Jo Sep 6, 2026
The Taedong Gate is the eastern gate of the Walled City of Pyongyang of Koguryo Kingdom (277 BC-668 AD) on the banks of the Taedong River in Taedongmun-dong in Central District of Pyongyang.
It was originally built as the eastern gate of the keep of the Walled City of Pyongyang, the capital of Koguryo, in the mid-6th century, and was rebuilt in 947 and 1392, the initial periods of the Koryo dynasty and the feudal Joson dynasty. The present structure was built in 1635.
It was the most important and biggest one among six gates of Pyongyang. It is 19 metres high. The embankment is 26.3 metres long, 14.25 metres wide and 6.5 metres high. In the middle of the embankment is an arch-shaped gateway whose four corners gradually widen outwards on the plane surface so that it can bear the weight of the structure.
The gate is considered as a typical example of gate construction of the feudal Joson dynasty embodying magnificence, beauty and airiness, as it has the architectural style and structural features of the early period of the feudal Joson dynasty, which inherited and developed the architectural art of Koryo.
Today, the gate serves as a recreational ground for the working people, youth and schoolchildren as it goes well with the Pyongyang Bell House, Ryongwang Pavilion and the Taedong River promenade’s flower bed facilities while boasting the long history of Pyongyang, which turned into a magnificent modern capital city.
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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 Sep 3, 2026
In September there are seasonal subdivisions of Paengno and Chubun.
Paengno means the time when clear dew falls with a sign of autumn. Chubun means that summer completely gives way to autumn. On the day of Chubun the length of day and night is almost the same and afterwards days gradually get shorter than nights.
This year’s Paengno and Chubun fall on September 7 and September 23 respectively.
From olden times, the Korean people gathered foxtail millet and other crops and sowed wheat and barley and they picked cotton in some regions in that period.
Medicinal herbs and wild fruits were gathered in mountainous regions.
Around Chubun, rice was harvested in paddy fields, soybean and other crops were gathered from dry fields and wheat and barley were planted in real earnest.
September 25 this year is Chusok (Harvest Moon Day), one of the greatest folk holidays of the Korean people.
Chusok means autumn evening and the Korean people enjoyed the folk holiday viewing the full moon in autumn when all kinds of cereals ripen. On the holiday, they first visited their forefathers’ graves to cut grass of them and hold memorial services arranging offerings made of new crops of the year. This was a reflection of the ennobling good manners and customs of the Korean people who always respect seniors and are polite and faithful to duty.
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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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