• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Transactions of Materials Processing
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• v.33 no.3
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• pp.200-207
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• 2024

In this study, we developed a semi-analytical approach for the numerical analysis of residual stress in oxide scales formed on hot-rolled steels. The oxide scale, formed during the hot rolling process, experiences complex interactions due to thermal and mechanical influences, significantly affecting the material's integrity and performance. Our research focuses on integrating various stress components such as thermal stress, growth stress, and creep behavior to predict the residual stress within the oxide layer. The semi-analytical method combines analytical expressions for each stress component with numerical integration to account for their cumulative effects. Validation through instrumented indentation tests confirms the reliability of our model, which considers thermal expansion coefficient (CTE) differences, scale growth, and creep-induced stress relaxation. Our findings indicate that thermal stress resulting from CTE differences significantly impacts the overall residual stress, with growth stress contributing a compressive component during cooling, and creep behavior playing a minor role in stress relaxation. This comprehensive approach enhances the accuracy of residual stress prediction, facilitating the optimization of material design and processing conditions for hot-rolled steel products.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.224-229
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• 2024

Quantum dot light-emitting diodes (QD-LEDs) are emerging as next-generation displays owing to their high color purity, wide color gamut, and solution processability. Enhancing the efficiency of QD-LEDs involves preventing non-radiative recombination mechanisms, such as Auger and interfacial recombination. Generally, ZnO serves as the electron transport layer, which is known for its higher mobility compared to that of organic semiconductors and can lead to excessive electron injection. Some of the injected electrons pass through the quantum dot emissive layer and undergo non-radiative recombination near or within the organic hole transport layer (HTL), resulting in HTL degradation. Therefore, the implementation of electron blocking layers (EBLs) is essential; however, studies on all-solution-processed inverted InP QD-LEDs are limited. In this study, poly(9-vinylcarbazole) (PVK) is introduced as an EBL to mitigate HTL degradation and enhance the emission efficiency of inverted InP QD-LEDs. Using a single-carrier device, PVK was confirmed to effectively inhibit electron overflow into the HTL, even at extremely low thicknesses. The optimization of the PVK thickness also ensured minimal disruption of the hole-injection properties. Consequently, a 1.5-fold increase in the maximum luminance was achieved in the all-solution-processed inverted InP QD-LEDs with the EBL.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.2
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• pp.17-22
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• 2023

In the midst of the development of science and technology based on the 4th industrial revolution, the ROK Army is moving forward with the ARMY TIGER 4.0 system, a ground combat system that combines future advanced science and technology. The system is developing around an AI-based hyper-connected ground combat system, and has mobility, intelligence, and networking as core concepts. Especially, the dronebot combat system is used as a compound word that refers to unmanned combat systems including drones and ground unmanned systems. In future battlefields, it is expected that the use of unmanned and artificial intelligence-based weapon systems will increase. During the transition to a complete unmanned system, it is a very important issue to ensure connectivity individual unmanned systems themselves or between manned and unmanned systems on the battlefield. This paper introduces the Multiplexer Allocation Problem (MAP) for effective command control and communication of UAV/UGV, and proposes a heuristic algorithm. In addition, the performance of the proposed algorithm is analyzed by comparing the solutions and computing time. Also, we discuss future research area for the MAP.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.57-64
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• 2024

The introduction of Smart Factories has transformed manufacturing towards more objective and efficient line management. However, most companies are not effectively utilizing the vast amount of sensor data collected every second. This study aims to use this data to predict product quality and manage production processes efficiently. Due to security issues, specific sensor data could not be verified, so semiconductor process-related training data from the "SAMSUNG SDS Brightics AI" site was used. Data preprocessing, including removing missing values, outliers, scaling, and feature elimination, was crucial for optimal sensor data. Oversampling was used to balance the imbalanced training dataset. The SVM (rbf) model achieved high performance (Accuracy: 97.07%, GM: 96.61%), surpassing the MLP model implemented by "SAMSUNG SDS Brightics AI". This research can be applied to various topics, such as predicting component lifecycles and process conditions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.4
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• pp.62-67
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• 2024

