• Nuclear Engineering and Technology
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• 제26권3호
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• pp.420-424
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• 1994

가압경수로형 원자력발전소의 저출력 및 과도상태에서의 개선된 증기발생기 수위 제어 방식을 제시하였다. 수축 및 팽창 현상에 의한 수위의 요동을 줄이기 위해 기존의 비례·적분 제어기에 증기발생기 압력 및 급수온도를 고려한 앞먹임 보상부를 첨가하였다. 원전 훈련용 시뮬레이터를 이용하여 시뮬레이션을 수행한 결과 기존방식에 비해 적은 수위오차, 훨씬 빠른 진정시간을 얻을 수 있었다. 제시된 알고리즘은 구현이 용이하고 실제 적용도 가능하리라 판단된다.

• Journal of Power Electronics
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• 제19권6호
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• pp.1544-1553
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• 2019

The efficiency of photovoltaic generation systems depends on the maximum power point tracking (MPPT) technique. Among the various schemes presented in the literature, the incremental conductance (INC) method is one of the most frequently used due to its superb tracking ability under changes in insolation and temperature. Generally, conventional INC algorithms implement a simple duty-cycle updating rule that is mainly found on the polarity of the peak-power evaluation function. However, this fails to maximize the performance in both steady-state and transient conditions. In order to overcome this limitation, a novel regulated INC (r-INC) method is proposed in this paper. Like the compensators in automatic control systems, this method applies a digital compensator to evaluate the INC function and improve the capability of power tracking. Precise modeling of a new MPPT system is also presented in the optimized design process. A 120W boost peak power tracker is utilized to obtain comparative test results and to confirm the superiority of the proposed method over existing techniques.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.317-320
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• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on the water quality such as turbidity, temperature, pH and alkalinity etc. However, the process of chemical reaction to improve water quality by the chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts or jar-test results. This paper presents the method of deriving the optimum dosing rate of coagulant, PAC(Polymerized Aluminium Chloride) for coagulant dosing process in water purification system. A neural network model is developed for coagulant dosing and purifying process. The optimum coagulant dosing rate can be derived the neural network model. Conventionally, four input variables (turbidity, temperature, pH, alkalinity of raw water) are known to be related to the process, while considering the relationships to the reaction of coagulation and flocculation. Also, the turbidity in flocculator is regarded as a new input variable. And the genetic algorithm is utilized to identify the neural network structure. The ability of the proposed scheme validated through the field test is proved to be of considerable practical value.

• Design & Manufacturing
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• 제10권2호
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• pp.18-23
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• 2016

Injection molded thermoplastic parts may need to be coated to facilitate paint adhesion, or to satisfy other surface property requirements, such as appearance, durability, and weather resistance. In this paper, a two-component polyurethane metering system was developed for the simultaneous injection and surface coating of a plastic substrate. The system was composed of storage tanks, feed pumps, axial piston pumps, mixing head. The tank was designed to be double-jacket structured and fabricated for polyol and isocyanate, respectively. A temperature chamber was used to maintain the material temperature to be $80^{\circ}C$ during flowing from storage tank to mixing head. Inside the chamber, feed pump, low pressure filter, high pressure pump, high pressure filter, pressure sensor, flow meter were installed. A mixing head of L-type was used for homogeneous mixing of polyol and isocyanate. Inside the mixing head, a cartridge heater and a temperature sensor were installed to control the temperature of the materials. The flow rate of axial-piston pump was controlled by using closed-loop feedback control algorithm. The input flow-rates were compared with the measured values. The output error was 6.7% for open-loop control, whereas the error was below 2.2% for closed-loop control. In addition, the pressure generated through mixing-head nozzle increased with increasing flow rate. It was found that the pressure drop between metering pump and mixing-head nozzle was almost 10 bar.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.59-67
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• 2024

Current straw mushroom farming in countries with large rice growing areas has great development potential, and was once considered a way to generate additional income and reduce poverty in rural areas. However, currently most people still grow mushrooms using traditional processes, leading to low productivity and unguaranteed output quality. Currently, due to climate change and unusual weather changes, people tend to switch to growing straw mushrooms indoors. In the process of growing straw mushrooms indoors, the design of an automatic control and monitoring system is very important to ensure the growing process is carried out effectively and achieves high yields. In this paper, we propose a system that can automatically control and monitor the humidity and temperature of the indoor straw mushroom growing process and other parameters that can be monitored through a network system using Internet of Things. The control algorithm automatically adjusts the grow house equipment based on feedback from sensors to maintain an optimal environment for growing straw mushrooms. Experimental results show that the straw mushroom growing system with automatically controlled and monitored environmental parameters helps improve efficiency, reduce costs and increase the sustainability of the current straw mushroom growing industry.

• 센서학회지
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• 제31권6호
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• pp.433-440
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• 2022

Recently, owing to global warming, average summer temperatures are increasing and the number of hot days is increasing is increasing, which leads to an increase in heat stroke. In particular, outdoor workers directly exposed to the heat are at higher risk of heat stroke; therefore, preventing heat-related illnesses and managing safety have become important. Although various wearable devices have been developed to prevent heat stroke for outdoor workers, applying various sensors to the safety helmets that workers must wear is an excellent alternative. In this study, we developed a smart helmet that measures various vital signs of the wearer such as body temperature, heart rate, and sweat rate; external environmental signals such as temperature and humidity; and movement signals of the wearer such as roll and pitch angles. The smart helmet can acquire the various data by connecting with a smartphone application. Environmental data can check the status of heat wave advisory, and the individual vital signs can monitor the health of workers. In addition, we developed an algorithm that classifies the risk of heat-related illness as normal and abnormal by inputting a set of vital signs of the wearer using a support vector machine technique, which is a machine learning technique that allows for rapid binary classification with high reliability. Furthermore, the classified results suggest that the safety manager can supervise the prevention of heat stroke by receiving feedback from the control system.