• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제5권10호
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• pp.319-326
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• 2016

밀집된 돈방에서 사육되는 돼지의 공격적 행동들은 돼지의 성장에 심각한 악영향을 주고, 이는 농가의 경제적 손실로 이어진다. 따라서 농가의 생산성 하락에 따른 경제적 손실과 직결되는 돈방 내의 비정상 상황들을 지속적으로 모니터링 할 수 있는 IT기반의 영상 감시 시스템이 요구된다. 본 논문에서는 돼지의 행동 분석 이전에 필수적으로 선행되어야만 하는 개별 돼지의 탐지를 위한 키넥트 카메라 기반의 새로운 모니터링 시스템을 제안한다. 제안된 시스템은 다음과 같다. 1) 키넥트 카메라로부터 취득한 깊이 영상에서 배경 차영상 기법과 깊이 임계값을 이용하여 서있는 돼지만을 탐지함으로써 영상 내의 탐색영역을 축소한다, 2) 서있는 돼지들 중에서 움직임이 있는 돼지들만을 관심영역으로 설정하여 탐지한다. 3) 서서 움직이는 돼지들 사이에서 발생하는 근접 문제를 깊이정보를 이용한 등고선기법을 제안 적용하여 돼지객체 탐지를 완성한다. 실제 세종에 위치한 한 돈사에서 취득한 깊이 영상 정보를 이용하여 본 논문에서 제안하는 시스템의 성능을 실험적으로 검증한다.

• 동력기계공학회지
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• 제15권4호
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• pp.37-41
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• 2011

This paper introduces the special eddy current sensor and its characteristic for bolt hole defect evaluation in gas turbine rotor. In the past, Fluorescent penetration inspection method was used for qualitative defect evaluation in gas turbine rotor bolt hole. This method can defect the bolt hole defect but can not evaluate the defect size. Nowadays, eddy current method is used quantitative defect evaluation due to advanced sensor design technology. And eddy current method is more time and cost saving than the old method. We developed bolt shape eddy current sensor for the rotor bolt hole defect detection and evaluation. The eddy current sensor moves to the bolt hole guided by screw nut and detects the defect on the bolt hole. The bolt hole mock-up and artificial defects were made and used for the signal detection & resolution analysis of eddy current sensor. The results show that signal detection capability is enough to detect 0.2 mm depth defect. And the resolution capability is enough to differentiate 02, 0.5, 1.0 and 2.0 mm depth defect.

• JSTS:Journal of Semiconductor Technology and Science
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• 제14권2호
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• pp.212-226
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• 2014

This paper presents the design and implementation of a pipelined architecture and a method for real-time human detection using depth image from a Time-of-Flight (ToF) camera. In the proposed method, we use Euclidean Distance Transform (EDT) in order to extract human body location, and we then use the 1D, 2D scanning window in order to extract human joint location. The EDT-based human extraction method is robust against noise. In addition, the 1D, 2D scanning window helps extracting human joint locations easily from a distance image. The proposed method is designed using Verilog HDL (Hardware Description Language) as the dedicated hardware architecture based on pipeline architecture. We implement the dedicated hardware architecture on a Xilinx Virtex6 LX750 Field Programmable Gate Arrays (FPGA). The FPGA implementation can run 80 MHz of maximum operating frequency and show over 60fps of processing performance in the QVGA ($320{\times}240$) resolution depth image.

• EDISON SW 활용 경진대회 논문집
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• 제5회(2016년)
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• pp.195-202
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• 2016

In this paper, crack detection method using eigen value analysis is presented. Three methods are used: theoretical analysis, finite element method with the cracked beam elements and finite element method with three dimensional continuum elements. Finite element formulation of the cracked beam element is introduced. Additional term about stress intensity factor based on fracture mechanics theory is added to flexibility matrix of original beam to model the crack. As using calculated stiffness matrix of cracked beam element and mass matrix, natural frequencies are calculated by eigen value analysis. In the case of using continuum elements, the natural frequencies could be calculated by using EDISON CASAD solver. Several cases of crack are simulated to obtain natural frequencies corresponding the crack. The surface of natural frequency is plotted as changing with crack location and depth. Inverse analysis method is used to find crack location and depth from the natural frequencies of experimental data, which are referred by another papers. Predicted results are similar with the true crack location and depth.

