Journal articles: 'Detection and location'

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Baidari, Dr Ishwar, and S. P. Sajjan. "Location Based Crime Detection Using Data Mining." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 208–12. http://dx.doi.org/10.9756/bijsesc.8279.

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Decker, Brooke K., and Tara N. Palmore. "Waterborne Pathogen Detection More than Just “Location, Location, Location…”." Infection Control & Hospital Epidemiology 35, no. 2 (February 2014): 130–31. http://dx.doi.org/10.1086/675067.

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Rodger, C. J., J. B. Brundell, and R. L. Dowden. "Location accuracy of VLF World-Wide Lightning Location (WWLL) network: Post-algorithm upgrade." Annales Geophysicae 23, no. 2 (February 28, 2005): 277–90. http://dx.doi.org/10.5194/angeo-23-277-2005.

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Abstract. An experimental VLF World-Wide Lightning Location (WWLL) network has been developed through collaborations with research institutions across the globe. The aim of the WWLL is to provide global real-time locations of lightning discharges, with >50% CG flash detection efficiency and mean location accuracy of <10km. While these goals are essentially arbitrary, they do define a point where the WWLL network development can be judged a success, providing a breakpoint for a more stable operational mode. The current network includes 18 stations which cover much of the globe. As part of the initial testing phase of the WWLL the network operated in a simple mode, sending the station trigger times into a central processing point rather than making use of the sferic Time of Group Arrival (TOGA). In this paper the location accuracy of the post-TOGA algorithm WWLL network (after 1 August 2003) is characterised, providing estimates of the globally varying location accuracy for this network configuration which range over 1.9-19km, with the global median being 2.9km, and the global mean 3.4km. The introduction of the TOGA algorithm has significantly improved the location accuracies. The detection efficiency of the WWLL is also considered. In the selected region the WWLL detected ~13% of the total lightning, suggesting a ~26% CG detection efficiency and a ~10% IC detection efficiency. Based on a comparison between all WWLL good lightning locations in February-April 2004, and the activity levels expected from satellite observations we estimate that the WWLL is currently detecting ~2% of the global total lightning, providing good locations for ~5% of global CG activity. The existing WWLL network is capable of providing real-time positions of global thunderstorm locations in its current form.

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Bae Kim, Jong, and Myung Jin Bae. "Location based FDS Framework." International Journal of Engineering & Technology 7, no. 3.33 (August 29, 2018): 72. http://dx.doi.org/10.14419/ijet.v7i3.33.18527.

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The FDS (Fraud Detection System) is a technological approach to prevent financial accidents by detecting abnormal behavior in financial transactions. In this paper, we present system components and considerations for efficient FDS construction and operation, and propose an optimized FDS operation framework based on IT governance. In addition, we propose a model that can improve the accuracy of abnormal transaction detection by using GPS information of user. This research is expected to be an operation model for Fintech based FDS that enables safe transactions without sacrificing the convenience of customers.

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Sharifi, Mohsen, Ali Aminfar, and Elnaz Abdollahzadeh. "A Minimalist Path Detection Approach for Wireless Sensor Networks." International Journal of Distributed Sensor Networks 5, no. 5 (October 1, 2009): 576–95. http://dx.doi.org/10.1080/15501320802300198.

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Most object tracking techniques find the exact locations of an object, while many other applications are only interested in the object's path rather than its locations. This provides an opportunity to reduce the existing tracking methods' resource usage by only focusing on the path detection. Taking advantage of this opportunity, this article presents two new minimalist approaches for accurately detecting unknown available passages in a sensor field without requiring the exact locations of the objects. In the first approach, each sensor sends its own location to a base station when it senses an object of interest. The base station uses b-spline curves to build the object's path online. Since each sensor sends its location data just once per new path, the first approach is a minimalist approach. The second approach is offline and uses non-uniform rational b-spline (NURBS) curves. Since NURBS needs weighted locations, each sensor sends its own location in addition to the number of times it has sensed an object based on the object's weight. Using the same simulation models, both approaches greatly reduce power consumption and improve the accuracy of the computed paths. The NURBS approach has proved to be robust on false alarms and improves the accuracy of path detection up to 95 percent, which is very close to detecting the object's actual path.

