• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.152-159
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• 2016

This paper describes an offset and 1/f noise cancellation technique based hall sensor signal processor. The hall sensor outputs a hall voltage from the input magnetic field, which direction is orthogonal to hall plate. The two major elements to complete the hall sensor operation are: the one is a hall sensor to generate hall voltage from input magentic field, and the other one is a hall signal process system to cancel the offset and 1/f noise of hall signal. The proposed hall sensor splits the hall signal and unwanted signals(i.e. offset and 1/f noise) using a spinning current biasing technique and chopper stabilizer. The hall signal converted to 100 kHz and unwanted signals stay around DC frequency pass through chopper stabilizer. The unwanted signals are bloked by highpass filter which, 60 kHz cut off freqyency. Therefore only pure hall signal is enter the ADC(analog to dogital converter) for digitalize. The hall signal and unwanted signal at the output of an amplifer and highpass filter, which increase the power level of hall signal and cancel the unwanted signals are -53.9 dBm @ 100 kHz and -101.3 dBm @ 10 kHz. The ADC output of hall sensor signal process system has -5.0 dBm hall signal at 100 kHz frequency and -55.0 dBm unwanted signals at 10 kHz frequency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.452-458
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• 2017

In this study, a device was developed for diagnosing depression using EEG signals from July 2016 to June 2017. For normal people, the left alpha rhythm is more activated than the right alpha rhythm, but for the depressed patients, the right alpha rhythm is more activated than the left one. An analog circuit and digital low pass filter were used for noise removal and amplification of EEG, and the Hamming window function was applied to eliminate the signal leakage generated by the fast Fourier transform. To verify the validity of the developed diagnosis system, the EEG of 20 university students in the 3rd and 4th grade with an average age of 24 years was measured. Calculations of the relative value of the left and right alpha rhythm for the depression diagnosis revealed a minimum, maximum, and mean value of 66.7, 113.3, and 92.2, respectively. In addition, 7 out of 20 subjects were between 90 and 95, and those with a higher mean deviation of approximately 20 tended to have mild depression. These results can provide meaningful data for the development of depression treatment equipment by solving the left and right brain asymmetry problem, and it may be applied usefully to diagnose depression after clinical trials on a large number of depressed patients.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.139-149
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• 2008

A load effect by vehicles running on a road and an increase of traffic is distinguished as a serious issue in the level of bridges' maintenance and management since it causes a quick damage of bridges. The expansion joint is the most important since it makes vehicles' traveling amicable and stress or additional load harmful to molding patterns minimized. However, it is very difficult to measure its expansion length since vehicles continue to pass on the expansion joint. Therefore, the study could see that it was possible to carry out a qualitative and quantitative maintenance and management if its expansion length is extracted with images. The study could acquire three dimensional coordinates of expansion joints with images. As the results of calculating RMSE of check point residual at 32 points in A area and at 28 points in B area, both A and B areas had very good results of RMSEsms 0.829mm~1.680mm. As the results of analyzing expansion length and immediate value extracted by images, the study analyzed that RMSE of A area was 0.64mm and RMSE of B area was 0.28. The average residual of A area was 0.60% and the average rresidual of B area was 0.27%. Therefore, it is judged that it is more scientific and efficient than the past to measure expansion length with images at the time of repairing and managing bridges in the future.

• Korean Journal of Optics and Photonics
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• v.22 no.4
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• pp.191-197
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• 2011

In this paper, we have designed an integrated triplexer which is the basic component for a FTTH(Fiber To The Home) system which can transmit CATV and voice/data at the same time in a single fiber. The integrated triplexer can be fabricated with a novel technique of "Micro-Block Stacking (MBS)" method which automatically aligns the optical components in the optical beam pass using accurate ceramic holders. We analyze the displacement of the optical focus according to the tolerances of the component dimensions and the assembly process using code V simulator. For the transmitter, the most serious shift of the focal points is caused by the displacements of the LD spot. So the focal point moves up to $72{\mu}m$ from the center point for ${\pm}25{\mu}m$, ${\pm}25{\mu}m$, ${\pm}30{\mu}m$ displacements. For the receiver the most serious shift of the focal points is caused by the displacements of a 0.8mm ball lens (for the analog receiving part) and a micro ball lens (for the digital receiving part), and the focal point moves up to $55{\mu}$ for ${\pm}55{\mu}m$, ${\pm}5{\mu}m$, ${\pm}55{\mu}m$ micro ball lens displacements.

