• 대한의용생체공학회:의공학회지
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• 제27권4호
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• pp.189-196
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• 2006

Micro computed tomography (micro-CT) has been used for in vivo animal study owing to its noninvasive and high spatial resolution capability. However, the sizes of existing detectors for micro-CT systems are too small to obtain whole-body images of a small animal object with $\sim$10 micron resolution and a part of its bones or other organs should be extracted. So, we have introduced the zoom-in micro-tomography technique which can obtain high-resolution images of a local region of an live animal object without extracting samples. In order to verify our zoom-in technique, we performed in vivo animal bone study. We prepared some SD (Sprague-Dawley) rats for making osteoporosis models. They were divided into control and ovariectomized groups. Again, the ovariectomized group is divided into two groups fed with normal food and with calcium-free food. And we took 3D tomographic images of their femurs with 20 micron resolution using our zoom-in tomography technique and observed the bone changes for 12 weeks. We selected ROI (region of interest) of a femur image and applied 2D FDT (fuzzy distance transform) to measure the trabecular bone thickness. The measured results showed obvious bone changes and big differences between control and ovariectomized groups. However, we found that the reliability of the measurement depended on the selection of ROI in a bone image for thickness calculation. So, we extended the method to 3D FDT technique. We selected 3D VOI (volume of interest) in the obtained 3D tomographic images and applied 3D FDT algorithm. The results showed that the 3D technique could give more accurate and reliable measurement.

• 산업경영시스템학회지
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• 제44권3호
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• 한국군사과학기술학회지
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• 제22권3호
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• pp.341-352
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• 2019

LTV(Light Tactical vehicle) operating in our military requires higher levels of performance and durability to withstand harsher conditions than ordinary vehicles, as they must travel on both rough-train and off-road as well as on public roads. Recently, developed light tactical vehicle is developed by a variety of test evaluations in order to satisfy ROC(Required Operational Capability) by the requirement military group. However, there is no standardized driving test condition for satisfying the durability performance of Korean tactical vehicle. Therefore, this study aims to provide basic data to establish reliable driving test conditions by analyzing the maneuver conditions and the driving data in order to select the representative drive course required. To do this, we analyzed the future operational environment, the area of operation analysis and the driving information of light tactical vehicle.

• Current Photovoltaic Research
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• 제9권2호
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• pp.51-58
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• 2021

Solar water pumping system (SWPS) is reliable and beneficial for Indian farmers in irrigation and crop production without accessing utility. The capability of easy installation and deployment, makes it an attractive option in remote areas without grid access. The selection of portable solar based pumps is pertaining to its longer life and economic viability due to lower running cost. The work presented in this manuscript intends to demonstrate performance analysis of portable systems. Consequent investigation reveals PSWS as the emerging option for rural household and marginal farmers. This can be attributed to the fact that, a considerable portion (around 45.7%) of the country's land is farmland and irrigation options are yet to reach farmers who entirely rely on rain water at present for harvesting of the crops. According to census 2010-2011 tube wells are the main source for irrigation amongst all other sources followed by canals. Out of the total 64.57-million-hectare net irrigation area, 48.16% is accounted by small and marginal holdings, 43.77% by semi-medium and medium holdings, and 8.07% by large holdings. As per 2015-16 census data, nearly 100 million farming households would struggle to make ends meet. The work included in this manuscript, presents the performance of different commercial brands and different technologies of DC surface solar water micro pumping systems have been studied (specifically, the centrifugal and reciprocating type pumps have been considered for analysis). The performance of the pumping systems has been analyzed and data is evaluated in terms of quantity of water impelled for specific head. The reciprocating pump has been observed to deliver the best system efficiency.

• 한국농공학회논문집
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• 제63권1호
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.133-140
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• 2013

The purpose of this study is identifying the relationship among the business strategy, order receiving capability and leverage variables of a construction company using industry characteristic variables, in addition to the explanation variables used in the previous studies. The samples of this study were limited to the construction companies listed in Korean stock market. This study built multiple regression analysis models, which have been frequently used in traditional previous studies, in the explanation of company capital structure. Empirical analysis on Static Trade-off Theory and Pecking Order Theory was done by the built model. The study results suggested that the capital structure determination behavior of a construction company generally follows Static Trade-off Theory; however, profitability was found to follow Pecking Order Theory. The explanation variables used in the previous capital structure studies mostly produced significant results; however, the variables, which this study experimentally used, did not produce significant results. It is believed that it implies that additional studies are required in the selection of variables and study methodology. Consequently, a case that unconditionally supports a particular theory is scarce. It has been also found that a case can support both theories at the same time. Therefore, it is believed that development study methodology or introduction of new study methodology that can identify the dynamic characteristic of construction company capital structure formation is required.