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Cheng, Yifang, Yehuda Ben-Zion, Florent Brenguier, Christopher W. Johnson, Zefeng Li, Pieter-Ewald Share, Aurélien Mordret, Pierre Boué, and Frank Vernon. "An Automated Method for Developing a Catalog of Small Earthquakes Using Data of a Dense Seismic Array and Nearby Stations." Seismological Research Letters 91, no. 5 (July 22, 2020): 2862–71. http://dx.doi.org/10.1785/0220200134.

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Abstract We propose a new automated procedure for using continuous seismic waveforms recorded by a dense array and its nearby regional stations for P-wave arrival identification, location, and magnitude estimation of small earthquakes. The method is illustrated with a one-day waveform dataset recorded by a dense array with 99 sensors near Anza, California, and 24 surrounding regional stations within 50 km of the dense array. We search a wide range of epicentral locations and apparent horizontal slowness values (0–15 s/km) in the 15–25 Hz range and time shift the dense array waveforms accordingly. For each location–slowness combination, the average neighboring station waveform similarity (avgCC) of station pairs <150 m apart is calculated for each nonoverlapping 0.5 s time window. Applying the local maximum detection algorithm gives 966 detections. Each detection has a best-fitting location–slowness combination with the largest avgCC. Of 331 detections with slowness <0.4 s/km, 324 (about six times the catalog events and 98% accuracy) are found to be earthquake P-wave arrivals. By associating the dense array P-wave arrivals and the P- and S-wave arrivals from the surrounding stations using a 1D velocity model, 197 detections (∼4 times of the catalog events) have well-estimated locations and magnitudes. Combining the small spacing of the array and the large aperture of the regional stations, the method achieves automated earthquake detection and location with high sensitivity in time and high resolution in space. Because no preknowledge of seismic-waveform features or local velocity model is required for the dense array, this automated algorithm can be robustly implemented in other locations.

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Li, Xuan, Dunant Halim, and Xiaoling Liu. "A study of the effects of structural delamination location on delamina-tion detection using a non-linear chaotic oscillator method." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4701–8. http://dx.doi.org/10.3397/in-2021-2804.

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This work aims to investigate the effects of structural delamination location on the effectiveness of delamination assessment using a vibration-based non-linear chaotic oscillator method. The change in structural vibration characteristics due to delamination at different structural locations can pose a challenge for accurate delamination detection due to the possible weak changes in the measured vibration signal and the existence of noise that can corrupt the signal. Thus in this work, a chaotic oscillator method was used due to its sensitivity to relatively small changes in measured vibration signal and robustness to measurement noise. The effects of vibration sensing location on the sensitivity in detecting the location of delamination was also investigated in this work. The Lyapunov Exponent was used in conjunction with the chaotic oscillator as a damage index, for the purpose of defining an effective measure to locate the delamination damage in a laminated structure. The correlation between the damage index and vibration sensing location for different delamination locations was investigated for a laminated beam structure, with a method for finding an optimal location for vibration sensors proposed. It was found that a vibration sensor placed in selected structural regions can provide an increased level of sensitivity in detecting certain delamination locations. The results from this work also demonstrated the effectiveness of the developed method in determining an optimal placement for vibration sensors for delamination detection.

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Chen, Chao, Shuai Li, and Y. Frank Chen. "An Accurate Detection and Location of Weld Surface Defect Based on Laser Vision." Key Engineering Materials 963 (October 13, 2023): 197–207. http://dx.doi.org/10.4028/p-vaqqo3.

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In order to effectively improve the efficiency of automatic detection and subsequent processing of welding defects in the construction field, this paper proposes a method for detecting and locating weld surface defects based on machine vision and laser vision. YOLOv5 is used for the initial detection and identification of weld hole defects to obtain the approximate location of the defect. Subsequently, the detailed features of the defect sites are extracted by scanning the approximate range of defect locations with a line laser 3D sensor based on the identification of weld defect holes. Finally, the defect location and depth are accurately located based on the extracted features. Experimental results show that the proposed method is capable of identifying weld surface hole defects with an accuracy rate of over 94%. Furthermore, the combination of the system with the line laser 3D sensor detection can significantly improve the accuracy compared to pure 2D visual inspection, while the manual measurement is neither convenient nor accurate. This indicates that the proposed system can be used for rapid and accurate feature information extraction of weld hole defects, making subsequent remedial welding in actual engineering more automatic and efficient.

