• Title/Summary/Keyword: Sensor Technique

Search Result 2,341, Processing Time 0.029 seconds

A Study on Development of Portable Concrete Crack Measurement Device Using Image Processing Technique and Laser Sensors (이미지 처리기법 및 레이저 센서를 이용한 휴대용 콘크리트 균열 측정 장치 개발에 관한 연구)

  • Seo, Seunghwan;Ohn, Syng-Yup;Kim, Dong-Hyun;Kwak, Kiseok;Chung, Moonkyung
    • Journal of the Korean Geosynthetics Society
    • /
    • v.19 no.4
    • /
    • pp.41-50
    • /
    • 2020
  • Since cracks in concrete structures expedite corrosion of reinforced concrete over a long period of time, regular on-site inspections are essential to ensure structural usability and prevent degradation. Most of the safety inspections of facilities rely on visual inspection with naked eye, so cost and time consuming are severe, and the reliability of results differs depending on the inspector. In this study, a portable measuring device that can be used for safety diagnosis and maintenance was developed as a device that measures the width and length of concrete cracks through image analysis of cracks photographed with a camera. This device captures the cracks found within a close distance (3 m), and accurately calculates the unit pixel size by laser distance measurement, and automatically calculates the crack length and width with the image processing algorithm developed in this study. In measurement results using the crack image applied to the experiment, the measurement of the length of a 0.3 mm crack within a distance of 3 m was possible with a range of about 10% error. The crack width showed a tendency to be overestimated by detecting surrounding pixels due to vibration and blurring effect during the binarization process, but it could be effectively corrected by applying the crack width reduction function.

Detection of Toluene Hazardous and Noxious Substances (HNS) Based on Hyperspectral Remote Sensing (초분광 원격탐사 기반 위험·유해물질 톨루엔 탐지)

  • Park, Jae-Jin;Park, Kyung-Ae;Foucher, Pierre-Yves;Kim, Tae-Sung;Lee, Moonjin
    • Journal of the Korean earth science society
    • /
    • v.42 no.6
    • /
    • pp.623-631
    • /
    • 2021
  • The increased transport of marine hazardous and noxious substances (HNS) has resulted in frequent HNS spill accidents domestically and internationally. There are about 6,000 species of HNS internationally, and most of them have toxic properties. When an accidental HNS spill occurs, it can destroys the marine ecosystem and can damage life and property due to explosion and fire. Constructing a spectral library of HNS according to wavelength and developing a detection algorithm would help prepare for accidents. In this study, a ground HNS spill experiment was conducted in France. The toluene spectrum was determined through hyperspectral sensor measurements. HNS present in the hyperspectral images were detected by applying the spectral mixture algorithm. Preprocessing principal component analysis (PCA) removed noise and performed dimensional compression. The endmember spectra of toluene and seawater were extracted through the N-FINDR technique. By calculating the abundance fraction of toluene and seawater based on the spectrum, the detection accuracy of HNS in all pixels was presented as a probability. The probability was compared with radiance images at a wavelength of 418.15 nm to select abundance fractions with maximum detection accuracy. The accuracy exceeded 99% at a ratio of approximately 42%. Response to marine spills of HNS are presently impeded by the restricted access to the site because of high risk of exposure to toxic compounds. The present experimental and detection results could help estimate the area of contamination with HNS based on hyperspectral remote sensing.

Image Data Loss Minimized Geometric Correction for Asymmetric Distortion Fish-eye Lens (비대칭 왜곡 어안렌즈를 위한 영상 손실 최소화 왜곡 보정 기법)

  • Cho, Young-Ju;Kim, Sung-Hee;Park, Ji-Young;Son, Jin-Woo;Lee, Joong-Ryoul;Kim, Myoung-Hee
    • Journal of the Korea Society for Simulation
    • /
    • v.19 no.1
    • /
    • pp.23-31
    • /
    • 2010
  • Due to the fact that fisheye lens can provide super wide angles with the minimum number of cameras, field-of-view over 180 degrees, many vehicles are attempting to mount the camera system. Not only use the camera as a viewing system, but also as a camera sensor, camera calibration should be preceded, and geometrical correction on the radial distortion is needed to provide the images for the driver's assistance. In this thesis, we introduce a geometric correction technique to minimize the loss of the image data from a vehicle fish-eye lens having a field of view over $180^{\circ}$, and a asymmetric distortion. Geometric correction is a process in which a camera model with a distortion model is established, and then a corrected view is generated after camera parameters are calculated through a calibration process. First, the FOV model to imitate a asymmetric distortion configuration is used as the distortion model. Then, we need to unify the axis ratio because a horizontal view of the vehicle fish-eye lens is asymmetrically wide for the driver, and estimate the parameters by applying a non-linear optimization algorithm. Finally, we create a corrected view by a backward mapping, and provide a function to optimize the ratio for the horizontal and vertical axes. This minimizes image data loss and improves the visual perception when the input image is undistorted through a perspective projection.

