• Korean Journal of Social Welfare Studies
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• v.48 no.2
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• pp.143-171
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• 2017

Along with the well-established evidence on the negative effect of social isolation on physical mental health and mortality, increasing attention has been paid to multi-dimensional nature of social isolation. In this study, the main effect and interaction effect of objective and subjective social isolation on heterogeneous age cohort related to the onset of dementia, which is becoming a social problem due to rapid aging of health issues, was examined through binary logistic regression analysis. Data came from the first wave of Korean Social Life, Health and Aging Project (KSHAP) (N= 814). Findings showed 1) in the young-old, objective isolation was a significant on the incidence of dementia, 2) in the old-old, subjective isolation increased the risk of dementia. In summary, the relative influence of objective and subjective social isolation related to the incidence of dementia varies depending on the young-old and old-old. On the other hand, the interaction effect of objective and subjective social isolation on dementia was not significant in both the young-old and old-old. Based on the findings, we discussed implications and suggestions for future research and relevant policy and program development(dementia-friendly communities) for ameliorating objective and subjective social isolation.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.457-464
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• 2019

The weight of pig is one of the main factors in determining the health and growth state of pigs, their shipment, the breeding environment, and the ration of feed, and thus measuring the pig's weight is an important issue in productivity perspective. In order to estimate the pig's weight by using the number of pig's pixels from images, acquired from a Top-view camera, the posture determining and the head removal from images are necessary to measure the accurate number of pixels. In this research, we propose the fast and accurate method to determine the pig's posture by using a fast image processing technique, find the head location by using a fast deep learning technique, and remove pig's head by using light weighted image processing technique. First, we determine the pig's posture by comparing the length from the center of the pig's body to the outline of the pig in the binary image. Then, we train the location of pig's head, body, and hip in images using YOLO(one of the fast deep learning based object detector), and then we obtain the location of pig's head and remove an outside area of head by using head location. Finally, we find the boundary of head and body by using Convex-hull, and we remove pig's head. In the Experiment result, we confirmed that the pig's posture was determined with an accuracy of 0.98 and a processing speed of 250.00fps, and the pig's head was removed with an accuracy of 0.96 and a processing speed of 48.97fps.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.21-28
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• 2021

This study examines factors affecting correction officer's preference toward institute for forensic psychiatric. The data were collected from the correction officers who worked at the six correctional facilities in 2019. Binary logistic regression was used to find the factors on the officer's preference. The result showed that the correction officers had their own preference toward prison for forensic psychiatric, and the preference was positively related to the age of the officer, work experience at the mental health center, mentally-ill prisoner's fighting as the rule-violation in prison, refusal of medical treatment, and lack of laws and regulations for the mentally-ill prisoners. In contrast, there was a negative relationship between the officer's rank and the preference for forensic psychiatric. More work needs to be done in the future research to collect more samples and include a broader ranger of variables than now.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Journal of the Korean Regional Science Association
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• v.34 no.4
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• pp.35-48
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• 2018

The purpose of this study is to empirically investigate the experiences and subjective opinions of urban agriculture in order to explore ways to vitalize urban agriculture. More specifically, we divides environmental value into three categories of ecology, landscape, and participation, and defines a function of urban agriculture to improve environmental values related to the three categories mentioned above. The main results of the empirical analysis using the survey data are summarized as follows. First, the probability of gathering information about urban agriculture is higher in metropolitan cities than small cities, and the larger the residence size, the higher the probability of actual urban agriculture participation. Second, the positive response rate was high for the three categories of urban agriculture, while the negative response rate was high for the surrounding environment. The implications derived from the analysis are as follows. First, the opposite results of experiences of urban agriculture suggests that local governments should further promote urban agriculture-based investment policies. In addition, these policies need to be preceded by analysis of the characteristics of population groups in the region Also, it is necessary to improve the environment through urban agriculture.

• Science of Emotion and Sensibility
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• v.22 no.1
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• pp.3-14
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• 2019

This exploratory study investigated the effects of the interaction between the experience of social exclusion and the presence or absence of a companion animal on the psychological well-being of individuals. Participants answered questions about whether or not they were respected by the community (yes or no); whether or not they currently have a companion animal (yes or no); and if they do, what kind of animal(s) they raise (multiple answers allowed). The study also assessed the psychological well-being of the participants. The group that experienced social exclusion demonstrated lower levels of psychological well-being than the group that did not. In addition, the group that reported the presence of at least one companion animal evinced higher levels of psychological well-being than the group that did not. Individuals who experienced social exclusion but lived with at least one companion animal were found to display superior psychological well-being than those who could not avail of the company of an animal. No difference in psychological well-being was found between those with a companion animal and those without one in the group that did not experience social exclusion. In conclusion, this study observed the effects of the binary interactions between social exclusion (experienced vs. not experienced) and the existence of companion animals (presence vs. absence) on the psychological well-being of people. The empirical data offer theoretical implications for the conditions in which companion animals do or do not improve psychological well-being in humans.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.63-71
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• 2006