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Yinsen Luan, Yinsen Luan, Bing Xu Bing Xu, Ping Yang Ping Yang, and and Guomao Tang and Guomao Tang. "Optic flaws detection and location based on a plenoptic camera." Chinese Optics Letters 15, no. 4 (2017): 041102–41106. http://dx.doi.org/10.3788/col201715.041102.

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Kim, Yuna, Sangho Song, Hyeonbyeong Lee, Dojin Choi, Jongtae Lim, Kyoungsoo Bok, and Jaesoo Yoo. "Regional Traffic Event Detection Using Data Crowdsourcing." Applied Sciences 13, no. 16 (August 19, 2023): 9422. http://dx.doi.org/10.3390/app13169422.

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Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data for detecting events that influence traffic. The proposed technique uses various machine-learning methods to accurately identify events and location information. Therefore, it can resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Performance evaluations also demonstrated its validity and effectiveness.

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Astley, Kenneth Richard. "Bearing anomaly detection and location." Journal of the Acoustical Society of America 122, no. 3 (2007): 1313. http://dx.doi.org/10.1121/1.2781411.

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Raheja, J. L., Ankit Chaudhary, and Shobhit Maheshwari. "Hand gesture pointing location detection." Optik 125, no. 3 (February 2014): 993–96. http://dx.doi.org/10.1016/j.ijleo.2013.07.167.

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Kincaid, Rex K., and Robin M. Givens. "Heuristics for Mixed Strength Sensor Location Problems." International Journal of Operations Research and Information Systems 11, no. 2 (April 2020): 53–65. http://dx.doi.org/10.4018/ijoris.2020040104.

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Location-detection problems are pervasive. Examples include the detection of faults in microprocessors, the identification of contaminants in ventilation systems, and the detection of illegal logging in rain forests. In each of these applications a network provides a convenient modelling paradigm. Sensors are placed at particular node locations that, by design, uniquely detect and locate issues in the network. Open locating-dominating (OLD) sets constrain a sensor's effectiveness by assuming that it is unable to detect problems originating from the sensor location. Sensor failures may be caused by extreme environmental conditions or by the act of a nefarious individual. Determining the minimum size OLD set in a network is computationally intractable, but can be modelled as an integer linear program. The focus of this work is the development and evaluation of heuristics for the minimum OLD set problem when sensors of varying strengths are allowed. Computational experience and solution quality are reported for geometric graphs of up to 150 nodes.

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Chen, Xiao Yu, Kun Ma, Jia Quan Wu, and Xiang Guo. "Damage Detection through Changes in Frequency Base on Reinforced Concrete Beam." Advanced Materials Research 255-260 (May 2011): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.188.

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The detection of the structur[1]al damage by the method of changes in frequency is limited in detecting single location of the structural damage. This paper try to solve the problems of how to detect the multi-location of the structural damages and the corresponding severity. Therefore, a simple supported large-size reinforced concrete beam in different damage conditions is simulated by the finite element software-ANSYS. Cruves of frequency changes ratio can be maped by the date of the simulation, the locations of damages and corresponding severity can be detection by judging the superposition of the intersections of many curves of the frequent changes ratio. The simulation results demonstrate that the method proposed in this paper cannot only detection the multi-locations of the structural damages accurately, but also analyze the severity of the structural damages qualitatively. Corresponding author: Makun, School of Science, Kunming University of Science and Technology, makun_box@sina.com

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Zhou, Zhengxian, Hao Liu, Dawei Zhang, Yashuai Han, Xinyan Yang, Xianfeng Zheng, and Jun Qu. "Distributed Partial Discharge Locating and Detecting Scheme Based on Optical Fiber Rayleigh Backscattering Light Interference." Sensors 23, no. 4 (February 6, 2023): 1828. http://dx.doi.org/10.3390/s23041828.

