• Journal of the Korean Geotechnical Society
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• v.27 no.9
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• pp.55-66
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• 2011

The compulsory replacement method for soft ground treatment is simple but excellent in economic feasibility. However, the accurate replacement depth is not easy to properly predicted since an theoretical algorithm has not presently been established so far. In this research a prediction equation is proposed in a new form based on the liquid limit and natural moisture content rather than on the bearing capacity of the soft soil layer. The equation is based on the monitoring as well as the confirmatory boring at the site. In addition, the equation has been derived from the data obtained from the analysis of the characteristics of silt/clay of Sihwa region. The final prediction equation has been drawn by applying the regression analysis method.

• Transactions of the Korean hydrogen and new energy society
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• v.14 no.1
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• pp.61-68
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• 2003

This paper presents the methodology to analyze flow duration characteristics and performance prediction technique for small hydro power(SHP) Plants and its application. The flow duration curve can be decided by using monthly rainfall data at the most of the SHP sites with no useful hydrological data. It was proved that the monthly rainfall data can be characterized by using the cumulative density function of Weibull distribution and Thiessen method were adopted to decide flow duration curve at SHP plants. And, the performance prediction technique has been studied and development. One SHP plant was selected and performance characteristics was analyzed by using the developed technique, Primary design specfications such as design flowrate, plant capacity, operational rate and annual electricity production for the SHP plant were estimated, It was found that the methodology developed in this study can be a useful tool to predict the performance of SHP plants and candidate sites in Korea.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.8 no.2
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• pp.61-69
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• 2012

Geothermal heat pump(GHP) systems have been shown to be an environmentally-friendly, efficient alternative to traditional cooling and heating systems in both residential and commercial applications. Although some experimental work related to performance evaluation of GHP systems with vertical borehole ground heat exchangers for commercial buildings has been done, relatively little has been reported on the performance simulation of these systems. The aim of this study is to evaluate the cooling and heating performance of the GHP system with 30 borehole ground heat exchangers applied to an commercial building($1,210m^2$) in Seoul. For this purpose, a typical design procedure was involved with a combination of design parameters such as building loads, heat pump capacity, circulating pump, borehole diameter, and ground effective thermal properties, etc. The cooling and heating performance prediction of the system was conducted with different prediction methods and then each result is compared.

• Structural Engineering and Mechanics
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• v.69 no.6
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• pp.677-691
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• 2019

In recent years, interest is growing in the engineering community on the experimental assessment and the theoretical prediction of the out-of-plane (OOP) seismic response of unreinforced masonry (URM) infills, which are widespread in Reinforced Concrete (RC) buildings in Europe and in the Mediterranean area. In the literature, some mechanical-based models for the prediction of the entire OOP force-displacement response have been formulated and proposed. However, the small number of experimental tests currently available has not allowed, up to current times, a robust and reliable evaluation of the predictive capacity of such response models. To enrich the currently available experimental database, six pure OOP tests on URM infills in RC frames were carried out at the Department of Structures for Engineering and Architecture of the University of Naples Federico II. Test specimens were built with the same materials and were different only for the thickness of the infill walls and for the number of their edges mortared to the confining elements of the RC frames. In this paper, the results of these experimental tests are briefly recalled. The main aim of this study is comparing the experimental response of test specimens with the prediction of mechanical models presented in the literature, in order to assess their effectiveness and contribute to the definition of a robust and reliable model for the evaluation of the OOP seismic response of URM infill walls.

• Transactions of the Korean hydrogen and new energy society
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• v.32 no.6
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• pp.592-611
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• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.93-104
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• 2021

In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.

• Earthquakes and Structures
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• v.22 no.4
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• pp.387-399
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• 2022

To study the seismic damage of masonry structures and understand the characteristics of the multi-intensity region, according to the Dujiang weir urbanization of China Wenchuan earthquake, the deterioration of 3991 masonry structures was summarized and statistically analysed. First, the seismic damage of multistory masonry structures in this area was investigated. The primary seismic damage of components was as follows: Damage of walls, openings, joints of longitudinal and transverse walls, windows (lower) walls, and tie columns. Many masonry structures with seismic designs were basically intact. Second, according to the main factors of construction, seismic intensity code levels survey, and influence on the seismic capacity, a vulnerability matrix calculation model was proposed to establish a vulnerability prediction matrix, and a comparative analysis was made based on the empirical seismic damage investigation matrix. The vulnerability prediction matrix was established using the proposed vulnerability matrix calculation model. The fitting relationship between the vulnerability prediction matrix and the actual seismic damage investigation matrix was compared and analysed. The relationship curves of the mean damage index for macrointensity and ground motion parameters were drawn through calculation and analysis, respectively. The numerical analysis was performed based on actual ground motion observation records, and fitting models of PGA, PGV, and MSDI were proposed.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.66-78
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• 2015

With the development of bio computing technology, DNA watermarking to do as a medium of DNA information has been researched in the latest time. However, DNA information is very important in biologic function unlikely multimedia data. Therefore, the reversible DNA watermarking is required for the host DNA information to be perfectively recovered. This paper presents a reversible DNA watermarking using least square based prediction error expansion for noncodng DNA sequence. Our method has three features. The first thing is to encode the character string (A,T,C,G) of nucleotide bases in noncoding region to integer code values by grouping n nucleotide bases. The second thing is to expand the prediction error based on least square (LS) as much as the expandable bits. The last thing is to prevent the false start codon using the comparison searching of adjacent watermarked code values. Experimental results verified that our method has more high embedding capacity than conventional methods and mean prediction method and also makes the prevention of false start codon and the preservation of amino acids.

• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.457-464
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• 2019

Objectives: Quantitative structure-activity relationship (QSAR) is one of the effective alternatives to animal testing, but its credibility in terms of toxicity prediction has been questionable. Thus, this work aims to evaluate its predictive capacity and find ways of improving its credibility. Methods: Using $TOPKAT^{(R)}$, OECD toolbox, and $Derek^{(R)}$, all of which have been applied world-wide in the research, industrial, and regulatory fields, an analysis of prediction credibility markers including accuracy (A), sensitivity (S), specificity (SP), false negative (FN), and false positive (FP) was conducted. Results: The multi-application of QSARs elevated the precision credibility relative to individual applications of QSARs. Moreover, we found that the type of chemical structure affects the credibility of markers significantly. Conclusions: The credibility of individual QSAR is insufficient for both the prediction of chemical toxicity and regulation of hazardous chemicals. Thus, to increase the credibility, multi-QSAR application, and compensation of the prediction deviation by chemical structure are required.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.989-996
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• 2021

In the case of an unmanned aerial vehicle using a battery as a power source, there are restrictions in performing the mission because the battery capacity is limited. To extend the mission capability, it is important to minimize battery usage while the flight to the mission area. In addition, by using the battery usage prediction model, the possibility of mission completeness can be determined and it can be a criterion for selecting an emergent landing point in the mission planning stage. In this paper, we propose a battery usage prediction model considering as one of the environmental factors in the three-dimensional space. The required power is calculated according to the flight geometry of an unmanned aerial vehicle. True battery usage which is predicted from the required power is verified through the comparison with the battery usage prediction model. The optimal flight trajectory that minimizes battery usage is produced and compared with the shortest travel distance.