• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.251-257
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• 2014

Digital RDA system is retransmit into the opposite direction of the incident signals. Digital RDA system have a disadventage that this system do not signal classification in multipath environment. because multipath signal is shown as vector sum of multipath signal, digital RDA system required complex signal process for multipath signal classification. In this paper, to solve these problem we propose hybrid digital RDA system which combination of the MUSIC algorithm and the digital RDA system. Proposed system has two modes. First mode is digital RDA mode. Secornd mode is digital beamforming mode. Digital RDA mode is used in situations where the less the impact of multipath. Digital beamforming mode is applied to multipath effects is greater. In secornd mode, we find optimal path using MUSIC algorithm. After than the proposed system uses only the optimal path. Through the proposed system in a multipath environment with digital RDA can be used to supplement a disadvantage.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.187-194
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• 2019

Traditional demand forecasting methods are difficult to meet the needs of companies due to rapid changes in the market and the diversification of individual consumer needs. In a diversified production environment, the right demand forecast is an important factor for smooth yield management. Many of the existing predictive models commonly used in industry today are limited in function by little. The proposed model is designed to overcome these limitations, taking into account the part where each model performs better individually. In this paper, variables are extracted through Gray Relational analysis suitable for dynamic process analysis, and statistically predicted data is generated that includes characteristics of historical demand data produced through ARIMA forecasts. In combination with the LSTM model, demand forecasts can then be calculated by reflecting the many factors that affect demand forecast through an architecture that is structured to avoid the long-term dependency problems that the neural network model has.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.685-690
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• 2022

Recent research on soft adhesives has sought to understand in depth how their chemical or mechanical structures interact strongly with living tissues. The aim is to optimally address the unmet needs of patients with acute or chronic diseases. Synergy adhesion, which includes both electrostatic (hydrogen bonds) and mechanical interactions (capillary stress), appears to be effective in overcoming challenges related to long-term unstable bonds to wet surfaces. Here, we report electrostatic and mechanically synergistic mechanisms of adhesion without chemical residues. To infer the mechanism, a thermodynamic model based on custom combination adhesives has been proposed. The model supported experimental results that thermodynamically controlled swelling of hydrogels embedded in elastomeric structures improves biofluidic insensitive on-site adhesion to wet surfaces and improves detachment without chemical residues in the direction of peeling.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.11-19
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• 2022

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.

• Journal of Marine Life Science
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• v.2 no.2
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• pp.83-89
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• 2017

In abalones, interspecific hybridization has been suggested as a possible means to increase production and desired traits for the industry. In Korea, Haliotis gigantea is considered a species with a larger size and higher temperature tolerance than H. discus hannai. However, H. discus hannai is considered the most valuable and popular fishery resource due to its better acceptance and higher market prices. Thus, viable interspecific hybrids have been produced by artificial inseminating H. gigantea eggs with H. discus hannai sperm. However, the reciprocal hybrid cross was not successful. In this study, the hybridity and the growth and thermal tolerance performance of the interspecific hybrids were examined. A combination of various assays revealed maximum growth occurrence at 21℃ and the higher growth rate in the hybrids than that of H. discus hannai parent. In addition, the growth and survival at high-temperature (28℃) of the hybrids was equivalent to that of the highly tolerant H. gigantea parent, suggesting new possibilities to overcome the mass mortality in H. discus hannai during high temperature periods of summer season in Korea. Furthermore, the induced interspecific hybrid status was confirmed by the presence of species-specific bands for each parental species of the random amplified polymorphic DNA (RAPD) profiles using universal rice primer (URP), which could be used as speciesspecific markers to distinguish the hybrids and their parental species.

• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• The Korean Journal of Cytopathology
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• v.16 no.1
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• pp.18-24
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• 2005

The objective of this study was to ascertain whether or not the high-risk human papillomavirus (HPV) test, when coupled with Papanicolaou (Pap) smears, would prove useful in the screening and management of patients in whom abnormal Pap smear results had been obtained. Concomitant high-risk HPV detection using the hybrid capture II test and colposcopy with a Pap smear were performed with 176 patients, all of whom had been screened for both cervical carcinoma and precancerous lesions. We concomitantly performed colposcopies on these patients. Upon the follow-ups, the histologic diagnoses of these patients were confirmed via either biopsy or hysterectomy. The rate of high-risk HPV detection was correlated with cytologic diagnoses and colposcopic findings. The group composed of the high-risk HPV-positive ASCUS patients exhibited a 55.7% rate of cervical intraepithelial neoplasia (CIN), a significantly higher rate than the 7.5% result obtained in the high-risk HPV-negative ASCUS group. HPV test showed high sensitivity (87%) and low specificity (62.6%) in detection of CIN and colposcopy also showed high sensitivity (88%) and low specificity (22%). Any combination of these tests improve sensitivity, but not specificity. High-risk HPV tests, when coupled with Pap smears, constituted a useful triage approach with regard to colposcopy-directed biopsies in patients in whom a cytologic diagnosis of ASCUS had been rendered.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.189-196
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• 2009

We propose a hybrid broadcast scheduling based on a combination of periodic and on-demand data scheduling methods for mobile data broadcasting in convergence networks from communication and broadcasting. We consider an environment in which the forward channel is for data broadcasting and the reverse channel is for sending data requests via cellular phones, WLAN, WiBro, etc. Collecting statistics of requests from clients, the server partitions the data items into hot-item and cold-item sets. Hot items are sent based on a push-based scheduling. An on-demand scheduling method is applied to cold items. Performance evaluation from simulations shows that our proposed scheduling algorithm yields small response time with high successful response ratio.

• Clean Technology
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• v.13 no.4
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• pp.251-256
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• 2007

Several studies regarding depolymerization of polycarbonate waste to get the essential monomer, bisphenol A, have been reported in recent years. However, those methods have some environmental safety problems of using highly toxic organic solvents as well as product separation problem due to the use of alkali catalyst. In this study, we proposed the combination of glycolysis and methanolysis to depolymerize the polycarbonate waste. Glycolysis reaction reached at the reaction equilibrium after about 180 minat 473.15K and dissolution of the polycarbonate was found to be a rate controlling step of the reaction. The yield of BPA was improved with the aid of combination of glycolysis and methanolysis. The methanolysis was carried out at a temperature range of $303.15K{\sim}363.15K$ and MeOH/PC molar ratio $0.5{\sim}3$. The yield of BPA had a maximum at 1.0 MeOH/PC molar ratio and increased with the reaction temperature.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.1-8
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• 2012

In this paper, we propose a new clustering combination based on numerical data driven for rule generation mechanism. In large and complicated space, a clustering method can obtain limited performance results. To overcome the single clustering method problem, hybrid combined methods can solve problem to divided simple cluster estimation. Fundamental structure of the proposed method is combined by mountain clustering and modified Chen clustering to extract detail cluster information in complicated data distribution of non-parametric space. It has automatic rule generation ability with advanced density based operation when intelligent systems including neural networks and fuzzy inference systems can be generated by clustering results. Also, results of the mechanism will be served to information of decision support system to infer the useful knowledge. It can extend to healthcare and medical decision support system to help experts or specialists. We show and explain the usefulness of the proposed method using simulation and results.