• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.159-175
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• 2024

In order to solve the problem of calculating the reasonable completed bridge state of a self-anchored hybrid cable-stayed suspension bridge (SA-HCSB), this paper proposes an analytical method. This method simplifies the main beam into a continuous beam with multi-point rigid supports and solves the support reaction forces. According to the segmented catenary theory, it simultaneously solves the horizontal forces of the main span main cables and the stay cables and iteratively calculates the equilibrium force system on the main beam in the collaborative system bridge state while completing the shape finding of the main span main cable and stay cables. Then, the horizontal forces of the side span main cables and stay cables are obtained based on the balance of horizontal forces on the bridge towers, and the shape finding of the side spans are completed according to the segmented catenary theory. Next, the difference between the support reaction forces of the continuous beam with multiple rigid supports obtained from the initial and final iterations is used to calculate the load of ballast on the side span main beam. Finally, the axial forces and strains of each segment of the main beam and bridge tower are obtained based on the loads applied by the main cable and stay cables on the main beam and bridge tower, thereby obtaining analytical data for the bridge in the reasonable completed state. In this paper, the rationality and effectiveness of this analytical method are verified through a case study of a SA-HCSB with a main span of 720m in finite element analysis. At the same time, it is also verified that the equilibrium force of the main beam under the reasonably completed bridge state can be obtained through iterative calculation. The analytical algorithm in this paper has clear physical significance, strong applicability, and high accuracy of calculation results, enriching the shape-finding method of this bridge type.

• Journal of The Korean Association For Science Education
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• v.28 no.5
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• pp.424-437
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• 2008

In this study, we collected teachers' opinions with regard to the effects of the instruction using analogy generation, the disadvantages of the instruction, the problem-solving methods of the instruction, and the teacher's role in it, and accordingly tried to investigate its effectiveness with the analysis of students' academic achievements and motivation, and through the student's interview, after applying the activities of creating generated analogies, finding the difference between the objects and comparisons, and presenting new-known genetics concepts as the students themselves generated analogies. As a result of a teachers' workshop on instruction using analogy development, it was expected to have a positive effect on students' understanding of scientific concepts in genetics, which were found to be difficult for students to understand in learning biology. Students found analogy examples for concepts in genetics in daily life, compared their analogs to those of peers, and examined inconsistencies between targets and analogs through the process of discussion, which finally led to their correct perception of scientific concepts in genetics. In addition, instruction using student-generated analogies proved to have a more positive effect on improving academic achievement and motivating learning, compared with traditional expository instruction.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.475-485
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• 2024

In many regions of Korea, including the Four Major Rivers, the seriousness of the problem of algal blooms due to eutrophication of water quality is being raised.In this study, in order to solve these social problems, we manufactured a filter using natural mineral fusion (red illite, zeolite, germanium ceramic, selenium ceramic, carbon ceramic) and independently developed a tank system for green algae experiments to observe and determine the stages of change in water quality. In order to study ways to improve water quality through quantitative analysis, 1 ton of severely polluted green algae water from the Nak dong River region was stored in a water tank and exposed to ultraviolet rays in the same environment as the Nak dong River. Then, the same environment as the Nak dong River was created. The results were derived from a 5-week water quality test. The results of this experiment confirmed that green-colored cyano bacteria were significantly reduced just by the turbidity results. The results were obtained through a request to the Korea Testing & Research Institute located in Changwon-si, Gyeong sang nam-do. CI-(chlorine ion) and NH3-N(ammonia nitrogen) had the effect of saving every week. The device used in this study was made of natural minerals free of heavy metals that are harmless to the human body and nature through long-term consideration and exploration to kill and prevent various strains living in water. Green purification system using natural hydrogen generating mineral filter were effective a non-chemical and physical methods. The results of this study are one way to contribute to the serious problems caused by green algae in many countries, and will contribute to the water quality environment by preventing the waste of environmental resources, improving the health of the people, and increasing the ability to purify environmental water quality at home and abroad.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.125-132
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• 2024

Optical satellite images are being used for national security and collection of information, and their utilization is increasing. However, it acquires low-quality images that are not suitable for the user's requirement due to weather conditions and time constraints. In this paper, a deep learning-based conversion of image and colorization model referring to high-resolution SAR images was created to simulate the occluded area with clouds of optical satellite images. The model was experimented according to the type of algorithm applied and input data, and each simulated images was compared and analyzed. In particular, the amount of pixel value information between the input black-and-white image and the SAR image was similarly constructed to overcome the problem caused by the relatively lack of color information. As a result of the experiment, the histogram distribution of the simulated image learned with the Gray-scale image and the high-resolution SAR image was relatively similar to the original image. In addition, the RMSE value was about 6.9827 and the PSNR value was about 31.3960 calculated for quantitative analysis.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.96-100
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• 2006