Development of Urban Wildlife Detection and Analysis Methodology Based on Camera Trapping Technique and YOLO-X Algorithm (카메라 트래핑 기법과 YOLO-X 알고리즘 기반의 도시 야생동물 탐지 및 분석방법론 개발)

  • Kim, Kyeong-Tae;Lee, Hyun-Jung;Jeon, Seung-Wook;Song, Won-Kyong;Kim, Whee-Moon
    • Journal of the Korean Society of Environmental Restoration Technology
    • /
    • v.26 no.4
    • /
    • pp.17-34
    • /
    • 2023
  • Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

Integrity evaluation of rock bolts in the field by using hammer-impact reflection method (해머 타격 반사법을 이용한 현장 록볼트 건전도 평가)

  • Yu, Jung-Doung;Bae, Myeong-Ho;Lee, Yong-Jun;Min, Bok-Ki;Lee, In-Mo;Lee, Jong-Sub
    • Journal of Korean Tunnelling and Underground Space Association
    • /
    • v.11 no.1
    • /
    • pp.47-56
    • /
    • 2009
  • Rock bolts and shotcrete play a crucial role as a main support system in the underground space. Thus, the safety of the underground space may be affected by the defect of rock bolts. In order to evaluate the rock bolt integrity by using non-destructive technique, the transmission method of the guided ultrasonic waves, which are generated by using the piezo disk elements has been successfully performed. The energy generated by the piezo disk elements, however, is not enough for the rock bolts in the field. In addition, the piezo disk elements should be installed at the end of the steel bar during construction of the rock bolts. The purpose of this study is the devolvement of the reflection method, which may generate enough energy, and the application in the field rock bolts. Both laboratory and field tests are carried out. The guided ultrasonic waves with high energy are generated by the hammer impact with the center punch, and the AE sensor is used to measure the reflected guided waves. The received guided waves are analyzed by the wavelet transform. The peak value of the wavelet transform produces the energy velocity, which is used for the evaluation of the rock bolt integrity. The energy velocity increases with an increase in the defect ratio in both laboratory and field rock bolts. This study demonstrates that the hammer-impact reflection method may be a suitable method for the evaluation of the rock bolt in the field.

Detection of Delay Attack in IoT Automation System (IoT 자동화 시스템의 지연 공격 탐지)

  • Youngduk Kim;Wonsuk Choi;Dong hoon Lee
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.33 no.5
    • /
    • pp.787-799
    • /
    • 2023
  • As IoT devices are widely used at home, IoT automation system that is integrate IoT devices for users' demand are gaining populrity. There is automation rule in IoT automation system that is collecting event and command action. But attacker delay the packet and make time that real state is inconsistent with state recongnized by the system. During the time, the system does not work correctly by predefined automation rule. There is proposed some detection method for delay attack, they have limitations for application to IoT systems that are sensitive to traffic volume and battery consumption. This paper proposes a practical packet delay attack detection technique that can be applied to IoT systems. The proposal scheme in this paper can recognize that, for example, when a sensor transmits an message, an broadcast packet notifying the transmission of a message is sent to the Server recognized that event has occurred. For evaluation purposes, an IoT system implemented using Raspberry Pi was configured, and it was demonstrated that the system can detect packet delay attacks within an average of 2.2 sec. The experimental results showed a power consumption Overhead of an average of 2.5 mA per second and a traffic Overhead of 15%. We demonstrate that our method can detect delay attack efficiently compared to preciously proposed method.

Performance Prediction for Plenoptic Microscopy Under Numerical Aperture Unmatching Conditions (수치 구경 불일치 플렌옵틱 현미경 성능 예측 방안 연구)

  • Ha Neul Yeon;Chan Lee;Seok Gi Han;Jun Ho Lee
    • Korean Journal of Optics and Photonics
    • /
    • v.35 no.1
    • /
    • pp.9-17
    • /
    • 2024
  • A plenoptic optical system for microscopy comprises an objective lens, tube lens, microlens array (MLA), and an image sensor. Numerical aperture (NA) matching between the tube lens and MLA is used for optimal performance. This paper extends performance predictions from NA matching to unmatching cases and introduces a computational technique for plenoptic configurations using optical analysis software. Validation by fabricating and experimenting with two sample systems at 10× and 20× magnifications resulted in predicted spatial resolutions of 12.5 ㎛ and 6.2 ㎛ and depth of field (DOF) values of 530 ㎛ and 88 ㎛, respectively. The simulation showed resolutions of 11.5 ㎛ and 5.8 ㎛, with DOF values of 510 ㎛ and 70 ㎛, while experiments confirmed predictions with resolutions of 11.1 ㎛ and 5.8 ㎛ and DOF values of 470 ㎛ and 70 ㎛. Both formula-based prediction and simulations yielded similar results to experiments that were suitable for system design. However, regarding DOF values, simulations were closer to experimental values in accuracy, recommending reliance on simulation-based predictions before fabrication.

