• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.6
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• pp.1299-1308
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• 1994

This paper is considered on the dynamic behavior and the dynamic impact coefficient on the cable-stayed bridge under the vehicle load. The method of static analysis, that is, the transfer matrix method is used to get influence values about displacements, section forces of girder and cable forces. Gotten influence values were used as basic data to analyse dynamic behavior. This paper used the transfer matrix method because it is relatively simpler than the finite element method, and calculating speed of computer is very fast and the precision of computation is high. In the process of dynamic analysis, the uncoupled equation of motion is derived from simultaneous equation of the motion of cable-stayed bridge and vehicle travelling by using mode shape, which was borne from system of undamped free vibration. The solution of the uncoupled equation of motion, that is, time history of response of deflections, velocity and acceleration on reference coordinate system, is found by Newmark-${\beta}$ method, a kind of direct integral method. After the time history of dynamic response was gotten, and it was transfered to the time history of dynamic response of cable-stayed bridge by linear transformation of coordinates. As a result of this numerical analysis, effect of dynamic behavior for cable-stayed bridge under the vehicle load has varied depending on parameter of design, that is, the ratio of span, the ratio of main span length, tower height, the flexural rigidity of longitudinal girder, the flexural rigidity of tower, and the cable stiffness, investigated. Very good agreements with the existing solution in the literature are shown for the uncracked plate as well as the cracked plate.

• Journal of Oriental Neuropsychiatry
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• v.29 no.3
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• pp.183-195
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• 2018

Objectives: To investigate validity and effectiveness of Saam five element acupuncture treatment in psychological problems. Methods: We selected a Korean medicine doctor known to apply Saam five element acupuncture treatment to the psychiatry field. An investigator visited the doctor's clinic to observe the cases and interview patients. Duration of each interview was 30 minutes to one hour. A semi-structured questionnaire was used for in-depth interviews. All interviews were recorded and converted into texts. Converted texts were then summarized and analyzed qualitatively. Results and Discussion: Treatment process of Saam five element acupuncture designed for psychiatric use is divided into four stages: qi transformation (氣化), acupuncture treatment, deep breathing, and acupressure. In the first stage, patients are asked to express their feelings and emotions in a physical sense; to imagine their feelings have colors, weight, and physical form, and to describe those characteristics. Next, the doctor evaluates each patient's qi according to the six qi (六氣), and then determines protocol of Saam five element acupuncture treatment. This treatment is focused on suffering of the patient and specific symptoms or emotions. The treatment duration is brief and changes in symptoms are frequently checked during treatment sessions. Without discussing details of stressful events, the doctor enables patients to express physical aspects of their feelings. He also provides them with psychoeducational resources. As a result, patients experienced immediate changes in body sensation, emotions and thinking, decreased response to triggers, and increased self-control after treatment. Conclusions: The Saam five element acupuncture treatment has the potential to be used as a treatment for psychiatric disorders. Treatment can be effective in managing psychiatric disorders when applied by primary Korean medicine clinics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.13-20
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• 2020

One of the major issues surrounding big data is the availability of massive time-based or telemetry data. Now, the appearance of low cost capture and storage devices has become possible to get very detailed time data to be used for further analysis. Thus, we can use these time data to get more knowledge about the underlying system or to predict future events with higher accuracy. In particular, it is very important to define custom tailored contract offers for many households and businesses having smart meter records and predict the future electricity usage to protect the electricity companies from power shortage or power surplus. It is required to identify a few groups with common electricity behavior to make it worth the creation of customized contract offers. This study suggests big data transformation as a side effect and clustering technique to understand the electricity usage pattern by using the open data related to smart meter and KNIME which is an open source platform for data analytics, providing a user-friendly graphical workbench for the entire analysis process. While the big data components are not open source, they are also available for a trial if required. After importing, cleaning and transforming the smart meter big data, it is possible to interpret each meter data in terms of electricity usage behavior through a dynamic time warping method.

• Geophysics and Geophysical Exploration
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• v.7 no.2
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• pp.109-116
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• 2004

Petroleum reservoir characterization is a process for quantitatively describing various reservoir properties in spatial variability using all the available field data. Porosity and permeability are the two fundamental reservoir properties which relate to the amount of fluid contained in a reservoir and its ability to flow. These properties have a significant impact on petroleum fields operations and reservoir management. In un-cored intervals and well of heterogeneous formation, porosity and permeability estimation from conventional well logs has a difficult and complex problem to solve by conventional statistical methods. This paper suggests an intelligent technique using fuzzy logic and neural network to determine reservoir properties from well logs. Fuzzy curve analysis based on fuzzy logics is used for selecting the best related well logs with core porosity and permeability data. Neural network is used as a nonlinear regression method to develop transformation between the selected well logs and core analysis data. The intelligent technique is demonstrated with an application to the well data in offshore Korea. The results show that this technique can make more accurate and reliable properties estimation compared with previously used methods. The intelligent technique can be utilized a powerful tool for reservoir characterization from well logs in oil and natural gas development projects.

