• Korean Journal of Construction Engineering and Management
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• v.11 no.3
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• pp.97-104
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• 2010

The expressway construction work is being recently diversified even the working environments and the working kinds due to getting large scale, complexity, and high technology. The accidents are increasing according to large scale even in construction equipment and to a rise in high-ground work, thereby being required an effort of reducing accidents. However, it is insufficient in a means of coping with the technically safety management of using specific and scientific method. In order to prevent accident, a specific plan is needed that can apply each in variables to safety management by analyzing the accident types and accident factors with statistical method. Accordingly, this study carried out investigate on accidents for 12 years in the expressway construction work, and aimed to analyze characteristics on the accident type and conversion disaster-victim number according to factors with occurrence of accidents. Thus, the empirical analysis was performed. As a result of research, first, as a result of verifying significant difference with accident type by accident factor, the significant difference was shown between a cause for occurrence of accident and height with occurrence of accident. Second, among factors by period, the time with occurrence of accident was indicated to have significant difference from conversion disaster-victim number. Among factors by work condition, the cause for occurrence of accident, the height with occurrence of accident, and the type with occurrence of accident were indicated to have significant difference from conversion disaster-victim number. What suggested by analyzing characteristics in these factors and variables has important significance as a countermeasure for safety management.

• Journal of Korean Society of Forest Science
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• v.63 no.1
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• pp.21-27
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• 1984

This study was to investigate the diurnal changes of shoot water potentials and the characteristics of xylem conductivity of branch in several conifers. The results obtained are as follows: 1) The diurnal shoot water potentials fluctuated with the sunlight intensities, and increase in shoot water potential lagged behind two hours as compared with the time of sunlight decrease in tree crown. 2) The shoot water potential reached the daily maximum ai twelve to fourteen o'clock in the afternoon, and the maximum shoot water potentials were -22 bar in Larix leptolepis, -18 bar in Pinus koraiensis, -15 bar in Pinus densiflora, -14 bar in Abies holophylla, and -10 in Pinus rigida. 3) The average gradient of shoot water potential per one meter height (${\varphi}_L/m$) in tree crown was -1.7 bar/m in Pinus koraiensis while that of Larix leptolepis was -2.1 bar/m. 4) The average of relative xylem conductivities (K, $cm^2/hr{\cdot}atm$) in branches was 2878 in Larix leptolepis, 2763 in Pinus rigida, 2652 in Pinus densiflora, and 2113 in Pinus koraiensis.

• Journal of Embryo Transfer
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• v.16 no.2
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• pp.127-131
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• 2001

The Jeju horse has been raised for centuries in Jeju island. Recently, as the number of this indigenous horse has been dropped dramatically, this breed became Natural Monument #347 to conserve and multiply this endangered breed. To provide the basic information for AI, sexual activity and semen characteristics in Jeju horse were investigated. Jeju horse semen was collected using Missouri style artificial vagina from fertile stallion.\\`she number of mount per ejaculation was 2..3$\pm$1.8, and the ejaculation time was 27.0$\pm$12.5 seconds. The total volume and gel-free volume of semen was 47.8$\pm$26.7 ml and 42.7$\pm$27.4ml, respectively, and the concentration of sperm and the total number of spermatozoa per ejaculation was 200.7$\pm$112.9$\times$10$^{6}$ ml and 7.6$\pm$3.9$\times$10$^{9}$ ml, respectively. The percentage of motile sperm and the number of live spermatozoa per ejaculation was 75.0$\pm$18.2% and 6.1$\pm$3.4$\times$10$^{9}$ ml, respectively, and the pH of gel-free semen was 7.3$\pm$0.2. The total percentage of abnormal sperm was 31.5%, and the percentage of sperm with abnormal head, midpiece and tail was 9.5$\pm$11.7%, 7.0$\pm$4.0% and 15.0$\pm$15.0%, respectively.

• Food Science of Animal Resources
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• v.28 no.1
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• pp.14-20
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• 2008

This study evaluated the effects of dietary fiber extracted from rice bran on the chemical composition, cooking characteristics and sensory properties of emulsion type sausage. Sausages were produced containing 0%, 1%, 2%, 3%, and 4% dietary fiber extracted from rice bran. The negative control had the highest fat, cooking loss, CIE L- and CIE a-values. The sausages containing rice bran had higher moisture, ash, pH, and CIE b-values than the control. Sausages with 3% rice bran had the lowest cooking loss. Sausages with 4% rice bran had the highest hardness and cohesiveness. There was a significant difference among the emulsion sausage samples with respect to sensory properties, with sausages containing 1% and 2% rice bran having a higher overall acceptability than the other sausages.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.489-498
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• 2022

Recently, the textile industry, which is labor-intensive, is attempting to reduce process costs and optimize quality through artificial intelligence. However, the fiber spinning process has a high cost for data collection and lacks a systematic data collection and processing system, so the amount of accumulated data is small. In addition, data imbalance occurs by preferentially collecting only data with changes in specific variables according to the purpose of fiber spinning, and there is an error even between samples collected under the same fiber spinning conditions due to difference in the measurement environment of physical properties. If these data characteristics are not taken into account and used for AI models, problems such as overfitting and performance degradation may occur. Therefore, in this paper, we propose an outlier handling technique and data augmentation technique considering the characteristics of the spinning process data. And, by comparing it with the existing outlier handling technique and data augmentation technique, it is shown that the proposed technique is more suitable for spinning process data. In addition, by comparing the original data and the data processed with the proposed method to various models, it is shown that the performance of the tensile tenacity and elongation prediction model is improved in the models using the proposed methods compared to the models not using the proposed methods.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.104-123
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• 2022

Style transfer based on neural network provides very high quality results by reflecting the high level structural characteristics of images, and thereby has recently attracted great attention. This paper deals with the problem of resolution limitation due to GPU memory in performing such neural style transfer. We can expect that the gradient operation for style transfer based on partial image, with the aid of the fixed size of receptive field, can produce the same result as the gradient operation using the entire image. Based on this idea, each component of the style transfer loss function is analyzed in this paper to obtain the necessary conditions for partitioning and padding, and to identify, among the information required for gradient calculation, the one that depends on the entire input. By structuring such information for using it as auxiliary constant input for partition-based gradient calculation, this paper develops a recursive algorithm for super high-resolution image style transfer. Since the proposed method performs style transfer by partitioning input image into the size that a GPU can handle, it can perform style transfer without the limit of the input image resolution accompanied by the GPU memory size. With the aid of such super high-resolution support, the proposed method can provide a unique style characteristics of detailed area which can only be appreciated in super high-resolution style transfer.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• Knowledge Management Research
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• v.25 no.1
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• pp.21-41
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• 2024

The global Industry has been shaped by start-ups that originated with knowledge-based innovative strategies or technologies in the 21st century. Specifically, laboratory start-ups that rely on research papers or patents for new technology development are recognized for their high survival rate and the creation of employment opportunities. Our study concentrated on 'I-Corps', which also introduced in Korea, standing for innovation corps is a laboratory startup program launched in 2011 by the NSF(National Research Foundation) to commercialize R&D results and foster entrepreneurship as part of the policy to build a start-up system at the national innovation level. In this study, we proposed and empirically tested a research model focusing on teams participating in the I-Corps program to determine how startup team diversity, among the team characteristics of laboratory startups, affected patent performance. As a result of the analysis, among the proposed variables, age diversity, educational background diversity, and value diversity had a significant impact on patent performance. The results of this study are expected to further strengthen the theoretical and practical foundations of researchers or practitioners of the I-Corps program, as well as related areas involving technology & laboratory startups, intellectual property and knowledge management fields in the future.