Jo Sep 18, 2026
The most efficient routing approach for data collection in WSNs is the cluster-based approach. The sensor nodes of WSNs are discriminated by several multi-criteria. Hence, a number of energy-efficient cluster-based routing protocols that comprehensively consider these mutually contradictory multi-criteria have been developed.
The typical algorithms which were used to comprehensively consider multi-criteria include fuzzy logic, MCDM, meta-heuristic optimization algorithm, and the combination of these approaches.
MCDM approach is mainly used to evaluate the completed alternatives defined by several criteria or factors. Thus, recently, there has been active research effort to exploit MCDM approaches for cluster-based routing.
The goal of cluster-based routing optimization is to maximize energy consumption balancing among nodes by taking into account various criteria in the whole process of clustering routing while maintaining stability, reliability and connectivity of network, and thus to extend network lifetime as much as possible. However, existing cluster-based routing protocols exploit either individual MCDM approaches or fuzzy logic or meta-heuristic optimization algorithms in the cluster head (CH) node selection of clustering stage.
Ri Man Gun, an institute head at the Faculty of Communication, proposed a novel clustering scheme using adaptive fuzzy C-means (AFCM) and an improved ant-lion optimization (ALO) approach.
This scheme first divides the whole network into k clusters using the AFCM algorithm. After that, an improved ALO is applied to each cluster to select the optimal CH nodes. The improved ALO prescribes a new fitness function by the multi-criteria based on the weights assigned by FCNP-VWA.
The simulation results revealed that the proposed scheme achieves superior energy consumption balance.
For more information, you can refer to his paper “An Energy Efficient Routing Scheme using a Hybrid MCDM and Meta-Heuristic Algorithm in WSNs” in “Proceedings of KUTIC-2025”.
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Jo Sep 17, 2026
The ball and plate system is a typical multivariable, nonlinear system, which is widely used in control engineering experiments and scientific research. As a two-dimensional extension of a ball and beam system, it not only stabilizes the ball at a desired position on the plate but also realizes random tracking control, which has involved a lot of research.
Model predictive control, which was mostly used for delay systems like chemical processes, has been used a lot in the control of mechanical systems recently. Prediction models are of different types, and recently, state space models have been used as a prediction model for many predictive controls. In this case, a state observer is necessary because it is impossible to measure all of the system states in practice. However, there are some problems with using a state observer. One of its drawbacks is robustness. And whether a state observer is included or not is also a problem for ball and plate systems.
Kim Hun Chol, a researcher at the Faculty of Automatics, presented a non-minimal state space (NMSS) model of ball and plate system, a typical multivariable, nonlinear system, and realized position control of the ball on the plate using NMSS model predictive control (MPC).
In conformity with the characteristics of MPC and the configuration of the proposed control system, he introduced the NMSS model without the need for a state observer to design MPC.
He verified the effectiveness and performance through MATLAB simulation and real experiments. The results validated that the proposed NMSS-MPC is a suitable control method for ball and plate systems.
You can find the details in his paper “Study on Stabilization and Tracking Control of Ball and Plate System using Non-Minimal State Space Model Predictive Control” in “Proceedings of KUTIC-2025”.
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Jo Sep 16, 2026
The particle swarm optimization (PSO) algorithm is a kind of optimization method based on the theory of cluster intelligence. The greatest advantage of PSO algorithm is its fast convergence speed, simplicity of evolution operation, low computational cost and few parameters needed.
Kim Yong Su, a researcher at the Faculty of Automatics, proposed a method of designing a PID controller using the PSO algorithm and the dominant pole placement.
He used the PSO algorithm to determine the position of dominant pole for minimizing the judge function consisting of the absolute error integral index (IAE) and overshoot value, and used Fuzzy Neural Network (FNN) to determine the parameters of PID controller.
The proposed method makes it possible to get both the optimal dominant pole and the exchanging frequency at the same time and to get good control results as the overshoot value is small. The simulation results demonstrated that the proposed method is effective.
