• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.333-344
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• 2022

In a container-based cloud environment, multiple containers can share a graphical processing unit (GPU), and GPU sharing can minimize idle time of GPU resources and improve resource utilization. However, in a cloud environment, GPUs, unlike CPU or memory, cannot logically multiplex computing resources to provide users with some of the resources in an isolated form. In addition, containers occupy GPU resources only when performing GPU operations, and resource usage is also unknown because the timing or size of each container's GPU operations is not known in advance. Containers unrestricted use of GPU resources at any given point in time makes managing resource contention very difficult owing to where multiple containers run GPU tasks simultaneously, and GPU tasks are handled in black box form inside the GPU. In this paper, we propose a container management technique to prevent performance degradation caused by resource competition when multiple containers execute GPU tasks simultaneously. Also, this paper demonstrates the efficiency of container management techniques that analyze and propose the problem of degradation due to resource competition when multiple containers execute GPU tasks simultaneously through experiments.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.917-925
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• 2021

This study aims to specify a constructivism-based instructional method using a modular data analysis tool. The value and meaning of a modular data analysis tool have been examined to be applied in the national curriculum for artificial intelligence education and the process of cultivating problem-solving ability based on computational thinking. The modular data analysis tool visually expresses the cognitive thinking process that forms the schema in equilibrating through assimilation and adjustment. Artificial intelligence education has features that embody abstract knowledge and structure the data analysis module through the represented schema as a BlackBox implemented as an algorithm. Therefore, the value of the modular data analysis tool could be examined because it has the advantage of connecting the conceptual and implicit schema.

• Land and Housing Review
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• v.14 no.1
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• pp.67-82
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• 2023

Using the Artificial Neural Network model and Binary Logistic Regression model, this study investigates influence factors on the quality of life in terms of housing environment during the COVID-19 in Seoul. The results show that the lower the satisfaction level of housing policy, the lower the quality of life in the employment field and the lower the quality of residential field. On the other hand, permanent workers and self-employed respondents have experienced improvement in residential quality during the pandemic. A limitation of this study is associated with disentangling the causal relationship using the 'black box' characteristics of ANN method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1081-1090
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• 2022

The face recognition model is used for identity recognition of smartphones, providing convenience to many users. As a result, the security review of the DNN model is becoming important, with adversarial attacks present as a well-known vulnerability of the DNN model. Adversarial attacks have evolved to decision-based attack techniques that use only the recognition results of deep learning models to perform attacks. However, existing decision-based attack technique[14] have a problem that requires a large number of queries when generating adversarial examples. In particular, it takes a large number of queries to approximate the gradient. Therefore, in this paper, we propose a method of generating adversarial examples using orthogonal space sampling and dimensionality reduction sampling to avoid wasting queries that are consumed to approximate the gradient of existing decision-based attack technique[14]. Experiments show that our method can reduce the perturbation size of adversarial examples by about 2.4 compared to existing attack technique[14] and increase the attack success rate by 14% compared to existing attack technique[14]. Experimental results demonstrate that the adversarial example generation method proposed in this paper has superior attack performance.

• Informatization Policy
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• v.30 no.1
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• pp.67-88
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• 2023

The expansion of the scope of product liability is necessary because the industrial environment has changed following the enactment of the Product Liability Act. Unlike human-coded algorithms, artificial intelligence is black-boxed according to machine learning, and even developers cannot explain the results. In particular, since the cause of the problem by artificial intelligence is unknown, the responsibility is unclear, and compensation for victims is not easy. This is because software or artificial intelligence is a non-object, and its productivity is not recognized under the Product Liability Act, which is limited to movable property. As a desperate measure, productivity may be recognized if it is stored or embedded in the medium. However, it is not reasonable to apply differently depending on the medium. The EU revise the product liability guidelines that recognize product liability when artificial intelligence is included. Although compensation for victims is the value pursued by the Product Liability Act, the essence has been overlooked by focusing on productivity. Even if an accident occurs using an artificial intelligence-adopted service, however, it is desirable to present standards according to practical risks instead of unconditionally holding product responsibility.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.113-118
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• 2023

Recently, as more and more cyclists ride bicycles for their health and more people commute by bicycle, the number of cyclists has increased. However, as the number of users increases, many accidents occur, and the handling of bicycle accidents is unstable. It is inadequate to prepare for accidents in other ways except for safety equipment. Therefore, there is a need for a safe and convenient way for modern adults to ride. Unlike other apps, in this study, by adding a safety function, you can shoot a black box while riding, and a function to inform you that it is an accident-prone area is implemented. In addition, a function that can detect an accident using the Android built-in sensor and automatically make emergency contact is added. Cyclists can secure safety and convenience in one app without the need to use additional apps. Furthermore it develops an app system that allows you to talk about riding and share your route through the Riding Community bulletin board.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.271-271
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• 2019

Estimating ship motion is difficult because it take place in complex environments.. Estimating ship motion is an important factor in ensuring the safety of ship, so accurate estimates are needed. Existing motion-related studies compare the apparent motion of the model acquired and the reference model by experimenting with the ship motion on a particular alignment, making it difficult to intuitively estimate the hull motion. This study introduces the concept of estimating the characteristics of ship motion as a transfer function through pole-zero interpretation and frequency response analysis by applying the method of transfer function of Linear-Time Invariant system. Ship motion analysis model using Linear-Time Invariant system is consist with 1) wave as input signal 2) ship motion as output signal 3) hull defined as black box. This model can be defined by numericalizing the ship motion as a transfer function and is expected to facilitate the characterization of the ship motion through pole-zero analysis and frequency response analysis.

• Convergence Security Journal
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• v.22 no.3
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• pp.101-110
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• 2022

As the cases of using artificial intelligence in various fields increase, attempts to solve various issues through artificial intelligence in the intrusion detection field are also increasing. However, the black box basis, which cannot explain or trace the reasons for the predicted results through machine learning, presents difficulties for security professionals who must use it. To solve this problem, research on explainable AI(XAI), which helps interpret and understand decisions in machine learning, is increasing in various fields. Therefore, in this paper, we propose an explanatory AI to enhance the reliability of machine learning-based intrusion detection prediction results. First, the intrusion detection model is implemented through XGBoost, and the description of the model is implemented using SHAP. And it provides reliability for security experts to make decisions by comparing and analyzing the existing feature importance and the results using SHAP. For this experiment, PKDD2007 dataset was used, and the association between existing feature importance and SHAP Value was analyzed, and it was verified that SHAP-based explainable AI was valid to give security experts the reliability of the prediction results of intrusion detection models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3D
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• pp.303-311
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• 2010

There have been various forms of systems such as a digital speedometer or a black box etc. to meet the social requirement for reducing traffic accidents and safe driving. However that systems are based on after-accident vehicle data, there is limit to prevent the accident before. So in our previous research, by storing, analyzing the Vehicle-dynamic information coming from driver's behavior, we are developing the decision-device which could provide driver with Alerting-Information in real-time to enhance the driver's safety drive. but the performance valuation is not yet executed. Finally, this study developed positional recognition system by using the DGPS for pre-developed risky driving decision device. The result of test analyzed with the same that the aggregated vehicle dynamics data in DGPS and dangerous risky driving decision device. If the performance of risky driving decision device is verified by precisely positional recognition system, the risky driving management of vehicle would be effected.