• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.149-156
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• 2024

The increased intelligence and speed of vehicle infotainment, whose main purpose was emergency and external communication, is showing the potential for application to various services such as navigation and autonomous driving. In particular, functionality for linking external devices and infrastructure is being strengthened due to advances in communication and networks. Under this trend, it is necessary to consider the direction of linkage with the cooperative intelligent transportation system (C-ITS) for advanced vehicle services and driving. In addition, in the case of automobiles, future vehicle development concepts are being established based on the concept of software-defined vehicles (SDVs) in line with the trend of electrification beyond telematics and infotainment advancements, and such SDV linkage must be considered at the same time. In this paper, we consider the future direction of ITS and SDV linkage based on analysis of vehicle infotainment structure. First, for this purpose, we analyze the existing vehicle infotainment structure and architecture, and also present the structure of the SDV linked to it. Based on this, analysis and implications are drawn on the possibility of applying and linking standard-based C-ITS services with SDV devices.

• Korean Journal of Radiology
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• v.23 no.9
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• pp.901-910
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• 2022

Objective: This study aimed to assess the technical feasibility, efficacy, and safety of the safe triangular working zone (STWZ) approach applied in percutaneous vertebroplasty (PV) for spinal metastases involving the posterior part of the vertebral body. Materials and Methods: We prospectively enrolled 87 patients who underwent PV for spinal metastasis involving the posterior part of the vertebral body, with or without the STWZ approach, from January 2019 to April 2022. Forty-nine patients (27 females and 22 males; mean age ± standard deviation [SD], 57.2 ± 11.6 years; age range, 31-76 years) were included in group A (with STWZ approach), accounting for 54 vertebrae. Thirty-eight patients (18 females and 20 males; 59.1 ± 10.9 years; 29-81 years) were included in group B (without STWZ approach), accounting for 57 vertebrae. Patient demographics, procedure-related variables, and pain relief as assessed using the visual analog scale (VAS) were collected at different time points. Tumor recurrence in the vertebrae after PV was analyzed using Kaplan-Meier curves. Results: The STWZ approach was successful from T1 to L5 without severe complications. Cement filling was satisfactory in 47/54 (87.0%) and 25/57 (43.9%) vertebrae in groups A and B, respectively (p < 0.001). Cement leakage was not significantly different between groups A and B (p = 1.000). Mean VAS score ± SD before and 1 week and 1, 3, 6, 9, and 12 months after PV were 7.6 ± 1.8, 4.2 ± 2.0, 2.7 ± 1.9, 1.9 ± 1.5, 1.7 ± 1.4, 1.7 ± 1.1, and 1.6 ± 1.3, respectively, in group A and 7.2 ± 1.7, 4.0 ± 1.3, 3.4 ± 1.6, 2.4 ± 1.2, 1.8 ± 1.0, 1.4 ± 0.5, and 1.7 ± 0.9, respectively, in group B. Kaplan-Meier analysis showed a lower tumor recurrence rate in group A than in group B (p = 0.001). Conclusion: The STWZ approach may represent a new, safe, alternative/auxiliary approach to target the posterior part of the vertebral body in the PV for spinal metastases.

• Journal of Pharmacopuncture
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• v.27 no.1
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• pp.1-13
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• 2024

Background: Diabetic nephropathy (DN) is one of the major complications of chronic hyperglycaemia affecting normal kidney functioning. The ayurvedic medicine curcumin (CUR) is pharmaceutically accepted for its vast biological effects. Objectives: The Curcuma-derived diferuloylmethane compound CUR, loaded on Poly (lactide-co-glycolic) acid (PLGA) nanoparticles was utilized to combat DN-induced renal apoptosis by selectively targeting and modulating Bcl2. Methods: Upon in silico molecular docking and screening study CUR was selected as the core phytocompound for nanoparticle formulation. PLGA-nano-encapsulated-curcumin (NCUR) were synthesized following standard solvent displacement method. The NCUR were characterized for shape, size and other physico-chemical properties by Atomic Force Microscopy (AFM), Dynamic Light Scattering (DLS) and Fourier-Transform Infrared (FTIR) Spectroscopy studies. For in vivo validation of nephro-protective effects, Mus musculus were pre-treated with CUR at a dose of 50 mg/kg b.w. and NCUR at a dose of 25 mg/kg b.w. (dose 1), 12.5 mg/kg b.w (dose 2) followed by alloxan administration (100 mg/kg b.w) and serum glucose levels, histopathology and immunofluorescence study were conducted. Results: The in silico study revealed a strong affinity of CUR towards Bcl2 (dock score -10.94 Kcal/mol). The synthesized NCUR were of even shape, devoid of cracks and holes with mean size of ~80 nm having -7.53 mV zeta potential. Dose 1 efficiently improved serum glucose levels, tissue-specific expression of Bcl2 and reduced glomerular space and glomerular sclerosis in comparison to hyperglycaemic group. Conclusion: This study essentially validates the potential of NCUR to inhibit DN by reducing blood glucose level and mitigating glomerular apoptosis by selectively promoting Bcl2 protein expression in kidney tissue.