For more information, you can refer to his paper “Self-Tuning Method of PID Controller using PSO and Fuzzy Neural Network” in “Proceedings of KUTIC-2025”.
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Jo Sep 15, 2026
Magnetic compass based on MEMS is a device that can display the course of ships and the heading of aircraft, and it is widely used because of its fast measurement and low cost.
Due to the characteristics of Earth’s magnetic field and the environmental effects on magnetic compasses, determination of heading based on Earth’s magnetic field measurement still exposes several problems to overcome. Thus, lots of efforts have been made to improve the accuracy of digital magnetic compasses in different external conditions, which resulted in the development of many calibration methods.
Preceding research papers described that the locus from the sensing axes of magnetic compass is an ellipse or ellipsoid due to the influence of magnetic interference on the magnetic compass.
Ri Yong Rim, a researcher at the Faculty of Naval Architecture and Ocean Engineering, proposed a method for compensating the accidental error and improving the accuracy of MEMS magnetic compass by using robust ellipse fitting.
In order to evaluate the effectiveness of the objective function, he performed ellipse approximations by the measured values under artificial random noise. As a result, he identified the trend and shortcomings of the new objective function, and further improved the objective function to overcome the shortcomings.
Then, he obtained compensation coefficients and compensation errors by the algebraic ellipse approximation and by the improved ellipse approximation, calculated the heading, and compared it with the actual heading. The result demonstrated that the improved ellipse approximation model had good noise suppression performance and adaptability.
You can find more information in his paper “Research on Improving Heading Accuracy of MEMS Magnetic Compass Based on Robust Ellipse and Spheroid Fitting” in “Proceedings of KUTIC-2025”.
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Jo Sep 14, 2026
Today, models of cooperative games with transferable utility (TU-games for short) have been widely used in many fields including economics. One of the most important problems in cooperative TU-games is to determine rational imputation, and computing such imputation within a limited time is of practical importance. So far, cooperative TU-games have used the Shapley value and the nucleolus to determine imputation, but the problem of computing the nucleolus of classical TU-games is NP-hard. In addition, the Shapley value computation in classical cooperative TU-games requires exponential time.
GWO (Grey Wolf Optimizer) is an algorithm to solve optimization problems by simulating the foraging process of grey wolves.
O Un Suk, a lecturer at the Faculty of Applied Mathematics, proposed an algorithm for computing rational imputation (an element of the core) in TU-games with nonempty core within a limited time.
The calculation experimental results demonstrated that the proposed algorithm is much better than the calculation of the Shapley value if the number of players exceeds four in classical TU-games.
If more details are needed, please refer to her paper “Algorithm for Computing the Rational Imputation using Grey Wolf Optimizer in Cooperative TU-Games” in “Proceedings of KUTIC-2025”.
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Jo Sep 13, 2026
The magnetometer is one of the most suitable attitude sensors for almost all low earth orbit (LEO) satellites.
There have been lots of efforts to develop attitude estimation algorithms using only a magnetometer and a spacecraft attitude estimation algorithm utilizing only magnetometer readings was developed. Although the algorithm can provide high accuracy of satellite attitude estimation, it is widely known that the unscented Kalman filter (UKF) faces heavy computational load.
Kim Hak Min, a researcher at the Faculty of Aerospace Engineering, proposed an attitude and angular rate estimation algorithm based on quaternion estimation (QUEST) and unscented Kalman filter (UKF).
The proposed algorithm uses only three-axis magnetometer as an attitude sensor and works in two stages. In the first stage, the QUEST uses magnetometer measurement vector and its derivative to estimate a coarse attitude. In the second stage, these estimated values are used as quaternion measurement data for UKF processing. The state vector for UKF consists of attitude parameters and angular rate vector. As a result, it is possible to estimate satellite attitude and get angular rate with high accuracy and small computation load.
The simulation result showed that the attitude of a satellite can be estimated with higher accuracy than 0.2° with small computational load.
You can find the details in his paper “Research on LEO Satellite Attitude Estimation Based on Quaternion and Unscented Kalman Filter” in “Proceedings of KUTIC-2025”.
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