• 한국태양에너지학회 논문집
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• 제38권5호
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• pp.27-36
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• 2018

The purpose of this paper is to apply the numerical solution procedure to compute conduction time series factors (CTSF) for construction materials with large thermal capacities. After modifying the procedure in Ref. [9], it is applied to find the CTSF for the wall type 19 and the roof type 18 of ASHRAE. The response periods for one hr pulse load are longer than 24hrs for these wall and roof. The CTSF generated using modified procedure agree well with the values presented in the ASHRAE handbook. The modified procedure is a general procedure that can be applied to find CTSF for materials with complex structures. For the large thermal capacity materials, it should be checked whether thermal response period of the material is over 24hr or not. With suggested solution procedure, it is easy to check the validity of the CTSF based on 24hr period.

• 대한약침학회지
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• 제26권3호
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• pp.238-246
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• 2023

Objectives: The purpose of this study was to evaluate the effectiveness of complex rehabilitation with and without acupuncture in a hospital setting. Methods: A randomized clinical trial was performed at Rehabilitation center "Kamenskoe Plato" in Almaty, Kazakhstan. 160 patients with Post COVID-19 condition were randomly equally divided into an acupuncture with complex rehabilitation methods and a only complex rehabilitation methods group in the period from March 1, 2022 to July 1, 2022. Either groups was performed for an 10-14 days period. The outcome measures were the Bartel index, the Borg scale, Modified Dyspnea Scale and the 6-minute walking test. Adverse events also were monitored and documented. Results: We found statistically significant improvement after the rehabilitation course with acupuncture in the all scales. And in the group without acupuncture, only on two scales: MDS and Borg scale. Conclusion: Rehabilitation with acupuncture is possible and effective in patients recovering from post-COVID-19. Our findings may be useful to guide clinicians taking care of patients with post-COVID-19.

• 한국지진공학회논문집
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• 제11권4호
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• pp.25-31
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• 2007

For areas such as the Korean Peninsula, which have moderate seismic activity but no available records of strong ground motion, synthetic seismograms can be used to evaluate ground motion without waiting for a strong earthquake. Such seismograms represent the estimated ground motions expected from a set of possible earthquake scenarios. Local site effects are especially important in assessing the seismic hazard and possible ground motion scenarios for a specific fault. The earthquake source and rupture dynamics can be described as a two-step process of rupture initiation and front propagation controlled by a frictional sliding mechanism. The seismic wavefield propagates through heterogeneous geological media and finally undergoes near-surface modulations such as amplification or deamplification. This is a complex system in which various scales of physical phenomena are integrated. A unified approach incorporates multi-scale problems of dynamic rupture, radiated wave propagation, and site effects into an all-in-one model using a three-dimensional, fourth-order, staggered-grid, finite-difference method. The method explains strong ground motions as products of complex systems that can be modified according to a variety of fine-scale rupture scenarios and friction models. A series of such deterministic earthquake scenarios can shed light on the kind of damage that would result and where it would be located.

• 한국표면공학회:학술대회논문집
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• 한국표면공학회 2016년도 추계학술대회 논문집
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• pp.147-147
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• 2016

In general, organic electrochromic (EC) materials have been known to be electrochemically unstable during the ionic exchange process. One effective method to realize stable EC materials is incorporating graphene derivatives in the polymer matrix, by using the strong interaction between graphene derivatives and polymer. However, previous studies are limited graphene derivatives. In this study, we developed a polymer-graphene derivative complex with the chemical assistance of a surfactant (octadecylamine, ODA). Surfactant-assisted graphene oxide (GO-ODA) was introduced as a protective layer on the electrochromic poly (3-hexyl thiophene) (P3HT) films by the Langmuir-Schaefer method. The deposition of GO-ODA protective layer with high coverage was confirmed by atomic force microscopy. The strong interactions between GO-ODA and P3HT were examined with UV-Vis spectrophotometry and X-ray photoelectron spectroscopy. Electrochemical and electrochromic investigations revealed that the GO-ODA layer greatly improved the long-term cyclability of the P3HT film. These findings imply that the GO-ODA complex has a significant role in creating stable EC cycling, due to its strong interaction with the P3HT film.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.540-549
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• 2018

This paper proposes the method of active distribution network expansion planning considering distributed generation integration and distribution network reconfiguration. The distribution network reconfiguration is taken as the expansion planning alternative with zero investment cost of the branches. During the process of the reconfiguration in expansion planning, all the branches are taken as the alternative branches. The objective is to minimize the total costs of the distribution network in the planning period. The expansion alternatives such as active management, new lines, new substations, substation expansion and Distributed Generation (DG) installation are considered. Distribution network reconfiguration is a complex mixed-integer nonlinear programming problem, with integration of DGs and active managements, the active distribution network expansion planning considering distribution network reconfiguration becomes much more complex. This paper converts the dual-level expansion model to Second-Order Cone Programming (SOCP) model, which can be solved with commercial solver GUROBI. The proposed model and method are tested on the modified IEEE 33-bus system and Portugal 54-bus system.

• International Journal of Aerospace System Engineering
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• 제2권2호
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• pp.58-61
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• 2015

Combustion instability in solid rocket motors is a long-term open problem since the first rockets were used. Based on the numerous previous studies, it is known that the limit cycle amplitude is one of the key characteristics of the nonlinear combustion instability in solid rocket motors. Flandro's extended energy balance corollary, aims to predict the limit cycle amplitude of complex, nonlinear pressure oscillations for rockets or air-breathing engines, and leads to a precise assessment of nonlinear combustion instability in solid rocket motors. However, based on the comparison with experimental data, it is revealed that the Flandro's method cannot accurately describe such a complex oscillatory pressure. Thus in this work we make modifications of the nonlinear term in the nonlinear wave equations which represents the interaction of different modes. Through this modified method, a numerical simulation of the cylindrical solid rocket has been carried out, and the simulated result consists well with the experimental data. It means that the added coefficient makes the nonlinear wave growth equations describe the experimental data better.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2003년도 추계학술대회
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• pp.55-63
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• 2003

Workflow Management System (WFMS) is a software system to support an efficient execution, control and management of complex business processes. Since traditional commercial systems mainly focus on automating processes, they don't have methods for enhancing the task performer's efficiency. In this paper, we propose a new method of executing business processes more efficiently in that a whole process is scheduled considering the degree of the participants' workload. The method allows managing the largest constraints among constituent resources of the process. We utilize DBR scheduling techniques to develop the method. We first consider the differences between workflow process models and DBR application models, and then develop the modified drum, buffer and rope. This leads us to develop WF-DBR (WorkFlow-DBR) that can control the proper size of the task performers' work list and arrival rate of process instances. Use of WF-DBR improves the efficiency of the whole process as well as the participants' working condition. We then carry out a set of simulation experiments and compare the effectiveness of our approach with that of scheduling techniques used in existing systems.

• 한국지능시스템학회논문지
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• 제8권6호
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.