Jo Sep 8, 2026
Running accuracy and stiffness of spindle units in a machine tool have a crucial influence on the accuracy and reliability of the whole machine. Fatigue life calculation of bearings in spindle units is important to ensure the good performance of spindle units and to design machine tools scientifically.
In order to predict the fatigue life of rolling bearings used in machine tools, the stresses generated at the contact surface of interface are needed. This stress distribution is mainly determined by the pressure acting on the contact surface. Accurate calculation of pressure distribution can be done by FEM, which considers not only the generator shape but also the length in the contact state. The calculation of pressure distribution requires great computational efforts, which is more important in the case of repeating the calculation of many contacts in complex rolling pairs than in the case of simple contacts.
In the previous studies, several methods were used for the fatigue life of bearings, but they failed to analyze the effect of the roll profile and ring misalignment on the bearing fatigue life in a direct way.
Having developed a mathematical model for the fatigue life calculation of cylindrical roller bearings widely used in machine tool spindles, Kim Myong Il, a researcher at the Faculty of Mechanical Science and Technology, proposed a reasonable roller profile by calculating the fatigue life of bearings of three roller profiles and comparing them by FEM.
The results showed that the stress distribution is significantly lower for circular or logarithmic correction than for rectilinear roller generators, but the stress distribution is higher for circular correction, and the overall logarithmic correction significantly improves the fatigue life by 4.5 times.
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Jo Sep 7, 2026
High-grade copper-bearing ores have been increasingly depleted and instead, low-grade and complex ores have now become dominant in copper production. The existing production process for low-grade copper molybdenum ore has the disadvantage of high cost and high energy consumption. Therefore, a great deal of attention is being paid to the simple and environmentally-friendly bioleaching. Without proper heap bioleaching technology, millions of tonnes of low-grade copper ore might be left as waste.
While many studies have been carried out on the bioleaching of chalcopyrite, which is the main copper-bearing material, few studies have been reported on the direct bioleaching of complex minerals containing chalcopyrite.
Cha Kwang Chon, a post-graduate student at the Faculty of Mining Engineering, investigated the feasibility of bioleaching for copper recovery from low-grade, complex chalcopyrite in a column reactor.
The ore mainly contains pyrite and chalcopyrite as sulphide minerals and quartz as the main gangue minerals.
He carried out the tests in a column reactor using a mixture of inoculums used for bioleaching of pyrite.
The results showed that the dissolution of copper reached 85% after 120 days at 35-45℃.
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Jo Sep 4, 2026
The water electrolysis technology using proton exchange membranes (PEM) has now become more widespread in its use due to its simple operation and maintenance and high productivity per unit volume as an intensive and high-efficiency water electrolysis technology that can directly produce high-purity, high-pressure hydrogen without special ancillary devices. Despite these advantages over alkali water electrolysis technology, which is widely industrialized across the world now, its disadvantage is high cost. It is attributable to the high cost of proton exchange membranes and electrode catalysts for them.
Ho Kuk Chol, a post-graduate student at the Institute of Nano Science and Technology, prepared ATO-supported IrO2-RuO2 composite catalysts as anodic catalysts in PEMWE by using the Adams method and investigated their electrochemical and water electrolytic properties.
When the Ir content was 30% for the IrO2-RuO2 composite catalyst and 80% for the supported catalyst, the catalysts had current density of 77.5mA/cm2 at the voltage of 1.5V in 0.5mol/L H2SO4 solution. The stability experiments in the same solution showed that the supported composite catalyst had almost the same stability as IrO2. It was concluded that the prepared catalyst showed almost the same performance with 60% reduction in cost compared to IrO2 catalysts.
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Jo Aug 28, 2026
UN and many countries are calling for urgent measures because air pollution caused by air pollutants seriously threatens human health and causes premature death. Many investigations and studies have revealed that PM2.5 is a main pollutant that destroys the environment of ecosystems and harms human health, and that air pollutants containing PM 2.5 cause various diseases such as respiratory, lung and heart diseases.
