• Journal of Ocean Engineering and Technology
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• v.18 no.6 s.61
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• pp.1-7
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• 2004

This paper deals with the performance analysis of the impulse turbine for a owe type wave energy conversion device. Numerical analysis was performed using the commercially-available software FLUENT. This parametric study includes variation of the setting angle of the guide vane. Since parametric study at various flow coefficients requires a tremendous amount of computing time, two-dimensional cascade flow approximation was employed to determine the optimum principal particulars in a rather simple manner. A Full three-dimensional calculation was also performed for several cases to confirm the validity of the two-dimensional approach. Results were compared to other experimental data, such as Setoguchi et al. (2001)'s extensive set of data, and found that the usefulness of 2-D analysis was well demonstrated. The advantages of each method were also evaluated.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.2
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• pp.207-214
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• 2006

The current paper focuses the analysis of leakage detection in water pipeline systems by means of the transient method. In order to obtain essential data for evaluation the existing methodology, an extensive experimental process has been carried out in a single pipeline system, Several experimental tests were performed with and without a leakage in the system. Using the unsteady friction and improved unsteady friction factors gives reasonable match between the computed and measured results on the condition of the flow situations presented in the paper. The transient method attempts to estimate the leakage in water pipelines using observed pressure data collected during transient events on the system.

• ETRI Journal
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• v.46 no.3
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• Membrane and Water Treatment
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• v.13 no.5
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• pp.219-225
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• 2022

5th Assessment Report of the Intergovernmental Panel on Climate Change Weather (AR5) predicts that recent severe hydrological events will affect the quality of water and increase water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed, and solar radiation) were compiled into a representative concentration curve (RC), defined using 8.5. AR5 and future use are calculated based on land use. Semi-distributed emission model Calculate emissions for each target period. Meteorological factors affecting water quality (precipitation, temperature, and flow) were input into a multiple linear regression (MLR) model and an artificial neural network (ANN) to analyze the data. Extensive experimental studies of flow properties have been carried out. In addition, an Acoustic Doppler Velocity (ADV) device was used to monitor the flow of a large open channel connection in a wastewater treatment plant in Ho Chi Minh City. Observations were made along different streams at different locations and at different depths. Analysis of measurement data shows average speed profile, aspect ratio, vertical position Measure, and ratio the vertical to bottom distance for maximum speed and water depth. This result indicates that the transport effect of the compound was considered when preparing the hazard analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2529-2547
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• 2012

In this paper, we use a quadrotor-based mobile sink to gather sensor data from the terrestrial deployed wireless sensor network. By analyzing the flight features of the mobile sink node, we theoretically study the flight constraints of height, velocity, and trajectory of the mobile sink node so as to communicate with the terrestrial wireless sensor network. Moreover, we analyze the data amount which the mobile sink can send when it satisfies these flight constraints. Based on these analysis results, we propose a data acquisition approach for the mobile sink node, which is discussed detailed in terms of network performance such as the transmission delay, packet loss rate, sojourning time and mobile trajectory when given the flying speed and height of the mobile sink node. Extensive simulation results validate the efficiency of the proposed scheme.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.166-174
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• 2021

A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.121-123
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• 2016

This paper studies the competitiveness and complementary among the major container ports in East Asia by analyzing their extensive and intensive dynamics in recent 8 years (2008-2015). Time series data on container throughput dividing into O-D and transshipment for the ports of Hong Kong, Kaohsiung, Shanghai, Busan, Ningbo-Zhoushan, and Shenzhen are calculated based on VAR and VECM model.

• Journal of the Korean Magnetic Resonance Society
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• v.8 no.2
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• pp.115-126
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• 2004

Four secondary metabolites from an unidentified tunicate were isolated by treatment with trichloroethyl chloroformate(TECF) or acetic anhydride in pyridine. Their structures were determined by an extensive NMR analysis and the configuration of diacetyl derivatives(3a, 4a) was assigned by comparing with NMR data of a similar compound. Three new naturally occurring compounds (1, 3, 4) showed potent brine shrimp lethality and antifungal effect against Candia albicans.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.45-54
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• 2022

There has been necessity to supplement the fin database to improve the accuracy of low-fidelity aerodynamic solver for missile configuration. In this study, fin database is expanded by in-house solver, utilized in the triservice data the previously established into regions beyond means of CFD. Fin alone data of CFD analysis results in the original region is matched well with triservice data originated from the wind tunnel tests. Extensive fin aerodynamic data from CFD analysis is added to the existing database of the low-fidelity solver. For confirmation, aerodynamic characteristics of body-tail and body-canard-tail missile configurations is computed using upgraded low-fidelity solver at transonic region. The result using improved solver shows good agreements with wind tunnel test and CFD analysis results, which implies that it becomes more accurate.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.717-728
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• 2011

An efficient method for the unsupervised classification of high resolution imagery is suggested in this paper. It employs pixel-linking and merging based on the adjacency graph. The proposed algorithm uses the neighbor lines of 8 directions to include information in spatial proximity. Two approaches are suggested to employ neighbor lines in the linking. One is to compute the dissimilarity measure for the pixel-linking using information from the best lines with the smallest non. The other is to select the best directions for the dissimilarity measure by comparing the non-homogeneity of each line in the same direction of two adjacent pixels. The resultant partition of pixel-linking is segmented and classified by the merging based on the regional and spectral adjacency graphs. This study performed extensive experiments using simulation data and a real high resolution data of IKONOS. The experimental results show that the new approach proposed in this study is quite effective to provide segments of high quality for object-based analysis and proper land-cover map for high resolution imagery of urban area.