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Optical fiber sensors are used for partial discharge detection in many applications due their advantage of strong anti-electromagnetic interference capability. Multi-point distributed partial discharge detection and location are important for electrical equipment. In this paper, a distributed partial discharge location and detection scheme based on optical fiber Rayleigh backscattering light interference is experimentally demonstrated. At the same time, the location and extraction algorithm is used to demodulate the partial discharge signal; furthermore, the high-pass filter is used to reduce the system low-frequency noise and environment noise. It is clear that the proposed system can detect a partial discharge signal generated by metal needle sensitivity, and the detectable frequency range is 0–2.5 kHz. We carried out 10 locating tests for two sensing units, the experimental results show that the maximum location error is 1.0 m, and the maximum standard deviation is 0.3795. At same time, the signal-to-noise ratio (SNR) of sensing unit 1 and sensing unit 2 are greatly improved after demodulation, which are 39.7 and 38.8, respectively. This provides a new method for a multipoint-distributed optical fiber sensor used for detecting and locating a long-distance electrical equipment partial discharge signal.

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Zhang, Zhi Fang, Krishna Shankar, Murat Tahtali, and Evgeny V. Morozov. "Graphical Detection Method for Delaminations." Applied Mechanics and Materials 66-68 (July 2011): 1410–15. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1410.

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In this paper, a simple graphical method for the detection of delaminations in damaged composite laminates is introduced. By using this method, both delamination size and location can be predicted accurately. First, a frequency database for the damaged beams with a range of delamination sizes and locations is generated using a Finite Element (FE) model; then for each mode, a surface plot relating the delamination size, location and frequency shift is generated. The next step is to look up the actual frequency shift (either from numerical simulation or experiment) from surface plots relating to three or more modes to get the intersection curves, which show the possible combination of delamination size and location for each mode. Finally the intersection curves of different modes are plotted together and the intersection point of all the curves indicates the possible delamination size and location, where the frequency shifts for all the modes can be matched. We demonstrate that this method is able to predict both delamination size and location fairly accurately. This method can be expanded to detect the propagation of delaminations by only monitoring the shifts in natural frequencies. It has the potential to detect multiple delaminations through continuous monitoring, provided that they do not occur simultaneously. This method has promising applications in the Structure Health Monitoring (SHM) of composite structures.

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Dashtdar, Majid, and Masoud Dashtdar. "Detecting the Fault Section in the Distribution Network with Distributed Generators Based on Optimal Placement of Smart Meters." Scientific Bulletin of Electrical Engineering Faculty 19, no. 2 (October 1, 2019): 28–34. http://dx.doi.org/10.1515/sbeef-2019-0017.

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AbstractOne of the most important issues in employing distribution networks is detecting the fault location in medium-voltage distribution feeders. Due to the vastness of distribution networks and growing distributed generation (DG) sources in this network, detection is difficult with the common methods. The aim of this paper is to present a method based on voltage distributed meters in a medium-voltage distribution network (by smart meters installed along the feeder) in order to detect the fault location in the presence of DG sources. Due to vastness of distribution network and cost of installing smart meters, it is not economically possible to install meters in all the Buses of the network. That’s why in this article, combination of genetic and locating algorithms and fault-based on voltage drop has been used to suggest a method to optimize the meter locations. In order to evaluate the efficiency of the method suggested, first we determine the optimal number and location of the meters and then we apply the fault that has been simulated in different Buses of the sample network, using PSCAD/EMTDC software. After results analysis, the fault location is estimated by MATLAB. Simulation results show that the fault locating method by optimal number of meters has good efficiency and accuracy in detecting faults in different spots and in different resistance ranges.

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Li, Jie, Bingzhe Dai, Jiahao Zhou, Junchao Zhang, Qilin Zhang, Jing Yang, Yao Wang, et al. "Preliminary Application of Long-Range Lightning Location Network with Equivalent Propagation Velocity in China." Remote Sensing 14, no. 3 (January 25, 2022): 560. http://dx.doi.org/10.3390/rs14030560.

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The equivalent propagation method adopts a variable propagation velocity in lightning location, minimizing the location error caused by various factors in the long-range lightning location network. To verify the feasibility of this method, we establish a long-range lightning location network in China. A new method is used to extract the ground wave peak points of the lightning sferics and is combined with the equivalent propagation velocity method for lightning location. By comparing with the lightning data detected by the lightning locating system called advanced direction and time-of-arrival detecting (ADTD) that has been widely used for tens of years in China, the feasibility of this method is initially verified. Additionally, it is found that the relative detection efficiency of our long-range lightning location network can reach 53%, the average location error is 9.17 km, and the detection range can reach more than 3000 km. The equivalent propagation method can improve the average location accuracy by ~1.16 km, compared with the assumed light speed of lightning-radiated sferic from the lightning stroke point to the observation station. The 50th percentile of the equal propagation velocity is 0.998c, which may be used in the long-range lightning location networks.

