• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.5
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• pp.37-43
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• 2008

The durability test of turbo-blower for PEM fuel cell is very important process of BOP development. It is a major barrier to the commercialization of these systems for stationary and transportation power applications. Commercial viability depends on improving the durability of the air supply system to increase the reliability and to reduce the lifetime cost. In this study, turbo-blower supported by oil-free bearing is introduced as the air supply system used by 80kW proton exchange membrane fuel systems. The turbo-blower is a turbo machine which operates at high speed, so air foil bearings suit their purpose as bearing elements. The impeller of blower was adopted mixed type of centrifugal and axial. So, it has several advantages for variable operating condition. The turbo-blower test results show maximum parasitic power levels below 1.67kW with the 30,000 rpm rotating speed, the flow rate of air has maximum 163SCFM(@PR1.1). For proper application of FCV, these have to durability test. This paper describes the experiment for confirming endurance and stability of the turbo-blower for 500 hours.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.90-105
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• 2004

Machines for crack sealing automation have been continually developed since the early 1990's because of the effectiveness of crack sealing that would be able to improve safety, quality and productivity. It has been considered challenging problem to detect crack network in pavement which includes noise (oil marks, skid marks, previously sealed cracks and inherent noise). Moreover, it is required to develop crack network mapping and modeling algorithm in order to accurately inject sealant along to the middle of cut crack network. The primary objective of this study is to propose machine vision algorithms (digital image processing algorithm and path planning algorithm) for fully automated pavement crack sealing. It is anticipated that the effective use of the proposed machine vision algorithms would be able to reduce error rate in image processing for detecting, mapping and modeling crack network as well as improving quality and productivity compared to existing vision algorithms.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• Tribology and Lubricants
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• v.33 no.6
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• pp.288-295
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• 2017

In order to improve the efficiency and reliability of the machine, the friction should be minimized. The most widely used method to minimize friction is to maintain the fluid lubrication state. However, we can reduce friction only up to a certain limit because of viscosity. As a result of several recent studies, surface texturing has significantly reduced the friction in highly sliding machine elements, such as mechanical seals and thrust bearings. Thus far, theoretical studies have mainly focused on isothermal/iso-viscous conditions and have not taken into account the heat generation, caused by high viscous shear, and the temperature conditions on the bearing surface. In this study, we investigate the effect of dimple depth and film-temperature boundary conditions on the thermohydrodynamic (THD) lubrication of textured parallel slider bearings. We analyzed the continuity equation, the Navier-Stokes equation, the energy equation, and the temperature-viscosity and temperature-density relations using a computational fluid dynamics (CFD) code, FLUENT. We compare the temperature and pressure distributions at various dimple depths. The increase in oil temperature caused by viscous shear was higher in the dimple than in the bearing outlet because of the action of the strong vortex generated in the dimple. The lubrication characteristics significantly change with variations in the dimple depths and film-temperature boundary conditions. We can use the current results as basic data for optimum surface texturing; however, further studies are required for various temperature boundary conditions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

• Tribology and Lubricants
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• v.37 no.1
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• pp.7-13
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• 2021

Ball bearing is a device that supports and transmits a load acting on a rotating shaft, and it is a type of rolling bearings that uses the rolling friction of the balls by inserting balls between the inner ring and the outer ring. Grease, which is prepared by mixing a thickener with a base oil, is a lubricant commonly used in bearings and has the advantage of a simple structure and easy handling. Bearings are increasingly being used in high value-added products such as semiconductors, aviation, and robots in the era of the 4th industrial revolution. Accordingly, there is an increasing demand for bearing grease. The selection of grease is an important factor in the bearing design. Therefore, a study must be conducted on the grease life evaluation to select an appropriate grease according to operating conditions such as a high temperature, high rotational speed, and high load. In this study, we evaluate the life of ball-bearing grease according to various operating conditions, namely, temperature, speed, and load changes. For this, we develop and theoretically verify a grease life test machine for ball bearings. We conduct a life test of grease according to various operating conditions of bearings and predict the grease life with a 10% and 50% failure probability using the Weibull analysis. In addition, we analyze the oxide characteristics of the grease over time using the Fourier transform infrared spectroscopy and the deterioration characteristics of the grease using the carbonyl index.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.19-27
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• 2022

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.

• 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.

• Journal of Navigation and Port Research
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• v.42 no.1
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• pp.9-16
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• 2018

The development of limited petroleum resources for use with mankind inevitably explores and seeks to develop oil fields in the deep sea area, under the rise of the oil prices market situation. The use of Oceanic Thermal Energy Conversion (OTEC) technology, which operates the power generation facility using the temperature differences between the deep water and the surface water, is progressing actively as a trend to follow. In this study, the application of the Discharged Thermal Energy Conversion (DTEC) was designed and analyzed under the condition that the supply condition of seawater used in the FPSO installed in the deep sea area is changed up to 400m depth. In this case, it was confirmed that the design of the system that can generate more electric power according to the depth of water is confirmed, by thus applying the DTEC system by taking the cooling water at a deeper water depth than the existing design water depth. The FPSO considers the similarity of the OTEC power generation facilities, and will apply the DTEC system to FPSO in the deep sea area to accumulate technology and the conversion to further utilize the OTEC power generation facilities after the end of life cycle of oil production, which could be a solution to two important issues, namely, resource development and sustainable development.