• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.730-732
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• 2017

While the short term effect of diet and exercise therapy has been proven, there has still been a problem of its long term effect. So, researchers has utilized behaviour modification therapy. It is expected to lead to natural weight loss by modifying wrong dietary life patterns and practices. However, this approach has turned out to be a more effective method for weight maintenance than loss of weight. In spite of its strength, as a matter of fact, persistent and continuous effort for weight management has not worked properly. This study proposes an artificial intelligence approach with the advantages of behaviour modification therapy, overcoming current approaches which is goal-driven and too uniform. For this, we plan to develop a health management program in which users get the messages that are customized for themselves according to different situations so that it can promotes persistent effort for exercise. Here, customized messages are handled by AI techniques, which eventually promotes soft persuasion, encouragement, and motivation.

• Safety and Health at Work
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• v.7 no.1
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• pp.6-11
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• 2016

Background: A permit to work (PTW) is a formal written system to control certain types of work which are identified as potentially hazardous. However, human error in PTW processes can lead to an accident. Methods: This cross-sectional, descriptive study was conducted to estimate the probability of human errors in PTWprocesses in a chemical plant in Iran. In the first stage, through interviewing the personnel and studying the procedure in the plant, the PTW process was analyzed using the hierarchical task analysis technique. In doing so, PTWwas considered as a goal and detailed tasks to achieve the goal were analyzed. In the next step, the standardized plant analysis risk-human (SPAR-H) reliability analysis method was applied for estimation of human error probability. Results: The mean probability of human error in the PTW system was estimated to be 0.11. The highest probability of human error in the PTW process was related to flammable gas testing (50.7%). Conclusion: The SPAR-H method applied in this study could analyze and quantify the potential human errors and extract the required measures for reducing the error probabilities in PTW system. Some suggestions to reduce the likelihood of errors, especially in the field of modifying the performance shaping factors and dependencies among tasks are provided.

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.168-178
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• 2019

In this paper I discuss a more complicated case of 'It'-Extraposition in English in the Optimality Theory [1] by further modifying and extending the analysis done in Khym (2018) [2] in which only the 'relatively' simple cases of 'It'-Extraposition such as 'CP-Predicate' was dealt with. I show in this paper that the constraints and the constraint hierarchy developed to explain the 'relatively' simple cases of 'It'-Extraposition are no longer valid for the more complicated cases of 'It'-Extraposition in configuration of 'CP-V-CP'. In doing so, I also discuss two important theoretic possibilities and suggest a new view to look at the 'It'-Extraposition: first, the long-bothering question of which syntactic approach between P&P (Chomsky 1985) [3] and MP (Chomsky 1992) [4] should be based on in projecting the full surface forms of candidates may boil down to just a simple issue of an intrinsic property of the Gen(erator). Second, the so-called 'It'- Extraposition phenomenon may not actually be a derived construction by the optional application of Extraposition operation. Rather, it could be just a representational construction produced by the simple application of 'It'-insertion after the structure projection with 'that-clause' at the post-verbal position. This observation may lead to elimination of one of the promising candidates of '$It_i{\ldots}[_{CP}that{\sim}]_i$' out of the computation table in Khym [2], and eventually to excluding the long-named 'It'-Extraposition case from Extrsposition phenomena itself. The final constraints and the constraint hierarchy that are explored are as follows: ${\bullet}$ Constraints: $^*SSF$, AHSubj, Subj., Min-D ${\bullet}$ Constraint Hierarchy: SSF<<>>Subj.>> AHSubj.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.110-116
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• 2022

