• Journal of the Microelectronics and Packaging Society
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• v.30 no.3
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• pp.11-19
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• 2023

Recently with the rapid advancement of artificial intelligence (AI) technologies such as Chat GPT, AI semiconductors have become important. AI technologies require the ability to process large volumes of data quickly, as they perform tasks such as big data processing, deep learning, and algorithms. However, AI semiconductors encounter challenges with excessive power consumption and data bottlenecks during the processing of large-scale data. Thus, the latest packaging technologies are required for AI semiconductor computations. In this study, the authors have described packaging technologies applicable to AI semiconductors, including interposers, Through-Silicon-Via (TSV), bumping, Chiplet, and hybrid bonding. These technologies are expected to contribute to enhance the power efficiency and processing speed of AI semiconductors.

• Structural Engineering and Mechanics
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• v.88 no.3
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• pp.273-285
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• 2023

This study examines how the interaction between soil and a wind turbine's supporting system affects the optimal design. The supporting system resting on an elastic soil foundation consists of a steel conical tower and a concrete circular raft foundation, and it is subjected to wind loads. The material cost of the supporting system is aimed to be minimized employing various metaheuristic optimization algorithms including teaching-learning based optimization (TLBO). To include the influence of the soil in the optimization process, modified Vlasov and Gazetas elastic soil models are integrated into the optimization algorithms using the application programing interface (API) feature of the structural analysis program providing two-way data flow. As far as the optimal designs are considered, the best minimum cost design is achieved for the TLBO algorithm, and the modified Vlasov model makes the design economical compared with the simple Gazetas and infinitely rigid soil models. Especially, the optimum design dimensions of the raft foundation extremely reduce when the Vlasov realistic soil reactions are included in the optimum analysis. Additionally, as the designated design wind speed is decreased, the beneficial impact of soil interaction on the optimum material cost diminishes.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1671-1678
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• 2023

The Korea multi-purpose satellite (KOMPSAT) series consisting of multi-sensors has been used in various fields such as land, environmental monitoring, and disaster analysis since its first launch in 1999. Recently, as various information processing technologies (high-speed computing technology, computer vision, artificial intelligence, etc.) that are rapidly developing are utilized in the field of remote sensing, it has become possible to develop more various satellite image processing and analysis algorithms. In this special issue, we would like to introduce recently researched technologies related to the KOMPSAT image application and research topics participated in the 2023 Satellite Information Application Contest.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.614-630
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• 2023

Dead trees significantly impact forest production and the ecological environment and pose constraints to the sustainable development of forests. A lightweight YOLOv4 dead tree detection algorithm based on unmanned aerial vehicle images is proposed to address current limitations in dead tree detection that rely mainly on inefficient, unsafe and easy-to-miss manual inspections. An improved logarithmic transformation method was developed in data pre-processing to display tree features in the shadows. For the model structure, the original CSPDarkNet-53 backbone feature extraction network was replaced by MobileNetV3. Some of the standard convolutional blocks in the original extraction network were replaced by depthwise separable convolution blocks. The new ReLU6 activation function replaced the original LeakyReLU activation function to make the network more robust for low-precision computations. The K-means++ clustering method was also integrated to generate anchor boxes that are more suitable for the dataset. The experimental results show that the improved algorithm achieved an accuracy of 97.33%, higher than other methods. The detection speed of the proposed approach is higher than that of YOLOv4, improving the efficiency and accuracy of the detection process.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1
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• pp.14-20
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• 2006

In this paper. a novel technique to set up the initial weights in BSS of delayed mixtures is proposed. After analyzing Eigendecomposition for the correlation matrix of mixing data. the initial weights are set from the Eigenvectors ith delay information. The Proposed setting of initial weighting method for conventional FDICA technique improved the separation Performance. The computer simulation shows that the Proposed method achieves the improved SIR and faster convergence speed of learning curve.