• Korean Journal of Applied Biomechanics
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• v.16 no.1
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• pp.101-108
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• 2006

The purpose of this study was to compare GRF characteristics during walking wearing jogging and roller shoes. Twelve male middle school students (age: $15.0{\pm}0.0\;yrs$, height: $173.6{\pm}5.0\;cm$, weight: $587.6{\pm}89.3\;N$) who have no known musculoskeletal disorders were recruited as the subjects. Kinematic data from six S-VHS camcorders(Panasonic AG456, 60 fields/s) and GRF data from two force platform; (AMII OR6-5) were collected while subjects walked wearing roller and jogging shoes in random order at a speed of 1.1 m/s. An event sync unit with a bright LED light was used to synchronize the video and GRF recordings. GRF data were filtered using a 20 Hz low pass Butterworth. digital filter and further normalized to the subject's body weight. For each trial being analyzed, five critical instants and four phases were identified from the recording. Temporal parameters, GRFs, displacement of center of pressure (DCP), and loading and decay rates were determined for each trial. For each dependent variable, paired t-test was performed to test if significant difference existed between shoe conditions (p <.05). Vertical GRFs at heel contact increased and braking forces at the end of initial double limb stance reduced significantly when going from jogging shoe to roller shoe condition. Robbins and Waked (1997) reported that balance and vertical GRF are closely related It seems that the ankle and knee joints are locked in an awkward fashion at the heel contact to compensate for the imbalance. The DCP in the antero-posterior direction for the roller shoe condition was significantly less than the corresponding value for the jogging shoe condition. Because the subjects tried to keep their upper body weight in front of the hip to prevent falling backward, the DCP for the roller shoe condition was restricted The results indicate that walking with roller shoes had little effect on temporal parameters, and loading and decay rates. It seems that there are differences in GRF characteristics between roller shoe and jogging shoe conditions. The differences in GRF pattern may be caused primarily by the altered position of ankle, knee, and center of mass throughout the walking cycle. Future studies should examine muscle activation patterns and joint kinematics during walking with roller shoes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.21-32
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• 2006

The license plate recognition (LPR) unit consists of the following core components: plate region segmentation, individual character extraction, and character recognition. Out of the above three components, accuracy in the performance of plate region segmentation determines the overall recognition rate of the LPR unit. This paper proposes an algorithm for segmenting the license plate region on the front or rear of a vehicle in a fast and accurate manner. In the case of the proposed algorithm images are captured on the spot where unmanned monitoring of illegal parking and stowage is performed with a variety of roadway environments taken into account. As a means of enhancing the segmentation performance of the on-the-spot-captured images of license plate regions, the proposed algorithm uses a mathematical model for license plate colors to convert color images into digital data. In addition, this algorithm uses Gaussian smoothing and double threshold to eliminate image noises, one-pass boundary tracing to do region labeling, and MBR to determine license plate region candidates and extract individual characters from the determined license plate region candidates, thereby segmenting the license plate region on the front or rear of a vehicle through a verification process. This study contributed to addressing the inability of conventional techniques to segment the license plate region on the front or rear of a vehicle where the frame of the license plate is damaged, through processing images in a real-time manner, thereby allowing for the practical application of the proposed algorithm.

• Journal of the Korean Geographical Society
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• v.28 no.2
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• pp.137-147
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• 1993

Terrain is on of the most important factors in the selection of defense areas. The objective of the study is selection optimum defense area between Seoul and Dongducheon using GIS technique. The contents of the study are: (1) to select the defense area by pure terrain factors, (2) to select the defense area with focusing on the avenues of approach, and (3) to compare the above two kinds of area. The study area is located in the northeastern part of Seoul metropolitan area. It is part of Choogaryung Rift Valley which is running from Seoul to Wonsan. Six factors are considered for the selection: tactical distance, direction, elevation, slope, aspect and the distance from main roads. The defense score of each area is calculated by the multiplication of scores of each factors. The optimum defense area I consists of high-mountain areas such as Mt. Dobong, Mt. Wan-gbang, etc. The optimum defense area II consists of high-mountain areas along the three main roads selected. An east-west line of optimum defense area from Kuksabong in the east to Mt. Bulkuk in the west through Chookseok pass is identified from the spatial pattern of the area II. The line is also a dividing line between the northern and the southern watersheds.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.195-201
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• 2016

Eddy current testing (ECT) is commonly applied for the inspection of automated production lines of metallic products, because it has a high inspection speed and a reasonable price. When ECT is applied for the inspection of a metallic object having an uneven target surface, such as the spline gear of a spline shaft, it is difficult to distinguish between the original signal obtained from the sensor and the signal generated by a defect because of the relatively large surface signals having similar frequency distributions. To facilitate the detection of defect signals from the spline gear, implementation of high-order filters is essential, so that the fault signals can be distinguished from the surrounding noise signals, and simultaneously, the pass-band of the filter can be adjusted according to the status of each production line and the object to be inspected. We will examine the infinite impulse filters (IIR filters) available for implementing an advanced filter for ECT, and attempt to detect the flaw signals through optimization of system design parameters for detecting the signals at the system level.

