• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.255-272
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• 2023

Spatial cable systems can provide more transverse stiffness and torsional stiffness without sacrificing the vertical bearing capacity compared with conventional vertical cable systems, which is quite lucrative for long-span earth-anchored suspension bridges' development. Higher economy highlights the importance of refined form-finding analysis. Meanwhile, the internal connection between the lateral and vertical sags has not yet been specified. Given this, an analytic algorithm of form-finding for the earth-anchored suspension bridge with spatial cables is proposed in this paper. Through the geometric compatibility condition and mechanical equilibrium condition, the expressions for cable segment, the recurrence relationship between catenary parameters and control equations of spatial cable are established. Additionally, the nonlinear general reduced gradient method is introduced into fast and high-precision numerical analysis. Furthermore, the analytic expression of the lateral and vertical sags is deduced and discussed. This is very significant for the space design above the bridge deck and the optimization of the sag-to-span ratio in the preliminary design stage of the bridge. Finally, the proposed method is verified with the aid of two examples, one being an operational self-anchored suspension bridge (with spatial cables and a 260 m main span), and the other being an earth-anchored suspension bridge under design (with spatial cables and a 500 m main span). The necessity of an iterative calculation for hanger tensions on earth-anchored suspension bridges is confirmed. It is further concluded that the main cable and their connected hangers are in very close inclined planes.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.59-60
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• 2023

The automation of container terminals is an important factor that determines port competitiveness, and global advanced ports tend to strengthen their competitiveness through container terminal automation. The operational efficiency of the AGV, which is an essential transport equipment of the automated terminal, can improve the productivity of the automated terminal. The operation of AGVs in automated container terminals differs from that of conventional container terminals, as it is based on an automated system in which AGVs travel along designated paths and operate according to assigned tasks, requiring consideration of factors such as workload, congestion, and collisions. To prevent such problems and improve the efficiency of AGV operations, a more sophisticated model is necessary. Thus, this paper proposes an AGV scheduling model that takes into account the AGV travel path and task assignment within the terminal The model prevent the problem of deadlock and. various cases are generated by changing AGV algebra and number of tasks to create AGV driving situations and evaluate the proposed algorithm through algorithm and optimization analysis.

• Journal of IKEEE
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• v.27 no.4
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• pp.587-600
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• 2023

This paper presents an optimal design of the slim type high frequency transformer used in the LLC DC to DC resonant converter for 65-inch UHD-TV with the rated power of 315W. This paper also performs an optimal design of the slim type high frequency through core loss analysis, AC winding loss analysis, and optimization design of the winding arrangement of the LLC resonant transformer. Particularly, the high-efficiency and slim type high frequency transformer based on the obtained results from theoretical analysis in this paper is constructed in the interleaved and vertical winding structures of its transformer to realize the winding method of automatic type and minimize AC winding loss. The primary and secondary windings of the slim type high frequency transformer the vertical winding structure proposed in this paper used the Litz-wire windings, PCB and copper plate windings, respectively. Finally, an optimal design of the slim type high frequency transformer proposed in this paper was carried out through the experimental results to confirm the validity of theoretical analysis based on the simulation results using Maxwell 2D and 3D tool.

• Membrane Journal
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• v.33 no.6
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• pp.344-351
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• 2023

The membrane separation process for carbon dioxide capture from hydrogen reformer exhaust gas has been developed. Using a commercial membrane module, a multi-stage process was developed to achieve 90% of carbon dioxide purity and 90% of recovery rate for ternary mixed gas. Even if a membrane module with being well-known properties such as material selectivity and permeability, the process performance of purity and recovery widely varies depending on the stage-cut, the pressure at feed and permeate side. In this study, we verify the limits of capture efficiency at single-stage membrane process under various operating conditions and optimized the two-stage recovery process to simultaneously achieve high purity and recovery rate.

• Journal of Hydrogen and New Energy
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• v.34 no.5
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• pp.413-420
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• 2023

Holmium-doped TiO₂ nanotubes (Ho-TNTs) were manufactured through anodization treatment and electrochemical deposition, and optimization experiments were conducted using various Holmium doping concentrations and time as variables. Surface as well as electrochemical characteristics were analyzed to study the prepared photocatalysts. Ho-TNTs were found to exist only in anatase phase through X-ray diffraction analysis. Ho-TNTs with 0.01 wt% 100 seconds shows a photocurrent density of 3.788 mA/cm² and an effective photo-conversion efficiency (PCE) of 4.30%, which is more efficient than pure TiO₂ nanotubes (pure-TNTs) (at bias potential 1.5 V vs. Hg/HgO). The photocatalytic activity of the aforementioned Ho-TNTs for hydrogen production was evaluated with the result of -29.20 µmol/h·cm².

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.569-582
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• 2023

Lead-acid Battery is the oldest rechargeable battery system and has maintained its position in the rechargeable battery field. The battery causes thermal runaway for various reasons, which can lead to major accidents. Therefore, preventing thermal runaway is a key part of the battery management system. Recently, research is underway to categorize thermal runaway battery cells into machine learning. In this paper, we present a thermal runaway hazard cell detection and verification algorithm using DBSCAN and statistical method. An experiment was conducted to classify thermal runaway hazard cells using only the resistance values as measured by the Battery Management System (BMS). The results demonstrated the efficacy of the proposed algorithms in accurately classifying thermal runaway cells. Furthermore, the proposed algorithm was able to classify thermal runaway cells between thermal runaway hazard cells and cells containing noise. Additionally, the thermal runaway hazard cells were early detected through the optimization of DBSCAN parameters using a grid search approach.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.502-509
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• 2023

This study presents a satellite constellation method that achieves optimal revisit performance by utilizing genetic algorithm techniques. The Walker method is a global coverage concept, and there are limitations to target-centered constellation considering the strategic environment of the Korean Peninsula. To overcome these limitations, targets are set in major areas of interest in North Korea, orbit elements with optimal revisit performance for each target are searched, and based on this, the number of satellites optimized for each target is derived using a genetic algorithm. The results of this study demonstrate the performance of the optimized constellation by applying phasing rules to achieve the desired revisit performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.1-7
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• 2023

This paper developed a training model to classify normal roads and flooded roads using artificial intelligence technology. We expanded the diversity of learning data using various data augmentation techniques and implemented a model that shows good performance in various environments. Transfer learning was performed using the CNN-based Resnet152v2 model as a pre-learning model. During the model learning process, the performance of the final model was improved through various parameter tuning and optimization processes. Learning was implemented in Python using Google Colab NVIDIA Tesla T4 GPU, and the test results showed that flooding situations were detected with very high accuracy in the test dataset.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.561-573
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• 2023

Korea Atomic Energy Research Institute (KAERI) constructed the KURT (KAERI Underground Research Tunnel) to analyze the hydrogeological/geochemical characteristics of deep rock mass. Numerous boreholes have been drilled to conduct various field tests. The selection of suitable investigation intervals within a borehole is of great importance. When objectives are centered around hydraulic flow and groundwater sampling, intervals with sufficient groundwater flow are the most suitable. This study defines such points as hydraulic outliers and aimed to detect them using borehole geophysical logging data (temperature and EC) from a 1 km depth borehole. For systematic and efficient outlier detection, machine learning algorithms, such as DBSCAN, OCSVM, kNN, and isolation forest, were applied and their applicability was assessed. Following data preprocessing and algorithm optimization, the four algorithms detected 55, 12, 52, and 68 outliers, respectively. Though this study confirms applicability of the machine learning algorithms, it is suggested that further verification and supplements are desirable since the input data were relatively limited.