Integrity evaluation of grouting in umbrella arch methods by using guided ultrasonic waves (유도초음파를 이용한 강관보강다단 그라우팅의 건전도 평가)

  • Hong, Young-Ho;Yu, Jung-Doung;Byun, Yong-Hoon;Jang, Hyun-Ick;You, Byung-Chul;Lee, Jong-Sub
    • Journal of Korean Tunnelling and Underground Space Association
    • /
    • v.15 no.3
    • /
    • pp.187-199
    • /
    • 2013
  • Umbrella arch method (UAM) used for improving the stability of the tunnel ground condition has been widely applied in the tunnel construction projects due to the advantage of obtaining both reinforcement and waterproof. The purpose of this study is to develop the evaluation technique of the integrity of bore-hole in UAM by using a non-destructive test and to evaluate the possibility of being applied to the field. In order to investigate the variations of frequency depending on grouted length, the specimens with different grouted ratios are made in the two constraint conditions (free boundary condition and embedded condition). The hammer impact reflection method in which excitation and reception occur simultaneously at the head of pipe was used. The guided waves generated by hitting a pipe with a hammer were reflected at the tip and returned to the head, and the signals were received by an acoustic emission (AE) sensor installed at the head. For the laboratory experiments, the specimens were prepared with different grouted ratios (25 %, 50 %, 75 %, 100 %). In addition, field tests were performed for the application of the evaluation technique. Fast Fourier transform and wavelet transform were applied to analyze the measured waves. The experimental studies show that grouted ratio has little effects on the velocities of guided waves. Main frequencies of reflected waves tend to decrease with an increase in the grouted length in the time-frequency domain. This study suggests that the non-destructive tests using guided ultrasonic waves be effective to evaluate the bore-hole integrity of the UAM in the field.

An Intelligent Intrusion Detection Model Based on Support Vector Machines and the Classification Threshold Optimization for Considering the Asymmetric Error Cost (비대칭 오류비용을 고려한 분류기준값 최적화와 SVM에 기반한 지능형 침입탐지모형)

  • Lee, Hyeon-Uk;Ahn, Hyun-Chul
    • Journal of Intelligence and Information Systems
    • /
    • v.17 no.4
    • /
    • pp.157-173
    • /
    • 2011
  • As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

A Technique Assessing Geological Lineaments Using Remotely Sensed Data and DEM : Euiseons Area, Kyungsang Basin (원격탐사자료와 수치표고모형을 이용한 지질학적 선구조 분석기술: 경상분지 의성지역을 중심으로)

  • 김원균;원중선;김상완
    • Korean Journal of Remote Sensing
    • /
    • v.12 no.2
    • /
    • pp.139-154
    • /
    • 1996
  • In order to evaluate the sensor`s look direction bias in the Landsat TM image and to estimate trends of primary geological lineaments, we have attempted to systematically compare lineaments in TM image, relief shadowed DEM's, and actual lineaments of geologic and topographic map through the Hough transform technique. Hough transform is known to be very effective to estimate the trend of geological lineaments, and help us to obtain the true trends of lineaments. It is often necessary to compensate the preferential enhancements of terrain lineaments in a TM image occurred by to look direction bias, and that can be achieved by utilizing an auxiliary data. In this study, we have successfully adopted the relief shadowed DEM in which the illuminating azimuth angle is perpendicular to look direction of a TM image for assessing true trends of geological lineaments. The results also show that the sum of four relief shadowed DEM's directional components can possibly be used as an alternative. In Euiseong-gun area where Sindong Group and Mayans Group are mainly distributed, geological lineaments trending $N5^{\circ}$~$10^{\circ}$W are dominant, while those of $N55^{\circ}$~$65^{\circ}$ W are major trends in Cheongsong-gun area where Hayang Group, Yucheon Group and Bulguksa Granite are distributed. Using relief shadowed DEM as an auxiliary data, we found the $N55^{\circ}$~$65^{\circ}$ W lineaments which are not cleanly observed in TM image over Euiseong-gun area. Compared with the trend of Gumchon and Gaum strike-slip faults, these lineaments are considered to be an extension of the faults. Therefore these strike-slip faults possibly extend up to Sindong Group in the northwest parts in the study area.