• Journal of Korea Water Resources Association
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• v.32 no.2
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• pp.143-152
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• 1999

South-eastern part of Kyungbuk Province is suffering from lack of suitable water development sources due to geographic condition and insufficient water sources condition. In order to find an appropriate solution, extensive studies are carried out such as investigation of new dam sites, regional water supply system, modification of existing water supply system, rehabilitation of old water resources structures and development of off-stream reservoirs. The network optimization model is applied for evaluation of the newly suggested water development alternatives. The results show that if water supply system is constructed until 2011, the reliability of water supply to Pohang and Kyungju region will be more than 95% and the network optimization model can be used to analyse the management of water resources system considering water rights or priority orders.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• Journal of Mechanical Science and Technology
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• v.16 no.8
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• pp.1033-1038
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• 2002

Genetic alsorithm (GA) , compared to the gradient-based optimization, has advantages of convergence to a global optimized solution. The genetic algorithm requires so many number of analyses that may cause high computational cost for genetic search. This paper proposes a personal computer network programming based on TCP/IP protocol and client-server model using socket, to improve processing speed of the genetic algorithm for optimization of composite laminated structures. By distributed processing for the generated population, improvement in processing speed has been obtained. Consequently, usage of network-based genetic algorithm with the faster network communication speed will be a very valuable tool for the discrete optimization of large scale and complex structures requiring high computational cost.

• Journal of the Korean Institute of Navigation
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• v.25 no.4
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• pp.323-333
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• 2001

This paper presents a decision making model for the cruise ship management. A network based optimization model has been developed for a single cruise ship operation. It gives optimal itinerary patterns over the planning period for the cruise ship managers wanting to maximize profit from the cruise ship operation. A network solution method to find the optimal solution is also developed. This network model can be equivalently transformed into a linear programming model, which makes the implementation of the model quite practical however complicated the given set of possible itineraries may be. The ship scheduling network developed in this study can also be used as a general framework to describe all possible cruise ship itineraries the cruise ship manager can figure out.

• Structural Engineering and Mechanics
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• v.7 no.5
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• pp.485-502
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• 1999

Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.

• Asia pacific journal of information systems
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• v.6 no.2
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• pp.125-148
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• 1996

This paper is primarily focused on the function of model management systems such as higher level representations and buildings of optimization models using them, especially in the area of the telecommunication network models. This research attempts to provide the model builders an intuitive language-namely higher level representation-using five distinctivenesses : Objective, Node, Link, Topological Constraint including five components, and Decision. The paper elaborates all components included in each of distinctivenesses extracted from structural characteristics of typical telecommunication network models. Higher level representations represented with five distinctivenesses should be converted into base level representations which are employed for semantic representations of linear and integer programming problems in knowledge: assisted optimization modeling system(UNIK-OPT). Furthermore, for formulating the network model using higher level representations, the reasoning process is proposed. A system called UNIK-NET is developed to implement the approach proposed in this research, and the system is illustrated with an example of the network model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.1007-1017
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• 1997

The existing survivability measure is defined as the only ratio of survival traffic volume on the given traffic demand. In this paper we suggest a new network survivability evaluation model. Sinceit depends on the importance of traffic, we can evaluatethe affect of telecommunication disaster. With the suggested evaluation model we formulate optimization models for restoration paths and traffic assinment on them. The optimization models are represented as mixed integer programming problems, which are difficult to find exact solutions. We develop heuristic algorithms according to the optimization models and apply them to an example network with 10 nodes and 17 links.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.131-138
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• 2022

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1357-1366
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• 2022

It is important to quickly and accurately build a disaster network or tactical mobile communication network adapting to the field. In configuring the traditional wireless communication systems, the parameters of the base station are set through cell planning. However, for cell planning, information on the environment must be established in advance. If parameters which are not appropriate for the field are used, because they are not reflected in cell planning, additional optimization must be carried out to solve problems and improve performance after network construction. In this paper, we present a rapid mobile communication network construction and optimization method using artificial intelligence and SON technologies in mobile communication base stations. After automatically setting the base station parameters using the CNN model that classifies the terrain with path loss prediction through the DNN model from the location of the base station and the measurement information, the path loss model enables continuous overage/capacity optimization.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.377-382
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• 2004

This paper presents a novel optimization method for the air- and gas-supplying network comprised of several air compression systems and air and gas streams in an industrial chemical plant. The optimization is based on the hybrid model developed by Han and $Han^1$ for predicting the power consumption of a compression system. A constrained optimization problem was formulated to minimize the total electric power consumption of all the compression systems in the air- and gas-supplying network under various operating constraints and was solved using a successive quadratic optimization algorithm. The optimization approach was applied to an industrial terephthalic acid manufacturing plant to achieve about 10% reduction in the total electric power consumption under varying ambient conditions.