• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.161-170
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• 2020

There is systematic spatial variations in environmental properties due to sensitive reaction to external conditions at plastic greenhouse occupied 99.2% of domestic agricultural facilities. In order to construct 3 dimensional distribution of temperature, relative humidity, CO₂ and illuminance, measurement matrix as 3 by 3 by 5 in direction of width, height and length, respectively, dividing indoor space of greenhouse was designed and tested at experimental site. Linear regression analysis was conducted to evaluate optimal estimation method in terms with horizontal and vertical variations. Even though sole measurement point for temperature and relative humidity could be feasible to assess indoor condition, multiple measurement matrix is inevitably required to improve spatial precision at certain time domain such as period of sunrise and sunset. In case with CO₂, multiple measurement matrix could not successfully improve the spatial predictability during a whole experimental period. In case with illuminance, prediction performance was getting smaller after a time period of sunrise due to systematic interference such as indoor structure. Thus, multiple sensing methodology was proposed in direction of length at higher height than growing bed, which could compensate estimation error in spatial domain. Appropriate measurement matrix could be constructed considering the transition of stability in indoor environmental properties due to external variations. As a result, optimal measurement matrix should be carefully designed considering flexibility of construction relevant with the type of property, indoor structure, the purpose of crop and the period of growth. For an instance, partial cooling and heating system to save a consumption of energy supplement could be successfully accomplished by the deployment of multiple measurement matrix.

• Composites Research
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• v.34 no.4
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• pp.241-248
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• 2021

As the electric vehicle market grows, there is an issue of light weight vehicles to increase battery efficiency. Therefore, it is going to replace the battery module cover that protects the battery module of electric vehicles with high strength/high heat-resistant polymer composite material which has lighter weight from existing aluminum materials. It also aims to respond to the early electric vehicle market where technology changes quickly by combining 3D printing technology that is advantageous for small production of multiple varieties without restrictions on complex shapes. Based on the composite material mechanics, the critical length of glass fibers in short glass fiber (GF)/polycarbonate (PC) composite materials manufactured through extruder was derived as 453.87 ㎛, and the side feeding method was adopted to improve the residual fiber length from 365.87 ㎛ and to increase a dispersibility. Thus, the optimal properties of tensile strength 135 MPa and Young's modulus 7.8 MPa were implemented as GF/PC composite materials containing 30 wt% of GF. In addition, the filament extrusion conditions (temperature, extrusion speed) were optimized to meet the commercial filament specification of 1.75 mm thickness and 0.05 mm standard deviation. Through manufactured filaments, 3D printing process conditions (temperature, printing speed) were optimized by multi-optimization that minimize porosity, maximize tensile strength, and printing speed to increase the productivity. Through this procedure, tensile strength and elastic modulus were improved 11%, 56% respectively. Also, by post-processing, tensile strength and Young's modulus were improved 5%, 18% respectively. Lastly, using the FEA (finite element analysis) technique, the structure of the battery module cover was optimized to meet the mechanical shock test criteria of the electric vehicle battery module cover (ISO-12405), and it is satisfied the battery cover mechanical shock test while achieving 37% lighter weight compared to aluminum battery module cover. Based on this research, it is expected that 3D printing technology of polymer composite materials can be used in various fields in the future.

• Information Systems Review
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• v.15 no.2
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• pp.1-19
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• 2013

Smart working sparked by iPhone3 opens a revolution in smart ways of working at any time, regardless of location and environment. Also, It provide real-time information processing and analysis, rapid decision-making and the productivity of businesses, including through the timely response and the opportunity to increase the efficiency. As a result, every company are developing mobile information systems. But company data is accessed from the outside, it has problems to solve like security, hacking and information leakage. Also, Mobile devices such as smart phones belonging to the privately-owned asset can't be always controlled to archive company security policy. In the meantime, public smart phones owned by company was always applied security policy. But it can't not apply to privately-owned smart phones. Thus, this paper is focused to archive company security policy, but also enable the individual's free to use of smart phones when we use mobile information systems. So, when we use smart phone as individual purpose, the normal operation of all smart phone functions. But, when we use smart phone as company purpose like mobile information systems, the smart phone functions are blocked like screen capture, Wi-Fi, camera to protect company data. In this study, we suggest the design and implementation of real time control and management of mobile device using MDM(Mobile Device Management) solution. As a result, we can archive company security policy and individual using of smart phone and it is the optimal solution in the BYOD(Bring Your Own Device) era.