• Journal of Pharmacopuncture
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• v.7 no.2
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• pp.57-64
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• 2004

This study was carried to identify the component of Small Intestine Meridian Muscle in human, dividing the regional muscle group into outer, middle, and inner layer. the inner part of body surface were opened widely to demonstrate muscles, nerve, blood vessels and the others, displaying the inner structure of Small Intestine Meridian Muscle. We obtained the results as follows; 1. Small Intestine Meridian Muscle is composed of the muscle, nerve and blood vessels. 2. In human anatomy, it is present the difference between a term of nerve or blood vessels which control the muscle of Meridian Muscle and those which pass near by Meridian Muscle. 3. The inner composition of meridian muscle in human arm is as follows ; 1) Muscle ; Abd. digiti minimi muscle(SI-2, 3, 4), pisometacarpal lig.(SI-4), ext. retinaculum. ext. carpi ulnaris m. tendon.(SI-5, 6), ulnar collateral lig.(SI-5), ext. digiti minimi m. tendon(SI-6), ext. carpi ulnaris(SI-7), triceps brachii(SI-9), teres major(SI-9), deltoid(SI-10), infraspinatus(SI-10, 11), trapezius(Sl-12, 13, 14, 15), supraspinatus(SI-12, 13), lesser rhomboid(SI-14), erector spinae(SI-14, 15), levator scapular(SI-15), sternocleidomastoid(SI-16, 17), splenius capitis(SI-16), semispinalis capitis(SI-16), digasuicus(SI-17), zygomaticus major(Il-18), masseter(SI-18), auriculoris anterior(SI-19) 2) Nerve ; Dorsal branch of ulnar nerve(SI-1, 2, 3, 4, 5, 6), br. of mod. antebrachial cutaneous n.(SI-6, 7), br. of post. antebrachial cutaneous n.(SI-6,7), br. of radial n.(SI-7), ulnar n.(SI-8), br. of axillary n.(SI-9), radial n.(SI-9), subscapular n. br.(SI-9), cutaneous n. br. from C7, 8(SI-10, 14), suprascapular n.(SI-10, 11, 12, 13), intercostal n. br. from T2(SI-11), lat. supraclavicular n. br.(SI-12), intercostal n. br. from C8, T1(SI-12), accessory n. br.(SI-12, 13, 14, 15, 16, 17), intercostal n. br. from T1,2(SI-13), dorsal scapular n.(SI-14, 15), cutaneous n. br. from C6, C7(SI-15), transverse cervical n.(SI-16), lesser occipital n. & great auricular n. from cervical plexus(SI-16), cervical n. from C2,3(SI-16), fascial n. br.(SI-17), great auricular n. br.(SI-17), cervical n. br. from C2(SI-17), vagus n.(SI-17),hypoglossal n.(SI-17), glossopharyngeal n.(SI-17), sympathetic trunk(SI-17), zygomatic br. of fascial n.(SI-18), maxillary n. br.(SI-18), auriculotemporal n.(SI-19), temporal br. of fascial n.(SI-19) 3) Blood vessels ; Dorsal digital vein.(SI-1), dorsal br. of proper palmar digital artery(SI-1), br. of dorsal metacarpal a. & v.(SI-2, 3, 4), dorsal carpal br. of ulnar a.(SI-4, 5), post. interosseous a. br.(SI-6,7), post. ulnar recurrent a.(SI-8), circuirflex scapular a.(SI-9, 11) , post. circumflex humeral a. br.(SI-10), suprascapular a.(SI-10, 11, 12, 13), first intercostal a. br.(SI-12, 14), transverse cervical a. br.(SI-12,13,14,15), second intercostal a. br.(SI-13), dorsal scapular a. br.(SI-13, 14, 15), ext. jugular v.(SI-16, 17), occipital a. br.(SI-16), Ext. jugular v. br.(SI-17), post. auricular a.(SI-17), int. jugular v.(SI-17), int. carotid a.(SI-17), transverse fascial a. & v.(SI-18),maxillary a. br.(SI-18), superficial temporal a. & v.(SI-19).