• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.39-51
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• 2024

This paper proposes a novel approach to enhance the security of container-based systems by analyzing system calls to dynamically detect race conditions without modifying the kernel. Container escape attacks allow attackers to break out of a container's isolation and access other systems, utilizing vulnerabilities such as race conditions that can occur in parallel computing environments. To effectively detect and defend against such attacks, this study utilizes eBPF to observe system call patterns during attack attempts and employs a AdaBoost model to detect them. For this purpose, system calls invoked during the attacks such as Dirty COW and Dirty Cred from popular applications such as MongoDB, PostgreSQL, and Redis, were used as training data. The experimental results show that this method achieved a precision of 99.55%, a recall of 99.68%, and an F1-score of 99.62%, with the system overhead of 8%.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.87-102
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• 2024

Digital transformation refers to the process by which a company or organization changes or innovates its existing business model or sales activities using digital technology. This requires the use of various digital technologies - cloud computing, IoT, artificial intelligence, etc. - to strengthen competitiveness in the market, improve customer experience, and discover new businesses. In addition, in order to derive knowledge and insight about the market, customers, and production environment, it is necessary to select the right data, preprocess the data to an analyzable state, and establish the right process for systematic analysis suitable for the purpose. The usefulness of such digital data is determined by the importance of pre-processing and the correct application of exploratory data analysis (EDA), which is useful for information and hypothesis exploration and visualization of knowledge and insights. In this paper, we reexamine the philosophy and basic concepts of EDA and discuss key visualization information, information expression methods based on the grammar of graphics, and the ACCENT principle, which is the final visualization review standard, for effective visualization.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.448-457
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• 2010

Vibrations and wave propagations in waveguide structures can be analysed efficiently by using waveguide finite element (WFE) method. The WFE method only models the 2-dimensional cross-section of the waveguide with finite elements so that the size of the model and computing time are much less than those of the 3-dimensional FE models. For cylindrical shells or pipes which have simple cross-sections, the external coupling with fluids can be treated theoretically. For waveguides of complex cross-sectional geometries, however, numerical methods are required to deal with external fluids. In this numerical approach, the external fluid is modelled by the boundary elements (BEs) and connected to WFEs. In order to validate this WFE/BE method, a pipe submerged in water is considered in this study. The dispersion diagrams and point mobilities of the pipe simulated are compared to those that theoretically obtained. Also the acoustic powers radiated from the pipe are predicted and compared in both cases of air and water as an external medium.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.447-453
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• 2009

This paper introduces the method for detect voices and exact end point at low SNR by maximizing voice energy. Conventional VAD (Voice Activity Detection) algorithm estimates noise level so it tends to detect the end point inaccurately. Moreover, because it uses relatively long analysis range for reflecting temporal change of noise, computing load too high for application. In this paper, the SEM-VAD (Speech Energy Maximization-Voice Activity Detection) method which uses psycho-acoustical bark scale filter banks to maximize voice energy within frames is introduced. Stable threshold values are obtained at various noise environments (SNR 15 dB, 10 dB, 5 dB, 0 dB). At the test for voice detection in car noisy environment, PHR (Pause Hit Rate) was 100%accurate at every noise environment, and FAR (False Alarm Rate) shows 0% at SNR15 dB and 10 dB, 5.6% at SNR5 dB and 9.5% at SNR0 dB.

• Convergence Security Journal
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• v.24 no.3
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• pp.3-11
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• 2024

Information security is crucial not only for protecting against external cyber-attacks but also for identifying and blocking internal data leakage risks in advance. To this end, many companies and institutions implement digital rights management(DRM) document security solutions, which encrypt files to prevent content access if leaked, and data loss prevention(DLP) solutions, which control devices such as USB ports on computing equipment to prevent data leaks. At a time when efforts to prevent internal data leaks are crucial, there is a growing need for control policies such as device control and the identification of information assets in standalone network environments, which could otherwise fall into unmanaged domains. In this study, we propose a Generation-Distribution-Application model for device control policies that are uniquely applied to standalone information assets that are not connected to internal networks. To achieve this, we developed an authentication technique linked with the asset management system, where information assets are automatically registered upon acquisition. This system allows for precise identification of information assets and enables flexible device control, and we have designed and implemented a system based on these principles.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.57-64
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• 2024

This paper aims to enhance the accuracy of music genre classification for music tracks where genre information is not provided, by utilizing machine learning to classify a large amount of music data. The paper proposes collecting and preprocessing data instead of using the commonly employed GTZAN dataset in previous research for genre classification in music. To create a dataset with superior classification performance compared to the GTZAN dataset, we extract specific segments with the highest energy level of the onset. We utilize 57 features as the main characteristics of the music data used for training, including Mel Frequency Cepstral Coefficients (MFCC). We achieved a training accuracy of 85% and a testing accuracy of 71% using the Support Vector Machine (SVM) model to classify into Classical, Jazz, Country, Disco, Soul, Rock, Metal, and Hiphop genres based on preprocessed data.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.97-108
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• 2024

Ethical and technical problems are becoming serious as the industrialization of artificial intelligence robots becomes active, research on risk is insufficient. In this situation, the researcher developed 52 verified indicators that can measure the body, rights, property, and social risk of artificial intelligence robots. In order to develop measurement indicators for each type of risk of artificial intelligence robots, 11 experts were interviewed in-depth after IRB deliberation. IIn addition, 328 workers in various fields where artificial intelligence robots can be introduced were surveyed to verify their fieldwork, and statistical verification such as exploratory factor analysis, reliability analysis, correlation analysis, and multiple regression analysis was verifyed to measure validity and reliability. It is expected that the measurement indicators presented in this paper will be widely used in the development, certification, education, and policies of standardized artificial intelligence robots, and become the cornerstone of the industrialization of artificial intelligence robots that are socially sympathetic and safe.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.