• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1019-1034
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• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.65-77
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• 2016

As respecting one's privacy becomes an important issue in mobile device data analysis, on-device analysis is getting attention, in which the data analysis is conducted inside a mobile device without sending data from the device to outside. One possible application of the on-device analysis is gender prediction using text data in mobile devices, such as text messages, search keyword, website bookmarks, and contact, which are highly private, and the limited computing power of mobile devices can be addressed by utilizing the word comparison method, where words are selected beforehand and delivered to a mobile device of a user to determine the user's gender by matching mobile text data and the selected words. Moreover, it is known that performing prediction after filtering instances using definite evidences increases accuracy and reduces computational complexity. In this regard, we propose a two-phase approach to on-device gender prediction, where both discriminability and popularity of a word are sequentially considered. The proposed method performs predictions using a few highly discriminative words for all instances and popular words for unclassified instances from the previous prediction. From the experiments conducted on real-world dataset, the proposed method outperformed the compared methods.

• Journal of the Korean Geotechnical Society
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• v.21 no.4
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• pp.5-12
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• 2005

Recently, geophysical prospecting methods have played very important roles in civil and environmental engineering problems. Technical advances in geophysical instruments and computer system made it possible to get underground images with very high resolution far purposes to resolve those problems. It was possible partly due to ever increasing demand for development of technologies needed to precisely detect polluted areas and prevent ground-related accidents. Based on the same demand, integrity tests of cast-in place piles draw more attention and development of accurate test procedures is required. Ultrasonic methods is one of most advanced non-destructive procedures. In the paper, a geotomography method is employed for the cast-in place pile integrity test using ultrasonic waves. The image of pile interior is scanned and scrutinized far better and more accurate decision in the cast-in place pile integrity. In this study, we firstly examined the accuracy fur tomography program with idealized synthetic models built in water tank: their position and size were changed in the tank and each case was studied. In the next stage, real concrete pile models were fabricated and images of anomaly areas inside the pile were scanned to successfully locate those areas.

• Journal of the Korean Geographical Society
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• v.49 no.3
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• pp.405-422
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• 2014

Two important issues in hedonic model are to specify accurate model and delineate submarkets. While the former has experienced much improvement over recent decades, the latter has received relatively little attention. However, the accuracy of estimates from hedonic model will be necessarily reduced when the analysis does not adequately address market segmentation which can capture the spatial scale of price formation process in real estate. Placing emphasis on improvement of performance in hedonic model, this paper tried to segment real estate markets in Gangnam-gu and Jungrang-gu, which correspond to most heterogeneous and homogeneous ones respectively in 25 autonomous districts of Seoul. First, we calculated variable coefficients from mixed geographically weighted regression model (mixed GWR model) as input for clustering, since the coefficient from hedonic model can be interpreted as shadow price of attributes constituting real estate. After that, we developed a spatially constrained data-driven methodology to preserve spatial contiguity by utilizing the SKATER algorithm based on a minimum spanning tree. Finally, the performance of this method was verified by applying a multi-level model. We concluded that submarket does not exist in Jungrang-gu and five submarkets centered on arterial roads would be reasonable in Gangnam-gu. Urban infrastructure such as arterial roads has not been considered an important factor for delineating submarkets until now, but it was found empirically that they play a key role in market segmentation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1A
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• pp.25-33
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• 2011

The impact damage behavior of steel-strengthened concrete panels exposed to explosive loading is investigated. Since real explosion experiments require the vast costs to facilities as well as the blast and impact damage mechanisms are too complicated, numerical analysis has lately become a subject of special attention. However, for engineering problems involving blast wave and fragment impact, there is no single numerical method that is appropriate to the various problems. In order to evaluate the retrofit performance of a steel-strengthened concrete panel subject to blast wave and fragment impact loading, an explicit analysis program, AUTODYN is used in this work. The multi-solver coupling methods such as Euler-Lagrange and SPH-Lagrange coupling method in order to improve efficiency and accuracy of numerical analysis is implemented. The simplified and idealized two dimensional and axisymmetric models are used in order to obtain a reasonable computation running time. As a result of the analysis, concrete panels subject to either blast wave or fragment impact loading without the steel plate are shown the scabbing and perforation. The perforation can be prevented by concrete panels reinforced with steel plate. The numerical results show good agreement with the results of the experiments.