• Journal of Wind Energy
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• v.14 no.3
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• pp.25-33
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• 2023

In order to secure the safety of increasing offshore activities such as offshore wind farm maintenance and fishing, IMPACT, a mid-term marine weather forecasting system, was established by predicting marine weather up to 7 days in advance. Forecast data from the Korea Hydrographic and Oceanographic Agency (KHOA), which provides the most reliable marine meteorological service in Korea, was used, but wind speed and wave height forecast errors increased as the leading forecast period increased, so improvement of the accuracy of the model results was needed. The Model Output Statistics (MOS) method, a post-correction method using statistical machine learning, was applied to improve the prediction accuracy of wave height, which is an important factor in forecasting the risk of marine activities. Compared with the observed data, the wave height prediction results by the model before correction for 6 to 7 days ahead showed an RMSE of 0.692 m and R of 0.591, and there was a tendency to underestimate high waves. After correction with the MOS technique, RMSE was 0.554 m and R was 0.732, confirming that accuracy was significantly improved.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1725-1732
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• 2024

In nuclear power plants, reactor coolant leakage can occur due to various reasons. Early detection of leaks is crucial for maintaining the safety of nuclear power plants. Currently, a detection system is being developed in Korea to identify reactor coolant system (RCS) leakage of less than 0.5 gpm. Typically, RCS leaks are detected by monitoring temperature, humidity, and radioactivity in the containment, and a water level in the sump. However, detecting small leaks proves challenging because the resulting changes in the containment humidity and temperature, and the sump water level are minimal. To address these issues and improve leak detection speed, it is necessary to quantify the leaks and develop an artificial intelligence-based leak detection system. In this study, we employed bidirectional long short-term memory, which are types of neural networks used in artificial intelligence, to predict the relative humidity in the leakage area for leak quantification. Additionally, an optimization technique was implemented to reduce learning time and enhance prediction performance. Through evaluation of the developed artificial intelligence model's prediction accuracy, we expect it to be valuable for future leak detection systems by accurately predicting the relative humidity in a leakage area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2199-2213
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• 2024

To enhance the quality of defect detection for Printed Circuit Board Assembly (PCBA) during electronic product manufacturing, this study primarily focuses on optimizing the YOLOv7-based method for PCBA defect detection. In this method, the Mish, a smoother function, replaces the Leaky ReLU activation function of YOLOv7, effectively expanding the network's information processing capabilities. Concurrently, a Squeeze-and-Excitation attention mechanism (SEAM) has been integrated into the head of the model, significantly augmenting the precision of small target defect detection. Additionally, considering angular loss, compared to the CIoU loss function in YOLOv7, the SIoU loss function in the paper enhances robustness and training speed and optimizes inference accuracy. In terms of data preprocessing, this study has devised a brightness adjustment data enhancement technique based on split-filtering to enrich the dataset while minimizing the impact of noise and lighting on images. The experimental results under identical training conditions demonstrate that our model exhibits a 9.9% increase in mAP value and an FPS increase to 164 compared to the YOLOv7. These indicate that the method proposed has a superior performance in PCBA defect detection and has a specific application value.

• Journal of Korean Ophthalmic Optics Society
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• v.18 no.2
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• pp.125-136
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• 2013

Purpose: The aim of this study was to investigate the effect of improving read speed with color filter or without color filter to improve reading disorder of teenager who were diagnosed as Meares-Irlen syndrome through survey inspection with Meares-Irlen syndrome visual stress (MISViS) score. Methods: MISViS subjects were selected from screening survey MISViS results given above 2.13 in the clinical criteria scores (MISViS score). Reading speed were measured quickly and efficiently the rate of reading via test in which randomly ordered common words are read aloud during a minute. Each of the subjects were worn a filter of the lowest concentration in each color filter group composed of 15 groups. Results: MISViS score of MISViS group and control group were 2.57 and 0.66, respectively. Results of reading speed with filter and without filter in MISViS group were $102.27{\pm}27.86$ wpm and $118.87{\pm}26.99$ wpm (p=0.001), respectively, as well as were $132.93{\pm}6.88$ wpm and $133.43{\pm}6.64$ wpm (p=0.131) in the normal group. Associated with error changes with filter and without filter between two groups, skipping in MISViS Group were from $0.25{\pm}0.62$ times to 0 times (p=0.191), Errors were from $1.83{\pm}1.69$ times to $0.17{\pm}0.38$ times (p = 0.004) and, repetitions were 0. skipping in control group were 0 times, errors were from $0.21{\pm}0.43$ times to $0.07{\pm}0.27$ times (p=0.336) and, repetitions were from $0.14{\pm}0.36$ times to 0 (p=0.165). The filter of blue series chosen in MISViS group had higher percentage (40%), whereas, subjects in normal group were more likely to prefer the filter of gray color (29%). Conclusions: This study showed that MISViS score have been used as a significant diagnosis for Irlen syndrome screening. This study found that wearing suitable color filter for MISViS patients were useful to improve learning with regard to reading. Unique color filter selection for MISViS subjects must be carefully considered since fit color filter are different personally.