• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.423-428
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• 2005

This paper proposes a new edge detection method using a $3{\times}3$ ideal binary pattern and lookup table (LUT) for the mobile robot localization without any parameter adjustments. We take the mean of the pixels within the $3{\times}3$ block as a threshold by which the pixels are divided into two groups. The edge magnitude and orientation are calculated by taking the difference of average intensities of the two groups and by searching directional code in the LUT, respectively. And also the input image is not only partitioned into multiple groups according to their intensity similarities by the histogram, but also the threshold of each group is determined by fuzzy reasoning automatically. Finally, the edges are determined through non-maximum suppression using edge confidence measure and edge linking. Applying this edge detection method to the mobile robot localization using projective invariance of the cross ratio. we demonstrate the robustness of the proposed method to the illumination changes in a corridor environment.

• The Journal of Korean Association of Computer Education
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• v.13 no.4
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• pp.77-86
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• 2010

In this paper, we propose a real time post-processing visual enhancement technique to reduce the blocking artifacts in block based DCT decoded image for mobile devices that have allocation of the restricted resource. In order to reduce the blocking artifacts effectively even while preserving the image edge to the utmost, the proposed algorithm uses the deblocking filtering or the directional filtering according to the edge detection of the each pixel. After it is discriminated that the pixel to apply the deblocking filtering belongs again to the monotonous area, the weighted average filter with the adaptive mask is applied for the pixel to remove the blocking artifacts. On the other hand, a new directional filter is utilized to get rid of staircase noise and preserve the original edge component. Experimental results show that the proposed algorithm produces better results than those of the conventional algorithms in both subjective and objective qualities.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.8-15
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• 2019

Recently, the importance of pre-shipment quality inspection has been emphasized due to the increase of STB supply. In this paper, we propose a method to support automation of quality inspection through simultaneous multi-channel input of STB video signal. The proposed method extracts a fingerprint using the center scan line of the image after stable video shot boundary detection using CeLDP combining color information and LDP code and performs synchronization between input video channels. The proposed method shows stronger shot boundary detection performance than the conventional shot detection method. Through the experiments applied to the real environment, it is possible to secure reliability and real-time quality check for synchronization between multi-channel inputs required for STB quality inspection. Also, based on the proposed method, we intend to study a large-scale quality inspection method in the future and propose a more effective quality inspection system.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2E
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• pp.46-53
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• 2005

Agency for Defense Development (ADD) developed the Submarine Acoustic Information Management System (SAIMS Version 1.0) capable of interfacing some submarine sensors in operation and predicting detection environments for sonars. The major design concepts are as follows: 1) A proper acoustic model is examined and optimized to cover wide spectra of frequency ranges for both active and passive sonars. 2) Interfacing the submarine sensors to an electric navigation chart, the system attempts to maximize the applicability of the information produced. 3) The state-of-the-art database in large area is built and managed on the system. 4) An algorithm, which is able to estimate a full sound speed profile from the limited oceanographic data, is developed and employed on the system. This paper briefly describes design concepts and algorithms embedded in the SAIMS. The applicability of the SAIMS was verified through three sea experiments in October 2003-February 2004.

• Korean Journal of Optics and Photonics
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• v.2 no.3
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• pp.139-148
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• 1991

A fiber-optic magnetic sensor system for the detection of small ac magnetic field(200Hz-2 kHz) was constructed. Magnetic field sensing part was fabricated by bonding a section of optical fiber to amorphous metallic glass(2605SC) having large magnetostriction effect. And with the directional coupler, all fiber type Mach-Zehnder interferometer was constructed to measure the variation of the external magnetic field by translating it into the optical phase shift in the interferometer. The signal fading problem of the interferometer, which is due to random phase drifts originated from the environment, i.e., temperature fluctuation, vibrations, etc., was elliminated by feedback phase compensation. This allows the sensitivity to be maintained at the maximum by keeping the interferometer in quadrature phase condition. The frequency response of metallic glass was found to be nearly flat in the range of 90 Hz-2 kHz and dc bias field for the maximum ac response was 3.5 Oe. The interferometer output showed good linearity over the range $\pm$0.5 Oe. For 1 kHz ac magnetic field the scale factor S and the minimum detectable magnetic field were measured to be 8.0 rad/Oe and $3X10^{-6} Oe/\sqrt{Hz}$at 1 Hz detection bandwidth respectively.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.18-22
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• 2022

