• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.168-175
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• 2021

In this study, a transdisciplinary convergence education program was developed to enhance the understanding for classification of reptiles and amphibians in biology education and also to increase AI (Artificial Intelligence) capability by using artificial intelligence education. The main content is to solve the classification of reptiles and amphibians that has been dealt with for a long time in biology education, using a decision tree and ML4K (Machine Learnig for Kids), it was designed for a total of 3 lessons. Experts review was conducted on the developed education program, as a result, the I-CVI(Item Content Validity Index) value was .88~1.00 so that can secure content validity. This education program has the advantage of being able to simultaneously learn about the learning contents of artificial intelligence in informatics and the classification of vertebrates in the biological education. In addition, since it is configured to minimize the cognitive load in the AI using part, it is characterized by the fact that all of any teachers can apply it their lesson easily.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.157-166
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• 2024

The Army TIGER System, which is being deployed to implement a future combat system, is expected to bring innovative changes to the army's combat methods and comabt execution capability such as mobility, networking and intelligence. To this end, the Army will introduce various systems using drones, robots, unmanned vehicles, AI(Artificial Intelligence), etc. and utilize them in combat. The use of various unmanned vehicles and AI is expected to result in the introduction of equipment with new technologies into the army and an increase in various types of transmitted information, i.e. data. However, currently in the military, there is an acceleration in research and combat experimentations on warfigthing options using Army TIGER forces system for specific functions. On the other hand, the current reality is that research on cyber threats measures targeting information systems related to the increasing number of unmanned systems, data production, and transmission from unmanned systems, as well as the establishment of cloud centers and AI command and control center driven by the new force systems, is not being pursued. Accordingly this paper analyzes the structure and characteristics of the Army TIGER force integration system and makes suggestions for necessity of building, available cyber defense solutions and Army TIGER integrated cyber protections system that can respond to cyber threats in the future.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.894-897
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• 2014

The speed of processes and the extremely large amount of data to be used in Intelligence Transportations System (ITS) cannot be handling by humans without considerable automation. However, it is difficult to develop software with conventional fixed algorithms (hard-wired logic on decision making level) for effectively manipulate dynamically evolving real time transportation environment. This situation can be resolved by applying methods of artificial intelligence and data mining that provide flexibility and learning capability. This paper presents a brief introduction of data mining and artificial intelligence (AI) applications in Intelligence Transportation System (ITS), analyzing the prospects of enhancing the capabilities by means of knowledge discovery and accumulating intelligence to support in decision making.

• Architectural research
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• v.26 no.1
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• pp.1-12
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• 2024

This study introduces a deep reinforcement learning (DRL)-based methodology for optimizing apartment space arrangements, addressing the limitations of human capability in evaluating all potential spatial configurations. Leveraging computational power, the methodology facilitates the autonomous exploration and evaluation of innovative layout options, considering architectural principles, legal standards, and client re-quirements. Through comprehensive simulation tests across various apartment types, the research demonstrates the DRL approach's effec-tiveness in generating efficient spatial arrangements that align with current design trends and meet predefined performance objectives. The comparative analysis of AI-generated layouts with those designed by professionals validates the methodology's applicability and potential in enhancing architectural design practices by offering novel, optimized spatial configuration solutions.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.237-238
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• 2023

Urbanization and the increase in building scale have amplified the complexity of M.E.P design. Traditional design methods face limitations when considering intricate pathways and variables, leading to an emergent need for research in automated design. Initial algorithmic approaches encountered challenges in addressing complex architectural structures and the diversity of M.E.P types. However, with the launch of OpenAI's ChatGPT-3.5 beta version in 2022, new opportunities in the automated design sector were unlocked. ChatGPT, based on the Large Language Model (LLM), has the capability to deeply comprehend the logical structures and meanings within training data. This study analyzed the potential application and latent value of LLMs in M.E.P design. Ultimately, the implementation of LLM in M.E.P design will make genuine automated design feasible, which is anticipated to drive advancements across designs in the construction sector.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.801-806
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• 2022

In the modern era, the necessity of color capability in the digital era is the demand of the era, and research on improving color practice on the subdivided digital four areas that are not in the existing practice is needed. For digital majors who are difficult to solve in existing paint color practice, classes in digital color practice in four more specialized areas are needed, and the use of efficient artificial intelligence was studied for classes in digitized color and color sense. In this paper, we tried to show the expansion of the color practice area by suggesting digital color practice and color matching method based on Photoshop artificial intelligence and big data technology that existing color and color matching were practice that only CMYK could do. In addition, based on the color quantification data of individual users provided by the latest Adobe Sceney program artificial intelligence, the purpose of the practice was to improve learners' predictions of actual color combinations and random colors using filter effects. In conclusion, it is a study on the use of programs that eliminate ambiguity in the mixing process of existing paint practice, secure digital color details, and propose a practical method that can provide effective learning methods for beginners and intermediates to develop their senses through artificial intelligence support. The Adobe program practice method necessary for coloration and main color through theoretical consideration and improvement of teaching skills that are better than existing paint practice were presented.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.