Human failure event (HFE) dependency analysis is a part of human reliability analysis (HRA). For efficient HFE dependency analysis, a maximum number of minimal cut sets (MCSs) that have HFE combinations are generated from the fault trees for the probabilistic safety assessment (PSA) of nuclear power plants (NPPs). After collecting potential HFE combinations, dependency levels of subsequent HFEs on the preceding HFEs in each MCS are analyzed and assigned as conditional probabilities. Then, HFE recovery is performed to reflect these conditional probabilities in MCSs by modifying MCSs. Inappropriate HFE dependency analysis and HFE recovery might lead to an inaccurate core damage frequency (CDF). Using the above process, HFE recovery is performed on MCSs that are generated with a non-zero truncation limit, where many MCSs that have HFE combinations are truncated. As a result, the resultant CDF might be underestimated. In this paper, a new method is suggested to incorporate HFE recovery into the MCS generation stage. Compared to the current approach with a separate HFE recovery after MCS generation, this new method can (1) reduce the total time and burden for MCS generation and HFE recovery, (2) prevent the truncation of MCSs that have dependent HFEs, and (3) avoid CDF underestimation. This new method is a simple but very effective means of performing MCS generation and HFE recovery simultaneously and improving CDF accuracy. The effectiveness and strength of the new method are clearly demonstrated and discussed with fault trees and HFE combinations that have joint probabilities.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.205-216
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• 2022

One of the many tools used for distributed deep learning training is Kubeflow, which runs on Kubernetes, a container orchestration tool. TensorFlow jobs can be managed using the existing operator provided by Kubeflow. However, when considering the distributed deep learning training jobs based on the parameter server architecture, the scheduling policy used by the existing operator does not consider the task affinity of the distributed training job and does not provide the ability to dynamically allocate or release resources. This can lead to long job completion time and low resource utilization rate. Therefore, in this paper we proposes a new operator that efficiently schedules distributed deep learning training jobs to minimize the job completion time and increase resource utilization rate. We implemented the new operator by modifying the existing operator and conducted experiments to evaluate its performance. The experiment results showed that our scheduling policy improved the average job completion time reduction rate of up to 84% and average CPU utilization increase rate of up to 92%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.997-1008
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• 2022

Today, AI(Artificial Intelligence) technology is being extensively researched in various fields, including the field of malware detection. To introduce AI systems into roles that protect important decisions and resources, it must be a reliable AI model. AI model that dependent on training dataset should be verified to be robust against new attacks. Rather than generating new malware detection, attackers find malware detection that succeed in attacking by mass-producing strains of previously detected malware detection. Most of the attacks, such as adversarial attacks, that lead to misclassification of AI models, are made by slightly modifying past attacks. Robust models that can be defended against these variants is needed, and the Robustness level of the model cannot be evaluated with accuracy and recall, which are widely used as AI evaluation indicators. In this paper, we experiment a framework to evaluate robustness level by generating an adversarial sample based on one of the adversarial attacks, C&W attack, and to improve robustness level through adversarial training. Through experiments based on malware dataset in this study, the limitations and possibilities of the proposed method in the field of malware detection were confirmed.

• Journal of Fashion Business
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• v.27 no.4
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• pp.88-100
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• 2023

This study analyzed the compositional characteristics of fashion videos and the characteristics of fashion design spreading as trends through Subversive Basics. Literature research and case studies were conducted concurrently. Based on the literature review, an analysis method was designed, focusing on the concept of online video content, Subversive Basics, and the video content type. For the case analysis, videos were collected and classified using Subversive Basics as the keyword. The content was observed, and design features were analyzed. Based on the results, the collected videos were classified into tutorial, curation, and creative content types according to their compositional characteristics. Tutorial content emphasizes practical actions that demonstrate how to make or modify clothing, thereby promoting user-generated content for dissemination. Curation contents provide users with style ideas and information about clothing and purchases to encourage clothing purchases and influence purchase decisions that lead to dissemination through clothing consumption and wear. Creative content showcases the process of modifying and creating clothes to enhance understanding and value of creative design. The characteristics of fashion design utilized in these contents include bold designs with high visual effects as the first category, designs that can be easily and quickly modified due to intentional incompleteness as the second category, and prominently featured body-positive, individualistic designs as the third category. The results of this study can be associated with balanced development between basic design elements and personalized unique designs, catering to consumer needs.

