• Journal of Surface Science and Engineering
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• v.53 no.6
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• pp.271-279
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• 2020

Non-ferrous metals, widely used in the mechanical industry, are difficult to machine, particularly by drilling and tapping. Since non-ferrous metals have a strong tendency to adhere to the cutting tool, the tool life is greatly deteriorated. Diamond-like carbon (DLC) is one of the promising candidates to improve the performance and life of cutting tool due to their low frictional property. In this study, a sacrificial DLC layer is applied on the hard nitride coated drill tool to improve the durability. The DLC coatings are fabricated by controlling the acceleration voltage of the linear ion source in the range of 0.6~1.8 kV. As a result, the optimized hardness(20 GPa) and wear resistance(1.4 x 10-8 ㎣/N·m) were obtained at the 1.4 kV. Then, the optimized DLC coating is applied as an sacrificial layer on the hard nitride coating to evaluate the performance and life of cutting tool. The Vickers hardness of the composite coatings were similar to those of the nitride coatings (AlCrN, AlTiSiN), but the friction coefficients were significantly reduced to 0.13 compared to 0.63 of nitride coatings. The drilling test were performed on S55C plate using a drilling machine at rotation speed of 2,500 rpm and penetration rate of 0.25 m/rev. The result showed that the wear width of the composite coated drills were 200 % lower than those of the AlCrN, AlTiSiN coated drills. In addition, the cutting forces of the composite coated drills were 13 and 15 % lower than that of AlCrN, AlTiSiN coated drills, respectively, as it reduced the aluminum clogging. Finally, the application of the DLC sacrificial layer prevents initial chipping through its low friction property and improves drilling quality with efficient chip removal.

• Journal of Korean Physical Therapy Science
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• v.16 no.3
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• pp.11-17
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• 2009

Background: The purpose of this study was designed to find out the effectiveness of reposition sense, muscle fatigue response on lumbar spine after apply lumbosacral stabilization exercise program to 4 patients with chronic low back pain and for 12 weeks. Method: In this study the reposition sense was measured in 3 angle(60, 30, 12) of the lumbar spine motion with blind by MedX test machine and the difference of instability to lumbar vertebra segments in flexion, extension test of standing position and spinal load test Mattress Test by Spinal Mouse. The stabilization exercise program was applied 2 times a week for 12 weeks in hospital and 2 times a day for 20 minutes at home. Result: The results of the present study were that the repositioning sense was appeared the most error in 12 angles of lumbar flexion and Men was appeared to decrease an error more than female in average value of 4 angles after 12 weeks. And average error of male was decrease more than female. Thus the effects of lumbosacral stabilization exercise was improved repositioning sense of prorioceptor. Fatigue response test(FRT) results, in male, was raised muscle fatigue rate during increase weight, on the other hand female appeared lower than male. Conclusion: As a results, lumbosacral stabilization exercise was aided to improvement of lumbar spine repositioning sense and vertebra segments stabilization. It was showed the rate of decrease in typically 12 degree angle point of each 3 angle(60, 36, 12). Especially, that spine instability patients will have a risk when in lifting a load or working with slight flexion posture around 12 degree during the daily of living life and it is probably to increase recurrence rate. Thus, not only lumbar extension muscle strength but also stability of vertebra segments in lumbar spine may be very important.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1229-1236
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• 2009

Puretone audiometer, which is a machine used for measuring the minimum hearing threshold, can be cost-effectively implemented using computer with sound card and software. In this paper, we describe a puretone audiometer which has been designed and implemented based on a general PC with sound card. It supports air conduction and bone conduction test taking with automatic masking. It also provides multiple modes consisted of self-test, auto-test and manual test mode. Such multiple modes makes it possible to use in various environments like as home and/or hospital. Through measure of waveform of output voltage and sound pressure, we verified that puretone audiometer of this paper properly operates.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• The Journal of Korean Academy of Prosthodontics
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• v.31 no.4
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• pp.482-505
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• 1993

The purpose of this study was to evaluate the fracture characteristics and the effect of resin bonding of laminate porcelain. In order to characterize the indentation-induced crack, Young's moduli and characteristic indentation dimensions were measured. The fatigue life under three point flexure test was measured using the electro-dynamic type fatigue machine, and the crack propagation with thermocycling was investigated on the condition of 15 second dwell time each in $5^{\circ}C\;and\;55^{\circ}C$ bath. The Vickers indentation pattern and the fracture surface were examined by an optical microscope and a scanning electron microscope (SEM). The results obtained were summarized as follows ; 1. Young's moduli(E) of the laminate porcelain and the resin cement used in this experiment were $62.56{\pm}3.79GPa$ and $15.01{\pm}0.12GPa$, respectively. 2. The initial crack size of the laminate porcelain was $69.19{\pm}5.94{\mu}m$ when an indentation load of 9.8N was applied, and the fracture toughness was $1.065{\pm}0.156MPa\;m^{1/2}$. 3. The fatigue life of laminate porcelain showed the constant fracture range at the stress level 27.46-35.30MPa. 4. When a cyclic flexure load was applied, the fatigue life of resin-bonded laminate porcelain was more decreased than that of laminate porcelain. 5. When a thermocycling was conducted, the crack growth rate of resin-bonded laminate porcelain was more increased than that of laminate porcelain. 6. Fracture surface showed the radial crack, the lateral crack, and the macroscopic crack branching region beneath the plastic deformation region when an indentation load of 9.8N was applied.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.87-96
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• 2006

This paper focused on the method for customizing software development path, considering the project environments and characteristics. he selection standard of development path is composed of ten items, based on the process of ISO/IEC TR 15721 Information Technology Guide for ISO/IEC 12207 (Software Life Cycle Process) and ISO/IEC 15504 Information technology - Process assessment. The ten items were reflected the project environments and characteristics, at the same time the items conduct the adjustment item of selecting project development path. An algorithm for selecting software development path through items of the project environments and characteristics is presented. To test the algerian in this paper, a system for selecting development path, which reflected algorithm was developed. The development project for web-based system were also adopted to the system for selecting development path. In addition, provened hand-worked project path process differed from machine-worked project path process. The reason why it differs is that outputs were mixed or their names were changed. The effect is to select easily software development route suitable for project environment and characteristics.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• Tunnel and Underground Space
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• v.13 no.5
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• pp.362-372
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• 2003

Since the occurrence of Portland cement, a great number of concrete structures were constructed. But the concrete structures have their own life times, which inevitably demand repairing treatments, especially on their surface parts. Currently many various methods have been developed and are being applied fer this purpose. In this study, a newly developed method using pneumatic chipping machine and anchor pin was adopted far repair of old concrete structure and the mechanical characteristics of cementing plane between existing and new concrete were tested. Comparing the removal methods for the decrepit part of existing concrete using pneumatic chipping machine and hydraulic breaker, the peak cohesion was higher when using chipping machine at the cementing plane. On the other hand, the residual cohesion was higher for the case of breaker. Step shaped chipping on the cementing plane was effective in increasing peak cohesion, which results 14% increase in the case of 30 mm step height and 22% in 50 mm height when compared with planar chipping plane. The use of anchor pin increased the residual cohesion, which restricted shear slip on the cementing plane after peak shear stress and the tensile strength of 32% compared with that of non-anchored case. According to the combined effect of step shaped chipping of 30 mm and anchor pin with an interval of 15 cm, the peak cohesion reached up to 77% and the residual cohesion showed 180% of the ones of the fresh concrete, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Smart Structures and Systems
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• v.13 no.3
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• pp.453-471
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• 2014

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.