• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.4
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• pp.1426-1436
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• 1996

The effectiveness of the regenerator pertaining to Stirling cycle machines is studied by analyzing the heat transfer characteristics of the oscillating flow in a tube, and a new consistent definition of the regenerator effectiveness can be expressed by the ratio of the heat transfer in axial direction (Q$\_$axial/) to that in transverse direction (Q/su w/), and its approximate form is obtained as a function of Nusselt number for practical utility. Since an approximate value of the ratio Q$\_$axial//Qsub w/ can be expressed in terms of the time-averaged rather than transient temperatures, this expression is useful to estimate the heat transfer coefficient in the Stirling machine regenerator experimentally.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.55-65
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• 2020

With the development of display hardware, image interpolation techniques have been used in various fields such as image zooming and medical imaging. Traditional image interpolation methods, such as bi-linear interpolation, bi-cubic interpolation and edge direction-based interpolation, perform interpolation in the spatial domain. Recently, interpolation techniques in the discrete cosine transform or wavelet domain are also proposed. Using these various existing interpolation methods and machine learning, we propose decision tree classification-based image interpolation methods. In other words, this paper is about the method of adaptively applying various existing interpolation methods, not the interpolation method itself. To obtain the decision model, we used Weka's J48 library with the C4.5 decision tree algorithm. The proposed method first constructs attribute set and select classes that means interpolation methods for classification model. And after training, interpolation is performed using different interpolation methods according to attributes characteristics. Simulation results show that the proposed method yields reasonable performance.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.236-243
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• 2004

In this paper, we proposed a brain-computer interface system using EEG signals. It can generate 4 direction command signal from EEG signals captured during imagination of subjects. Bandpass filter used for preprocessing to detect the brain signal, and the power spectrum at a specific frequency domain of the EEG signals for concentration status and non-concentration one is used for feature. In order to generate an adequate signal for controlling the 4 direction movement, we propose a new interface system implemented by using a support vector machine and a time-multiplexing method. Moreover, bio-feed back process and on-line adaptive pattern recognition mechanism are also considered in the proposed system. Computer experimental results show that the proposed method is effective to recognize the non-stational brain wave signal.

• Transactions of the Society of Information Storage Systems
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• v.2 no.4
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• pp.230-235
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• 2006

The machine locking system is an important device for the safety of persons using the machine. In this study, a locking system using electromagnetic fields is proposed to decrease the defects and the cost for repairing and maintenance of the existing locking system using structural mechanism. We analyze the electromagnetic locking system and calculate the generated force considering direction by the finite element method. Also, we set up two design domains for the topology optimization; first domain is optimized to reduce the volume and the other is optimized to maximize the generated force keeping the volume, especially. The optimal design is obtained by integration of the two optimized results. An improved design is obtained by the optimal topology and it is confirmed by comparison with the initial locking system.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.8
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• pp.1057-1064
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• 2004

Silk, polyester, cotton, and wool fabrics were embroidered with varying stitch length of 3mm, 5mm, and 10mm to examine the difference in fabric shrinkage in terms of sewing direction, fabric thickness, cover factor, stitch length, and fiber type. Warp, filling, and bias direction of sewing resulted in no difference in fabric shrinkage. Within the samples with same stitch length, there was less fabric shrinkage in fabrics with higher fabric thickness or higher cover factor. There was larger shrinkage when sewn with longer stitch length. Comparing fabrics with different fiber types but similar fabric thickness, silk and polyester fabrics showed the smallest fabric shrinkage and wool fabrics showed the largest shrinkage. It is shown that similar fabric shrinkage between silk and polyester is due to the similarity in cover factor.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.34 no.5
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• pp.49-55
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• 2002

A new method is proposed for the on-line measurement of the fiber orientation of sheet materials. The measurement of fiber orientation is very important in manufacturing paper sheets, non-woven fabrics, and glass sheets, because fiber orientation strongly affects product properties represented by, for example, dimensional stability of paper. A method developed in this research utilizes anisotropy of dielectric constants of sheet materials as a key characteristic to determine the fiber orientation. The new on-line sensor, consisting of 5 microwave dielectric resonators set in different directions, was designed to detect the fiber orientation while paper is running with high speed on a paper machine. This sensor can determine the direction and the degree of fiber orientation from the measured direction of the maximal dielectric constant and its variation, respectively. The fundamental performance of this system was examined by the static measurement of printing grade paper, which gave a satisfactory result. Then, the dynamic measurements were done at a speed of 1,000 m/min by using a high-speed test-coating machine.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.47 no.6
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• pp.49-56
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• 2015

To investigate fiber orientation of Korean traditional paper, Hanji, the fiber arrangements on the surface and cross section of Hanji were observed by scanning electron microscope (SEM). The surface observation showed that the fibers of traditional paper were arranged in vertical and horizontal directions, and those of ssangbal sheet forming were arranged more in vertical direction. The fibers of machine made Hanji and copying paper were usually arranged in vertical direction. As for the observation of cross section of traditional paper, fibers arranged in horizontal direction tended to be increased with the increase of thicknesses. The thick traditional paper have cracks in the middle part of structural section of paper. They were larger in cross section in horizontal direction than in vertical direction. The horizontal arrangement of traditional paper seemed to be about 8% to 14% higher to the vertical arrangement, which is very lower to other papers.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.111-116
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• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.11-17
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• 2019

In this paper, we propose a method to recognize the moving direction in the indoor environment by using the sensing system equipped with passive infrared (PIR) sensors and a deep learning algorithm. A PIR sensor generates a signal that can be distinguished according to the direction of movement of the user. A sensing system with four PIR sensors deployed by $45^{\circ}$ increments is developed and installed in the ceiling of the room. The PIR sensor signals from 6 users with 10-time experiments for 8 directions were collected. We extracted the raw data sets and performed experiments varying the number of sensors fed into the deep learning algorithm. The proposed sensing system using deep learning algorithm can recognize the users' moving direction by 99.2 %. In addition, with only one PIR senor, the recognition accuracy reaches 98.4%.

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.179-189
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• 2007

Most of the seeds of cucurbitaceous rootstock species used for grafting were mainly sown by hand. This study was carried out to develop an on-line discriminating algorithm of seed direction using machine vision and an automatic seeding system. The seeding system was composed of a supplying device, feeding device, machine vision system, reversing device, seeding device and system control section. Machine vision was composed of a color CCD camera, frame grabber, image inspection chamber, lighting and personal computer. The seed image was segmented into a region of seed part and background part using thresholding technique in which H value of HSI color coordinate system. A seed direction was discriminated by comparing position between the center of circumscribed rectangle to a seed and the center of seed image. It took about 49ms to identify and redirect seed. Line-up status of seed was good the more than 95% of a sowed seed. Seeding capacity of this system was shown to be 10,140 grains per hour, which is three times faster than that of a typical worker.