• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.1063-1070
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• 2011

The manufacturing execution system (MES) for product production handles different production processes according to the product characteristics and different types of data according to the process being considered. For efficiently providing the data pertaining to production equipment to production systems such as the MES, data collection through the equipment interface is required for obtaining the production data pertaining to field equipment. In this paper, a method is proposed for collecting the production data through the equipment interface in order to collect the various types of production-equipment data from the field. The proposed method is applied to a real manufacturing system to verify its efficiency. A more powerful MES can be constructed with a data acquisition system that acquires the status data at the shop-floor level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.248-256
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• 2007

Recently, the network Infrastructure grows rapidly and the digital image compression technique has made remarkable progress. Therefore, the demand of the real-time image surveillance system which uses a network camera server has been increasing. Network Camera Server has emerged as an attractive alternative to the CCTV for the wireless video surveillance. In this article, the model of JPEG Streamer for collecting and delivering JPEG image is designed and realized as a key module for the wireless video surveillance system. The thread pool and shared memory have been used to improve the stability and efficiency of the JPEG Streamer. In addition, the concept of double buffering is of much benefit to improve the quality of real-time image. In this article, the wireless video surveillance system by using JPEG Streamer is suggested to send the real-time image through the wireless internet with the personal digital assistance (PDA).

• Journal of Power System Engineering
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• v.22 no.6
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• pp.51-57
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• 2018

In Korea, the Air Quality Conservation Act and the Petroleum and Petroleum Substitute Fuel Business Act stipulate certain quality standards for fuels distributed in Korea, thereby striving to reduce vehicle performance and emissions. Domestic petroleum products import and produce all the crude oil from each oil refiner so that the quality of the petroleum product is different according to the characteristics of the crude oil. As a result, vehicles have been improved by using the physical properties calculated through the physical property measurement that has tried to improve the accuracy of the measurement of the energy consumption efficiency of the automobile by using standard fuel from abroad. In this study, the same test procedure and method as the test method of domestic gasoline vehicle emission are applied using four samples of gasoline and the latest gasoline vehicle which are actually distributed, and the performance evaluation is performed. The purpose of this study is to contribute to improvement of vehicle technology and fuel quality by collecting necessary basic data and obtaining data on the effect of differences in gasoline property on vehicle emissions. The results of the test showed that the emission of gases (NMOG, CO) from gasoline vehicles was the most influenced by the sulfur content, unlike the previous studies that the vehicles emission had the greatest influence on the distillation characteristics and the specific gravity of aromatic compounds. The catalytic reaction such as the poisoning action of the three-way catalyst which is the abatement device was interfered and the emission was increased. The distillation characteristics and specific gravity of aromatic compounds were found to affect the emission of vehicles. According to the physical properties of the fuel, the emission difference was 28.0% in the urban mode and 17.6 % in the highway mode.

• Journal of the Korean Society for Railway
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• v.11 no.6
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• pp.562-568
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• 2008

The modern logistics has tried not only to convert the conventional packing system into unit load system using pallet but also to enhance the fitness between packing facilities and transport modes. This is based on the goal to reduce total logistics cost by improving logistics efficiency. Since the packing unit can affect both loading rate and loading facilities, basic form of packing unit is very important to unit load system. The object of this study is to develop a new method for evaluating the effectiveness of standardized packing module. The new method is based on measure of effectiveness (MOE) which are identified by expert survey. This study has originality in that the collecting method for effectiveness of standardized packing module has not been developed up to now.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.16-21
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• 2019

Small and medium-sized Korean companies are currently changing their industrial structure faster than in the past due to various environmental factors (such as securing competitiveness and developing excellent products). In particular, the importance of collecting and utilizing data produced in smart factory environments is increasing as diverse devices related to artificial intelligence are put into manufacturing sites. This paper proposes an artificial intelligence-based smart factory model to improve the process of products produced at the manufacturing site with the recent smart factory. The proposed model aims to ensure the increasingly competitive manufacturing environment and minimize production costs. The proposed model is managed by considering not only information on products produced at the site of smart factory based on artificial intelligence, but also labour force consumed in the production of products, working hours and operating plant machinery. In addition, data produced in the proposed model can be linked with similar companies and share information, enabling strategic cooperation between enterprises in manufacturing site operations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.618-620
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• 2019

Smart Farm, which combines agriculture and ICT convergence technology, is at a lower stage than other industries in Korea, but it is also one of the most active research and development fields. Smart Farm aims to improve the efficiency of each step by collecting, processing and analyzing various information of agriculture sector through convergence between agriculture and ICT technology. In this study, we studied the image processing method that can distinguish strawberry which can be harvested at harvest time by color for smart farm composition of strawberry which is a horticultural crop. Strawberry harvesting requires a lot of labor in the process of growing strawberries. In this study, we aim to collect information necessary for labor saving in strawberry harvester. As a precedent study, we plan to implement a form in which the color temperature changes according to the light direction and brightness value through OpenCV color detection using Python. In the future, it is planned to study strawberry color value suitable for harvest by applying compensation value to color temperature change.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.12
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• pp.43-53
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• 2019

The construction industry has made great strides in the past decades by utilizing computer programs including CAD. However, compared to other manufacturing sectors, labor productivity is low due to the high proportion of workers' knowledge-based task in addition to simple repetitive task. Therefore, the knowledge-based task efficiency of workers should be improved by recognizing the visual information of computers. A computer needs a lot of training data, such as the ImageNet project, to recognize visual information. This study, aim at proposing building facade datasets that is efficiently constructed by quickly collecting building facade data through portal site road view and automatically labeling using deep learning as part of construction of image dataset for visual recognition construction by the computer. As a method proposed in this study, we constructed a dataset for a part of Dongseong-ro, Daegu Metropolitan City and analyzed the utility and reliability of the dataset. Through this, it was confirmed that the computer could extract the significant facade information of the portal site road view by recognizing the visual information of the building facade image. Additionally, In contribution to verifying the feasibility of building construction image datasets. this study suggests the possibility of securing quantitative and qualitative facade design knowledge by extracting the facade design knowledge from any facade all over the world.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.297-305
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• 2021

A contractor or a main contractor is a company with full capacity to construct all project's works for the owner. A subcontractor is an organization that works with the main contractor to execute and complete work packages for the project. Selecting an effective subcontractor will help the efficiency and success of any projects in the construction industry. Therefore, this study identified subcontractor evaluation factors in Vietnam by collecting questionnaire survey data from engineers and staffs in the construction industry project environment. An exploratory factor analysis (EFA) was then performed to identify the critical factors when evaluating and selecting the subcontractor in construction projects. Moreover, when considering the impact level in terms of the average value, the research results showed that the most critical concern was the subcontractor's reputation. Furthermore, the top five factors affecting the sub-contractor evaluation and selection are (i) reputation, (ii) price, (iii) construction techniques, (iv) ability to implement projects according to commitments, and (v) subcontractor competence (the team of workers, technician staff, engineers with full capacity according to regulations). These research results provide an overall perspective that will help main contractors develop suitable subcontractors' evaluation and selection factors in their projects in the construction industry.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.