In this paper, we developed and optimized a chitosan-based polymer microbial bead carrier that is cell-friendly, has a high moisture absorption rate, and effectively provides the conditions for microbial biomineral formation as an optimal microbial carrier that protects microorganisms in concrete, and evaluated the self-healing performance of mortar using it. In order to incorporate circular-shaped microbial endospores, a circular-shaped microbial bead carrier was developed by combining chitosan and alginate polymers, and the amount of calcium carbonate produced could be actively controlled by adjusting the composition of the carrier. The amount of biominerals formed and the size of crystals were maximized in the hydrogel bead carrier containing chitosan, and in the case of mortar cracks using this, it was confirmed that self-healing of cracks with a maximum crack width of 0.3mm was achieved within 96 hours after crack generation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.3
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• pp.95-103
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• 2024

The diversity of smart EV(electric vehicle)-related industries is increasing due to the growth of battery-based eco-friendly electric vehicle component material technology, and labor-intensive industries such as logistics, manufacturing, food, agriculture, and service have invested in and studied automation for a long time. Accordingly, various types of robots such as autonomous mobile robots and collaborative robots are being utilized for each process to improve industrial engineering such as optimization, productivity management, and work management. The technology that should accompany this unmanned automobile industry is unmanned automatic charging technology, and if autonomous mobile robots are manually charged, the utility of autonomous mobile robots will not be maximized. In this paper, we conducted a study on the technology of unmanned charging of autonomous mobile robots using charging terminal docking and undocking technology using an unmanned charging system composed of hardware such as a monocular camera, multi-joint robot, gripper, and server. In an experiment to evaluate the performance of the system, the average charging terminal recognition rate was 98%, and the average charging terminal recognition speed was 0.0099 seconds. In addition, an experiment was conducted to evaluate the docking and undocking success rate of the charging terminal, and the experimental results showed an average success rate of 99%.

• Safety and Health at Work
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• v.15 no.2
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• pp.228-235
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• 2024

Background: Glove reuse poses risks, as chemicals can persist even after cleaning. Decontamination methods like thermal aeration, recommended by US OSHA, vary in effectiveness. Some studies show promising results, while others emphasize the importance of considering both permeation and tensile strength changes. This research advocates for informed glove reuse, emphasizing optimal thermal aeration temperatures and providing evidence to guide users in maintaining protection efficiency. Methods: The investigation evaluated Neoprene and Nitrile gloves (22 mils). Permeation tests with toluene and acetone adhered to American Society for Testing Materials (ASTM) F739 standards. Decontamination optimization involved aeration at various temperatures. The experiment proceeded with a maximum of 22 re-exposure cycles. Tensile strength and elongation were assessed following ASTM D 412 protocols. Breakthrough time differences were statistically analyzed using t-test and ANOVA. Results: At room temperature, glove residuals decreased, and standardized breakthrough time (SBT)₂ was significantly lower than SBT₁, indicating reduced protection. Higher temperature decontamination accelerated residual removal, with ∆SBT (SBT₂/SBT₁) exceeding 100%, signifying restored protection. Tensile tests showed stable neoprene properties postdecontamination. Results underscore thermal aeration's efficacy for gloves reuse, emphasizing temperature's pivotal role. Findings recommend meticulous management strategies, especially post-breakthrough, to uphold glove-protective performance. Conclusions: Thermal aeration at 100℃ for 1 hour proves effective, restoring protection without compromising glove strength. The study, covering twenty cycles, suggests safe glove reuse with proper decontamination, reducing costs significantly. However, limitations in chemical-glove combinations and exclusive focus on specific gloves caution against broad generalization. The absence of regulatory directives on glove reuse highlight the importance of informed selection and rigorous decontamination validation for workplace safety practices.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.67-76
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• 2024

In this study, we address the problem of target detection using multiple agents. Specifically, the detection problem involving mobile agents necessitates additional strategies for path planning. The objective is to maximize the total utility derived from the detection process over a specific period. This detection problem incorporates realistic utility values by considering a stochastic process based on the Poisson process, which accounts for the changing probability of target event occurrence over time. The objective function is nonlinear and is classified as an NP-hard problem. To identify an effective solution within an efficient computation time, this study demonstrates that the objective function possesses the characteristic of submodularity. Using this property, we propose a heuristic algorithm designed to obtain a reasonable strategy with relatively low computational time. The proposed algorithm shows solution performance and the ability to generate solutions within an appropriate computation time through theoretical and experimental results.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.