• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.329-344
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• 2021

The use of structural control devices to minimize structural response to seismic/dynamic excitations has attracted increased attention in recent years. The use of magnetorheological (MR) dampers as a control device have captured the attention of researchers in this field due to its flexibility, adaptability, easy control, and low power requirement compared to other control devices. However, little attention has been paid to the effect of configuration and number of dampers installed in a structure on responses reduction. This study assesses the control of a five-story structure using one and two MR dampers at different stories to determine the optimal damper positions and configurations based on performance indices. This paper also addresses the fail-safe current value to be applied to the MR damper at each floor in the event of feedback or control failure. The model is mathematically simulated in SIMULINK/MATLAB environment. Linear control strategies for current at 0 A, 0.5 A, 1 A, 1.5 A, 2 A, and 2.5 A are implemented for MR dampers, and the response of the structure to these control strategies for different configurations of dampers is compared with the uncontrolled structure. Based on the performance indices, it was concluded that the dampers should be positioned starting from the ground floor, then the 2^nd floor followed by 1^st and rest of the floors sequentially. The failsafe value of current for MR dampers located in lower floors (G+1) should be kept at a higher value compared to dampers at top floors for effective passive control of multi-story structures.

• Advanced Industrial SCIence
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• v.1 no.2
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• pp.24-29
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• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• Korean Journal of Exercise Nutrition
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• v.25 no.2
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• pp.20-25
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• 2021

[Purpose] The aim of this review was to discuss the effects of vitamin D on physical performance and musculoskeletal injuries in athletes and provide information on the field applications of vitamin D. [Methods] A systematic review was conducted to identify studies on vitamin D in athletes that assessed serum vitamin D levels, vitamin D and physical performance, vitamin D and musculoskeletal injuries, and practical guidelines for supplementation of vitamin D. [Results] Several studies reported that a high proportion of athletes had vitamin D insufficiency or deficiency. Low serum levels of vitamin D in athletes were more pronounced in winter than in other seasons, and indoor athletes had lower serum vitamin D levels than outdoor athletes. Low vitamin D levels have been demonstrated to have negative effects on muscle strength, power, and endurance; increase stress fractures and other musculoskeletal injuries; and affect acute muscle injuries and inflammation following high-intensity exercises. Therefore, periodic assessment and monitoring of vitamin D levels are necessary in athletes; the recommended serum level of 25(OH)D is > 32 ng/mL and the preferred level is > 40 ng/mL (-1). In those with low levels of vitamin D, exposure to sunlight and an improved diet or supplements may be helpful. Particularly, 2000-6000 IU of supplemental vitamin D3 can be consumed daily. [Conclusion] Vitamin D is a potential nutritional factor that can significantly affect physical performance and musculoskeletal injuries in athletes. The importance and role of vitamin D in athletes should be emphasized, and the current levels of vitamin D should be assessed. Therefore, it is essential to periodically evaluate and monitor serum vitamin D levels in athletes.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.29-42
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• 2022

In mountainous areas of China, concrete poles with connectors are widely employed in power transmission due to its convenience of manufacture and transportation. The bearing capacity of the poles must have degenerated over time, and most of the steel connectors have been corroded. Carbon fiber reinforced polymer (CFRP) offers a durable, light-weight alternative in strengthening those poles that have served for many years. In this paper, the bearing capacity and failure mechanism of CFRP sheet strengthened existing reinforced concrete poles with corrosion steel connectors were investigated. Four poles were selected to conduct flexural capacity test. Two poles were strengthened by single-layer longitudinal CFRP sheet, one pole was strengthened by double-layer longitudinal CFRP sheets and the last specimen was not strengthened. Results indicate that the failure is mainly bond failure between concrete and the external CFRP sheet, and the specimens fail in a brittle pattern. The cross-sectional strains of specimens approximately follow the plane section assumption in the early stage of loading, but the strain in the tensile zone no longer conforms to this assumption when the load approaches the failure load. Also, bearing capacity and stiffness of the strengthened specimens are much larger than those without CFRP sheet. The bearing capacity, initial stiffness and elastic-plastic stiffness of specimen strengthened by double-layer CFRP are larger than those strengthened by single-layer CFRP. Weighting the cost-effective effect, it is more economical and reasonable to strengthen with single-layer CFRP sheet. The results can provide a reference to the same type of poles for strengthening design.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.2
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• pp.119-128
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• 2022

Recent advancement of Internet of Things (IoT) and energy harvesting technology enable realization of flexible thermoelectric energy harvester (f-TEH), with technological prowess for use in biomedical monitoring system integrated applications. To expand a flexible thermoelectric energy harvesting platform, the f-TEH must be required for optimized flexible thermoelectric materials and device structure. In response to these demands related to thermoelectric energy harvesting, many research groups have investigated various f-TEHs applied as a power source for wearable electronics. As a key member of the f-TEH, film-based f-TEHs possess significant applicability in research to realize self-powered wearable electronics, owing to their excellent flexibility, low thermal conductivity, and convenient fabrication process. Thus, based on the rapid growth of thermoelectric film technology, this review aims to overview comprehensively the f-TEH made of various inorganic/organic thermoelectric materials including developed fabrication methods, high thermoelectric performance, and wide-range applications.

• Journal of Korean Academy of Nursing
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• v.53 no.2
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• pp.236-248
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• 2023

Purpose: Nursing informatics competency is used to manage and improve the delivery of safe, high-quality, and efficient healthcare services in accordance with best practices and professional and regulatory standards. This study examined the relationship between nursing informatics competency (NIC), nursing care left undone, and nurse reported quality of care (NQoC) and nursing productivity. A path model for their effects on nursing productivity among clinical nurses was also established. Methods: Data were collected using structured questionnaires answered by 192 nurses working in a tertiary hospital located in J city, Korea, and analyzed using SPSS/WIN 23.0 and AMOS 21.0 program. Results: The fit indices of the alternative path model satisfied recommended levels χ² = .11 (p = .741), normed χ² (χ² /df) = .11, SRMR = .01, RMSEA = .00, GFI = 1.00, NFI = 1.00, AIC = 18.11. Among the variables, NIC (β = .44, p < .001), NQoC (β = .35, p < .001) had a direct effect on nursing productivity. Due to the mediating effect of NQoC on the relationship between NIC and nursing productivity, the effect size was .14 (95% CI .08~.24). Meanwhile, nursing care left undone through NQoC in the relationship between NIC and nursing productivity, has a significant mediation effect (estimate .01, 95% CI .00~.03). The explanatory power of variables was 44.0%. Conclusion: Education and training for enhancing NIC should be provided to improve nursing productivity, quality of care and to reduce missed nursing care. Furthermore, monitoring the quality of nursing care and using it as a productivity index is essential.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.