Because it is difficult to apply direct and optical detection techniques to sunken hazardous and noxious substances (HNS), effective acoustic detection techniques are required to detect sunken HNS in water. In this study, the possibility of acoustic detection of sunken HNS was investigated through backscattering signal measurement experiments using chloroform, a sunken HNS. After establishing a pool in an acrylic tank, backscattering signals were measured according to the presences or absence of chloroform by varying the grazing angle from 90° to 50° in 0.5° intervals using a pan&tilt system. A directional transducer transmitted and received sinusoidal signals with a frequency of 200 kHz and a pulse length of 25 ㎲ in a monostatic state. When chloroform was deposited, the received level of the backscattering signal at the interface between water and chloroform became low at a grazing angle of approximately 80° or smaller. Based on the backscattering signal results obtained at the interface between water and chloroform, the possibility of acoustic detection of sunken HNS was demonstrated.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.318-329
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• 2022

Plant phenology including flowering, leaf unfolding, and leaf coloring in a forest is important to understand the forest ecosystem. Temperature rise due to recent climate change, however, can lead to plant phenology change as well as snowfall in winter season. Therefore, accurate monitoring of forest environment changes such as plant phenology and snow cover is essential to understand the climate change effect on forest management. These changes can monitor using a digital camera system. This paper introduces the detection methods for plant phenology and snow cover at the mountain region using an unmanned camera system that is a way to monitor the change of forest environment. In this study, the Automatic Mountain Meteorology Stations (AMOS) operated by Korea Forest Service (KFS) were selected as the testbed sites in order to systematize the plant phenology and snow cover detection in complex mountain areas. Multi-directional Internet Protocol (IP) camera system that is a kind of unmanned camera was installed at AMOS located in Seoul, Pyeongchang, Geochang, and Uljin. To detect the forest plant phenology and snow cover, the Red-Green-Blue (RGB) analysis based on the IP camera imagery was developed. The results produced by using image analysis captured from IP camera showed good performance in comparison with in-situ data. This result indicates that the utilization technique of IP camera system can capture the forest environment effectively and can be applied to various forest fields such as secure safety, forest ecosystem and disaster management, forestry, etc.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.543-550
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• 2011

In order to improve readability, a leakage-signal canceller has been proposed in UHF-band RFID readers. The proposed canceller is composed of two blocks: an environment monitoring block for detection of Tx and RX power levels and a leakage cancellation block. The leakage canceller consists of directional-couplers, digital attenuators and phase shifters based on the feed-forward scheme. The leakage canceller is located between antenna and reader. An improved experimental characterization of the scheme is presented with results from the reader operating in the Korean RFID frequency band, conforming the validity of the approach with more than 29 dB of leakage cancellation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2087-2092
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• 2011

In this paper, we use the earth's magnetic field is measured by the MR device. By analyzing the measured data to determine the direction to implement the system. The construction of the system to determine the direction of neural networks, using input data based on an analysis of the relationship between pattern formation and characteristics of study and related information through a pattern when it is remembered that the output feedback input to associative networks and proves its feasibility for implementation. The entire detection system with regional changes in the Earth's magnetic field to adapt to the environment should aim to build a system.

• Annual Conference of KIPS
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• 2019.05a
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• pp.419-422
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• 2019

The recent increase in single-person households and family income has led to an increase in the number of pet owners. However, due to the owners' difficulty to communicate with them for 24 hours, pets, and especially dogs, tend to display unwanted behavior that can be harmful to themselves and their environment when left alone. Therefore, detecting those behaviors when the owner is absent is necessary to suppress them and prevent any damage. In this paper, we propose a camera-based system that detects a set of normal and unwanted behaviors using deep learning algorithms to monitor dogs when left alone at home. The frames collected from the camera are arranged into sequences of RGB frames and their corresponding optical flow sequences, and then features are extracted from each data flow using pre-trained VGG-16 models. The extracted features from each sequence are concatenated and input to a bi-directional LSTM network that classifies the dog action into one of the targeted classes. The experimental results show that our method achieves a good performance exceeding 0.9 in precision, recall and f-1 score.