In the air pollution concentration prediction task, it was reported that the artificial neural network (ANN) model improves prediction performance while avoiding the complexity and annoyance of modeling, compared to the deterministic method which has lack in representing the heterogeneity and nonlinearity of many factors related to pollutant formation.
Therefore, many researchers have begun to apply artificial neural networks extensively to air pollution prediction, and since then they have ensured the accurate prediction performance by combining deep neural networks with several other optimization techniques that have shown dramatic effectiveness in the prediction of large time series data. In a word, their research results show that deep learning models significantly improve their performance when combined with several other effective analytical methods on time series data rather than being used as a single model.
Based on this analysis, Pak Un Jin, a researcher at the Faculty of Automatics, proposed a hybrid model consisting of convolutional neural network (CNN)-long short-term (LSTM) with multifractal detrended fluctuation analysis (MF-DFA) for air pollution and meteorological time series data, and used it to predict the next day’s 24-h average PM2.5 concentration in Beijing City.
The comparison of the performance indexes of the proposed model with MLP and LSTM models showed that the proposed model provides higher prediction accuracy.
For further details, please refer to his paper “A deep learning approach via multifractal detrended fluctuation analysis for PM2.5 prediction” in “Journal of Atmospheric and Solar-Terrestrial Physics” (SCI).
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Jo Aug 27, 2026
The evacuated tube solar collector (ETSC) shows superior performance over the flat-plate collector (FPC) as it can reduce convective and radiation heat loss, and the thermal efficiency of ETSC has a linear relationship with the difference between the fluid inlet temperature and the ambient temperature divided by the solar radiation incident on the collector.
Under steady-state operating conditions, the useful output power of FPC is a function of many parameters, and one of them is the overall heat loss coefficient of FPC.
WGETSC (water-in-glass evacuated tube solar collector), unlike FPC, typically consists of 16-28 flooded solar tubes in direct connection to a horizontal water tank, so it is difficult to use the single overall heat loss coefficient as in FPC.
Having considered the solar tube and the water tank respectively in WGETSC, Jong Hyon Il, a section head at the Faculty of Thermal Engineering, derived a formula to determine the thermal performance of WGETSC and compared the calculated value with measured one.
The mean relative error between the measured and calculated values was 9.8%.
For more information, you can refer to his paper “On evaluating thermal performance of water-in-glass evacuated tube solar collector” in “International Journal of Research in Engineering”.
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Jo Aug 26, 2026
As face is rather invariable among human biometric features, face recognition is considered as the most important biometric identification task. Face recognition has been widely used for user authentication in security systems such as electronic payment systems as a technology to search faces from facial images from photographs or videos and therefore, it has been studied for years.
Over the past decade, the mainstream of face recognition has been based on deep learning and it has been developed to a much higher level than average human ability. Nevertheless, face recognition is still far from “perfect”.
Recently, face recognition systems using convolutional neural networks are considered as the best methods among existing face recognition systems. Face recognition network models that have been developed and proved to be superior in performance cannot perform real-time face recognition in devices with constrained computational resources such as low base computers or mobile phones because their structure is very complex and they need a large amount of computation. What is more, reducing the number of layers continuously to reduce computational burden affects recognition performance.
In GhostFaceNets, they improved the trade-off between speed and accuracy by performing the attention operation using a DFC (decoupled fully-connected) attention. However, the DFC attention has limitations in capturing wide spatial information, which may lead to the degradation of recognition performance.
Jo Kwang Chol, a researcher at the Institute of Information Technology, has designed a network structure with low computational cost and improved performance by combining the self-attention module with the extended Ghost module based on the backbone of GhostFaceNets, and verified its accuracy using international standard databases.
The results showed that the proposed network model brings significant improvement in face recognition performance with 99.74% in LFW and 97.7% in AgeDB-30 and that with 42 MFLOP, it can support stable real-time face recognition in embedded devices.
For more details, you can refer to his paper “GhostFormerNet: A Lightweight Face Recognition Method based on Extended Ghost Module and Self-Attention” in “2025 International Conference on Graphics and Signal Processing”.
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