• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.5 no.1
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• pp.45-67
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• 2010

On July 21st in 2005, People's Bank of China (PBOC) turned the currency peg against the U.S. dollar into managed currency system based on a basket of unnamed currencies under China's exchanged rate regime. This change means that China's enterprises are not free from currency fluctuations. The purpose of this study is to analyze the relations between Renminbi fluctuations in the floating exchange rate and financial results of venture companies. The process and outcomes of this study are as follows, First, in order to measure the financial results of venture companies, I choose venture companies in Shandong Province listed on the Shanghai Stock Exchange (SSE) at random and several quarter financial sheets according to safety ratios, profitability ratios, growth ratios, activity ratios. Second, I arrange the daily Renminbi exchange rate data announced from July 21st, 2005 to December 31st, 2008 by PBOC into the quarterly data. Third, in order to confirm the relations between Renminbi fluctuations and financial results of venture companies, I carry out Pearson's correlation analysis. As a result, the revaluation of the Chinese Renminbi has weakly negative effects on debt ratio, total assets turnover ratio and equity turnover ratio in statistics. But the revaluation of the Chinese Renminbi is not related to other financial index in statistics. The result of this study is that the revaluation of the Chinese Renminbi has little influence on the export and import of Chinese venture companies and certifies the fact that Chinese venture companies have much foreign currency assets. In addition to avoid the currency exposure risk, this study shows the effective method about currency exposure risk which adjusts proportion of Renminbi to foreign currency.

• 한국벤처창업학회:학술대회논문집
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• 2010.08a
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• pp.139-160
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• 2010

On July 21st in 2005, People's Bank of China (PBOC) turned the currency peg against the U.S. dollar into managed currency system based on a basket of unnamed currencies under China's exchanged rate regime. This change means that China's enterprises are not free from currency fluctuations. The purpose of this study is to analyze the relations between Renminbi fluctuations in the floating exchange rate and financial results of venture companies. The process and outcomes of this study are as follows, First, in order to measure the financial results of venture companies, I choose venture companies in Shandong Province listed on the Shanghai Stock Exchange (SSE) at random and several quarter financial sheets according to safety ratios, profitability ratios, growth ratios, activity ratios. Second, I arrange the daily Renminbi exchange rate data announced from July 21st, 2005 to December 31st, 2008 by PBOC into the quarterly data. Third, in order to confirm the relations between Renminbi fluctuations and financial results of venture companies, I carry out Pearson's correlation analysis. As a result, the revaluation of the Chinese Renminbi has weakly negative effects on debt ratio, total assets turnover ratio and equity turnover ratio in statistics. But the revaluation of the Chinese Renminbi is not related to other financial index in statistics. The result of this study is that the revaluation of the Chinese Renminbi has little influence on the export and import of Chinese venture companies and certifies the fact that Chinese venture companies have much foreign currency assets. In addition to avoid the currency exposure risk, this study shows the effective method about currency exposure risk which adjusts proportion of Renminbi to foreign currency.

• Radiation Oncology Journal
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• v.21 no.4
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• pp.291-298
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• 2003

Purpose: We report upon a web-based system for Patterns of Care Study (PCS) devised for Korean radiation oncology. This PCS was designed to establish standard tools for clinical quality assurance, to determine basic parameters for radiation oncology processes, to offer a solid system for cooperative clinical studies and a useful standard database for comparisons with other national databases. Materials and Methods: The system consisted of a main server with two back-ups in other locations. The program uses a Linux operating system and a MySQL database. Cancers with high frequencies in radiotherapy departments in Korea from 1998 to 1999 were chosen to have a developmental priority. Results: The web-based clinical PCS .system for radiotherapy in www.pcs.re.kr was developed in early 2003 for cancers of the breast, rectum, esophagus, larynx and lung, and for brain metastasis. The total number of PCS study items exceeded one thousand. Our PCS system features user-friendliness, double entry checking, data security, encryption, hard disc mirroring, double back-up, and statistical analysis. Alphanumeric data can be input as well as image data. In addition, programs were constructed for IRB submission, random sampling of data, and departmental structure. Conclusion: For the first time in the field of PCS, we have developed a web-based system and associated working programs. With this system, we can gather sample data in a short period and thus save, cost, effort and time. Data audits should be peformed to validate input data. We propose that this system should be considered as a standard method for PCS or similar types of data collection systems.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.6
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• pp.177-187
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• 2016