• Progress in Medical Physics
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• v.19 no.1
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• pp.63-72
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• 2008

With recent advancement of the medical imaging systems and picture archiving and communication system (PACS), installation of digital radiography has been accelerated over past few years. Moreover, Computed Radiography (CR) which was well established for the foundation of digital x-ray imaging systems at low cost was widely used for clinical applications. This study analyzes imaging characteristics for two systems with different pixel sizes through the Modulation Transfer Function (MTF), Noise Power Spectrum (NPS) and Detective Quantum Efficiency (DQE). In addition, influence of radiation dose to the imaging characteristics was also measured by quantitative assessment. A standard beam quality RQA5 based on an international electro-technical commission (IEC) standard was used to perform the x-ray imaging studies. For the results, the spatial resolution based on MTF at 10% for Agfa CR system with I.P size of $8{\times}10$ inches and $14{\times}17$ inches was measured as 3.9 cycles/mm and 2.8 cycles/mm, respectively. The spatial resolution based on MTF at 10% for Fuji CR system with I.P size of $8{\times}10$ inches and $14{\times}17$ inches was measured as 3.4 cycles/mm and 3.2 cycles/mm, respectively. There was difference in the spatial resolution for $14{\times}17$ inches, although radiation dose does not effect to the MTF. The NPS of the Agfa CR system shows similar results for different pixel size between $100{\mu}m$ for $8{\times}10$ inch I.P and $150{\mu}m$ for $14{\times}17$ inch I.P. For both systems, the results show better NPS for increased radiation dose due to increasing number of photons. DQE of the Agfa CR system for $8{\times}10$ inch I.P and $14{\times}17$ inch I.P resulted in 11% and 8.8% at 1.5 cycles/mm, respectively. Both systems show that the higher level of radiation dose would lead to the worse DQE efficiency. Measuring DQE for multiple factors of imaging characteristics plays very important role in determining efficiency of equipment and reducing radiation dose for the patients. In conclusion, the results of this study could be used as a baseline to optimize imaging systems and their imaging characteristics by measuring MTF, NPS, and DQE for different level of radiation dose.

• The Korean Society of Law and Medicine
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• v.20 no.1
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• pp.163-201
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• 2019

Doctor has the duty of an inter-hospital transfer, known as inter-facility or secondary transfer, when the diagnostic and therapeutic facilities required for a patient are not available at the given hospital. Also, the decision to transfer the patient to an another facility is rely on whether ill patient is the benefits of care, including clinical and non-clinical reasons, available at the another facility against the potential risks. Crucial point to note is that issues about 'inter-hospital transfer' is limited to questions occurred in the course of transfer between emergency medicals (facilities). 'emergency medical (facility)' is specified by Medical Law, article 3 and the duty of an inter-hospital transfer includes any possible adverse events, medical or technical, during the transfer. Because each medical facility has an different ability to care for a patient in an emergency condition, coordination between the referring and receiving hospitals' emergency medicals would be important to ensure prompt transfer to the definitive destination avoiding delay at an emergency. Simultaneously, transfer of documents about the transfer process, medical record and investigation reports are important materials for maintaining continuity of medical care. Although the duty of an inter-hospital transfer is recognized as one of duty of doctor and more often than not it occurs, there is constant legal conflict between a doctor and a patient related to the duty of the inter-hospital transfer. Therefore, we need clear and specific legal standard about the inter-hospital transfer. This paper attempts to review the Supreme Court's cases associated to the inter-hospital transfer and to compare opinion of the cases with guideline for an inter-hospital transfer already given. Furthermore, this article is intended to broaden our horizons of understanding the duty of an inter-hospital transfer and I wish this article helps to resolve the settlement and case dealt with the duty of inter-hospital transfer.