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Ko, Daijin, and Judith E. Zeh. "Detection of Migration Using Sound Location." Biometrics 44, no. 3 (September 1988): 751. http://dx.doi.org/10.2307/2531589.

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Ray, S., Wei Lai, and I. C. Paschalidis. "Statistical location detection with sensor networks." IEEE Transactions on Information Theory 52, no. 6 (June 2006): 2670–83. http://dx.doi.org/10.1109/tit.2006.874376.

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Ray, S., D. Starobinski, A. Trachtenberg, and R. Ungrangsi. "Robust Location Detection With Sensor Networks." IEEE Journal on Selected Areas in Communications 22, no. 6 (August 2004): 1016–25. http://dx.doi.org/10.1109/jsac.2004.830895.

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Schieferdecker, Dennis. "Location-Free Detection of Network Boundaries." ACM Transactions on Sensor Networks 11, no. 4 (December 23, 2015): 1–40. http://dx.doi.org/10.1145/2795232.

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Junnila, Ville, and Tero Laihonen. "Tolerant location detection in sensor networks." Advances in Applied Mathematics 112 (January 2020): 101938. http://dx.doi.org/10.1016/j.aam.2019.101938.

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Frangos, William. "Electrical detection of leaks in lined waste disposal ponds." GEOPHYSICS 62, no. 6 (November 1997): 1737–44. http://dx.doi.org/10.1190/1.1444274.

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A method for detecting and locating leaks in the plastic liner of a waste disposal pond has been implemented and tested at a site near Budmerice in Slovakia. The method is based on detecting electric current flowing through holes in the insulating lining membrane. Unlike similar methods employed elsewhere, this implementation allows monitoring for leaks that may develop during and after filling the pond with electrically inhomogeneous solid waste. To accomplish this goal, sensing electrodes were placed below the membrane during construction. In operation, current was passed between an electrode inside the pond and another outside; the voltage caused by this current was observed on the buried sensing electrodes. The data were then processed to detect and locate any leaks in the membrane. An important practical concern is achieving acceptable detectability and location accuracy while using a sufficiently sparse grid of sensing electrodes. This problem was overcome by two processing steps: (1) calculating electrical potentials from the observed voltages and (2) performing a nonlinear inversion on subsets of the data. With this technique, observations made with a 10- × 8-m grid of electrodes, a relatively low‐power current source, and a simple receiver can provide accurate location information, even for small leaks. In a blind test, the system accurately predicted the locations of six leaks that were subsequently verified visually. Five of the leaks were cuts in the plastic typically measuring less than 2 × 0.1 cm, whereas the sixth leak was a group of many small holes. For the five, the typical location accuracy was about 30 cm, comparable to the basic survey location accuracy of the sensing electrodes.

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Cui, Ce, Cheng Hao, and Duan Duan Li. "The Research of Gray Bus Positioning System." Applied Mechanics and Materials 385-386 (August 2013): 496–99. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.496.

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Position detection method is applied in industrial production. Some of these methods can ensure positioning accuracy but poor ability to resist pollution, some lower cost but positioning is not accurate. In this paper, we study the Gray bus location system can be used in linear or circular displacement detection. Gray bus location system has a precise location. Its ability to resist pollution is strong, waterproof. Gray bus location system detecting the unloading cars real time location, then, let the signal of real-time location transmitted to MCU.MCU converts the signal into switch signal to control the discharging car running. Through Gray bus position detection device and control system to work together, can realize the unloading car walk smoothly, the stop position accurately.

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Berlucchi, Giovanni, Leonardo Chelazzi, and Giancarlo Tassinari. "Volitional Covert Orienting to a Peripheral Cue Does Not Suppress Cue-induced Inhibition of Return." Journal of Cognitive Neuroscience 12, no. 4 (July 2000): 648–63. http://dx.doi.org/10.1162/089892900562408.