• Korean Journal of Psychosomatic Medicine
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• v.5 no.2
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• pp.185-194
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• 1997

A comparison was made regarding illness behavior among patients with somatoform disorders, depressive disorders and psychosomatic disorders. The subjects consisted of out-patients with somatoform disorders(N=52), depressive disorders(N=52) and psychosomatic disorders(N=51). illness behavior was assessed by illness Behavior Assessment Schedule and the questionnaire about help-seeking behavior. The patients with somatoform disorders and psychosomatic disorders more often affirmed the presence of somatic disease, were more likely to have phobia of disease, had more preoccupation with ideas of disease and more frequently shopped around oriental clinics than the patients with depressive disorders. The patients with somatoform disorders more often attributed its cause to physical factors, less often attributed the origin of affective disturbance to psychological causes, showed Less depression and irritability, and were less likely to accept psychiatric treatment recommended by other physicians than depressive patients. The patients with somatoform disorders were more likely to report having been told that they suffered from a mild illness than those with psychosomatic disorders. The patients with somatoform disorders with psychological problems tended to inhibit expression of their emotion. Female patients with somatoform disorders more often affirmed the presence of psychological disorder and attributed its cause to psychological factors than male ones. These results suggest that in illness behavior, patients with somatoform disorders are different from depressive patients, whereas the former patients are similar to psychosomatic patients except the discrepancy between therapists and patients regarding evaluation of their symptoms. Thus, it is emphasized that first, therapists need to approach patients with somatoform disorders somatically with understanding of their underlying need to deny psychological problems, followed by either psychological or biopsychosocial approach.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.1
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• pp.28-39
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• 2019

In the design process of counter measures against the beach erosion, information like the main sediment transport mode and yearly net amount of longshore and cross shore transport is of great engineering value. In this rationale, we numerically analyzed the yearly sediment budget of the Mang-Bang beach which is suffering from erosion problem. For the case of cross sediment transport, Bailard's model (1981) having its roots on the Bagnold's energy model (1963) is utilized. In doing so, longshore sediment transport rate is estimated based on the assumption that longshore transport rate is determined by the available wave energy influx toward the beach. Velocity moments required for the application of Bailard's model (1981) is deduced from numerical simulation of the nonlinear shoaling process over the Mang-Bang beach of the 71 wave conditions carefully chosen from the wave records. As a wave driver, we used the consistent frequency Boussinesq Eq. by Frelich and Guza (1984). Numerical results show that contrary to the Bailard's study (1981), Irribaren NO. has non negligible influence on the velocity moments. We also proceeds to numerically simulate the yearly sediment budget of Mang-Bang beach. Numerical results show that for ${\beta}=41.6^{\circ}$, the mean orientation of Mang-Bang beach, north-westwardly moving longshore sediment is prevailing over the south-eastwardly moving sediment, the yearly amount of which is simulated to reach its maxima at $125,000m^3/m$. And the null pint where north-westwardly moving longshore sediment is balanced by the south-eastwardly moving longshore sediment is located at ${\beta}=47^{\circ}$. For the case of cross shore sediment, the sediment is gradually moving toward the shore from the April to mid October, whereas these trends are reversed by sporadically occurring energetic wind waves at the end of October and March. We also complete the littoral drift rose of the Mang-Bang beach, which shows that even though the shore line is temporarily retreated, and as a result, the orientation of Mang-Bang beach is larger than the orientation of null pont, south-eastwardly moving longshore sediment is prevailing. In a case that the orientation of Mang-Bang beach is smaller than the orientation of null pont, north-westwardly moving longshore sediment is prevailing. And these trend imply that the Mang-Bang beach is stable one, which has the self restoring capability once exposed to erosion.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.