In this paper, we propose a secure solution for user authentication by using fingerprint verification on the sensor-client-server model, even with the client that is not necessarily trusted by the sensor holder or the server. To protect possible attacks launched at the untrusted client, our solution makes the fingerprint sensor validate the result computed by the client for the feature extraction. However, the validation should be simple so that the resource-constrained fingerprint sensor can validate it in real-time. To solve this problem, we separate the feature extraction into binarization and minutiae extraction, and assign the time-consuming binarization to the client. After receiving the result of binarization from the client, the sensor conducts a simple validation to check the result, performs the minutiae extraction with the received binary image from the client, and then sends the extracted minutiae to the server. Based on the experimental results, the proposed solution for fingerprint verification can be performed on the sensor-client-server model securely and in real-time with the aid of an untrusted client.

• The Journal of Internal Korean Medicine
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• v.43 no.4
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• pp.542-558
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• 2022

Objectives: The purpose of this study was to assess the effect of Yigong-san on anorexia in children by conducting a systematic review and meta-analysis of randomized controlled trials (RCTs). Methods: Eleven electronic databases were searched on May 30, 2022 to collect relevant studies. All studies published up to the search date were considered. RCTs reporting the effect of Yigong-san on the treatment of anorexia in children were included. The primary outcome was an improvement in clinical anorexia symptoms after treatment. In this meta-analysis, continuous and binary outcomes were assessed, and the data were presented as the mean difference and risk ratio with their 95% confidence intervals. The risk of bias was assessed using the Cochrane Collaboration's risk of bias tool. Results: A total of nine studies were included in this systematic review. The treatment group (Yigong-san only or Yigong-san plus conventional treatment) showed a statistically significant effect compared to the control group (conventional treatment only) in total effective rate (Yigong-san only: RR 1.26, 95% CI 1.17, 1.36, I²=0%; Yigong-san plus conventional treatment: RR 1.32, 95% CI 1.18, 1.47, I²=0%), clinical symptoms, some of the anthropometric outcomes, and biological markers related to appetite and growth in children with anorexia. No serious adverse events related to Yigong-san were reported. Conclusions: Yigong-san showed statistically significant effects as a treatment for anorexia in children. However, the number of studies included in the meta-analysis was insufficient, and the herbs contained in the Yigong-san used in the included studies were not standardized. Future research should focus on the implementation of methodologically robust clinical research.

• Journal of IKEEE
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• v.26 no.4
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• pp.736-741
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• 2022

Lattice-based cryptography is the most practical post-quantum cryptography because it enjoys strong worst-case security, relatively efficient implementation, and simplicity. Ring learning with errors (R-LWE) is a public key encryption (PKE) method of lattice-based encryption (LBC), and the most important operation of R-LWE is the modular polynomial multiplication of rings. This paper proposes a method for optimizing modular multipliers based on approximate computing (AC) technology, targeting the medium-security parameter set of the R-LWE cryptosystem. First, as a simple way to implement complex logic, LUT is used to omit some of the approximate multiplication operations, and the 2's complement method is used to calculate the number of bits whose value is 1 when converting the value of the input data to binary. We propose a total of two methods to reduce the number of required adders by minimizing them. The proposed LUT-based modular multiplier reduced both speed and area by 9% compared to the existing R-LWE modular multiplier, and the modular multiplier using the 2's complement method reduced the area by 40% and improved the speed by 2%. appear. Finally, the area of the optimized modular multiplier with both of these methods applied was reduced by up to 43% compared to the previous one, and the speed was reduced by up to 10%.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.95-107
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• 2022

Shipping companies earn profits through cargo transportation, and therefore, investment decisions to purchase ships are more important than anything else. Nevertheless, the cash flow discount method was mainly used in the economic analysis method, which assumes that all situations are static. This study shows that the real option model is useful in the economic analysis of ship investment. This economic analysis took into account the irreversibility of investment and uncertainty of benefits. In particular, this study used a binary option price determination model among real options. In addition, the simulation was conducted using actual investment data of A shipping company. As a result of the analysis, the investment value of used ships according to the net present value method was analyzed as negative (-), but the investment value in the real option model reflecting the flexibility of decision-making was evaluated as having positive (+) economic feasibility. It was analyzed that economic feasibility is affected by profit volatility and discount rate. Therefore, this study is expected to help shipping companies make more flexible decisions by using the real option model along with the existing net present value method when making ship investment decisions.