• Therapeutic Science for Rehabilitation
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• v.6 no.1
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• pp.55-69
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• 2017

Objective : The purpose of this study was to investigate the reliability and concurrent validity of the computerized cognitive function test system (called CNFT) for evaluating the cognitive function and to provide its normative data. Methods : For this purpose, 140 normal adults participated in a investigation to provide the normative data of CNFT. 40 normal adults participated in an evaluating experiment to verify the reliability and validity. CNFT consists of attention, memory, sensori-motor coordination, and frontal lobe & higher cognitive function domains. Because CNFT is a computerized evaluation tool, all results and operations are processed consistently and automatically. Results : In the results, as the age of subjects increased, the average accuracy decreased and response time increased. Additionally, memory and frontal lobe & higher cognitive function was lower than other domains. Test-retest reliability of 2 weeks interval was highly correlated (r=.48~.85) and there is no significant difference between test and retest scores. CNFT was highly correlated with computerized neurocognitive function test (r=.67~.79; p<.05). Conclusion : Normative data of CNFT were obtained, and the guidelines for the interpretation were provided. A reliable and valid clinically applicable computerized cognitive function test was developed.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.416-429
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• 2018

Typhoons and floods are natural disasters that occur frequently, and the damage resulting from these disasters must be in advance predicted to establish appropriate responses. Direct damages such as building collapse, human casualties, and loss of farms and fields have more attention from people than indirect damages such as increase of consumer prices. But indirect damages also need to be considered for living. The agricultural products are typical consumer items affected by typhoons and floods. Sudden, powerful typhoons are mostly accompanied by heavy rains and damage agricultural products; this increases the retail price of such products. This study analyzes the influence of natural disasters on the price of agricultural products by using a deep learning algorithm. We decided rice, onion, green onion, spinach, and zucchini as target agricultural products, and used data on variables that influence the price of agricultural products to create a model that predicts the price of agricultural products. The result shows that the model's accuracy was about 0.069 measured by RMSE, which means that it could explain the changes in agricultural product prices. The accurate prediction on the price of agricultural products can be utilized by the government to respond natural disasters by controling amount of supplying agricultural products.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.96-110
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• 2018

As the development of ICT has made it easier to collect various traffic information, research on creating new traffic attributes is drawing attention. Estimation and forecasts of demand and traffic volume are one of the main indicators that are essential to traffic operation, assuming that the traffic pattern at a particular node or link is repeated. Traditionally, a survey method was used to demonstrate this similarity on trip behavior. However, the method was limited to achieving high accuracy with high costs and responses that relied on the respondents' memory. Recently, as traffic data has become easier to gather through ETC system, smart card, studies are performed to identify the regularity of trip in various ways. In, this study, route-level trip data collected in Daegu metropolitan city were analyzed to confirm that individual traveler forms a spatially similar trip chain over several days. For this purpose, we newly define the concept of spatial trip regularity and assess the spatial difference between daily trip chains using the sequence alignment algorithm, Dynamic Time Warping. In addition, we will discuss the applications as the indicators of fixed traffic demand and transportation services.

• The Journal of Bigdata
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• v.7 no.2
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• pp.31-41
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• 2022

Steel is one of the most fundamental components to mechanical industry. However, the quality of products are greatly impacted by the surface defects in the steel. Thus, researchers pay attention to the need for surface defects detector and the deep learning methods are the current trend of object detector. There are still limitations and rooms for improvements, for example, related works focus on developing the models but don't take into account real-time application with practical implication on industrial settings. In this paper, a real-time application of steel surface defects detection based on YOLOv4 is proposed. Firstly, as the aim of this work to deploying model on real-time application, we studied related works on this field, particularly focusing on one-stage detector and YOLO algorithm, which is one of the most famous algorithm for real-time object detectors. Secondly, using pre-trained Yolov4-Darknet platform models and transfer learning, we trained and test on the hot rolled steel defects open-source dataset NEU-DET. In our study, we applied our application with 4 types of typical defects of a steel surface, namely patches, pitted surface, inclusion and scratches. Thirdly, we evaluated YOLOv4 trained model real-time performance to deploying our system with accuracy of 87.1 % mAP@0.5 and over 60 fps with GPU processing.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.1-11
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• 2024

In the midst of the current turbulent global economy, traditional investment metrics are undergoing a metamorphosis, signaling the onset of what's often referred to as an "Investment cold season". Early-stage startups, despite their boundless potential, grapple with immediate revenue constraints, intensifying their pursuit of critical investments. While financial indicators once took center stage in investment evaluations, a notable paradigm shift is underway. Organizational culture, once relegated to the sidelines, has now emerged as a linchpin in forecasting a startup's resilience and enduring trajectory. Our comprehensive research, integrating insights from CVF and OCAI, unveils the intricate relationship between organizational culture and its magnetic appeal to investors. The results indicate that startups with a pronounced external focus, expertly balanced with flexibility and stability, hold particular allure for investment consideration. Furthermore, the study underscores the pivotal role of adhocracy and market-driven mindsets in shaping investment desirability. A significant observation emerges from the study: startups, whether they secured investment or failed to do so, consistently display strong clan culture, highlighting the widespread importance of nurturing a positive employee environment. Leadership deeply anchored in market culture, combined with an unwavering commitment to innovation and harmonious organizational practices, emerges as a potent recipe for attracting investor attention. Our model, with an impressive 88.3% predictive accuracy, serves as a guiding light for startups and astute investors, illuminating the intricate interplay of culture and investment success in today's economic landscape.