• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.381-394
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• 2022

Recently, due to the personalization and customization of data, Internet-based services have increased requirements for real-time processing, such as real-time AI inference and data analysis, which must be handled immediately according to the user's situation or requirement. Real-time tasks have a set deadline from the start of each task to the return of the results, and the guarantee of the deadline is directly linked to the quality of the services. However, traditional container systems are limited in operating real-time tasks because they do not provide the ability to allocate and manage deadlines for tasks executed in containers. In addition, tasks such as AI inference and data analysis basically utilize graphical processing units (GPU), which typically have performance impacts on each other because performance isolation is not provided between containers. And the resource usage of the node alone cannot determine the deadline guarantee rate of each container or whether to deploy a new real-time container. In this paper, we propose a monitoring technique for tracking and managing the execution status of deadlines and real-time GPU tasks in containers to support real-time processing of GPU tasks running on containers, and a node list management technique for container placement on appropriate nodes to ensure deadlines. Furthermore, we demonstrate from experiments that the proposed technique has a very small impact on the system.

• Applied Chemistry for Engineering
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• v.33 no.2
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• pp.202-209
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• 2022

Effects of the support and amount of NiCl₂ on ammonia adsorption capacity were investigated to improve the ammonia adsorption performance. NiCl₂ was impregnated onto the surface of various supports under ultrasonic irradiation. The physicochemical properties and ammonia adsorption performance of NiCl₂-impregnated adsorbents were investigated. Among the various supports, it was found that the adsorption capacity of ammonia was the best when NiCl₂ was impregnated on activated carbon (AC) with the highest specific surface area. As a result of changing the amount of NiCl₂ impregnated on AC, the NiCl₂(2.0)/AC adsorbent impregnated with 2 mmol·g^-1 of NiCl₂ showed the highest ammonia adsorption capacity of 5.977 mmol·g^-1. In addition, the adsorption capacity was found to be maintained at an almost constant level in five repeated cycle tests under the condition that low-temperature heat could be utilized. This indicates that the adsorbent has excellent regeneration ability.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.101-106
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• 2023

In this busy world actually stress is continuously grow up in research and monitoring social websites. The social interaction is a process by which people act and react in relation with each other like play, fight, dance we can find social interactions. In this we find social structure means maintain the relationships among peoples and group of peoples. Its a limit and depends on its behavior. Because relationships established on expectations of every one involve depending on social network. There is lot of difference between emotional pain and physical pain. When you feel stress on physical body we all feel with tensions, stress on physical consequences, physical effects on our health. When we work on social network websites, developments or any research related information retrieving etc. our brain is going into stress. Actually by social network interactions like watching movies, online shopping, online marketing, online business here we observe sentiment analysis of movie reviews and feedback of customers either positive/negative. In movies there we can observe peoples reaction with each other it depends on actions in film like fights, dances, dialogues, content. Here we can analysis of stress on brain different actions of movie reviews. All these movie review analysis and stress on brain can calculated by machine learning techniques. Actually in target oriented business, the persons who are working in marketing always their brain in stress condition their emotional conditions are different at different times. In this paper how does brain deal with stress management. In software industries when developers are work at home, connected with clients in online work they gone under stress. And their emotional levels and stress levels always changes regarding work communication. In this paper we represent emotional intelligence with stress based analysis using machine learning techniques in social networks. It is ability of the person to be aware on your own emotions or feeling as well as feelings or emotions of the others use this awareness to manage self and your relationships. social interactions is not only about you its about every one can interacting and their expectations too. It about maintaining performance. Performance is sociological understanding how people can interact and a key to know analysis of social interactions. It is always to maintain successful interactions and inline expectations. That is to satisfy the audience. So people careful to control all of these and maintain impression management.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.3
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• pp.263-276
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• 2023

The purpose of this study was to evaluate the performance of a model using You Only Look Once (YOLO) for object detection of proximal caries in periapical radiographs of children. A total of 2016 periapical radiographs in primary dentition were selected from the M6 database as a learning material group, of which 1143 were labeled as proximal caries by an experienced dentist using an annotation tool. After converting the annotations into a training dataset, YOLO was trained on the dataset using a single convolutional neural network (CNN) model. Accuracy, recall, specificity, precision, negative predictive value (NPV), F1-score, Precision-Recall curve, and AP (area under curve) were calculated for evaluation of the object detection model's performance in the 187 test datasets. The results showed that the CNN-based object detection model performed well in detecting proximal caries, with a diagnostic accuracy of 0.95, a recall of 0.94, a specificity of 0.97, a precision of 0.82, a NPV of 0.96, and an F1-score of 0.81. The AP was 0.83. This model could be a valuable tool for dentists in detecting carious lesions in periapical radiographs.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.557-564
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• 2023

In this study, 3D printing technology was used to compensate for the shortcomings of the use of lead, which has proven to have excellent shielding performance, and to control unnecessary human exposure. 3D printers can implement three-dimensional shapes and can immediately apply individual ideas, which has great advantages in maintaining technology supplementation while reducing the cost and duration of prototyping. Among the various special 3D printers, the FDM method was adopted, and the filament used for output was manufactured using a research extruder by mixing two materials, PLA (Poly-Lactic-Acid) and tungsten. The purpose was to verify the validity through dose evaluation and to provide basic information on the production of chapezones of various materials. The mixed filament was implemented as a morphological shield. Filaments made of a research extruder by mixing PLA and tungsten were divided into 10 %, 20 %, 30 %, 40 %, and 50 % according to the tungsten content ratio. Through the process of 3D Modeling, STL File storage, G-code generation, and output, 10 cm × 10 cm × 0.5 cm was manufactured, respectively, and dose and shielding ability were evaluated under the conditions of tube voltages of 60 kVp, 80 kVp, 100 kVp, 120 kVp, and tube currents of 20 mAs and 40 mAs.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.6
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• pp.229-240
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• 2016

