• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.25-31
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• 2009

In this paper, the implementation of a production control system based on RFID has been studied in order to obtain an exact Cost Center data such as the name of workers included a process of work and a time period to finish the process. The cost center of a worker will be correctly obtained by checking the work time using RFID tag data and by transmitting the data to a server of ERP or POP system. And also warming up time, cleaning time, power failure, and out of order sign will be checked and calculated using the data stored in RFID tags attached in workers and machine facilities. Therefore, exact Cost Center data will be obtained by the production control system with touch screens entering the data according to the situation in real time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.12
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• pp.1140-1145
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• 2008

In conventional cutting system of Heat-Shrink-Tube, workers operate cutting system after considering about length and quantity of heat-shrink-tube. So, not only work time and production cost is increased but also material is wasted because the data that workers have to consider is so much. In this paper, an effective cutting system of heat-shrink-tube was developed to reduce production cost, work time and waste of material. The cutting system consists of a supervisory computer installed inside a control room, a on-site computer installed on the work area, and a PLC system. In the developed system, a supervisory computer send work order to the on-site computer using LAN and the on-site computer operates the cutting system of the heatshrink-tube after it makes an array production order. Also, the on-site computer reports information to the supervisory computer when an accident happened.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.268-272
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• 1988

In this paper, we introduce the supermicro computer- based nonreal-time BTR type DNC system, which can provide valuable datas for production and management by monitoring the operation of NC equipments. (upto 48 NC machine) In accordance with manual job schedulinq, part program is combined with tool presetting datas and enables rapid downloading and uploading. An application to the production system of die & Mold plant is presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.73-85
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• 1997

In the product liability age the demand on quality is extremely high and inspection and test are automated. The process capability indices $C_p, {\;}C_{pk}$ and p control chart widely used to provide unitless measure of process performance and process control. Traditional process capability indices $C_p, {\;}C_{pk}$ do not represent the process variation from target value. The convention p chart for control of fraction nonconforming becomes inadequate when the fraction nonconforming becomes very small such as PPM level production system. This paper proposes process performance measure considering quadratic loss function and cumulative counts control chart for control of PPM level production system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.8
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• pp.495-497
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.18-20
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.103-108
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• 2015

The electric utility has the responsibility of reducing the impact of peaks on electricity demand and related costs. Therefore, they have introduced Direct Load Control System (DLCS) to automate the external control of shedding customer load that it controls. Since the number of customer load participating in the DLC program are keep increasing, DLCS operators a re facing difficulty in monitoring and controlling customer load. The existing DLCS needs constant operator intervention, e.g., whenever the load is about to exceed a predefined amount, it needs operator's intervention to control the on/off status of the load. Therefore, DLCS operators need the state-of-the-art DLCS, which can control automatically the on/off status of the customer load without intervention as much as possible. This paper presents an intelligent DLCS using the active database. The proposed DLCS is applying the active database to DLCS which can avoid operator's intervention as much as possible. To demonstrate the validity of the proposed system, variable production rules and intelligent demand controller are presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.79-86
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• 2017

This research focused on deciding optimal manufacturing WIP (Work-In-Process) limit for a small production system. Reducing WIP leads to stable capacity, better manufacturing flow and decrease inventory. WIP is the one of the important issue, since it can affect manufacturing area, like productivity and line efficiency and bottlenecks in manufacturing process. Several approaches implemented in this research. First, two strategies applied to decide WIP limit. One is roulette wheel selection and the other one is elite strategy. Second, for each strategy, JIT (Just In Time), CONWIP (Constant WIP), Gated Max WIP System and CWIPL (Critical WIP Loops) system applied to find a best material flow mechanism. Therefore, pull control system is preferred to control production line efficiently. In the production line, the WIP limit has been decided based on mathematical models or expert's decision. However, due to the complexity of the process or increase of the variables, it is difficult to obtain optimal WIP limit. To obtain an optimal WIP limit, GA applied in each material control system. When evaluating the performance of the result, fitness function is used by reflecting WIP parameter. Elite strategy showed better performance than roulette wheel selection when evaluating fitness value. Elite strategy reach to the optimal WIP limit faster than roulette wheel selection and generation time is short. For this reason, this study proposes a fast and reliable method for determining the WIP level by applying genetic algorithm to pull system based production process. This research showed that this method could be applied to a more complex production system.

• The Plant Pathology Journal
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• v.19 no.6
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• pp.280-283
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• 2003

A soybean-sprout rot epidemic occurred in a mass production soybean sprout factory in 2000 and 2001 in Korea, which caused up to 20% production loss. Among the causal pathogenic bacteria and fungi, Pythium deliense was found to be the dominant pathogen of severe root and hypocotyls rot, particularly in recirculating water system. An average of 90% of the isolated fungi from the rotted sprout on potato dextrose agar were Pythium sp. The fungal density of Pythium in the sampled water was monitored in the recycled water system for 1 year using a selective medium (com meal agar with Pimaricin, 10 mg； Rifampicin, 10 mg； and Ampicillin, 100 mg per 1 liter). The drained water from the soybean-sprout cultivation always had a certain amount of fungus in it. The removal of Pythium from the recycling water system must be thorough, safe, and environment friendly. However, the pathogen in the water was easily found even after ozone and chlorine treatments, which were devised on the recycling system for the removal of microorganisms. 5-$\mu\textrm{m}$ pore size filter was applied and was able to successfully control the disease. As the sprout industry increasingly shifts into mass production, the demand for water will increase continuously. Recycling water for sprout production is eco-friendly. However, a process must be devised to be able to first decompose organic matters before Pythium zoospores are filtered.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2002.04b
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• pp.78-102
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• 2002