• Journal of the Korea Society for Simulation
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• v.8 no.2
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• pp.87-100
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• 1999

A manufacturing environment without a computerized system causes numerous problems, since many important decisions are made based on the experience of veteran staffs. Especially, when a strategy for the improvement of manufacturing efficiency is considered, it is hard to predict the effect of the strategy. A solution to the problem without large investment of the computerized system is the simulation study. This paper shows the modeling and simulation based on DEVS(Discrete Event System Specification). Two types of models are implemented, one for representing the current production strategy and the other for the new strategy. The new strategy is expressed as priority rules within the model. The process in concern is the metal grating production process in which the size of the group, for applying a specific cutting and scheduling strategies, is one of the important factors in improving the production efficiency. Some reliable criteria for the evaluation related to the production effeciency are established from the simulation study.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.550-555
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• 1993

The aim of this paper is to present the development of visual seam tracking system equipped with visual range finder. The visual range finder, which consists of a CCD camera and a diode laser system with line generating optics, developed to recognize the types of weld joints and detect the location of weld joints. In practical applications, however, images of the weld joints are often degraded due to spatters, are flares, surface specularity, and welding smoke. To overcome the problem, this paper proposes a syntactic approach which is a class of artificial intelligence techniques. In the approach, the type of weld joint is inferred based upon the production rules which are linguiques grammars consisting of a set of line and junction primitives of laser strip image projected on weld joint. The production rules eliminate several noisy primitives to create new primitives through the merging process of primitives. After the recognition of weld joint, arc welding is started and the location of weld joints is repeatedly detected using a spring model-based template matching in which the template model is a by-product of the recognition process of weld joint. To show the effectiveness of the proposed approach a series of experiments-identification and robotic tracking-are conducted for four different types of weld joints.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.80-92
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• 1997

This research focuses onan efficient control method of temperature for multiple points using only one processor. For a yarn production system, the surface temperature control of heaters are very important for quality control. Therefore, we designed a temperature controller for a draw and twist machine and applied Fuzzy-PWM algorithm to the controller. If we use a processor for the temperature control of multiple points with the conventional ON/OFF control, the control performance of the system becomes poor. To overcome these problems, we developed a new Fuzzy-PWM algorithm for the adjustment of power rate to the heaters in the conventional ON/OFF control. It is shown that this algorithm has the same effects as the PID algorithm for the temperature control of each point. The proposed algorithm is robust against the production condition and environment such as the reference temperature and the thickness of yarn, since the power rate to the heater is adjusted by Fuzzy Rules derived from the values of the reference termperatureand the thickness of yarn. To obtain optimal Fuzzy rulees, the control simulations are perfodrmed through the modelling of the heater and simulation of Fuzzy rules. This algorithm is applied for the multiple pont temperature controller and showed satisfactory performance.

• Korean Journal of Computational Design and Engineering
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• v.5 no.3
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• pp.255-262
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• 2000

A computer-aided process planning (CAPP) system for rotationally symmetric deep drawing products has been developed. The application for non-axisymmetric components, however, has not been reported yet. Therefore, this study investigates process sequence design in deep drawing process and constructs an expert system of process planning for non-axisymmetric motor frame products with elliptical shape. The system developed consists of four modules. The first one is recognition of shape module to recognize the products. The second one is a 3-D modeling module to calculate surface area for non-axisymmetric products. The third one is a blank design module that creates an oval-shaped blank with the identical surface area. The forth one is a process planning module based on production rules that play the best important role in an expert system for manufacturing. The production rules are generated and upgraded by interviewing with field engineers. The constructed system using AutoLISP language under the AutoCAD environment is baled on the knowledge base system which is involved a lot of expert's technology. Results of this system will be provide effective aids to the designer and engineer in this field.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.123-132
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• 1999

This paper describes a research work of developing computer-aided design of lead frame, semiconductor, with blanking operation which is very precise for progressive working. Approach to the system is based on the knowledge-based rules. Knowledge for the system is formulated from pasticity theories, experimental results and the empirical knowledge of field experts. This system has been written in AutoLISP on the AutoCAD using a personal computer and in I-DEAS Drafting Programming Language on the I-DEAS Master Series Drafting with Workstation, HP9000/715(64). Transference of data between AutoCAD and I-DEAS Master Series Drafting is accomplished by DXF(drawing exchange format) and IGES(initial graphics exchange specification) methods. This system is composed of five modules, which are input and shape treatment, production feasibility check, strip-layout, data-conversion and die-layout modules. The process planning and Die design system is designed by considering several factors, such as complexities of blank geometry, punch profiles, and the availability of a press equipment and standard parts. This system provides its efficiecy for strip-layout, and die design for lead frame, semiconductor.