Kim et al. Proposed Normalized Confidence Measure (NCM) [1-2] and it was successfully used for rejecting mis-recognized words in isolated word recognition. However their experiments were performed on the fixed word speech recognition. In this Paper we apply NCM to the domain of vocabulary independent speech recognition (VISP) and shows the rejection Performance of NCM in VISP. Specialty we Propose vector quantization (VQ) based method for overcoming the problem of unseen triphones. It is because NCM uses the statistics of triphone confidence in the case of triphone-based normalization. According to speech recognition experiments Phone-based normalization method shows better results than RLJC[3] and also triphone-based normalization approach. This results are different with those of Kim et al [1-2]. Concludingly the Phone-based normalization shows robust Performance in VISP domain.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1
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• pp.40-46
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• 2008

Weak target detection and localization in the presence of loud surface ship noise is a critical problem for matched field processing (MFP) in shallow water. For stationary sources, each signal component of received signal can be separated and interference can be suppressed using eigen space analysis schemes. However, source motion, in realistic cases, causes spreading of signal energies in their subspace. In this case, eigenvalues of target and interfere signal components are mixed and hard to be separated with usual phone space eigenvector decomposition (EVD) approaches. Our technique is based on mode space and utilizes the difference in their physical characteristics of surface and submerged sources. Performing EVD for modal cross spectral density matrix, interference components in the mode amplitude subspace can be classified and eliminated. This technique is demonstrated with synthetic data, and results are discussed.

• Convergence Security Journal
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• v.24 no.2
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• pp.189-200
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• 2024

In recent years, the development of defense technology has become digital with the introduction of advanced assets such as drones equipped with artificial intelligence. These assets are integrated with modern information technologies such as industrial IoT, artificial intelligence, and cloud computing to promote innovation in the defense domain. However, the convergence of the technology is increasing the possibility of transfer of cyber threats, which is emerging as a problem of increasing the vulnerability of defense assets. While the current cybersecurity methodologies focus on the vulnerability of a single asset, interworking of various military assets is necessary to perform the mission. Therefore, this paper recognizes these problems and presents a mission-based asset management and evaluation methodology. It aims to strengthen cyber security in the defense sector by identifying assets that are important for mission execution and analyzing vulnerabilities in terms of cyber security. In this paper, we propose a method of classifying mission dependencies through linkage analysis between functions and assets to perform a mission, and identifying and classifying assets that affect the mission. In addition, a case study of identifying key assets was conducted through an attack scenario.

• Smart Media Journal
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• v.12 no.11
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• pp.198-210
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• 2023

After COVID-19, team's collaborations are conducted online using whiteboard tools as remote working increases. In order to understand the problems of the convergence process using online whiteboard tools, an observation study comparing online and offline collaboration and a focus group interview were conducted. In addition, a questionnaire was conducted to confirm the found problem, and a solution idea was proposed. through in-depth interviews, we validate the proposed ideas. The convergence process of collaboration using online whiteboard tools had problems ; "excessive amount of information", "shift of view", "role of facilitator". To solve the problems, we proposed the idea of classifying each stage of the collaboration process, providing a navigator, and facilitator request system window. This paper proposed an idea that can effectively help the convergence process directly related to decision-making during the online collaboration process through analysis of advantages and problems of online and offline collaboration.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.

• The Mathematical Education
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• v.63 no.2
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• pp.209-231
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• 2024

The education sector is undergoing significant changes due to the Fourth Industrial Revolution and the advancement of artificial intelligence. Particularly, the importance of education based on artificial intelligence is being emphasized. Accordingly, the purpose of this study is to develop a statistics education program using deep learning prediction in high school mathematics and to examine the impact of such statistically problem-solvingcentered statistics education programs on high school students' statistical literacy and computational thinking. To achieve this goal, a statistics education program using deep learning prediction applicable to high school mathematics was developed. The analysis revealed that students' understanding of context improved through experiencing how data was generated and collected. Additionally, they enhanced their comprehension of data variability while exploring and analyzing various datasets. Moreover, they demonstrated the ability to critically analyze data during the process of validating its reliability. In order to analyze the impact of the statistics education program on high school students' computational thinking, a paired sample t-test was conducted, confirming a statistically significant difference in computational thinking between before and after classes (t=-11.657, p<0.001).