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Detection reaction time (RT) at an extrafoveal location can be increased by noninformative precues presented at that location or ipsilaterally to it. This cue-induced inhibition is called inhibition of return or ipsilateral inhibition. We measured detection RT to simple light targets at extrafoveal locations that could be designated for covert orienting by local or distant cues. We found that cue-induced inhibition co-occurred in an additive fashion with the direct effects of covert orienting, i.e., it detracted from facilitation at attended locations and increased the disadvantage for unattended locations. Thus, cue-induced inhibition cannot be suppressed by a volitional covert orienting to the cued location; the cooccurrence of different facilitatory and inhibitory effects confirms the simultaneous operation of multiple independent, attentional mechanisms during covert orienting.

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Hassoubah, Rawan S., Suhare M. Solaiman, and Manal A. Abdullah. "Intrusion Detection of Hello Flood Attack in WSNs Using Location Verification Scheme." International Journal of Computer and Communication Engineering 4, no. 3 (2015): 156–65. http://dx.doi.org/10.17706/ijcce.2015.4.3.156-165.

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Panchenko, Anatoliy, Yuliia Musairova, Yevheniia Zarichniak, Volodymyr Yevchenko, and Mykola Klymenko. "Method for determining the locations of power cable damage." Vehicle and electronics. Innovative technologies, no. 23 (June 29, 2023): 50–58. http://dx.doi.org/10.30977/veit.2023.23.0.6.

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Problem. Existing methods for remotely detecting cable damage locations, except in the case of cable breakage, have a common drawback. They are unable to accurately separate the cable core resistance from the transient resistance at the short-circuit location, leading to low accuracy in fault detection. The posterior transient resistance at the short-circuit location can vary widely, depending on when the repair crew arrives. Goal. The goal of this study is to propose a method for identifying the location of "floating breakdown" cable damage. Methodology. The method involves using short-circuit indicators to determine the type and area(s) of the short circuit. The UNI-TUT255A device, a current clamp type, is then installed on the damaged cable core. The Imax option with memory is set, and the cable is switched on at the rated voltage. By comparing the recorded shock current value with the values obtained from a short-circuit model of the cable in MATLAB, the location of the damage along the length of the cable is determined. The point where the shock current value of the model matches the recorded value corresponds to the location of the short circuit. Originality. The proposed method addresses the challenge of locating faults in power cables, specifically those of the "floating breakdown" type. This type of cable damage, which occurs when the cable is accidentally disconnected during a short circuit, poses difficulties in detection, as traditional methods may show normal readings. The method presented in this study overcomes these limitations and provides a practical solution for identifying "floating breakdown" faults. Practical value. The results obtained from this method allow for the accurate detection of faults at the nominal voltage, without the need for burning the cable. This reduces the probability of additional damage caused by overvoltage. Furthermore, the method requires only one operator to carry the necessary equipment, eliminating the need for transport with powerful burning installations typically used in traditional methods.

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Bae, Bum Won, In Pil Kang, and Yeon Sun Choi. "A Gear Chain Fault Detection Method Using an Adaptive Interference Canceling." Key Engineering Materials 345-346 (August 2007): 1303–6. http://dx.doi.org/10.4028/www.scientific.net/kem.345-346.1303.

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A fault diagnosis method based on wavelet and adaptive interference canceling is presented for the identification of a damaged gear tooth. A damaged tooth of a certain gear chain generates impulsive signals that could be informative to fault detections. Many publications are available not only for the impulsive vibration signal analysis but the application of signal processing techniques to the impulsive signal detections. However, most of the studies about the gear fault detection using the impulsive vibration signals of a driving gear chain are limited to the verification of damage existence on a gear pair. Requirements for more advanced method locating damaged tooth in a driving gear chain should be a motivation of further studies. In this work an adaptive interference canceling combined with wavelet method is used for a successful identification of the damaged tooth location. An application of the wavelet technique provides a superior resolution for the damage detection to the traditional frequency spectrum based methods. An analysis and experiment with three pair gear chain show the feasibility of this study yielding a precise location of the damaged gear tooth.

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Lu, Haoran, Qingchuan Zhao, Yongliang Chen, Xiaojing Liao, and Zhiqiang Lin. "Detecting and Measuring Aggressive Location Harvesting in Mobile Apps via Data-flow Path Embedding." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 1 (February 27, 2023): 1–27. http://dx.doi.org/10.1145/3579447.

