• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.12
• /
• pp.1048-1054
• /
• 2003

In laser surface hardening processes, the geometrical parameters such as the depth and the width of a hardened layer can be utilized to assess the hardened layer quality. However, accurate monitoring of the geometrical parameters for on-line process control as well as for on-line quality evaluation is very difficult because the hardened layer is formed beneath a material surface and is not visible. Therefore, temperature monitoring of a point of specimen surface has most frequently been used as a process monitoring method. But, a hardened layer depends on the temperature distribution and the thermal history of a specimen during laser surface hardening processing. So, this paper describes the estimation results of the geometric parameters using multi-point surface temperature monitoring. A series of hardening experiments were performed to find the relationships between the geometric parameters and the measured temperature. Estimation results using a neural network show the enhanced effectiveness of multi-point surface temperature monitoring compared to one-point monitoring.

• Journal of information and communication convergence engineering
• /
• v.7 no.3
• /
• pp.377-382
• /
• 2009

In this paper, theoretical analysis for metropolitan optical networks is performed. First, analytical optical SNR is derived assuming each node consists of an EDFA, an optical filter, an optical switch, and a VOA, and then the relationship between OSNR and BER is studied. In a metropolitan optical network, an optical signal can be dropped to deliver data, and we also studied the effect of drop loss on system performance. When the drop loss is relatively small, the receiver structure of the node can be treated as a preamplifier receiver which is widely used in long-haul systems. In that case, ASE noise from EDFAs is the dominant noise source in the receiver. However, system performance is relatively insensitive to OSNR when the drop loss is significant because of the noise sources in the receiver (thermal and shot noise).

• Nuclear Engineering and Technology
• /
• v.30 no.3
• /
• pp.222-234
• /
• 1998

The Westinghouse OT$\Delta$T DNB protection logic heavily restricts the operation region by applying the same logic for a full range of operating pressure in order to maintain its simplicity. In this work, a fuzzy neural network method is used to estimate the DNB protection limit using the measured average temperature and pressure of a reactor core. Fuzzy system parameters are optimized by a hybrid learning method. This algorithm uses a gradient descent algorithm to optimize the antecedent parameters and a least-squares algorithm to solve the consequent parameters. The proposed method is applied to Yonggwang 3&4 nuclear power plants and the proposed method has 5.99 percent larger thermal margin than the conventional OT$\Delta$T trip logic. This simple algorithm provides a good information for the nuclear power plant operation and diagnosis by estimating the DNB protection limit each time step.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.1
• /
• pp.78-86
• /
• 2003

Multivariable control system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, Pill Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the Pill controller has to be manually tuned by trial and error. This paper suggests a tuning method of the Pill Controller for the multivariable power plant using an immune algorithm, through computer simulation. Tuning results by immune algorithms based neural network are compared with the results of genetic algorithm.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.24 no.12
• /
• pp.830-836
• /
• 2012

In this study, the effects of heating control methods on heating control performance and energy consumption in the floor radiant heating control system of residential apartments were research by computer simulation. A general regression neural network(GRNN) control method for reducing indoor temperature overshoot and saving energy in floor radiant heating system is suggested. The GRNN control method shows good responses in comparison with the conventional and outdoor reset control methods for improving indoor thermal environment and reducing energy consumption.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
• /
• v.16 no.4
• /
• pp.16-23
• /
• 2020

In this study, the control characteristics and effects of control methods on heating performance and energy consumption for the hot water floor radiant heating control system of a residential apartment were research by experiment. As a control method, On-Off control and outdoor reset control methods with GRNN(General Regression Neural Network) and without GRNN are considered. Also, the control performances with regard to improvement of indoor thermal environment and reduction of energy consumption are compared, respectively. Experiment results show that the performance of the control method with GRNN is better than that of conventional on-off control method without GRNN in the responses of room set temperature and energy saving.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.89-94
• /
• 2022

The Saudi Vision 2030 defined the directions of the national economy and market towards diversifying sources of income, and developing energy to become less dependent on oil. The study sought through a theoretical review to identify the reality of the energy sector and the areas of investment available in the field of renewable energy. Findings showed that investment in the renewable energy sector is a promising source according to solar, wind, hydrogen, geothermal energy and burning waste than landfill to extract biogas for less emission. The renewable energy sector faces challenges related to technology, production cost, price, quantity of production and consumption, and markets. The study revealed some recommendations providing and suggested electronic marketing system to provide investors and consumers with energy available from renewable sources.

• Advances in concrete construction
• /
• v.15 no.4
• /
• pp.269-286
• /
• 2023

In the present study, we aim to utilize the numerical solution frequency results of functionally graded beam under thermal and dynamic loadings to train and test an artificial neural network. In this regard, shear deformable functionally-graded beam structure is considered for obtaining the natural frequency in different conditions of boundary and material grading indices. In this regard, both analytical and numerical solutions based on Navier's approach and differential quadrature method are presented to obtain effects of different parameters on the natural frequency of the structure. Further, the numerical results are utilized to train an artificial neural network (ANN) using AdaGrad optimization algorithm. Finally, the results of the ANN and other solution procedure are presented and comprehensive parametric study is presented to observe effects of geometrical, material and boundary conditions of the free oscillation frequency of the functionally graded beam structure.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.6
• /
• pp.96-106
• /
• 2020

To solve the limitation of traffic monitoring that occur from embedded sensor such as loop and piezo sensors, the thermal imaging camera was installed on the roadside. As the length of Class 1(passenger car) is getting longer, it is becoming difficult to classify from Class 3(2-axle truck) by using an embedded sensor. The collected images were labeled to generate training data. A total of 17,536 vehicle images (640x480 pixels) training data were produced. CNN (Convolutional Neural Network) was used to achieve vehicle classification based on thermal image. Based on the limited data volume and quality, a classification accuracy of 97.7% was achieved. It shows the possibility of traffic monitoring system based on AI. If more learning data is collected in the future, 12-class classification will be possible. Also, AI-based traffic monitoring will be able to classify not only 12-class, but also new various class such as eco-friendly vehicles, vehicle in violation, motorcycles, etc. Which can be used as statistical data for national policy, research, and industry.

• Journal of the Korean Ceramic Society
• /
• v.26 no.6
• /
• pp.795-801
• /
• 1989

The polycrystalline mullite fiber was synthesized from various combination of starting materials including metal alkoxides and colloidal sol by the sol-gel process. The best spinnability was observed in the sol which showed shear thinning and hysteresis (i.e., thixotropic flow), indicating that the network structure was broken down as the shear rate increased. The mullite fiber was polycrystalline after firing and characterized by thermal analysis, XRD, FT-IR spectroscopy, rheological measurements, and SEM.