• Korean Journal of Computational Design and Engineering
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• v.7 no.1
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• pp.9-17
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• 2002

The study is insufficient on process planning of the elliptical deep drawing product. Thus, in this present study, the expert system for elliptical deep drawing products was constructed by using process sequence design. The expert system was developed to be based on the general concept of each entity. The system was developed in this work consists of sixth modules. The first one is a shape recognition module to recognize non-axisymmetric products and to generate Entity_list. The second one is three dimensional (3-D) modeling module to calculate the surface area for non-axisymmetric products. The third one is a blank design module to create suggested blanks of three shapes with the identical surface area. The fourth one is shape design module based on the production rules that play the most important role in an expert system for manufacturing. The production rules are generated and upgraded by inter- viewing field engineers, plastic theory and experiments. The fifth and sixth ones are a graphic module to visualize results of the expert system and a post module to rise user's convenience, respectively. According to constructed the expert system for process sequence design, it was possible to reduce the lead time.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.10-16
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• 2023

The ever-increasing amount of data generated by various industries and systems has led to the development of data mining techniques as a means to extract valuable insights and knowledge from such data. The electrical energy industry is no exception, with the large amounts of data generated by SCADA systems. This study focuses on the analysis of historical data recorded in the SCADA database of the Libyan Electricity Company. The database, spanned from January 1st, 2013, to December 31st, 2022, contains records of daily date and hour, energy production, temperature, humidity, wind speed, and energy consumption levels. The data was pre-processed and analyzed using the WEKA tool and the Apriori algorithm, a supervised machine learning technique. The aim of the study was to extract association rules that would assist decision-makers in making informed decisions with greater efficiency and reduced costs. The results obtained from the study were evaluated in terms of accuracy and production time, and the conclusion of the study shows that the results are promising and encouraging for future use in the Libyan Electricity Company. The study highlights the importance of data mining and the benefits of utilizing machine learning technology in decision-making processes.

• English Language & Literature Teaching
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• v.18 no.1
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• pp.23-43
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• 2012

This study investigates the effects of three different types of instruction, namely form-based, comprehension-based, and production-based on the development of Korean university students' (n=137) comprehension and production of English relative clauses (RCs). The extent of improvements was analyzed by administering pre-and post-tests consisting of two comprehension tests (selecting the right form of RCs and the right picture descriptions) and one production test (combining two sentences). Findings of this study suggest that all three types of instruction increased participants' comprehension and productions of RCs. However, there appeared differential effects by the instruction type. It was found production-based instruction was most effective in promoting comprehension, followed by comprehension-based instruction. Comprehension-based instruction worked best with the development of production, suggesting that the effects of comprehension training did not only work for increasing comprehension skills, but also transfer to production skills. The type or level of tasks employed for each instruction appeared to play an important role in causing such results. Form-based instruction displayed the lowest improvements in both comprehension and production of RCs. A sentence-combination task employed for form-based instruction appear to result in mere explicit rule explanations without chances to notice rules in context or use their knowledge in practice.

• The Journal of Information Systems
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• v.27 no.3
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• pp.25-42
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• 2018

Purpose The purpose of this study focused on adopting production systems in cognitive psychology to improve the extraction process of persuasive design characteristics for healthcare-related mobile applications. Design/Methodology/Approach A research approach with four stages was developed. We developed and updated the evaluation guideline for persuasive design characteristics (PDC). We tried to summarize and analyze each of 28 PDC and prepared related production rules. Verification process for both guideline approach and production system approach were performed. Top one hundred apps from both medical category and health and Fitness category were selected and evaluated by two approaches. By comparing the results of the two approaches, we tried to explain the improvement and reliability of introducing the production system in the PDC extraction process. Findings Based on the updated guideline for healthcare-related mobile applications, a production system in cognitive psychology was developed. By comparing the PDC extraction results by two approaches, production system showed a better improvement for evaluation precision and efficiency for decision-making process. The findings of this study can be used for researchers and app developers to apply production system to analyze, evaluate, and develop better healthcare-related apps with persuasion.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.145-152
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• 2020

In this paper, we propose the Crop Classification Management System (CCMS) that detects changes in growth environment to improve crop production rate. The CCMS consists of two modules. First, the Crop Classification Module (CCM) classifies crops through CNN. Second, the Farm Anomaly Detection Module (FADM) detects abnormal crops by comparing accumulated data of farms. The CCM recognizes crops currently grown on farms and sends them to the FADM, and the FADM picks up the weather data from the past to the present day of the farm growing the crops and applies them to the Nelson rules. The FADM uses the Nelson rules to find out weather data that has occurred and adjust farm conditions through IoT devices. The performance analysis of CCMS showed that the CCM had a crop classification accuracy of about 90%, and the FADM improved the estimated yield by up to about 30%. In other words, managing farms through the CCMS can help increase the yield of smart farms.