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Today, location-based services have become prevalent in the mobile platform, where mobile apps provide specific services to a user based on his or her location. Unfortunately, mobile apps can aggressively harvest location data with much higher accuracy and frequency than they need because the coarse-grained access control mechanism currently implemented in mobile operating systems (e.g., Android) cannot regulate such behavior. This unnecessary data collection violates the data minimization policy, yet no previous studies have investigated privacy violations from this perspective, and existing techniques are insufficient to address this violation. To fill this knowledge gap, we take the first step toward detecting and measuring this privacy risk in mobile apps at scale. Particularly, we annotate and release thefirst dataset to characterize those aggressive location harvesting apps and understand the challenges of automatic detection and classification. Next, we present a novel system, LocationScope, to address these challenges by(i) uncovering how an app collects locations and how to use such data through a fine-tuned value set analysis technique,(ii) recognizing the fine-grained location-based services an app provides via embedding data-flow paths, which is a combination of program analysis and machine learning techniques, extracted from its location data usages, and(iii) identifying aggressive apps with an outlier detection technique achieving a precision of 97% in aggressive app detection. Our technique has further been applied to millions of free Android apps from Google Play as of 2019 and 2021. Highlights of our measurements on detected aggressive apps include their growing trend from 2019 to 2021 and the app generators' significant contribution of aggressive location harvesting apps.

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Lu, Haoran, Qingchuan Zhao, Yongliang Chen, Xiaojing Liao, and Zhiqiang Lin. "Detecting and Measuring Aggressive Location Harvesting in Mobile Apps via Data-flow Path Embedding." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (June 26, 2023): 45–46. http://dx.doi.org/10.1145/3606376.3593535.

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Today, location-based services have become prevalent in the mobile platform, where mobile apps provide specific services to a user based on his or her location. Unfortunately, mobile apps can aggressively harvest location data with much higher accuracy and frequency than they need because the coarse-grained access control mechanism currently implemented in mobile operating systems (e.g., Android) cannot regulate such behavior. This unnecessary data collection violates the data minimization policy, yet no previous studies have investigated privacy violations from this perspective, and existing techniques are insufficient to address this violation. To fill this knowledge gap, we take the first step toward detecting and measuring this privacy risk in mobile apps at scale. Particularly, we annotate and release the first dataset to characterize those aggressive location harvesting apps and understand the challenges of automatic detection and classification. Next, we present a novel system, LocationScope, to address these challenges by (i) uncovering how an app collects locations and how to use such data through a fine-tuned value set analysis technique, (ii) recognizing the fine-grained location-based services an app provides via embedding data-flow paths, which is a combination of program analysis and machine learning techniques, extracted from its location data usages, and (iii) identifying aggressive apps with an outlier detection technique achieving a precision of 97% in aggressive app detection. Our technique has further been applied to millions of free Android apps from Google Play as of 2019 and 2021. Highlights of our measurements on detected aggressive apps include their growing trend from 2019 to 2021 and the app generators' significant contribution of aggressive location harvesting apps.

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Martin, K. F., and M. Moavenian. "Failure Detection and Location Using Residual Difference Generation Detection Filters." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 210, no. 4 (November 1996): 283–90. http://dx.doi.org/10.1243/pime_proc_1996_210_467_02.

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The paper describes a theoretical investigation of failure detection using a new type of filter. The latter, called a residual difference generation detection filter, is based upon using (a) a main detection filter and (b) a faulty parameter modifier. Both of these use a reference model of the system; this is a mathematical model of the system without faults. In (a) the reference model is used in parallel with the real system in order to generate residuals (functions of differences between the real system and the reference model); in (b) the reference model is used in parallel with another mathematical model of the system which contains a known fault which again generates residuals that are a function of the known fault. By analysing these residuals while changing the known fault in (b) it is possible to detect which fault is occurring. The technique is applied to faults in a servo-motor, faults being assumed to occur singly. Tests were carried out using a mathematical model of the real system which incorporated an ‘unknown’ fault.

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Et.al, Park Gi-Hun. "Development of a Cable Damage Detection Deep Learning Method based on Acceleration Response of Cable-Stayed Bridge." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 638–47. http://dx.doi.org/10.17762/turcomat.v12i6.2059.