• Horticultural Science & Technology
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• v.30 no.3
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• pp.270-277
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• 2012

Greenhouse industry has been growing in many countries due to both the advantage of stable year-round crop production and increased demand for fresh vegetables. In greenhouse cultivation, $CO_2$ concentration plays an essential role in the photosynthesis process of crops. Continuous and accurate monitoring of $CO_2$ level in the greenhouse would improve profitability and reduce environmental impact, through optimum control of greenhouse $CO_2$ enrichment and efficient crop production, as compared with the conventional management practices without monitoring and control of $CO_2$ level. In this study, a mathematical model was developed to estimate the $CO_2$ emission from soil as affected by environmental factors in greenhouses. Among various model types evaluated, a linear regression model provided the best coefficient of determination. Selected predictor variables were solar radiation and relative humidity and exponential transformation of both. As a response variable in the model, the difference between $CO_2$ concentrations at the soil surface and 5-cm depth showed are latively strong relationship with the predictor variables. Segmented regression analysis showed that better models were obtained when the entire daily dataset was divided into segments of shorter time ranges, and best models were obtained for segmented data where more variability in solar radiation and humidity were present (i.e., after sun-rise, before sun-set) than other segments. To consider time delay in the response of $CO_2$ concentration, concept of time lag was implemented in the regression analysis. As a result, there was an improvement in the performance of the models as the coefficients of determination were 0.93 and 0.87 with segmented time frames for sun-rise and sun-set periods, respectively. Validation tests of the models to predict $CO_2$ emission from soil showed that the developed empirical model would be applicable to real-time monitoring and diagnosis of significant factors for $CO_2$ enrichment in a soil-based greenhouse.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.198-209
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• 2016

The Movie, A Werewolf Boy, describes metaphorically the hidden desire of female audience. The majority of stories about wolves contains fear of wolves inherited by and learned through human history struggling with the nature. They also shows punishment and reward toward wolves as a cultural symbol of evil. This study focuses on analyzing wolves as a metaphor of hidden original desire rather than as a subject of fear and punishment. Soon-Hee's experience with a wolf-boy in her period of puberty and her remembering the experience in her old age expresses her original sexual desire. The movie, A Werewolf boy does not directly deal with sexual content. However, SU-NI's growth through the interaction with the wolf-boy informs us Freudian theory of sex and the mechanism of sexual desire and its repression or realization. Brun Bettelheim's "Little Red Riding Hood" was used to analyze Soon-hee's desire during her puberty and Clarissa Estes's "Women Who Run with the Wolves" were used to explore SU-NI's wildness forgotten in her elderlyhood. This study shows in the process of wolf boy's transformation from biological being into social being awakened the hidden and original desire of woman.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.897-904
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• 2005

Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.

• Journal of Navigation and Port Research
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• v.28 no.1
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• pp.97-104
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• 2004

Introduction of wave model, take into account the effect of tide, wind and wave induced currents at the coastal waters of complex bathymetry, is a very important factor for most coastal engineering design and disaster protection problems. As the steady state spectral wave model could simulate depth induced wave shoaling and refraction, current induced refraction effect, steepness induced wave breaking, diffraction, wind wave growth, wave-wave interaction, and wave-current interaction that redistribute energy, this would support and compensate the gap in the real field of design where other wave models could not deal and cause wrong estimation. In this study, for better understanding and analysis of wave transformation process, we applied the spectral wave model to the large coastal waters near Gaduck Island where the Busan new port construction project is going on. We also compared the simulation results with the calculatea from the existing model. From such a trial of this study, we hope that broader and safer use of the spectral model in the area of port design and disaster prevention system come through in near future.

• Journal of Korea Water Resources Association
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• v.41 no.8
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• pp.807-823
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• 2008

This paper presents a technique for estimating discharge rating curve parameters. In typical practical applications, the original non-linear rating curve is transformed into a simple linear regression model by log-transforming the measurement without examining the effect of log transformation. The model of pseudo-likelihood estimation is developed in this study to deal with heteroscedasticity of residuals in the original non-linear model. The parameters of rating curves and variance functions of errors are simultaneously estimated by the pseudo-likelihood estimation(P-LE) method. Simulated annealing, a global optimization technique, is adapted to minimize the log likelihood of the weighted residuals. The P-LE model was then applied to a hypothetical site where stage-discharge data were generated by incorporating various errors. Results of the P-LE model show reduced error values and narrower confidence intervals than those of the common log-transform linear least squares(LT-LR) model. Also, the limit of water levels for segmentation of discharge rating curve is estimated in the process of P-LE using the Heaviside function. Finally, model performance of the conventional log-transformed linear regression and the developed model, P-LE are computed and compared. After statistical simulation, the developed method is then applied to the real data sets from 5 gauge stations in the Geum River basin. It can be suggested that this developed strategy is applied to real sites to successfully determine weights taking into account error distributions from the observed discharge data.

• The KIPS Transactions:PartC
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• v.12C no.6 s.102
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• pp.781-792
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• 2005

This study proposes an Architecture for user level End-to-end Quality of Service(QoS) using overlay In Next Generation Network(NGN). Inexisting NGNs, the IMS of a control plane provides user QoS through direct traffic control and resource-reservation over the IP packet transport network of a user plane. Further, a set of torrent studies are ongoing not only to maximize the QoS for users, but also to minimize the quality deterioration for supporting the user End-to-end QoS. Along with that, an extended QoS in user level must be considered, for Instance, differentiating service quality to support users' expectation, providing optimized contents by users' equipments, and so forth. Accordingly, the Overlay Service Network Architecture proposed by this study provides protocol adaptation for maximum throughput on transport layer by using the most efficient transport layer protocol to various network circumstances. Also, the Overlay Service Network Architecture on application layer distributes processing delay from the data transformation process of the user equipment to the network, and it is capable of intermediate processing depending on user service level. application service feature, and equipment circumstance as well. Thus, this study mainly proposes the Overlay Service Network Architecture for user level end-to-end QoS in NGN with the quality control features both on the transport layer and the application layer, an internal component feature, and a service scenario providing the QoS linking with 3GPP.