Accurate positioning of nodes distributed in wireless sensor networks is very important for ensuring the reliability of data transmission and guaranteeing the completeness of collected data by determining the phase and monitoring range of sensor networks. However, guaranteeing the accuracy of positioning in sensor networks needs a large consumption of node resource as well as an increasing cost of installation.

Pak Sin Il, a section head at the Faculty of Communication, proposed a new algorithm for positioning the whole network by iterating the process of estimating the coordinates and path loss factors of the connected unknown nodes from the anchor nodes whose positions are known, based on Received Signal Strength Indication (RSSI) model.

In order to increase the accuracy of position decision and path loss factor estimation, he calculated the power level of white noise and introduced weighting coefficient that is proportional to the information quantities.

The experimental results indicated that the proposed algorithm can guarantee much higher accuracy than other modern positioning methods.

You can find the details in his paper “A Method for Improving Accuracy of Positioning Based on RSS Model in 3D Wireless Sensor Networks” in “Proceedings of KUTIC-2025”.

Continuous measurement of oxygen concentration is essential in production processes, environmental analysis, medical applications, etc. An electrochemical oxygen sensor measures oxygen concentration by measuring produced current on the basis of electrochemical principles. It has a relatively long response time.

A moving average filter is not sensitive to whether the phase response is linear or not, and therefore it is used in many applications for noise processing in various measurement systems. There are several types of moving average filters including simple moving average (SMA) filter, linear decay moving average (LDMA) filter and raised cosine moving average (RCMA) filter. The disadvantage of these moving average filters is that they cannot achieve sufficient denoising effects if the filter order is limited. A special case of the moving average filter is an exponential moving average (EMA) filter.

Han Chol Hak, a section head at the Faculty of Automatics, proposed an improved exponential moving average filter (EMA) by determining the optimal coefficient to suit smooth noisy signals of sensors.

First, he analyzed the variation of deterministic signal accuracy and noise reduction characteristics according to the coefficient value of EMA filter. Through simulation, he determined the optimal coefficient which depends on the signal to noise ratio (SNR) and the frequency of input signals by approximation. Then, he set sample frequency constraints to obtain good filtering effect. Finally, he designed an improved EMA filter that changes the optimal coefficient according to input signals, and compared it with a simple moving average filter (SMA) for oxygen sensor signals.

The results show that the proposed algorithm outperforms other moving average filters for smooth noisy signals.

If more information is needed, you can refer to his paper “An Improved Exponential Moving Average Filter Design and Application in Electrochemical Oxygen Sensor Signal Denoising” in “Proceedings of KUTIC-2025”.

Time delay produces detrimental effects on the stability and performance of control systems, and the control is generally more difficult unless the delay is compensated.

IMC has a remarkable effect on improving the control performance of systems with large time delays.

Hong Kwang Hyok, a researcher at the Faculty of Automatics, has proposed a modified internal model control based PID (IMC-PID) control strategy by incorporating the structure of predictive functional control (PFC) together with DOB.

First, he used particle swarm optimization (PSO) for the optimal calculation of the IMC filter time constant. Then, he combined PFC and DOB with IMC-PID controller to compensate for the effect of time delay and disturbance. Thus, he could not only enhance disturbance rejection ability but also ensure predictive control.

He applied the proposed method to a first order plus dead time (FOPDT) process to analyze the effectiveness. The simulation results showed that the proposed controller can effectively compensate for the effects of time delay and disturbance.

You can find the details in his paper “Improved IMC-PID Controller Combining Predictive Functional Control Structure and Disturbance Observer” in “Proceedings of KUTIC-2025”.