• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.2
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• pp.3-9
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• 2009

When washing machine is rotated in the laundry, it tends to lean toward one side. This tendency causes a serious vibration. The balance of washing machine plays an important role in order to reduce the vibration by injecting the sand or the salt water into the balance of washing machine. The hot plate welder is used to prevent from outflow of contents. The hot plate welder brings about many problems which is concerned with accidents. The direction recognition and location information of the balance are required in this system. In this paper, the recognition direction of balance in washing machine using machine vision system is studied. The template matching algorithm compares sub-image with original image acquired in real-time to obtain a center point of balance image. The mid points and the edges of balance are estimated by the edge detection and gauging algorithms. The data acquired by these results is used for recognition direction of balance. The automation software for image processing is developed by using LabVIEW.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.31 no.5
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• pp.73-85
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• 1999

Yield and fracture are separated in the tensile failure of paper. Failure in the machine direction of photocopy paper is contrasted with failure in the cross-machine direction . The ratios of distortion (shape change) to dilatation (volume change) for individual elements at yield and fracture are described. The ratios of distortion to dilatation are measured and compared to predicted values of the strain energy density theory. To evaluate the effect of the angle from the principal material direction on the strain energy density theory. To evaluate the effect of the angle from the principal material direction on the strain energy density factor, samples are prepared from machine direction to cross-machine direction in 15 degree intervals. the strain energy density of individual elements are obtained by the integration of stress from finite element analysis with elastic plus plastic strain energy density theory. Poison's ratio and the angle from the principal material direction have a great effect ion the ratio fo distortion to dilatation in paper. During the yield condition, distortion prevails over dilatation . At fracture, dilatation is at a maximum.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1660-1663
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• 2005

Unwanted vibrations are inevitably induced in other directions when pure unidirectional vibration motion is desired for the vertical scratching mechanism. Pure vertical vibration motion of the scratching machine can be obtained by driving identical two motors with symmetrically positioned eccentric unbalance masses. The desired optimal condition for driving pure vertical vibration for the scratching machine is assumed to be the resonance condition in that direction. Imposing the flexibility of the scratching machine in the horizontal direction, we can find out the amount of horizontal vibration component while maintaining the resonance in vertical direction. The desired stiffness in horizontal direction which produces the minimum vibration in horizontal direction are defined which can be used as a guide line to design the supporting structure of the scratching machine.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.1 s.190
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• pp.7-13
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• 2007

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.7 no.1
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• pp.9-15
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• 2001

Top-to-bottom compression strength of corrugated fiberboard boxes is partly dependent on the load-carrying ability of the central panel areas. The ability of these central areas to resist bending under load will increase the stacking strength of the box. The difference of box compression strengths, among boxes which are made with identical dimensions and fabricated with same components but different flute sizes, is primarily due to difference of the flexural stiffness of the box panels. Top-to-bottom compression strength of a box is accurately predicted by flexural stiffness measurements and the edge crush test of the combined boards. This study was carried out to analyze the flexural stiffness, maximum bending force and maximum deflection for various corrugated fiberboards by experimental investigation. There were significant differences between the machine direction (MD) and the cross-machine direction (CD) of corrugated fiberboards tested. It was about 50% in SW and DW, and $62%{\sim}74%$ in dual-medium corrugated fiberboards(e.g. DM, DMA and DMB), respectively. There were no significant differences of maximum deflection in machine direction among the tested fiberboards but, in cross direction, DM showed the highest value and followed by SW, DMA, DMB and DW in order. For the corrugated fiberboards tested, flexural stiffness in machine direction is about $29%{\sim}48%$ larger than cross direction, and difference of flexural stiffness between the two direction is the lowest in DMA and DMB.

• Korean Journal of Applied Biomechanics
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• v.32 no.1
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• pp.1-8
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• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.7 no.2
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• pp.25-30
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• 2001

Corrugated board is composed of cellulose fibers which are arranged with the same direction as the board manufactured. The direction is classified with machine direction (MD) and cross-machine direction (CD). Therefore, corrugated board is orthotropic material that has totally different strength properties at each direction and especially, at machine direction, the mechanical properties of fiberboard is superior. The compression strength of the corrugated fiberboard boxes is very important information to the manufacturers and the end users. This study was carried out to design the optimum pattern, size, and location of ventilating hole for ventilating container through the finite element analysis. The optimum pattern and location of ventilating and hand hole were vertical oblong, a short distance to the right and left from the center of panel, and center or a short distance to the top of both sides, respectively. We identified the effect on both stress dispersion and stress level from the analysis of redisigned hand hole.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.225-230
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• 2002

Machine of textiles let off is equipment supplying constantly fabrics. Nowdays, as it is replaced band - brake type with hydraulic motor driven type, we looked into characteristic of hydraulic solenoid pilot direction valve(SPDV) for controlling acceleration performance of hydraulic motor. This study deals with controlling the initial speed of textiles let off machine. Finally, to control the initial speed of hydraulic motor, we controlled the adjustment screw of SPDV by a hand. Test which was carried out in the laboratory shows that initial speed of textiles let off could be improved by controlling adjustment screw of SPDV. Also, the results of experiment work were compared with dynamic characteristic of other on/off solenoid direction valve(SDV).

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.184-189
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• 2017

With the advent of robust deep learning method, Neural machine translation has recently become a dominant paradigm and achieved adequate results in translation between popular languages such as English, German, and Spanish. However, its results in under-resourced languages Korean and Vietnamese are still limited. This paper reports an attempt at constructing a bidirectional Korean-Vietnamese Neural machine translation system with the supporting of Korean analysis tool - UTagger, which includes morphological analyzing, POS tagging, and WSD. Experiment results demonstrate that UTagger can significantly improve translation quality of Korean-Vietnamese NMT system in both translation direction. Particularly, it improves approximately 15 BLEU scores for the translation from Korean to Vietnamese direction and 3.12 BLEU scores for the reverse direction.