• Journal of Preventive Medicine and Public Health
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• v.10 no.1
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• pp.94-101
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• 1977

Noise pollution, both in the environment and in the workplace, has been recognized as a major health hazard -one that can impair not only a person's hearing but also his physical and mental well-being. As industrialization progresses, the prevalence rate of occupational diseases is increasing, especially hearing loss, which has the highest prevalence rate among the occupational diseases. The major cause of noise is the construction of various large industries without any regulation of noise sources. Therefor, we must establish an enactment to control mechanical noise sources. as soon as possible. For the purpose of controlling the noise source, we must have exact data about such things as the sound level, the frequency of the peak sound and the revolutions per minute (r.p.m.) of the machine (a measure of the power of its motor). This study was undertaken in order to define the noise characteristics, the power of the machine's motor, the change of the sound level and the peak sound as the r.p.m. increases, and the permissible exposure time. The sample size of this study was 74 machines at 11 plants in 6 industries. The results are as follows; 1. The breakdown of the types of mechanical noise noted was : 63.6% continuous normal sound, 26.9% intermittent sound, 4.7% continuous repeating sound and 4.6% impulsive sound. 2. With respect to the type of industry, the overall sound level was the highest in the mechanical industry, with $103.8{\pm}2.8dB(A)$, and lowest in the textile industry, with $89.2{\pm}1.43dB(A)$. 3. With respect to the type of machine, the highest sound level was 124 dB(A) caused by Gauzing(II), in the mechanical industry, and the lowest was 76 dB(A) caused by Attachment (Jup Chack) (I) in the timber industry. 4. The shortest permissible exposure time to Gauzing(II) in the mechanical industry was less than 15 minutes. 5. Among 74 machines, 68.2% of the peak sound was situated in the high frequency range (52.7% at 2 KHz, 4.1% at 4 KHz and 1.4% at 8 KHz). 41.8% of the peak sound was in the middle frequency range (4.1% at 250Hz, 14.8% at 500Hz and 22.9% at 1KHz). 6. If one machine had two motors or more, the peak sound was shifted to the low frequency range. 7. As the r.p.m. increased, the overall and peak sound levels were increased without any change of the frequency of the peak sound. 8. Whenever the machines had the same kind and the same r.p.m., the overall and peak sounds were changed by the physicochemical characteristics of the raw materials and the management.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1071-1081
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• 2021

In addition to the current quality control procedures for the weather observation performed by the Korea Meteorological Administration (KMA), this study proposes quality inspection standards for Internet of Things (IoT) urban weather observed data based on machine learning that can be used in smart cities of the future. To this end, in order to confirm whether the standards currently set based on ASOS (Automated Synoptic Observing System) and AWS (Automatic Weather System) are suitable for urban weather, usability was verified based on SKT AWS data installed in Seoul, and a machine learning-based quality control algorithm was finally proposed in consideration of the IoT's own data's features. As for the quality control algorithm, missing value test, value pattern test, sufficient data test, statistical range abnormality test, time value abnormality test, spatial value abnormality test were performed first. After that, physical limit test, stage test, climate range test, and internal consistency test, which are QC for suggested by the KMA, were performed. To verify the proposed algorithm, it was applied to the actual IoT urban weather observed data to the weather station located in Songdo, Incheon. Through this, it is possible to identify defects that IoT devices can have that could not be identified by the existing KMA's QC and a quality control algorithm for IoT weather observation devices to be installed in smart cities of future is proposed.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.384-395
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• 2020

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.134-139
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• 2023

Rehabilitators perform outpatient treatment and daily rehabilitation exercises to recover physical function with the aim of quickly returning to society after surgical treatment. Unlike performing exercises in a hospital with the help of a professional therapist, there are many difficulties in performing rehabilitation exercises by the patient on a daily basis. In this paper, we propose a CNN-LSTM-based upper limb rehabilitation real-time monitoring system so that patients can perform rehabilitation efficiently and with correct posture on a daily basis. The proposed system measures biological signals through shoulder-mounted hardware equipped with EMG and IMU, performs preprocessing and normalization for learning, and uses them as a learning dataset. The implemented model consists of three polling layers of three synthetic stacks for feature detection and two LSTM layers for classification, and we were able to confirm a learning result of 97.44% on the validation data. After that, we conducted a comparative evaluation with the Teachable machine, and as a result of the comparative evaluation, we confirmed that the model was implemented at 93.6% and the Teachable machine at 94.4%, and both models showed similar classification performance.