• Journal of Korean geriatric psychiatry
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• v.16 no.1
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• pp.17-23
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• 2012

The diagnosis of Alzheimer's disease (AD) is still obscure even to specialists. To improve the diagnostic accuracy, to find at-risk people as early as possible, to predict the efficacy or adverse reactions of pharmacotherapy on an individual basis, to attain more reliable results of clinical trials by recruiting better defined participants, to prove the disease-modifying ability of new candidate drugs, to establish prognosis-based therapeutic plans, and to do more, is now increasing the need for biomarkers for AD. Among AD-related biochemical markers, cerebrospinal beta-amyloid and tau have been paid the most attention since they are materials directly interfacing the brain interstitium and can be obtained through the lumbar puncture. Level of beta-amyloid is reduced whereas tau is increased in cerebrospinal fluid of AD patients relative to cognitively normal elderly people. Remarkably, such information has been found to help predict AD conversion of mild cognitive impairment. Despite inconsistent findings from previous studies, plasma beta-amyloid is thought to be increased before the disease onset, but show decreasing change as the disease progress. Regarding other peripheral biochemical markers, omics tools are being widely used not only to find useful biomarkers but also to generate novel hypotheses for AD pathogenesis and to lead new personalized future medicine.

• Journal of Powder Materials
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• v.30 no.6
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• pp.509-515
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• 2023

Lead-free perovskite ceramics, which have excellent energy storage capabilities, are attracting attention owing to their high power density and rapid charge-discharge speed. Given that the energy-storage properties of perovskite ceramic capacitors are significantly improved by doping with various elements, modifying their chemical compositions is a fundamental strategy. This study investigated the effect of Zn doping on the microstructure and energy storage performance of potassium sodium niobate (KNN)-based ceramics. Two types of powders and their corresponding ceramics with compositions of (1-x)(K,Na)NbO₃-xBi(Ni_2/3Ta_1/3)O₃ (KNN-BNT) and (1-x)(K,Na)NbO₃-xBi(Ni_1/3Zn_1/3Ta_1/3)O₃ (KNN-BNZT) were prepared via solid-state reactions. The results indicate that Zn doping retards grain growth, resulting in smaller grain sizes in Zn-doped KNN-BNZT than in KNN-BNT ceramics. Moreover, the Zn-doped KNN-BNZT ceramics exhibited superior energy storage density and efficiency across all x values. Notably, 0.9KNN-0.1BNZT ceramics demonstrate an energy storage density and efficiency of 0.24 J/cm³ and 96%, respectively. These ceramics also exhibited excellent temperature and frequency stability. This study provides valuable insights into the design of KNN-based ceramic capacitors with enhanced energy storage capabilities through doping strategies.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.1
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• pp.41-48
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• 2024

Purpose : This study aims to understand the effects of job-seeking stress on depression and self-esteem in college students majoring in health science. Methods : In this study, in order to measure college students' employment stress, depression, self-esteem, and social support, a measurement tool was used by modifying and supplementing the questionnaire to suit the purpose and method of this study. The subjects, 210 students (46 men and 164 women) enrolled in colleges located in Busan, had their scores on the Beck depression inventory (BDI), job-seeking stress scale, and self-esteem scale measured. Results : There was a positive correlation between all sub-variables of employment stress, and a negative correlation between employment stress and self-esteem. Additionally, a positive correlation was found between employment stress and depression, and a negative correlation was found between self-esteem and depression. The result taken from the job-stress scale showed that women experienced an overall higher level of stress than men while seeking jobs. Furthermore, in terms of the BDI, men exhibited a higher level of depression than women when subjected to job-seeking stress. Finally, the results from the self-esteem scale reveal that men exhibited less self-esteem than women. Conclusion : A comprehensive review of the study's findings suggested that women had greater job-seeking stress than men. Additionally, it found that, for men, job-seeking stress had a greater impact on depression levels and self-esteem. These results suggest that a higher level of job-seeking stress may lead to higher levels of depression and lower levels of self-esteem. It will be useful to conduct follow-up research by operating self-esteem and depression programs.