Academic resilience of individual competencies that have overcome fatal or chronic adversity seen as a excessive threat in academic scene is the psychological ability to overcome stress. This study is a survey research carried try to identify the factors that affect the academic resilience of nursing students. Interpersonal communication competence and happiness to target the nursing students of the total 554 people to investigate the effect on the academic resilience, were analyzed using the SPSS version 21.0. The main results are as follows. (1) academic resilience could vary depending on the grade, living arrangements, Interpersonal relationship, personality and academic performance. (2) academic resilience had a positive correlation with interpersonal communication competence(r=.46, p<.001) and happiness(r=.35, p<.001). (3) factors that had significant impact on academic resilience of nursing students were grade(p=.002), living arrangements(p=.001), academic performance(p<.001), interpersonal communication competence(p<.001) and happiness(p<.001) and explanatory power was 36.9%. Through this study, interpersonal communication competence and happiness were identified as having the positive effect on academic resilience. As a follow-up, in order to improve academic resilience, a study to develop programs that would promote interpersonal communication competence and happiness are suggested.

• Journal of Life Science
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• v.33 no.6
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• pp.490-497
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• 2023

Pregnant women may need to take medications to treat preexisting diseases or diseases that develop during pregnancy. However, some drugs may be fetotoxic and lead to, for example, teratogenicity and growth retardation. Predicting the fetotoxicity of drugs is thus important for the health of the mother and fetus. The fetotoxicity of many drugs has not been established because various challenges hinder the ability of researchers to determine their fetotoxicity. The need exists for in silico-based fetotoxicity assessment models, as they can modernize the testing paradigm, improve predictability, and reduce the use of animals and the costs of fetotoxicity testing. In this study, we collected data on the fetotoxicity of drugs and constructed fetotoxicity prediction models based on various machine learning algorithms. We optimized the models for more precise predictions by tuning the hyperparameters. We then performed quantitative performance evaluations. The results indicated that the constructed machine learning-based models had high performance (AUROC >0.85, AUPR >0.9) in fetotoxicity prediction. We also analyzed the feature importance of our model's predictions, which could be leveraged to identify the specific features of drugs that are strongly associated with fetotoxicity. The proposed model can be used to prescreen drugs and drug candidates at a lower cost and in less time. It provides a predictive score for fetotoxicity risk, which may be beneficial in the design of studies on fetotoxicity in human pregnancy.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• (The) Research of the performance art and culture
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• no.22
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• pp.413-439
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• 2011

This study aims to look for the proper directions of following and developing Gyeonggi sinawi dance in the fashion of Kim Sukja by closely examining its kinds and patterns. First, its characteristics and education reality were investigated. Second, the seven kinds of Gyeonggi sinawi dance Kim Sukja allegedly handed down (according to the 121st Report of the Intangible Cultural Assets) were concretely examined. Third, the composition of each dance pattern was studied. Fourth, various beats used in Gyeonggi sinawi dance were revealed. The late Kim Sukja had outstanding artistic talent and ability in Gyeonggi sinawi dance movements, musical composition, gayageum accompanied singing, and pansori episodes. Behind her were master singer Kim Seokchang (grandfather), father Kim Deoksun (belonging to Hwaseong Artist Board), shaman-mother Jeong Gwiseong, and great dancer Jo Jinyeong. Kim sukja's seven Gyeonggi sinawi dance types were bujeong nori, teo beollim, jinsoe, jeseok, kkaekkeum, ollimchae, and dosal puri (designated as Important Intangible Cultural Asset in 1990). Such beats as seopchae (dosal puri), mori, bal ppeodeurae, bujeong nori, ollimchae, jinsoe, and teo beollim (ban seoreum) were mainly used in Gyeonggi sinawi dance. In sum, Kim Sukja's dance was more than an individual's dance to represent the cultural types and life at that time in Gyeonggi-do and be a very important academic historic material. Therefore, it is the responsibility of the present generation to hand down and develop such invaluable traditional cultural materials.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.86-93
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• 2023

As the hysteretic behavior of reinforced concrete members under cyclic loading progresses, the energy dissipation ability decreases due to a decrease in stiffness and strength and pinching effects. However, the guideline "Nonlinear Analysis Model for Performance-Based Seismic Design of Reinforced Concrete Building Structures, 2021" requires calculating a single energy dissipation factor for each member and all histeric step, so the decrease in energy dissipation capacity according to histeric step cannot be considered. It is judged that Therefore, in this study, the energy dissipation factor according to the histeric step was examined by comparing the existing experimental results and the nonlinear time history analysis results for a general beam under cyclic loading. The energy dissipation factor was calculated as the ratio of the energy dissipation amount of the actual specimen to the energy dissipation amount of the idealized elastoplastic behavior obtained as a result of nonlinear time history analysis. In the existing experiment results, the energy dissipation factor was derived by calculating one cycle for each histeric step, and the energy dissipation factor was derived based on the nonlinear modeling process in the guidelines. In the existing experimental study, the energy dissipation factor was calculated by setting each histeric step (Y-L-R), and the energy dissipation factor was found to be 0.36 in the Y-L step and 0.28 in the L-R step, and the energy dissipation factor in the guideline was found to be 0.31. This shows that the energy dissipation factor calculation formula in the guidelines does not indicate a decrease in the energy dissipation capacity of reinforced concrete members.