News Process Optimization for Electrochemical Synthesis ...

Jo Feb 20, 2026

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).

News Novel Optimal Design of IMC-PID Controller

Jo Feb 19, 2026

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).

Coordination Control of Area Traffic Networks based...

Jo Feb 18, 2026

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).

An Improved Fast Convergent Sliding Mode Control De...

Jo Feb 17, 2026

A cascade control system is a multi-loop control system that can be implemented effectively for a controlled object that is capable of measuring any intermediate control variable directly affecting the primary control variable. In cascade control systems, the intermediate sensor and controller are used to effectively reject the disturbances before they affect the primary control variable.

In a cascade control system, the inner loop control system is usually designed as a fast response system, and the outer loop control system is designed as a little slower system than the inner loop system.

Due to the good disturbance rejection and fast convergence performance, many studies have been conducted to design a sliding mode control (SMC) for the inner loop controller of a cascade system. With the improvement of chattering effect, SMC controllers have been used for not only the inner loop system, but also the outer loop control system.

To the best of our knowledge, no efforts have been focused on the relationship between the outer loop sliding surface and the inner loop sliding surface of cascade control systems.

Kim Sok Min, a researcher at the Faculty of Automatics, designed the inner loop sliding surface as a hierarchical sliding surface containing the outer loop sliding surface, and proposed a hierarchical sliding mode controller so as to reduce the outer loop reaching phase and improve the entire convergence. Furthermore, in order to improve the steady-state performance, he designed an adaptive PID sliding surface to improve the reaching phase using the proportional-integral-differential (PID) sliding surface.

The numerical simulations verified the excellent performance of the proposed design method.

If more information is needed, you can refer to his paper “An improved fast convergent sliding mode control design of a cascade system based on the hierarchical structure and an adaptive PID sliding surface” in “Second International Conference on Electronics, Electrical, and Control System (EECS 2025)” (EI).

Data-Driven Design of Fractional Order H

You can find the details in his paper “Data-Driven Design of Fractional Order H^∞ Controller by Convex Optimization” in “International Journal of Dynamics and Control” (EI).

Improved Genetic Algorithm for Flow Shop Scheduling...

Jo Feb 15, 2026

Flow shop scheduling with blocking time is a problem with zero buffer and it has been widely studied in recent years. In particular, minimizing makespan is the most popular objective function in blocking flow shop scheduling problems. Hence, it is urgent to develop an effective algorithm for minimizing the makespan in the blocking flow shop.

So far, many researches have been conducted on blocking flow shop scheduling for minimizing makespan, but no literature presents a mathematical model considering the machine blocking time for this problem.

Rim Kum Chol, a researcher at the Faculty of Automatics, proposed a flow shop scheduling model aimed at makespan minimization considering the machine blocking, and an improved genetic algorithm based on some genetic operations tailored to the problem.

First, he presented a mathematical model of the blocking flow shop scheduling problem by introducing the new consideration of machine blocking. Second, he proposed an improved genetic algorithm using some tailored genetic operators such as the order crossover and the fragment inversion interchange mutation.

The proposed algorithm can be applied to not only blocking flow shop scheduling problems but also other scheduling problems and even to many optimization problems with some modifications.

For more information, please refer to his paper “Improved Genetic Algorithm for Flow Shop Scheduling Problem with Machine Blocking to Minimize Makespan” in “Second International Conference on Intelligent Transportation and Smart Cities (ICITSC 2025)” (EI).