• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.11
• /
• pp.51-57
• /
• 2014

This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.

• Journal of Welding and Joining
• /
• v.21 no.4
• /
• pp.31-38
• /
• 2003

Line heating is an effective and economical process for forming flat metal plates into three-dimensional shapes for plating of ships. Because the nature of the line heating process is a transient thermal process, followed by a thermo elastic plastic stress field, predicting deformed shapes of plate is very difficult and complex problem. In this paper, neural network model o3r solving the inverse problem of metal forming is proposed. The backpropagation neural network systems for determining line-heating positions from object shape of plate are reported in this paper. Two cases of the network are constructed-the first case has 18 lines which have different positions and directions and the second case has 10 parallel heating lines. The input data are vertical displacements of plate and the output data are selected heating lines. The train sets of neural network are obtained by using an analytical solution that predicts plate deformations in line heating process. This method shows the feasibility that the neural network can be used to determine the heating-line positions in line heating process.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.6B
• /
• pp.875-887
• /
• 2010

The workgroup of IEEE 802 proposed the IEEE 802.16 standard, also known as WiMAX, to provide broadband wireless access (BWA). The standard specifies two operational modes, one is popular PMP mode, and the other is optional mesh mode. In the mesh mode, the network entry process-NetEntry is the pivotal procedure for mesh network topology formulation and thus, influences the accessibility of whole mesh network. Unfortunately, the NetEntry process suffers from the hidden neighbor problem, in which new neighborship emerges after a new node comes in and results in possible collisions. In this paper, we propose a new SN-protected NetEntry process to address the problem. Simulation results show that the new proposed NetEntry process is more stable compared with the standard-based NetEntry process.

• Journal of Information Technology Services
• /
• v.10 no.1
• /
• pp.47-64
• /
• 2011

In the IT(Information Technology) service, which supply the solutions related to business management and IT, network construction and application trends, the related service business are increasing according to the enlargement of project scope and the diversity of project types as the need of service customers. In this paper, I propose the significant effect factors on the network management of IT service firms. The key findings are from the analysis result about 94 IT service firms as follows. For implementation the high performance of network management in the IT service firms, the strategic elements in the process of network construction are more conceived highly than the basic element in them. Also the perspective of project objectives are considered than the nominal perspectives in the partner selection process. The competency of partner firms', the cooperation process between the partner firms', network relation operation management and network relation structure management are the significant effect factors of network management.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2164-2168
• /
• 2003

Real time control system such as in factory automation fields, defense field, aerospace, railway industries, financial trading and so forth, includes multiple computers on multiple nodes, and share data to process various actions and functions. This is similar to multitasking in a multiprocessor computer system. The task processing efficiency of such system is proportionally increased by process speed of each process computer. And also it is greatly influenced by communication latencies of each node. To provide proper operation of such real time system, a network that can guarantee deterministic exchange of certain amount of data within a limited time is required. Such network is called as a real time network. As for modern distributed control system, the timeliness of data exchange gives important factor for the dynamics of entire control system. In a real time network system, exchanged data are determined by off-line design process to provide the timeliness of data. In other word, designer of network makes up a network data table that describes the specification of data exchanged between control equipments. And by this off-line design result, the network data are exchanged by predetermined schedule. First, this paper explains international standard real time network TCN (Train Communication Network) applied to the KHST (Korean High Speed Train) project. And then it explains the computer program developed for design tool of network data table of TCN.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.3
• /
• pp.196-200
• /
• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used fur on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.2
• /
• pp.136-139
• /
• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.476-479
• /
• 1992

This paper presents a neural network method for tuning PlD controller of a time-varying process. Three gains of PlD controller are tuned for a certain desirable response pattern by back-propagation neural network. The neural network is trained using changes of output features vs. changes of PlD gains. But sometimes it needs longer training time and larger structure to train the correlation between the process and controller on entire region of the process. The difficulty in system identification is that the inverse function of the system can not be clearly stated. To cope with the problem, we do not train the neural network to respond correctly for the entire regions but train for only local region where the system is heading toward by training the neural network and tuning of the PlD controller. It may be trained for fine-tuning itself. Simulation results show that the adaptive PID controller using neural network trained in the local area performs remarkably for time-varying second order process.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.12 no.20
• /
• pp.71-76
• /
• 1989

In this paper, GERT network in modeled to improve the network of manufacturing process with feedback loop. A lot of Information on the GERT network can be derived from the equivalent W-function and MGF(moment generating function) using Mason's rule. These equations are used in calculating the variations of the performance measure and in improving the system performance. System improvement in manufacturing process is achieved by increasing the equivalent probabilities of each branches and by decreasing the expected equivalent time.