• Journal of Korean Medicine Rehabilitation
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• 제32권1호
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• pp.51-62
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• 2022

Objectives Objective of this study is to investigate the role of paraspinal muscles by examining the correlation between slip percentage (SP) of spondylolisthesis and fatty infiltration of lumbar paraspinal muscle. Methods Retrospective analysis was performed on 45 patients diagnosed with spondylolisthesis based on medical records. Using T2-weighted axial magnetic resonance imaging, cross-sectional areas (CSAs) of psoas major (PM), multifidus (MU) and erector spinae (ES) were calculated and divided by CSA of lower level vertebral body (VB). SP was measured using sagittal T2-weighted images. Correlation of SP with muscle relative cross-sectional area (RCSA) and muscle fatty infiltration by Goutallier classification was respectively analyzed using Spearman correlation. Statistic assessment conducted by Wilcoxon signed rank test and paired t-test using program GraphPad prism 5 (GraphPad Software, Inc., San Diego, CA, USA). Results Spondylolisthesis forward slip percentage by Taillard's method was negatively associated with both side MU RCSAs. No significant correlation was found between PM RCSA, ES RCSA and SP. Forward slippage was significantly correlated with fatty infiltration of lumbar paraspinal muscle measured by Goutallier classification. Conclusions This study is to understand the role of paraspinal muscle affecting spinal instability by investigating correlation between statistical deviation of lumbar muscle characters (RCSA, fatty infiltration of lumbar muscle) and SP. We found that spondylolisthesis SP is positively related to fatty infiltration of lumbar paraspinal muscle. and is negatively associated with both side MU RCSAs.

• Journal of Intelligence and Information Systems
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• 제28권3호
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• pp.209-235
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• 2022

Starting from 2010, blockchain technology, along with the development of artificial intelligence, has been in the spotlight as the latest technology to lead the 4th industrial revolution. Furthermore, previous research regarding blockchain's technological applications has been ongoing ever since. However, few studies have been examined the standards for classifying the blockchain economic ecosystem from a capital market perspective. Our study is classified into a collection of interviews of software developers, entrepreneurs, market participants and experts who use blockchain technology to utilize the blockchain economic ecosystem from a capital market perspective for investing in stocks, and case study methodologies of blockchain economic ecosystem according to application fields of blockchain technology. Additionally, as a way that can be used in connection with equity investment in the capital market, the blockchain economic ecosystem classification methodology was established to form an investment universe consisting of global blue-chip stocks. It also helped construct an intelligent portfolio through quantitative and qualitative analysis that are based on quant and artificial intelligence strategies and evaluate its performances. Lastly, it presented a successful investment strategy according to the growth of blockchain economic ecosystem. This study not only classifies and analyzes blockchain standardization as a blockchain economic ecosystem from a capital market, rather than a technical, point of view, but also constructs a portfolio that targets global blue-chip stocks while also developing strategies to achieve superior performances. This study provides insights that are fused with global equity investment from the perspectives of investment theory and the economy. Therefore, it has practical implications that can contribute to the development of capital markets.

• Journal of Nutrition and Health
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• 제56권4호
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• pp.391-403
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• 2023

Purpose: This study evaluated the validity of a physical activity classification table (PACT) based on total energy expenditure (TEE) and physical activity level (PAL) measured using the doubly labeled water (DLW) method in Korean adults and the elderly. Methods: A total of 141 (male 70, female 71) adults and elderly were included. The reference standards TEE_DLW, PAL_DLW were measured over a 14-day period using DLW. A 24-hour physical activity diary was kept for three days (two days during the week and one day on the weekend). PAL_PACT was calculated by classifying the activity type and intensity using the PACT. PAL_PACT was multiplied by resting energy expenditure measured by indirect calorimetry to estimate TEE_PACT. Results: The mean age of the study participants was 50.5 ± 18.8 years, and the mean body mass index was 23.4 ± 3.3 kg/m². A comparison of TEE_DLW and TEE_PACT by sex and age showed no significant differences. The bias, the difference between TEE_DLW and TEE_PACT, was male 17.3 kcal/day and female -4.5 kcal/day. The percentage of accurate predictions (values within ± 10% of the TEE_DLW) of TEE_PACT was 58.6% in males and 54.9% in females, with the highest prediction values in the age group 40-64 years (70.9%) in males and over 65 years (73.9%) in females. The spearman correlation coefficient (r) between TEE_PACT and TEE_DLW was 0.769, indicating a significant positive correlation (p < 0.001). Conclusion: In this study, the use of a new PACT for calculating TEE and PAL was evaluated as valid. A web version of the software program and a smartphone application need to be developed using PACT to make it easier to apply for research purposes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.365-368
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• 2021

In this study, a plastic bottle recycling bin system that utilizes an infrared sensor was implemented. The proposed system consists of a recognition unit, a control unit, an alarm unit, and a driving unit. The recognition unit detects the plastic bottle, measures the distance between the plastic bottle and the infrared sensor, extracts the value of the bottle, compares the extracted value with a standard range, and then transmits the control value to the control unit if the extracted value of the bottle is outside the standard range. In this case, the result of the presence or absence of a brand label or bottle cap is transmitted to the controller. The control unit opens the entrance of the recycling bin or alerts the alarm unit according to the result value transmitted from the sensor unit. In order to implement the proposed system, the recognition unit was implemented with an infrared sensor, and the control unit was made with an Arduino IDE controller, based on the C programming language. Additionally, the recognition unit and the control unit are able to communicate using analog signals. The proposed system accurately judges the presence or absence of a brand label and bottle cap of plastic bottles according to a predetermined algorithm. It then blocks the entrance of the recycling bin when a brand label or bottle cap is still attached. As the amount of waste discharged per person is relatively high and the majority of such waste is incinerated rather than recycled, the system proposed in this study is expected to increase the recycling rate of plastic bottles.

