Zinc oxide (ZnO) is one of the oxide semiconductor materials with excellent electrical, piezo-electrical and optical characteristics. It is widely used in many applications such as solar cells, gas sensors and field-emission displays. ZnO nanomaterials are also used in electronic, thermal and quantum devices, catalysis and wastewater disposal as adsorbents and photocatalysts.

For synthesis of ZnO nanoparticles, various techniques have been proposed and applied. Among them, the electrochemical synthesis method is widely used due to its simplicity, low-temperature operation, low energy consumption and great purity of synthesized products.

So Hyong Dok, a student at the Faculty of Material Science and Technology, has proposed a process optimization method, namely, TOPSIS-Taguchi method, for electrochemical synthesis of ZnO nanoparticles with respect to productivity and consumption using technique for order preference by similarity to ideal solution (TOPSIS) and Taguchi methods. TOPSIS is used to convert multiple responses into a single integrated response (IR), and Taguchi method is used to design experiments and find optimal process parameters (PPs) by optimizing multiple responses.

He determined the optimal pH, concentration (CC), voltage (VL), and conductivity (CD) to maximize the productivity of ZnO nanoparticles and minimize the specific energy consumption and specific electrode consumption.

The optimal PPs were pH of 5, CC of 0.05 M, VL of 8V and CD of 30ms/cm, and their effect ranking on the IR was CC (37.657%), CD (32.498%), pH (15.614%) and VL (14.231%).

The proposed method could be widely used not only to electrochemical synthesis process optimization of ZnO nanoparticles but also to various materials fabrication process optimization problems.

For more information, please refer to his paper “Process optimization for electrochemical synthesis of ZnO nanoparticles with respect to productivity and consumption using TOPSIS and Taguchi methods” in “Scientific Reports” (SCI).

In automatic control of industrial processes, the determination of controller parameters is very important to prevent dangerous accidents and to increase quality and production. However, it is not easy to calculate controller parameters suitable for real plants, since most real processes have immeasurable model uncertainties.

PID controllers are most frequently applied in many fields where automatic control including process control is necessary. A PID controller has a great variation in control performance depending on how the parameters are determined, and thus many methods have been developed to adjust the parameters appropriately.

Conventional feedback control methods may not be good for time-delay plants because the control action is lagged. A suitable alternative to this kind of plant is the predictive control.

Hong Kwang Hyok, a researcher at the Faculty of Automatics, has investigated an improved internal model control based PID (IMC-PID) controller by combining particle swarm optimization (PSO) together with predictive functional control (PFC) framework.

First, he determined the optimal filter time constant, which is the core element of IMC, using PSO algorithm. Then, by employing the PFC idea to eliminate the effect of delay, he constructed a modified PID control system with PFC features. According to the framework of PFC, he carried out output prediction of the plant with delay and determined the optimal manipulated value by the IMC-PID control strategy.

The control method he investigated proved effective through the control of a first plus dead time (FOPDT) plant.

If further details are needed, please refer to his paper “A novel optimal design of IMC-PID controller incorporating PSO and predictive functional control framework” in “Second International Conference on Electronics, Electrical, and Control System” (EI).

The urban traffic problem has become more and more serious with the development of social and economic development. Researches are being carried out in many directions to solve this problem. In particular, smart traffic control systems that can effectively manage urban traffic without increasing infrastructure are being widely studied.

However, due to the characteristics peculiar to the traffic system itself, there arise many difficulties in building an intelligent traffic system. And it is difficult to realize due to the fact that the amount of data related to its decision-making are vast and artificial. In order to overcome this, artificial intelligence techniques such as multi-agent systems (MAS), deep learning, and Q learning are being actively applied.

With the advent of AI technology, distributed autonomous control was made possible, and MAS technology has become an important technology to solve the traffic coordination problem. From the viewpoint of area traffic coordination control, it is particularly important to adopt more intelligent control strategies at urban intersections where most traffic congestions usually occur.

Kim Thae Yong, a researcher at the Faculty of Automatics, proposed a Multi-Agent architecture by which agents (intersections) can implement traffic signal control and coordination control over intersections, mutually utilizing the characteristics of the low-dimensioned traffic state of other agents within the traffic networks, thus improving the performance of area traffic coordination.

He simulated the algorithm in several possible cases and the control strategy for coordination control by means of VISSIM. The simulation results show that the proposed method can reduce the total traffic time and queue length under a congested traffic environment.

For more details, you can refer to his paper “Coordination control of area traffic networks with multi-agent architecture based on deep recurrent Q-learning networks” in “Second International Conference on Intelligent Transportation and Smart Cities (ICITSC 2025)” (EI).