and telecommunication technologies, and their capabilities are becoming unprecedented for human development, effective solutions to many professional problems. The analysis of information and communication technologies of education used in higher educational institutions of Ukraine confirmed that for the effective use of special teaching methods, as well as software and technical teaching aids, it is necessary to have a trained teaching staff and students.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.85-90
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• 2023

The Ant Colony System (ACS) is a variant of Ant colony optimization algorithm which is well-known in Traveling Salesman Problem. This paper proposed a hybrid method based on genetic algorithm (GA) and ant colony system (ACS), called GACS, to solve traffic routing problem. In the GACS, we use genetic algorithm to optimize the ACS parameters that aims to attain the shortest trips and time through new functions to help the ants to update global and local pheromones. Our experiments are performed by the GACS framework which is developed from VANETsim with the ability of real map loading from open street map project, and updating traffic light in real-time. The obtained results show that our framework acquired higher performance than A-Star and classical ACS algorithms in terms of length of the best global tour and the time for trip.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.130-138
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• 2023

The Smart grids are considered as multi-disciplinary power systems where the communication networks are highly employed. This paper presents optimal wide area measurement system (WAMS) configuration in Nordic power system. The transition from SCADA to WAMS becomes now trend in all power systems to ensure higher reliability and data visibility. The optimization applied in this research considered the geographical regions of the Nordic power system. The research considered all the devices of WAMS namely phasor measurement units (PMUs), phasor data concentrators (PDCs) and communication links. The study also presents two scenarios for optimal WAMS namely base case and N-1 contingency as different operating conditions. The result of this research presents technical and financial results for WAMS configuration in a real power system. The optimization results are performed using MATLAB 2017a software application.

• Annual Conference of KIPS
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• 2023.11a
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• pp.442-445
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• 2023

최근 딥러닝 및 로봇기술의 발전으로 인해 대량의 데이터를 빠르게 수집하고 처리하는 연구 분야들로 확대되었다. 이와 관련된 한 가지 분야로써 다중 로봇을 이용한 분산학습 연구가 있으며, 이는 단일 에이전트를 이용할 때보다 대량의 데이터를 빠르게 수집 및 처리하는데 용이하다. 본 연구에서는 기존 Distributed Neural Network Optimization (DiNNO) 알고리즘에서 제안한 정적 분산 학습방법과 달리 단계적 분산학습 방법을 새롭게 제안하였으며, 모델 성능을 향상시키기 위해 원시 변수를 근사하는 단계수를 상수로 고정하는 기존의 방식에서 통신회차가 늘어남에 따라 점진적으로 근사 횟수를 높이는 방법을 고안하여 새로운 알고리즘을 제안하였다. 기존 알고리즘과 제안된 알고리즘의 정성 및 정량적 성능 평가를 수행하기 MNIST 분류와 2 차원 평면도 지도화 실험을 수행하였으며, 그 결과 제안된 알고리즘이 기존 DiNNO 알고리즘보다 동일한 통신회차에서 높은 정확도를 보임과 함께 전역 최적점으로 빠르게 수렴하는 것을 입증하였다.