• Journal of Animal Science and Technology
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• 제65권5호
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• pp.939-950
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• 2023

This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa L.) productivity. A total of 197 alfalfa yield results were acquired from the alfalfa field trials conducted by the South Korean National Agricultural Cooperative Federation or Rural Development Administration between 1983 and 2008. The corresponding climate and soil data were collected from the database of the Korean Meteorological Administration. Twenty-three growing condition variables were developed as explaining variables for alfalfa forage biomass production. Among them, twelve variables were chosen based on the significance of the partial-correlation coefficients or potential agricultural values. The selected partial correlation coefficients between the variables and alfalfa forage biomass ranged from -0.021 to 0.696. The influence of the selected twelve variables on yearly alfalfa production was summarized into three dominant factors through factor analysis. Along with the accumulated temperature variables, the loading scores of the daily mean temperature higher than 25℃ were over 0.88 in factor 1. The sunshine duration at temperature between 0℃-25℃ was 0.939 in factor 2. Precipitation days were 0.82, which was the greatest in factor 3. Stepwise regression applied with the three dominant factors resulted in the coefficients of factors 1, 2, and 3 for 0.633, 0.485, and 0.115, respectively, and the R-square of the model was 0.602. The environmental conditions limiting alfalfa growth, such as daily temperature higher than 25℃ or daily mean temperature affected annual alfalfa production most substantially among the growing condition variables. Therefore, future cultivar selection should consider the capability of alfalfa to be tolerant to extreme summer weather along with biomass production potential.

• 아태비즈니스연구
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• 제14권4호
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• pp.87-113
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• 2023

Purpose - Focusing on Haier, a successful corporate venture in China, we analyse the operating mechanism and success factors of corporate venture, and reveal the necessary factors for the successful implementation of corporate venture. Design/methodology/approach - This study is a single case study centred on Haier, a successful corporate venture in China. Findings - The operational process of Haier's corporate venture includes six key aspects: project selection, team building, resource allocation and support, project implementation, risk control measures, performance evaluation and rewards. In terms of success factors, the support of top management with leadership capability of value creation and sharing is very important for the success of corporate venture. Secondly, a multi reward mechanism can be introduced to motivate employees and improve performance. Thirdly, it is important to integrate corporate culture into the operating mechanism of an corporate venture. Fourthly, flexible operations that break down rigid organisational boundaries and transform the organisation into a more open platform for entrepreneurship can increase the likelihood of success. Finally, empowering employees with operational discretion can also have a positive impact on the success of an Corporate Venture. Research implications or Originality - This study contributes to theory and practice by analysing the success conditions of corporate venture, providing new understanding and drawing new perspectives, especially from the experience of Haier. The results suggest strategies and flexibility for successfully pursuing corporate venture, and provide important experience for international companies to help them gain competitive advantage in global competition. It also helps corporate leaders to promote new directions and innovations and improve their strategies to respond to dynamic environments.

• 벤처창업연구
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• 제13권6호
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• pp.49-62
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• 2018

본 연구는 정부 창업지원정책의 효율성 제고를 위하여 성공가능성이 높은 창업기업 선별을 위한 선정평가지표 개발을 목적으로 진행하였다. 창업기업 선정평가지표 개발을 위해 첫째, 관련 문헌고찰을 통해 평가지표에 대한 이론적 재정립을 하였다. 둘째, 창업생태계의 각 분야 전문가 대상 델파이 기법을 활용하여 주요 평가항목을 도출하였다. 셋째, 항목별 중요도 산정을 위하여 AHP 기법을 적용하여 가중치를 도출하였다. 연구결과를 요약하면 다음과 같다. 첫째, 균형성과표(BSC) 관점에서 창업자 선정평가지표의 적용을 시도하였다. 둘째, 선행연구 고찰을 통한 창업자 역량평가 요인 및 관련 분야의 창업 전문가를 대상으로 의견 수렴과 구조화된 설문의 최종 주요 문항을 도출하였다. 셋째, 도출된 주요 선정평가지표의 가중치 적용결과, 관점별, 대항목인 사업화 관점 > 시장관점 > 기술개발관점 > 조직역량관점 순으로 나타났다. 중항목의 사업화 관점에서는 제품화 역량, 시장관점에서는 시장경쟁력, 기술개발관점에서는 제품 차별성 역량, 조직역량관점에서는 창업자의 역량이 중요하게 인지되었으며, 전반적으로 주요 중항목은 창업자 역량 > 시장경쟁력 > 제품화 역량 > 제품 차별성 순으로 나타났다. 소항목 간의 중요도는 경쟁제품의 비교 우수성이 가장 우선순위가 높았으며, 시장 진입 가능성 > 창업자의 보유역량 > 자본조달능력 > 창업자의 추진 의지 및 열정 순으로 나타났으며 파트너의 보유역량, 구성원의 명확한 역할과 기능적 구성 정도, 협력 관계 정도, 연구개발투자 등이 낮게 나타났다. 이러한 연구결과는 기존 선정평가지표 개발에 관한 선행연구의 개념적 대안 제시 및 향후 정교한 지표개발을 위한 의미 있는 시사점을 제시하였다.