• Journal of Platform Technology
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• v.10 no.3
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• pp.21-29
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• 2022

Recently, the scope of the smart factory has been expanded, and process research to minimize the part that requires manpower in many processes is increasing. In the case of detecting defects in the appearance of small products, precise verification using a vision system is required. Reliability and speed of inspection are inefficient for human inspection. In this paper, we propose an algorithm for inspecting product appearance defects using a machine vision system. In the case of the remote control targeted in this paper, the appearance is different for each product. Due to the characteristics of the remote control product, the data obtained using two cameras is compared with the master data after denoising and stitching steps are completed. When the algorithm presented in this paper is used, it is possible to detect defects in a shorter time and more accurately compared to the existing human inspection.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.979-983
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• 1993

A power operated (0.5 hp electric motor) grain flour separator was designed and developed for separation of grain (wheat, corn, chickpea and soybean) flour it no various fractions based on the size of the particles of the product. The separator is made of mild steel and consists of a hopper, power driven agitating mechanism, feed control , cylindrical separator unit and an eccentric mechanism. The machine was tested for wheat (variety : Subjata) flour separation into four fraction, viz : semolina ; Gr-I and II, flour (coarse) and white (fine) flour. Wheat samples (6.8% m.c., db) were first pearled by CIAE pearler for 15.8% bran removal . The product and machine characteristics were determined at different capacities varying from 24 kg/h to 143 kg/h. It was found that 76 kg/h capacity gave reasonably best results in terms of purity and recovery of semolina vis-a-vis the market product. The energy requirement of the machine at no-load was found to be 230 w and at load conditio s, it varied between 36.3-6.4kj per kg of feed separation. The machine could be used by small flour millers, small/medium size traders and retailers and other processors for making available various flour products of different particle size in the market for ready use fo the consumers.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.473-476
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• 2005

Today's manufacturing industry is facing challenges from advanced difficult-to-machine materials (WC-Co alloys, ceramics, and composites), stringent design requirements (high precision, complex shapes, and high surface quality), and machining costs. Advanced materials play an increasingly important role in modem manufacturing industries, especially, in aircraft, automobile, tool, die and mold making industries. The greatly-improved thermal, chemical, and mechanical properties of the material (such as improved strength, heat resistance, wear resistance, and corrosion resistance), while having yielded enormous economic benefits to manufacturing industries through improved product performance and product design, are making traditional machining processes unable to machine them or unable to machine them economically. In this paper, mechanical characteristic evaluation test of fine powder type WC-Co alloy was accomplished to obtain clear data for miniaturized special die parts machining with high reliability and high quality.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.59-60
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• 2012

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.3
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• pp.315-320
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• 2009

The purpose of this study is to predict the roundness of Numerical Control Machining so that helps the operator to choose the right machining conditions to produce a product within the given error limits. Learning of neural network is Backpropagation theory. From this study, the base was set to setup the database to produce precisely machined product by predicting the rate of error in the fabrication facility which does not have the environment to analyze it.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.1
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• pp.59-73
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• 1991

In this paper, how to evaluate the assembly system was studied. The traction machine(TRM/C) is a kind of medium sized small quantity product. This work was performed on restriction of satisfying the customer's demand by simulating on the criteria of the queuing quantity in production system, the effectiveness of assembly machine and the average time spent on assembling. The problems about assembling were grasped through the analysis of products and process. The outputs of simulations that used SIMAN on four alternatives were evaluated on the basis of the average queuing quantity, the line effectiveness, the assembly time of a product and the production capacity.

• Korean Journal of Mathematics
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• v.28 no.4
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• pp.973-984
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• 2020

This paper is devoted to study the reproducing property on 2-inner product Hilbert spaces. We focus on a new structure to produce reproducing kernel Hilbert and Banach spaces. According to multi variable computing, this structures play the key role in probability, mathematical finance and machine learning.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.5 no.2
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• pp.53-61
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• 2001

We introduce sums and joins of fuzzy finite state machines and investigate their algebraic structures.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3
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• pp.349-355
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• 2013

Recently, there is an increasing need to produce more precise products with small deviations from defined target values. Machine capability is the ability of a machine tool to produce parts within a tolerance interval. Capability indices are a statistical way of describing how well a product is machined compared to defined target values and tolerances. Today, there is no standardized way to acquire a machine capability value. This paper describes a method for evaluating machine capability indices in machining centers. After the machining of specimens, the straightness, roundness, and positioning accuracy were measured by using CMM (coordinate measuring machine). These measured values and defined tolerances were used to evaluate the machine capability indices. It will be useful for the industry to have standardized ways to choose and calculate machine capability indices.