• The International Journal of Costume Culture
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• v.7 no.2
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• pp.77-86
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• 2004

The purpose of this study is to characterize physical and tensile properties according to a knitting method as basic materials for solving the difficulties that occur due to the fact that the crosswise elongation is most different among knit stitch at the time of measuring elongation of knitwear. The sample used for this study was wool $100\%$ and was knitted into two, that is, controlled loop length controlled to properties of structure and fixed loop length by using Shimaseiki SES-124S 12G computer automatic flat knitting machine with DSCS device. Also, the density of rib fabric was 12gauge and its quantity was a total of seven of $0{\times}0,\;1{\times}1,\;2{\times}1,\;2{\times}2,\;3{\times}3,\;4{\times}4$ and including plain fabric, and knitted 2 pieces of sample of 300 wale${\times}400$ course. In conclusion, rib stitch has the much higher stretch rate in the direction of the course than other stitches.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1999.06a
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• pp.215-219
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• 1999

One of the innovative physical methods that provide insight into the basic processes which determine friction and wear behavior of coated machine tools is acoustic emission (AE). In this study, an investigation of the relation between AE and friction signal produced during repeated sliding test is presented. The material of test specimens is CrN coated 0.2% plain carbon steel with 1 Um thickness. The obtained results demonstrate that AE signal is very related with friction, and AE signal is more sensitive than friction when CrN coated film come off the substrate.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.123-124
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• 2008

The appearance inspection of various electronic products and parts has been executed by the eyesight of human. But inspection by eyesight can't bring about uniform inspection result. Because the appearance inspection result by eyesight of human is changed by condition of physical and spirit of the checker. So machine vision inspection system is currently used to many appearance inspection fields instead of the checker. Therefore we proposes a inspection system in this paper. it will be able to secure the objectivity of the prosecuting attorney using inspection system. Also this system has been developed only using PC, CCD Camera and Visual C++ for universal workplace.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.20-26
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• 2017

Process control is crucial in many industries, especially in semiconductor manufacturing. In such large-volume multistage manufacturing systems, a product has to go through a very large number of processing steps with reentrant) before being completed. This manufacturing system has many machines of different types for processing a high mix of products. Each process step has specific quality standards and most of them have nonlinear dynamics due to physical and/or chemical reactions. Moreover, many of the processing steps suffer from drift or disturbance. To assure high stability and yield, on-line quality monitoring of the wafers is required. In this paper we develop a real-time fault detection system on semiconductor manufacturing process. Proposed system is superior to other incremental fault detection system and shows similar performance compared to batch way.

• Journal of Industrial Technology
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• v.25 no.B
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• pp.127-133
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• 2005

Manufacturing methods of Nano particles can be distinguished by top-down technology as physical method and bottom-up technology as chemical synthetic method. Top-down technology is a kind of method for making microstructure as like carving after forming a macroscopic structure in advance and its typical methods are ball milling, gas condensation method and so on. Nano Particles synthesized by bottom-up method have got to do dispersing process for using them as actual nano particles because their viscosity are very strong and so easy to shape cohesive substances. Therefore, this study is about a particle separating device which separates a certain constant size of grains processed already in mill and mixer because we mostly use media agitating mill as a device of milling and dispersing and we necessarily use very slight balls as media for manufacturing nano particles in the machine. The centrifugal device has been designed for passing and separating below a certain type of grain size after final process of particles in the mill.