This study started with the question, "Is the cost of landscape construction work in residential areas measured by public enterprises, 'in response to the needs of consumers?" The study analyzed whether the landscape construction expenditure is being introduced at an appropriate ratio according to the value the residents have regarding landscape elements. Following this, research was conducted for the purpose of providing basic data for improving the efficiency of formulating apartment landscape construction costs in the future. This research proceeded based on a questionnaire survey of residents of apartments, and the content of the questionnaire used frequency analysis and descriptive statistics research methods. To take a look at a comparative analysis of value recognition, in particular, a comparative analysis was performed based on the actual input cost based on the ratio of landscape elements by layer. Conclusions were found as follows: First, the degree of interest in the apartment landscape of the tenants was high, and the value of the landscape was high but realistic satisfaction appeared comparatively low. Second, the awareness of residents' values regarding landscape elements appeared to give "plantings" more value than "facilities". Thirdly, as a result of a mutual comparison between the values recognized by the resident regarding landscape elements and the construction input fee, depending on the landscape elements, it appeared that there is a difference in the ratio of up to 52 times from 1.25. Fourth, the fact that there is a difference in the relative proportion of value recognition and inputting construction cost indicates that it is not possible to respond to the needs of tenants during the construction cost development process. It also shows that the utility of inputting construction costs is low. Therefore, a macro-level examination such as reflecting the existing inflation rate is necessary to develop the efficient landscape construction cost of apartment such as the awareness of the value of the residents regarding landscape elements, out of the customary construction cost formulation method based on the microscopic dimensions of the consumer side.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Quality Improvement in Health Care
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• v.4 no.1
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• pp.50-63
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• 1997

Background : A pressure sore was defined as any skin lesion caused by unrelieved pressure and resulting in damage to underlying tissue. The health care institutions in the United States were reported the incident rate of pressure sores ranging from 6 to 14 %. Intensive Care Unit needed highest quality of care has been found over 40% incidence rate of pressure sore. Also, Annual expenditures for the care of pressure sores in patients in the United States have been estimated to be $7.5 billion; furthermore, 50 percent more nursing time is required to care for patients with pressure sore in comparison to the time needed to implement preventive measures against pressure sore formation. However, In Korea, there were little reliable reports, or researches, about incidence rates of pressure sore in health care institution including intensive care unit and about the integrated approach like CQI action team for risk assessment, prevention and treatment of pressure ulcers. Therefore, this study was to develop pressure sore risk assessment tool and the protocol for prevention of pressure sore formation through CQI action team activities, to monitor incident rate of pressure sore and the length of sore formation for patients at high risk, and to approximately estimate nursing time for sore dressing during research period as the effect of CQI action team. Method : CQI action team in intensive care unit, launched since early 1996, reviewed the literature for the standardized risk assessment tool, developed the pressure sore assessment tool based on the Braden Scale, tested its validity, compared on statistics including incidence rate of pressure sore for patients at high risk. Throughout these activities, CQI action team was developed the protocol, called as St. Marys hospital Intensive Care Unit Pressure Sore Protocol, shifted the emphasis from wound treatment to wound prevention. After applied the protocol to patients at high risk, the incident rate and the period of prevention against pressure development were tested with those for patients who received care before implementation of protocol by Chi-square and Kaplan-Meier Method of Survival Analysis. Result : The CQI action team found that these was significant difference of in incidence rate of pressure sores between patients at high risk (control group) who received care before implementation of protocol and those (experimental group) who received it after implementation of protocol (p<.05). 25% possibility of pressure sore formation was shown for the patients with 6th hospital day in ICU in control group. In experimental group, the patients with 10th hospital day had 10% possibility of pressure sore. Therefore, there was significant difference(p<.05) in survival rate between two groups. Also, nursing time for dressing on pressure sore in experimental group was decreased as much as 50% of it in control group. Conclusion : The collaborative team effort led to reduced incidence, increased the length of prevention against pressure sore, and declined nursing care times for sore dressing. However, there have had several suggestions for future study. The preventive care system for pressure sore should be applied to patients at moderate, or low risk throughout continuous CQI team activities based on Bed Sore Indicator Fact Sheet. Hospital-wide supports, such as incentives, would be offered to participants for keeping strong commitment to CQI team. Also, Quality Information System monitoring incidents and estimating cost of poor quality, like workload (full time equivalence) or financial loss, regularly in a hospital has to be developed first for supporting CQI team activities as well as empowering hospital-wide QI implementation. Being several limitations, this study would be one of the report cards for the CQI team activities in intensive care unit of an acute hospital and a trial of quality improvement of health care in Korea.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Women's Health Nursing
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• v.6 no.2
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• pp.234-257
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• 2000

