• 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 Korean Society of Water and Wastewater
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• v.33 no.5
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• pp.329-339
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• 2019

Recently, the advancement of information and communication technology(ICT) is expanding the connectivity through Internet of Things(IoT), and the media of connection is also expanding from wire/cable transmission to broadband wireless communication, which has significantly improved mobility. This hyperconnectivity has become a key element of the fourth industrial revolution, whereas the supervisory control network of purification plants in korea is operated as a communication network separated from the outside, thereby lagging in terms of connectivity. This is considered the best way to ensure security, and thus there is hardly any consideration of establishing alternatives to operate an efficient and stable communication network. Moreover, security for management of a commercialized communication network and network management solution may be accompanied by immense costs, making it more difficult to make new attempts. Therefore, to improve the conditions for the current supervisory control network of purification plants, this study developed a industrial security L2 switch that supports modbus TCP(Transmission Control Protocol) communication and encryption function of the transmission section. As a result, the communication security performance improved significantly, and the cost for implementing the network management system using Historical Trend and information of HMI(Human Machine Interface) could be reduced by approximately KRW 200 million. The results of this study may be applied to systems for gas, electricity and social safety nets that are infrastructure communication networks that are similar to purification plants.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.75-78
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• 1995

In this paper it is concerned to develop control method using jar-test results in order to predict the optimum dosage of coaglant, PAC(PoliAluminum Chloride). Considering the relations with the reactions with the reaction of coagulation and flocculation, the five independent variables ( e, g, turbidity of raw water, water turbidity in flocculators, temperature, pH, and alkalynity) are selected out of parameters and they are put into calculation to develop a neural network model for PAC dosing process in water purification system. This model is utilized to predict optimum dosage of PAC. That is, the optimum dosage of PAC is searched in neural network model for PAC dosing process to minimize the water turbidity in flocculators. This searching is implemented by means of expert heuristics. The efficacy of the proposed contorl schemem and feasibility of acquired neural network model for PAC dosing contorl in water purification system is evaluated by means of computer simulation.

• Journal of Korean Society of Water and Wastewater
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• v.32 no.4
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• pp.309-315
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• 2018

Currently, the commercialization of the $5^{th}$ Generation (5G) service is becoming more prevalent in domestic communication network technology. This has reduced communication delay time and enabled large-capacity data transmission and video streaming services in real-time. In order to keep pace with these developments, K-water has introduced a smart process control system in water purification plants to monitor the status of the water purification process. However, since wireless networks are based on the public Long Term Evolution (LTE) network, communication delay time remains high, and high-resolution video services are limited. This is because communication networks still have a closed structure due to expense and security issues. Therefore, with 5G in its current form, it is very difficult to accommodate future services without improving the infrastructure of its communication networks. In recognition of these problems, this study implemented the authentication and management function of wireless networks on a wired network management system in the K-water Bansong water purification plant. The results confirmed that wired Local Area Network (LAN) services give a higher security performance than an expensive commercial wireless LAN system. This was achieved by using an Internet Protocol (IP) address management system of wired networks and the packet filtering function of the Layer2 (L2) switch. This study also confirmed that it is possible to create a wireless LAN service that is 3.7 times faster than the existing LTE communication network.

• 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.

• Proceedings of the KIEE Conference
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• 2000.11c
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• pp.476-478
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• 2000

Objective is to design a high performance purification filter system of reactor coolant and seal injection system at nuclear power station. The purification filter systems play an important role in the stability of the nuclear and volume control system which consist the primary network systems of the nuclear power station. But the users of the purification filter systems frequently suffer from high maintenance cost which comes from lack of understanding of the system technology and domestic suppliers. It is time to establish a high performance domestic filter system manufacturing technology and optimum design for wide use in industrial applications.

• 수도
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• s.14
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• pp.10-29
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• 1978

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.449-458
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• 2023

Artificial Intelligence is providing convenience in various fields using big data and deep learning technologies. However, deep learning technology is highly vulnerable to adversarial examples, which can cause misclassification of classification models. This study proposes a method to detect and purification various adversarial attacks using StarGAN. The proposed method trains a StarGAN model with added Categorical Entropy loss using adversarial examples generated by various attack methods to enable the Discriminator to detect adversarial examples and the Generator to purification them. Experimental results using the CIFAR-10 dataset showed an average detection performance of approximately 68.77%, an average purification performance of approximately 72.20%, and an average defense performance of approximately 93.11% derived from restoration and detection performance.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.282-285
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• 1996

In the water purification plant, the raw water is promptly purified by injecting chemicals. The amount of chemicals is directly related to water quality such as turbidity, temperature, pH and alkalinity. At present, however, the process of chemical reaction to the turbidity has not been clarified as yet. Since the process of coagulant dosage has no feedback signal, the amount of chemical can not be calculated from water quality data which were sensed from the plant. Accordingly, it has to be judged and determined by Jar-Test data which were made by skilled operators. In this paper, it is concerned to model and control the coagulant dosing process using jar-test results in order to predict optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride). The considering relations to the reaction of coagulation and flocculation, the five independent variables(turbidity, temperature, pH, Alkalinity of the raw water, PAC feed rate) are selected out and they are put into calculation to develope a neural network model and a fuzzy model for coagulant dosing process in water purification system. These model are utilized to predict optimum coagulant dosage which can minimize the water turbidity in flocculator. The efficacy of the proposed control schemes was examined by the field test.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.5
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• pp.421-430
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• 2017

In this study, a model was developed to predict for Disinfection By-Products (DBPs) generated in water supply networks and consumer premises, before and after the introduction of advanced water purification facilities. Based on two-way ANOVA, which was carried out to statistically verify the water quality difference in the water supply network according to introduce the advanced water treatment process. The water quality before and after advanced water purification was shown to have a statistically significant difference. A multiple regression model was developed to predict the concentration of DBPs in consumer premises before and after the introduction of advanced water purification facilities. The prediction model developed for the concentration of DBPs accurately simulated the actual measurements, as its coefficients of correlation with the actual measurements were all 0.88 or higher. In addition, the prediction for the period not used in the model development to verify the developed model also showed coefficients of correlation with the actual measurements of 0.96 or higher. As the prediction model developed in this study has an advantage in that the variables that compose the model are relatively simple when compared with those of models developed in previous studies, it is considered highly usable for further study and field application. The methodology proposed in this study and the study findings can be used to meet the level of consumer requirement related to DBPs and to analyze and set the service level when establishing a master plan for development of water supply, and a water supply facility asset management plan.