• Journal of the Korean Society of Food Culture
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• v.37 no.3
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• pp.179-195
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• 2022

For any research study, in order to achieve the researcher's intended purpose, the depth of research is added, and the area of the subject is expanded by clearly defining the scope and objective. The study was undertaken to analyze the bibliographic data of 254 papers in the field of foodservice and restaurant published in the Journal of the Korean Dietary Culture from 2002 to 2021. The study was divided into two periods: 2002 to 2011, and 2012 to 2021. Research topics were derived and research trends according to temporal changes were confirmed through analysis of keyword networks by period. In addition, analyzing the keyword network of simultaneous appearance of "foodservice" and "restaurant", the research topics were compared and analyzed in relation to which keywords were expanded by period. Our analysis revealed that the research topics were mostly studied for satisfaction and nutrition. Additionally, they were classified into procurement, Korean food before employee menu, marketing, restaurant industry, and quality. In the period from 2002 to 2011, it was confirmed that studies encompassed a wide range of research topics, focusing on foodservice and restaurant; in the second period from 2012 to 2021, the research topics were more classified and subdivided.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

• Journal of IKEEE
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• v.26 no.2
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• pp.211-218
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• 2022

This paper presents a S-band phased array antenna system for receiving LEO satellite telemetry signals. The proposed antenna, which is performed to be beam-tiled along the elevation direction, consists of 16 sub-array assemblies, 16 active circuit modules, a perpendicular feed network and a control/power unit. In order to precisely track an LEO satellite, the developed antenna is placed with its elevation axis along the projected trajectory of the satellite on the earth. The center of antenna aperture is facing to the maximum elevation angle in the LEO trajectory. The beam-tilted angles for tracking LEO satellite are obtained by calculating accurately satellite points. Satellite tracking measurements are carried out in the range of ±30° with the respect to the maximum elevation angle. The S/N ratio of 16.5 dB and the Eb/No of 13.3 dB at the maximum elevation angle are obtained from the measurements. The measured result agrees well with the pre-analyzed system margin.

• Journal of Acupuncture Research
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• v.39 no.3
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• pp.213-221
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• 2022

Background: This study investigated the clinical effectiveness of Korean medicine (KM) treatment for shoulder impingement syndrome (SIS). Methods: There were 61 patients who were diagnosed with SIS in the Jaseng hospital network of KM (7 hospitals located in Korea: Gangnam, Daejeon, Bucheon, Haeundae, Bundang, Ulsan, and Gwangju) between January 1^st, 2015 and December 31^st, 2020 who were retrospectively reviewed. The patients were grouped according to complications, intake of analgesics, duration of illness preadmission, and treatment. Treatments consisted of herbal medicine, acupuncture, cupping, Chuna, pharmacopuncture, bee venom pharmacopuncture, medicinal steaming, Daoyin exercises, and physical therapy. By comparing the Numeric Rating Scale (NRS), Shoulder Pain and Disability Index, and European Quality of Life 5-Dimensions questionnaire scores, the effectiveness of integrated KM treatment was evaluated. Results: There were 14 males and 47 females. For inpatients diagnosed with SIS, the mean NRS score decreased from 5.78 ± 1.33 to 3.40 ± 1.43 (p < 0.001). The mean Shoulder Pain and Disability Index score decreased from 53.87 ± 14.76 to 38.56 ± 18.87 (p < 0.001), and the mean European Quality of Life 5-Dimensions questionnaire increased from 0.67 ± 0.13 to 0.76 ± 0.09 (p < 0.001) after KM treatment. Medicinal steaming (0.398; p < 0.001), acupuncture (0.290), cupping (0.288), bee venom pharmacopuncture (0.282), and Daoyin exercises (0.262; p < 0.05) had a positive correlation with improved changes in the NRS score. Conclusion: Conclusion: Treatment with integrated KM treatment improved the pain, range of motion, shoulder function, and quality of life of patients with SIS.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3879-3891
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• 2021

A heat pipe residual heat removal system is proposed to be incorporated into the reactor driven subcritical (RDS) facility, which has been proposed by MIT Nuclear Reactor Laboratory for testing and demonstrating the Fluoride-salt-cooled High-temperature Reactor (FHR). It aims to reduce the risk of the system operation after the shutdown of the facility. One of the main components of the system is an air-cooled heat pipe heat exchanger. The alkali-metal high-temperature heat pipe was designed to meet the operation temperature and residual heat removal requirement of the facility. The heat pipe model developed in the previous work was adopted to simulate the designed heat pipe and assess the heat transport capability. 3D numerical simulation of the subcritical facility active zone was performed by the commercial CFD software STAR CCM + to investigate the operation characteristics of this proposed system. The thermal resistance network of the heat pipe was built and incorporated into the CFD model. The nominal condition, partial loss of air flow accident and partial heat pipe failure accident were simulated and analyzed. The results show that the residual heat removal system can provide sufficient cooling of the subcritical facility with a remarkable safety margin. The heat pipe can work under the recommended operation temperature range and the heat flux is below all thermal limits. The facility peak temperature is also lower than the safety limits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.239-250
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• 2022

LoRa is a physical layer technology designed to secure highly reliable long-range communication with introducing loco parentis tree network and chirp spreading spectrum. Since since a leaf can send message to more than one parents simultaneously with a single transmission in a region, packet delivery ratio increases logarithmically as the number of gateways increases. The delivery ratio, however, dramatically collapses even under loco parentis tree topology due to the limitations of ALOHA-like primitive MAC, . The proposed method is intended to exploit SDMA approach to reuse frequency in an area. With the view, TxPower of each sender for each message in a concurrent transmission is elaborately controlled to survive the collision at different gateway. Thus, the gain from the capture effect improves the capacity of resource-hungry Low Power and Wide Area Networks.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.1
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• pp.54-65
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• 2022

Species distribution models are a useful tool for predicting future distribution and establishing a preemptive response of invasive species. However, few studies considered the possibility of habitat for the aquatic organism and the number of target sites was relatively small compared to the area. Environmental DNA (eDNA) is the emerging tool as the methodology obtaining the bulk of species presence data with high detectability. Thus, this study applied eDNA survey results of Micropterus salmoides and Lepomis macrochirus to species distribution modeling by seasons in the Anyang stream network. Maximum Entropy (MaxEnt) model evaluated that both species extended potential distribution area in October compared to July from 89.1% (12,110,675 m²) to 99.3% (13,625,525 m²) for M. salmoides and 76.6% (10,407,350 m²) to 100% (13,724,225 m²) for L. macrochirus. The prediction value by streams was varied according to species and seasons. Also, models elucidate the significant environmental variables which affect the distribution by seasons and species. Our results identified the potential of eDNA methodology as a way to retrieve species data effectively and use data for building a model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.855-867
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• 2022

As the need for a deep learning accelerator increases with the development of IoT equipment, research on the implementation and safety verification of the deep learning accelerator is actively. In this paper, we propose a new side channel analysis methodology for secret information that overcomes the limitations of the previous study in Usenix 2019. We overcome the disadvantage of limiting the range of weights and restoring only a portion of the weights in the previous work, and restore the IEEE754 32bit single-precision with 99% accuracy with a new method using CPA. In addition, it overcomes the limitations of existing studies that can reverse activation functions only for specific inputs. Using deep learning, we reverse activation functions with 99% accuracy without conditions for input values with a new method. This paper not only overcomes the limitations of previous studies, but also proves that the proposed new methodology is effective.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.