• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.319-326
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• 2016

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. In this paper, we propose a new Kinect camera-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The moving-pigs are labeled as regions of interest. 3) A contour method is proposed and applied to solve the touching-pigs problem in the Kinect-depth image. The experimental results with the depth videos obtained from a pig farm located in Sejong illustrate the efficiency of the proposed method.

• Journal of Power System Engineering
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• v.15 no.4
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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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• v.14 no.2
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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.

• Proceeding of EDISON Challenge
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• 2016.03a
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1119-1128
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• 2013

In this paper, we propose an algorithm to detect and track the human face with a GPU-based high speed. Basically the detection algorithm uses the existing Adaboost algorithm but the search area is dramatically reduced by detecting movement and skin color region. Differently from detection process, tracking algorithm uses only depth information. Basically it uses a template matching method such that it searches a matched block to the template. Also, In order to fast track the face, it was computed in parallel using GPU about the template matching. Experimental results show that the GPU speed when compared with the CPU has been increased to up to 49 times.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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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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• v.1 no.2
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.321-323
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• 2004

We developed the monte-carlo simulation code for analysis of the On signal in dental medium. In calculation, we obtain the two different propagation signals as a function of the probing depth. Signal 2 begins to exceed the signal 1 at a very small probing depth(=60${\mu}{\textrm}{m}$). For reduce the signal, detection area is limited to radius and detection angle. As numerical result, probing depth becomes appoximately 500${\mu}{\textrm}{m}$.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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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.

• Journal of Korea Multimedia Society
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• v.19 no.12
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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.