At present in the medical care, the study and effort for producing health service to consider efficiency, effectiveness, and quality are urgently called for because of the difficulty in the keen competition according to the inter- nationalization and opening, the operation in the medical institution service testing system, the change in the medical policy of KDRGs, and the lack of the health care cost increasing rate. As an alternative, the case management for the new management system is introduced in the U.S., and the Critical Pathway that is the method designing the contents of activity and its result has been developed and applied in order to anticipate and manage the patient-outcome for the realization of the cost-effective case-management. Thus, this study intended to analyze the effectiveness to obtain by developing the Critical Pathway presented as the method to improve the quality-betterment and cost effectiveness through the continuous and consistent patient management for the hysterectomy patient and applying it to the real practice. As a study method, this author formed a conceptual framework through considering five Critical Pathway used in the current U.S. and three Critical Pathway presented in the literature to develop the Critical Pathway for the hysterectomy patient, and made out the preliminary Critical Pathway through reviewing the old chart. This author made the verified the validity of the expert group about the developed Critical Pathway, and to confirm the possibility of practice application, completed and settled the final Critical Pathway after using the Critical Pathway to the hysterectomy patient from March 1st to 15th, 1997. Finally, to analyze the application-effect of the developed Critical Pathway, this author offered health care service applying the Critical Pathway to the hysterectomy patient from April 15th to August 31th, 1997. The guide for the Critical Pathway was carried out in advance by outpatient setting nurse for outpatient setting visit before the operation, and after hospitalization the primary nurse monitored the execution degree on the every duty. After discharge this author surveyed the complication through phone visiting, and one month after discharge surveyed the patient's reaction about the offered service when outpatient setting visit and analyzed the result. The source for health care cost was obtained by the statistics about the hospital charge which was offered by the General Business Department. The results were as follows. 1. It was decided that the vertical line of the Critical Pathway was made up of eight items such as monitoring/assessment, treatment, line/drains, activity, medication, lab test, diet, patient teaching, and the horizontal line of the Critical Pathway was made up of from hospitalization to discharge. 2. After the analysis of service contents through reviewing the old chart, it was decided that the horizontal line of the preliminary Critical Pathway was made up of from hopitalization to fourth postoperative day, and the vertical line of it was divided into eight items which were the contents to occur with the time frame of the horizontal line. 3. After the verifying the validity of the expert group about the preliminary Critical Pathway, the horizontal line was amended from hopitalization to third postoperative day, and taking their consensus, some contents of the horizontal line was amended and deleted. 4. From March 1st to 15th, 1997, to confirm the clinical suitability, this author offered eight hysterectomy patients the medical service through the Critical Pathway. The result was that three of them could be discharged at the expected discharge day, and the others later than that day. Supplementing the preliminary Critical Pathway through analyzing the cause of that delay- case, this author developed the final Critical Pathway. 5. There were no significant differences between the experimental and the control group in the incidence of complication(P > 0.05). 6. The 92.4% of experimental group was satisfied with the Critical Pathway service. 7. The length of hospital stay of the experimental group offered with the Critical Pathway service was 4.6 days and there was a significant difference that it was 1.3 days shorter than that of the control group(t=-29.514, P=0.000). 8. There wsa a significant difference that the mean medical charge per one patient of the experimental group offered the Critical Pathway service was cheaper \124,150 than that of the control group(t=-9.826, P=0.000). 9. The result that the author assumed and analyzed hospital income with the rate of turning bed was assumed that the increase of hospital income was \63,245,072 for that study, and the income increase was expected with \68,704,864 for a year. The result that this author applied the Critical Pathway to the hysterectomy patient have no differences in the incidence of complication, high satisfaction with that service, and the length of hospital stay decreased in the experimental group, and the mean hospital charge per one patient decreased, but hospital income increased. Suggestions for further study and nursing practice are as follows. 1. The study to apply the Critical Pathway for a year, verify the validity, and measure the effect repeatedly is needed. 2. To apply and manage the Critical Pathway effectively, the study to computerize it is needed. 3. The study to develop hospital-based Critical Pathway about other diseases or procedure, and measure the effect is needed.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.