• The Science & Technology
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• v.33 no.12 s.379
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• pp.73-79
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• 2000

1983년 12월 레이건 미 대통령의 승인으로 잉태한 우주정거장 사업은 그동안 숱한 진통을 겪으면서 국제협력사업으로 바뀌어 마침내 2005년 가동을 목표로 지상 4백60km의 우주상공에서 미국을 비롯한 16개국이 참가한 국제우주정거장(ISS)에는 최근 우리나라도 참여할 길이 열려서 그동안 남의 일로만 생각해 오던 국제우주정거장 사업에 대한 우리의 관심이 차츰 높아지고 있다. 우리나라의 항공우주연구소가 우주정거장에 탑재할 우주선 검출기인악세스를 수용할 모듈의 설계와 제작을 맡게 될 것으로 알려졌다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.409-411
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• 2001

본 논문에서는 소프트웨어 테스트 단계중에 발생되는 테스트노력 소요량을 고려한 소프트웨어 신뢰도 성장 모델을 제시하여 시간종속적인 테스트 노력소요량 동태를 일정 테스트 노력일 때와 웨이블 테스트 노력일 때를 비교하여 연구한다. 테스트 단계중에 소요되는 테스트노력의 양에 대한 결함 검출비를 현재의 절함 내용에 비례하는 것으로 가정하여 모델을 NHPP로 공식화하되, 이 모델을 이용하여 소프트웨어 신뢰도 척도에 대한 데이터 분식기법을 개발한다. 테스트 시간의 경과와 신뢰도와의 관계를 연구한다. 목표신뢰도를 만족시키는 최적발행시각을 정한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.10
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• pp.909-916
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• 2016

The beacon receiver is an equipment which detects and measures the signal strength of transmitting satellite beacon signal. Beacon signals transmitted by satellites are low power continuous wave(CW) signals without any modulation intended for antenna steering to satellite direction and power control purposes on the earth. The beacon signal detection method using a very narrow band analog filter and RSSI(Received Signal Strength Intensity) has been typically used. However, it requires the implementation to track the frequency at the beacon receiver, thus a beacon frequency variation of the satellite due to temperature changes and long-term operation. Therefore, in this paper, the beacon signal detection receiver is designed by using a very narrow band digital filter array for a faster acquisition and SNR(Signal to Noise Ratio) method detection. For this purpose, by calculating the satellite link budget with the rain attenuation between satellite and ground station, and then extracting the received $C/N_o$ of the beacon signal, this work derives the bandwidth and the array number of the configured digital filter that gives the required C/N.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.949-958
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• 2004

race detection using a boosting based algorithm requires a very large size of face and nonface data. In addition, the fact that there always occurs a need for adding additional training data for better detection rates demands an efficient incremental teaming algorithm. In the design of incremental teaming based classifiers, the final classifier should represent the characteristics of the entire training dataset. Conventional methods have a critical problem in combining intermediate classifiers that weight updates depend solely on the performance of individual dataset. In this paper, for the purpose of application to face detection, we present a new method to combine an intermediate classifier with previously acquired ones in an optimal manner. Our algorithm creates a validation set by incrementally adding sampled instances from each dataset to represent the entire training data. The weight of each classifier is determined based on its performance on the validation set. This approach guarantees that the resulting final classifier is teamed by the entire training dataset. Experimental results show that the classifier trained by the proposed algorithm performs better than by AdaBoost which operates in batch mode, as well as by ${Learn}^{++}$.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.258-266
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• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.249-254
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• 2007

A mobile robot with various types of sensors and wireless camera is introduced. We show this mobile robot can detect objects well by combining the results of active sensors and image processing algorithm. First, to detect objects, active sensors such as infrared rays sensors and supersonic waves sensors are employed together and calculates the distance in real time between the object and the robot using sensor's output. The difference between the measured value and calculated value is less than 5%. We focus on how to detect a object region well using image processing algorithm because it gives robots the ability of working for human. This paper suggests effective visual detecting system for moving objects with specified color and motion information. The proposed method includes the object extraction and definition process which uses color transformation and AWUPC computation to decide the existence of moving object. Shape information and signature algorithm are used to segment the objects from background regardless of shape changes. We add weighing values to each results from sensors and the camera. Final results are combined to only one value which represents the probability of an object in the limited distance. Sensor fusion technique improves the detection rate at least 7% higher than the technique using individual sensor.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.593-599
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• 2013

In this paper, the GPS cycle-slip detection for carrier-phase based positioning of land vehicle is presented. For the carrier phase based positioning, cycle-slip detection is necessary to get the reliability of positioning result. There exists many cycle-slip detection algorithms, but we detect the cycle-slip by using the monitoring value which is defined as residual between the carrier phase measurement and estimated value from low-cost inertial sensor. To achieve goal of paper, low-cost cycle-slip detection system, permissible specification region of inertial sensor is derived. By using the result of permissible region, appropriate inertial sensor of cycle-slip detection can be decided, proper cost and proper specification. To verify the result of this paper, we conduct the rate table test. As a result, required cycle-slip detection performance is satisfied conservatively.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.599-604
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• 2008

Recently, many researches for autonomous mobile system have been proposed, which can recognize surrounded environment and navigate to destination without outside intervention. The basic sufficient condition for the autonomous mobile system is to navigate to destination safely without accident. In this paper, we propose a path planning method in local region for safe navigation of autonomous system through evaluation of the road surface distortion(damaged/deformed road, unpaved road, obstacle and etc.). We use laser distance sensor to get the information on the road surface distortion and apply image binalization method to evaluate safe region in the detected local region. We show the validity of the proposed method through the computer simulation based on the artificial local road map.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.77-82
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• 2005

Partial response maximum likelihood (PRML) detection using the Viterbi algorithm involves the calculation of likelihood metrics that determine the most likely sequence of decoded data. In general, it is assumed that branches at each node in the trellis diagram have same probabilities. If modulation code with minimum and maximum run-length constraints is used, the occurrence ratio (Ro) of each particular pattern is different, and therefore the assumption is not true. We present a calculation scheme of the likelihood metrics for the PRML detection using the occurrence ratio. In simulation, we have tested the two (1,7) run-length-limited codes and calculated the occurrence ratios as the orders of PR targets are changed. We can identify that the PRML detections using the occurrence ratio provide more than about 0.5dB gain compared to conventional PRML detections at 10/sup -5/ BER in high-density magnetic recording and optical recording channels.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.702-709
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• 2010

The performance of mass segmentation is greatly influenced by an initial position of a mass. Some researchers performed mass segmentation with the initial position of a mass given by radiologists. The purpose of our research is to find the initial position for mass segmentation and to notify the segmented mass to radiologists without any additional information on mammograms. The proposed system consists of breast segmentation by region growing and opening operations, decision of an initial seed with characteristics of masses, and mass segmentation by a level set segmentation. A seed for mass segmentation is set based on mass scoring measure calculated by block-based variances and masked information in a sub-sampled mammogram. We used a DDSM database to evaluate the system. The accuracy of mass detection is 78% sensitivity at 4 FP/image, and it reached 92% if multiple views for masses were considered.