• 한국정보통신학회논문지
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• 제17권5호
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• pp.1119-1128
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• 2013

본 논문에서는 얼굴을 검출하고 GPU 기반으로 얼굴을 고속으로 추적하는 알고리즘을 제안하였다. 얼굴 검출에서는 깊이영상과 RGB영상을 사용하고, 기존의 방법인 Adaboost을 이용하지만 움직임 영역과 피부색 영역을 이용하여 Adaboost의 입력영상을 제한하여 얼굴을 검출하였다. 얼굴 검출과는 다르게 얼굴 추적은 깊이 정보만을 사용하였다. 기본적으로 얼굴 추적에서는 템플릿과 매칭 된 블록을 찾는 템플릿 매칭 방법을 사용하였다. 또한 고속으로 얼굴을 추적하기 위해서 GPU를 이용하여 템플릿 매칭을 병렬하여 연산하였다. 실험결과 CPU와 GPU을 비교 하였을 때 GPU 수행속도가 최대 49배까지 향상되는 것을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

• Journal of Electrical Engineering and Technology
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• 제1권2호
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• pp.233-236
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• 2006

A 3-D numerical simulation of a buried-channel CCD (Charge Coupled Device) with a deep depletion has been performed to investigate its electrical and physical behaviors. Results are presented for a deep depletion CCD (EEV CCD12; JET-X CCD) fabricated on a high-resistivity $(1.5k\Omega-cm)\;65{\mu}m$ thick epi-layer, on a $550{\mu}m$ thick p+ substrate, which is optimized for X-ray detection. Accurate predictions of the Potential minimum and barrier height of a CCD Pixel as a function of mobile electrons are found to give good charge transfer. The depletion depth approximation as a function of gate and substrate bias voltage provided average errors of less than 6%, compared with the results estimated from X-ray detection efficiency measurements. The result obtained from the transient simulation of signal charge movement is also presented based on 3-Dimensional analysis.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.321-323
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• 2004

치아 조직안에서의 OCT 신호의 검출 및 최적화를 위해 몬테카를로 수치해석 프로그램을 개발하였다. 수치해석 결과에 의해 치아 조직 내에서의 깊이에 따라 서로 다른 전파 특성을 갖는 두가지 신호를 얻었다. 검출 신호는 약 60w 이상의 깊이에서 잡음 신호가 특성 신호에 비해 커짐으로 검출이 어려웠으나, 검출 영역 및 각도의 제한에 의해 5000n이상으로 검출 깊이가 증가함을 알수 있었다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.178-187
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• 2000

The tillage operation by rotary implements is widely done in Korea. In the case of rotary implements, the tillage depth control system is one of important implement control systems. A contact type-sensor for measurement of the ground height was designed and fabricated to evaluate the possibility of application of the sensor on the tillage depth control system. Indoor experiments were conducted to obtain static and dynamic detection characteristics of the sensor under various conditions; 1) several moisture contents for four soil samples, 2) two soil surfaces with a designed configuration, 3) four heights of the sensor from the soil surface, 4) five traveling speeds of the carrier on which the sensor was attached, and so on. The experimental results showed the detection characteristics of the sensor sufficient as the ground height sensor of the tillage depth control system.

• 한국멀티미디어학회논문지
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• 제19권12호
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• pp.1927-1935
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• 2016

This paper propose a framework for human body parts in RGB-D image. We conduct tasks of obtaining person area, finding candidate areas and local detection in order to detect hand, foot and head which have features of long accumulative geodesic distance. A person area is obtained with background subtraction and noise removal by using depth image which is robust to illumination change. Finding candidate areas performs construction of graph model which allows us to measure accumulative geodesic distance for the candidates. Instead of raw depth map, our approach constructs graph model with segmented regions by quadtree structure to improve searching time for the candidates. Local detection uses HOG based SVM for each parts, and head is detected for the first time. To minimize false detections for hand and foot parts, the candidates are classified with upper or lower body using the head position and properties of geodesic distance. Then, detect hand and foot with the local detectors. We evaluate our algorithm with datasets collected Kinect v2 sensor, and our approach shows good performance for head, hand and foot detection.