• 대한공업교육학회지
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• v.33 no.2
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• pp.97-119
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• 2008

The purpose of this research is to clarify the elements of the function of the role of industrial high schools that its experts perceived. The content of this research is verify the function element required for the performance of the role of specialized education through content validity ratio(CVR). This research adopted the method of literature research and Delphi method, which is to collect and come to an agreement of the opinions of the 26 research panels. The first round is constructed by the semi-constructed questionnaire for the analysis of the opinions of the panels by inductive method. The second round is to categorize the result of the first one into 7 domains, and asked each category by Likert's 5 scale checklists, and statistically analyzed mean, medium, standard deviation, and quartile. The third round is to statistically analyze Mean, standard deviation, medium, and validity ratio(CVR) to reassure the opinions of the panels on the basis of the result of the first one. The categorized contents of the function required for the performance of the specialized education in this research is 'in-service visit and in-service training', 'licence acquiring education', 'employment counseling and job employment information', 'custom-made education connected with industry', 'career education' and 'enhancement of basic career competency'. The panels are divided into professors, teachers, professionals, and policy administrators, and they verified the validity rate of the function role and priority of emphasis. The result showed that the tendency of the education is converting from physical function-centered education to education of emotional attitude and competence of thought.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.365-374
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• 2015

The regular quality assurance (RQA) of X-ray images is essential for maintaining a high accuracy of diagnosis. This study was to evaluate the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE) of a computed radiography (CR) system for various periods of use from 2006 to 2015. We measured the pre-sampling MTF using the edge method and RQA 5 based on commission standard international electro-technical commission (IEC). The spatial frequencies corresponding to the 50% MTF for the CR systems in 2006, 2009, 2012 and 2015 were 1.54, 1.14, 1.12, and $1.38mm^{-1}$, respectively and the10% MTF for 2006, 2009, 2012, and 2015 were 2.68, 2.44, 2.44, and $2.46mm^{-1}$, respectively. In the NPS results, the CR systems showed the best noise distribution in 2006, and with the quality of distributions in the order of 2015, 2009, and 2012. At peak DQE and DQE at $1mm^{-1}$, the CR systems showed the best efficiency in 2006, and showed better efficiency in order of 2015, 2009, and 2012. Because the eraser lamp in the CR systems was replaced, the image quality in 2015 was superior to those in 2009 and 2012. This study can be incorporated into used in clinical QA requiring performance and evaluation of the performance of the CR systems.

• Journal of Energy Engineering
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• v.28 no.2
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• pp.18-35
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• 2019

The Korea District Heating Corporation operates a gas engine generator with a capacity of $4500m^3 /day$ of biogas generated from the sewage treatment plant of the Nanji Water Recycling Center and 1,500 kW. However, the actual operation experience of the biogas power plant is insufficient, and due to lack of accumulated technology and know-how, frequent breakdown and stoppage of the gas engine causes a lot of economic loss. Therefore, it is necessary to prepare technical fundamental measures for stable operation of the power plant In this study, a series of process problems of the gas engine plant using the biogas generated in the sewage treatment plant of the Nanji Water Recovery Center were identified and the optimization of the actual operation was made by minimizing the problems in each step. In order to purify the gas, which is the main cause of the failure stop, the conditions for establishing the quality standard of the adsorption capacity of the activated carbon were established through the analysis of the components and the adsorption test for the active carbon being used at present. In addition, the system was applied to actual operation by applying standards for replacement cycle of activated carbon to minimize impurities, strengthening measurement period of hydrogen sulfide, localization of activated carbon, and strengthening and improving the operation standards of the plant. As a result, the operating performance of gas engine # 1 was increased by 530% and the operation of the second engine was increased by 250%. In addition, improvement of vent line equipment has reduced work process and increased normal operation time and operation rate. In terms of economic efficiency, it also showed a sales increase of KRW 77,000 / year. By applying the strengthening and improvement measures of operating standards, it is possible to reduce the stoppage of the biogas plant, increase the utilization rate, It is judged to be an operational plan.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.