• Journal of Environmental Health Sciences
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• 제47권4호
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• pp.330-338
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• 2021

Background: This study was conducted to provide background information for the proper management of radon contamination in apartments using mechanical ventilation facilities in residential environments. Objectives: To this end, this study compared and evaluated changes in radon concentrations based on different operating intensities of mechanical ventilation with or without natural ventilation. Methods: For the continuous measurement of radon concentrations, an RAD7 instrument was installed in four apartments equipped with a ventilation system. The measurements were done for comparison of ventilation types and different ventilation intensities ("high", "middle", "low"). Results: The results confirmed that both mechanical and natural ventilation sufficiently reduced the radon concentration in the apartments. In particular, mechanical ventilation at "high" intensity was the most effective. Natural ventilation combined with mechanical ventilation and then natural ventilation alone were the second and the third most effective, respectively. Conclusions: When using ventilation to reduce indoor radon concentrations, it is most effective to operate mechanical ventilation ("high") or natural ventilation and mechanical ventilation at the same time. In cases where mechanical ventilation is available alone, it is recommended to operate it at a minimum of "middle" intensity.

• Journal of Powder Materials
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• 제28권6호
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• pp.470-477
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• 2021

Energy storage systems should address issues such as power fluctuations and rapid charge-discharge; to meet this requirement, CoFe₂O₄ (CFO) spinel nanoparticles with a suitable electrical conductivity and various redox states are synthesized and used as electrode materials for supercapacitors. In particular, CFO electrodes combined with carbon nanofibers (CNFs) can provide long-term cycling stability by fabricating binder-free three-dimensional electrodes. In this study, CFO-decorated CNFs are prepared by electrospinning and a low-cost hydrothermal method. The effects of heat treatment, such as the activation of CNFs (ACNFs) and calcination of CFO-decorated CNFs (C-CFO/ACNFs), are investigated. The C-CFO/ACNF electrode exhibits a high specific capacitance of 142.9 F/g at a scan rate of 5 mV/s and superior rate capability of 77.6% capacitance retention at a high scan rate of 500 mV/s. This electrode also achieves the lowest charge transfer resistance of 0.0063 Ω and excellent cycling stability (93.5% retention after 5,000 cycles) because of the improved ion conductivity by pathway formation and structural stability. The results of our work are expected to open a new route for manufacturing hybrid capacitor electrodes containing the C-CFO/ACNF electrode that can be easily prepared with a low-cost and simple process with enhanced electrochemical performance.

• Journal of the Daesoon Academy of Sciences
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• 제34집
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• pp.141-169
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• 2020

This study focuses on the interpretation and transformation of "The Great Learning" within the Korean new religion, Daesoon Jinrihoe. Joseon Dynasty Korea was a member of the Chinese Character Cultural Sphere in East Asia. The examination and recruitment system of the Yuan Dynasty influenced the Joseon Dynasty for a long historical period. Zhu Xi's (朱熹) version of The Four Books were accepted and applied in imperial examinations during the Joseon Dynasty. The 18th century Confucian thinker, Jeong Yak-Yong (丁若鏞), overturned and rebuilt his own system for studying and interpreting The Four Books (四書學). Zhu Xi and Jeong Yak-Yong's systems of thought influenced Confucianism knowledge in that era. The historical figure deified as the Supreme God by Daesoon Jinrihoe, Kang Jeungsan (姜甑山), was trained in the study of The Four Books within that cultural and philosophical context, and this is especially evident in his interpretation and transmission of "The Great Learning." Kang Jeungsan regarding The Great Learning as deeply important. That text combined Confucian discourse on Principle, Mind, and Practice. In his interpretation, The Great Learning was also a medical and religious book that had holy and mysterious powers. In Mugeuk-do and Taegeuk-do (direct predecessors to Daesoon Jinrihoe), Jo Jeongsan interpreted the concept of Sincerity and Regularizing the Mind and incorporated them into doctrine as 'Sincerity, Respectfulness, and Faithfulness' and 'Guarding against Self-deception.' Park Wudang practiced and spread those doctrines to Korea, and Daesoon Jinrihoe devotees continue to follow those doctrines in present times.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• Korean Journal of Remote Sensing
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• 제38권1호
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• pp.111-126
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• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

• Journal of Korean Society for Quality Management
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• 제50권3호
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• pp.473-490
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• 2022

Purpose: The purpose of this study is to propose a new useful suggestion to monitor the stability of process by developing a stability ratio or index related to investigating how well the process is controlled or operated to the specified target. Methods: The proposed method to monitor the stability of process is building up a new measure index which is making up for the weakness of the existing index in terms of short or long term period of production. This new index is a combined one considering both stability and capability of process to the specification limits. We suppose that both process mean and process variation(or deviation) are changing on time period. Results: The results of this study are as follows: regarding the stability of process as well as capability of process, it was shown that two indices, called SI(stability index) and PI(performance index), can be expressed in two-dimensional X-Y graph simultaneously. This graph is categorized as 4 separated partitions, which are characterized by its numerical value intervals of SI and PI which are evaluated by test statistics. Conclusion: The new revised index is more robust than the existing one in investigating the stability of process in terms of short and long period of production, even in case both process mean and variation are changing.

• Medical Lasers
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• 제10권3호
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• pp.161-169
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• 2021

Background and Objectives Fractional microneedle radiofrequency systems are popular options to increase elasticity in aging skin. Laser-assisted drug delivery is a promising method for the epidermal injection of topically applied drugs and cosmetic ingredients. This study assesses the safety and efficacy of topical delivery of L-ascorbic acid, vitamin E, and ferulic acid after fractional microneedle radiofrequency treatment for reducing photodamage. Materials and Methods In this prospective, single-center, split-face, controlled pilot study, six women (mean age, 48.0 years; range, 35-57 years; Fitzpatrick skin types III and IV) exhibiting mild to moderate photodamage, underwent a single session of fractional microneedle radiofrequency treatment. The patients were instructed to apply the antioxidant formulation to only one side of the face. Patients were evaluated 3 days, 7 days, and 4 weeks thereafter, using three-dimensional imaging and ultrasound. Ex vivo, the full-thickness human skin was used for molecular and histological evaluation. Statistical analysis was achieved by applying t-tests, Mann-Whitney U tests, and one-way analyses of variance. Results Compared to the untreated side, the antioxidant-treated side exhibited a significant increase in dermal thickness (10.32% vs. 17.54%, p < 0.05), but not in skin elasticity (4.76% vs. 4.69%, p > 0.05). The difference in erythema between the sides was statistically not significant (p > 0.05). In the ex vivo model, expression of FGF2 in the skin was significantly increased after application of the antioxidant formulation, as compared to results obtained subsequent to fractional microneedle radiofrequency treatment only (p < 0.01). Conclusion This study demonstrates that for the treatment of photodamaged skin, laser-assisted delivery of the antioxidant formulation is a safe and effective adjuvant modality following fractional microneedle radiofrequency.

• Nuclear Engineering and Technology
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• 제53권1호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1224-1248
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• 2022

In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.

• Smart Structures and Systems
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• 제29권1호
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.