• Journal of Industrial Convergence
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• 제21권9호
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• pp.41-48
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• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.

• KIPS Transactions on Software and Data Engineering
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• 제9권3호
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• pp.101-108
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• 2020

Since the performance of deep learning techniques has recently been proven in the field of image processing, there are many attempts to perform classification, analysis, and detection of images using such techniques in various fields. Among them, the expectation of medical image analysis software, which can serve as a medical diagnostic assistant, is increasing. In this study, we are attention to the capsule endoscope image, which has a large data set and takes a long time to judge. The purpose of this paper is to distinguish the gastrointestinal landmarks and to estimate the gastrointestinal transition location that are common to all patients in the judging of capsule endoscopy and take a lot of time. To do this, we designed CNN-based Classifier that can identify gastrointestinal landmarks, and used it to estimate the gastrointestinal transition location by filtering the results. Then, we estimate gastrointestinal transition location about seven of eight patients entered the suspected gastrointestinal transition area. In the case of change from the stomach to the small intestine(pylorus), and change from the small intestine to the large intestine(ileocecal valve), we can check all eight patients were found to be in the suspected gastrointestinal transition area. we can found suspected gastrointestinal transition area in the range of 100 frames, and if the reader plays images at 10 frames per second, the gastrointestinal transition could be found in 10 seconds.

• Journal of the Korean Society of Marine Environment & Safety
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• 제24권2호
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• pp.146-156
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• 2018

The ship's chart system was changed from the use of paper chart to the ENC (Electronic Navigational Chart) using ECDIS (Electronic Chart Display and Information System). The introduction of ENC in ships is necessary for ship automation and for the digitalizing of data and integration of information, but unexpected various problems have occurred and are posing a great threat to safe navigation in the transitional period when the new system has been applied to the sea. In this paper, to assess whether ECDIS contributes to the safety of navigation for its intended purposes as new navigation equipment, a usability test of ECDIS was conducted on masters and crew who have used ECDIS on ocean-going vessels. The result was verified with a paired sample T-test, and it was significantly analyzed with the effectiveness of a simplified task; cost efficiency was decreased since ECDIS was used. By analyzing 'MSC.1/Circ.1503 ECDIS - Guidance for good practice', we found that the effects of the maintenance of ECDIS software, operating anomalies identified within ECDIS, differences between raster chart display system (RCDS) and ECDIS, and matters of identification were compounded by the overlapping information on the safety of ships. The anomalies were also grouped according to their characteristics, and we proposed suitable improvements accordingly. The reason for the reduction in efficiency in the usability test was that the problems with ECDIS were intended to be solved only with the careful use of navigational officers who did not have systematic solutions. To solve these problems, the maintenance of software, the improvement of ECDIS anomalies, the reliable ENC issuance including the global oceans, and S-mode development are a priority.

• Journal of the Society of Naval Architects of Korea
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• 제54권2호
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• pp.151-160
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• 2017

Growing demand and rapid development of the synthetic fiber rope in mooring system have taken place since it has been used in deep water platform lately. Unlike a chain mooring, synthetic fiber rope composed of lightweight materials such as Polyester(polyethylene terephthalate), HMPE(high modulus polyethylene) and Aramid(aromatic polyamide). Non-linear stiffness and another failure mode are distinct characteristics of synthetic fiber rope when compared to mooring chain. When these ropes are exposed to environmental load for a long time, the length of rope will be increased permanently. This is called 'the creep phenomenon'. Due to the phenomenon, The initial characteristics of mooring systems would be changed because the length and stiffness of the rope have been changed as time goes on. The changed characteristics of fiber rope cause different mooring tension and vessel offset compared to the initial design condition. Commercial mooring analysis software that widely used in industries is unable to take into account this phenomenon automatically. Even though the American Petroleum Institute (API) or other classification rules present some standard or criteria with respect to length and stiffness of a mooring line, simulation guide considers the mechanical properties that is not mentioned in such rules. In this paper, the effect of creep phenomenon in the fiber rope mooring system under specific environment condition is investigated. Desiged mooring system for a Mobile Offshore Drilling Unit(MODU) with HMPE rope which has the highest creep is analyzed in a time domain in order to investigate the effects creep phenomenon to vessel offset and mooring tension. We have developed a new procedure to an analysis of mooring system reflecting the creep phenomenon and it is validated through a time domain simulation using non-linear mooring analysis software, OrcaFlex. The result shows that the creep phenomenon should be considered in analysis procedure because it affects the length and stiffness of synthetic fiber rope in case of high water temperature and permanent mooring system.

• Journal of KIISE:Software and Applications
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• 제29권7호
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• pp.465-475
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• 2002

An object-oriented framework supports efficient component-based software development by providing a flexible architecture that can be decomposed into easily modifiable and composable classes. Object-oriented frameworks require thorough testing as they are intended to be reused repeatedly In developing numerous applications. Furthermore, additional testing is needed each time the framework is modified and extended for reuse. To test a framework, it must be instantiated into a complete, executable system. It is, however, practically impossible to test a framework exhaustively against all kinds of framework instantiations, as possible systems into which a framework can be configured are infinitely diverse. If we can classify possible configurations of a framework into a finite number of groups so that all configurations of a group have the same structural or behavioral characteristics, we can effectively cover all significant test cases for the framework testing by choosing a representative configuration from each group. This paper proposes a systematic method of classifying object structures of a framework hotspot and extracting structural test patterns from them. This paper also presents how we can select an instance of object structure from each extracted test pattern for use in the frameworks hotspot testing. This method is useful for selection of optimal test cases and systematic construction of executable test target.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제22권4호
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.