• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.644-651
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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 water quality such as turbidity, temperature, pH and alkalinity. However, the process of chemical reaction to improve water quality (e.g., turbidity) by 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 using Jar-test data. In this paper, a systematic control strategy is proposed to derive the optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride), using Jar-test results. A neural network model is developed for coagulant dosing and purifying process by means of six input variables (turbidity, temperature, pH, alkalinity of raw water, PAC feed rate, turbidity in flocculation) and one output variable, while considering the relationships to the reaction of coagulation and flocculation. The model is utilized to derive the optimum coagulant dosage (in the sense of minimizing turbidity of water in flocculator). The ability of the proposed control scheme validated through the field test has proved to be of considerable practical value.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.7
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• pp.1-8
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• 2004

A simple AMC (Adaptive Modulation and Coding) technique using ARQ (Automatic Repeat Request) for a MIMO (Multiple Input Multiple Output) system is proposed which does not require the additional feedback. In addition, the proposed AMC technique is different from the conventional technique in the aspect of considering the MCS (Modulation and Coding Scheme) level from the previous packet. The proposed technique can discard fewer amounts of unsuitable packets than the conventional technique. In the proposed system not only same rate control method for transmit antennas but also individual rate control method can be applied. The performance of the proposed technique is verified under a MIMO-OFDM (Orthogonal Frequency Division Multiplexing) system based on WLAN (Wireless Local Area Network), IEEE 802.11a. The results of the computer simulation show that a MIMO system with the proposed technique achieves higher throughput than one with a fixed transmission rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.751-758
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• 2020

As the application fields using various types of communication, including the Internet of Things, have emerged, the form of communication has been diversified. Some applications require fast feedback and the others continue to send data regardless of whether it is received or not. Transmitting data in one way can be acknowledged by the opposite direction response. These information exchanges form a two-way communication. For applications that need to issue commands remotely, such as network control systems, it is important to give a fast response because the sender decides the next action based on the response from the recipient. In this paper, we propose the base station (BS) cooperation to improve the latency performance of the two-way communication in cellular networks. We design the two-way communication strategy utilizing cooperating BSs with the same direction of traffic as well as bidirectional traffic. We show that the proposed scheme improves the latency performance than the previous works.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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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.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.4
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• pp.403-411
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• 2001

A criterion for evaluation of burners has changed recently, and the environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the $NO_x$ and CO regulation. Consequently. 'good burner' means one whose thermal efficiency is high under the constraint of $NO_x$ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of $NO_x$ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro- Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro- Fuzzy learning algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of $NO_x$ and CO of the combustion gas was successfully inferred.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.7
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• pp.539-548
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• 1989

This paper describes an instantaneous reactive power compensator aimed at the compensation of reactive power and current harmonics of a thyristor load. A new definition of the instantaneous reactive power consisting of both displacement of fundamental current and harmonic distortion current is proposed and the physical meaning is investigated from the viewpoint of an instantaneous power flow. The instantaneous reactive power is calculated from the feedback of instantaneous voltage, current and is compensated by the current controlled PWM converter connected in parallel with the load. The PWM converter operates as a high performance current control scheme, because adopts the excellent current controlled PWM technique based on the current deviation vector. Both simulation and experimental results show good compensating performances in steady and transient state.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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• pp.587-595
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• 2002

Recent trend changes a criterion for evaluation of humors that environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the NO/sub x/ and CO regulation. Consequently, 'good burner'means one whose thermal efficiency is high under the constraint of NO/sub x/ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of NO/sub x/ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro-Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro-Fuzzy loaming algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of NO/sub x/ and CO of the combustion gas was successfully inferred.

• Current Optics and Photonics
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• v.5 no.4
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• pp.384-390
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• 2021

This paper presents a wide-range tunable wavelength-locking technology based on optoelectronic oscillation (OEO) loops for optical fiber sensors and microwave photonics applications, explains the theoretical fundamentals of the design, and demonstrates a method for locking the relative wavelength differences between a leader semiconductor laser and its follower lasers. The input of the OEO loop in the proposed scheme (the relative wavelength difference) determines the radio-frequency (RF) signal frequency of the oscillation output, which is quantized into an injection current signal for feedback to control the wavelength drift of follower lasers so that they follow the wavelength change of the leader laser. The results from a 10-hour continuous experiment in a field environment show that the wavelength-locking accuracy reached ±0.38 GHz with an Allan deviation of 6.1 pm over 2 hours, and the wavelength jitter between the leader and follower lasers was suppressed within 0.01 nm, even though the test equipment was not isolated from vibrations and the temperature was not controlled. Moreover, the tunable range of wavelength locking was maintained from 10 to 17 nm for nonideal electrical devices with limited bandwidth.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.1116-1123
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• 2024

-In recent years, unmanned underwater vehicles (UUV) have been vigorously developed, and with the continuous deepening of marine exploration, traditional energy can no longer meet the energy supply. Nuclear energy can achieve a huge and sustainable energy supply. The heat pipe reactor has no flow system and related auxiliary systems, and the supporting mechanical moving parts are greatly reduced, the noise is relatively small, and the system is simpler and more reliable. It is more favorable for the control of unmanned systems. The use of heat pipe reactors in unmanned underwater vehicles can meet the needs for highly compact, long-life, unmanned, highly reliable, ultra-quiet power supplies. In this paper, a heat pipe reactor scheme named UPR-S that can be applied to unmanned underwater vehicles is designed. The reactor core can provide 1 MW of thermal power, and it can operate at full power for 5 years. UPR-S has negative reactive feedback, it has inherent safety. The temperature and stress of the reactor are within the limits of the material, and the core safety can still be guaranteed when the two heat pipes are failed.