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The purpose of this thesis was to select a cable-stayed bridge to which external force may cause damage as the subject, to develop a damage detection deep learning method capable of detecting cable damage, and to test and verify the developed damage detection deep learning method. The damage detection method was developed as a system that utilizes the acceleration response of a structure measured for maintenance purposes. To extract information capable of identifying the damage locations from among the measured acceleration responses, a CNN ID was used to develop the damage detection deep learning method. The developed damage detection deep learning method was developed in a way not independently arranging 1 machine learning model per each measuring point and finally predicting the damage location based on the decision-making results collected from each machine learning model. The developed damage detection deep learning method performed the learning per each machine learning model by utilizing the acceleration response of a structure acquired based on the preliminary damage test. Finally, the damage detection deep learning method that completed the learning verified the cable damage location detection performance by utilizing the data acquired based on the cable-stayed bridge damage test. As a result, it was confirmed that the developed damage detection deep learning method predicted the damage location of a cable-stayed bridge at an average accuracy of 89%. In the current research, only the cable-stayed bridge of the Seohaegyo Bridge was studied, but in the improved study, the research will be conducted on other bridges and damage assessment will be conducted on all cables.

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Luštrek, Mitja, Hristijan Gjoreski, Simon Kozina, Božidara Cvetkovic, Violeta Mirchevska, and Matjaž Gams. "Detecting Falls with Location Sensors and Accelerometers." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 2 (August 11, 2011): 1662–67. http://dx.doi.org/10.1609/aaai.v25i2.18857.

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Due to the rapid aging of the population, many technical solutions for the care of the elderly are being developed, often involving fall detection with accelerometers. We present a novel approach to fall detection with location sensors. In our application, a user wears up to four tags on the body whose locations are detected with radio sensors. This makes it possible to recognize the user’s activity, including falling any lying afterwards, and the context in terms of the location in the apartment. We compared fall detection using location sensors, accelerometers and accelerometers combined with the context. A scenario consisting of events difficult to recognize as falls or non- falls was used for the comparison. The accuracy of the methods that utilized the context was almost 40 percentage points higher compared to the methods without the context. The accuracy of pure location-based methods was around 10 percentage points higher than the accuracy of accelerometers combined with the context.

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Colombero, Chiara, Cesare Comina, and Laura Valentina Socco. "Imaging near-surface sharp lateral variations with surface-wave methods — Part 1: Detection and location." GEOPHYSICS 84, no. 6 (November 1, 2019): EN93—EN111. http://dx.doi.org/10.1190/geo2019-0149.1.

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Near-surface sharp lateral variations can be either a target of investigation or an issue for the reconstruction of reliable subsurface models in surface-wave (SW) prospecting. Effective and computationally fast methods are consequently required for detection and location of these shallow heterogeneities. Four SW-based techniques, chosen between available literature methods, are tested for detection and location purposes. All of the techniques are updated for multifold data and then systematically applied on new synthetic and field data. The selected methods are based on computation of the energy, energy decay exponent, attenuation coefficient, and autospectrum. The multifold upgrade is based on the stacking of the computed parameters for single-shot or single-offset records and improves readability and interpretation of the final results. Detection and location capabilities are extensively evaluated on a variety of 2D synthetic models, simulating different target geometries, embedment conditions, and impedance contrasts with respect to the background. The methods are then validated on two field cases: a shallow low-velocity body in a sedimentary sequence and a hard-rock site with two embedded subvertical open fractures. For a quantitative comparison, the horizontal gradients of the four parameters are analyzed to establish uniform criteria for location estimation. All of the methods indicate ability in detecting and locating lateral variations having lower acoustic impedance than the surrounding material, with errors generally comparable or lower than the geophone spacing. More difficulties are encountered in locating targets with higher acoustic impedance than the background, especially in the presence of weak lateral contrasts, high embedment depths, and small dimensions of the object.

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Bayoumi, Ahmed, Tobias Minten, and Inka Mueller. "Determination of Detection Probability and Localization Accuracy for a Guided Wave-Based Structural Health Monitoring System on a Composite Structure." Applied Mechanics 2, no. 4 (December 2, 2021): 996–1008. http://dx.doi.org/10.3390/applmech2040058.

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The capabilities of detection and localization of damage in a structure, using a guided wave-based structural health monitoring (GWSHM) system, depend on the damage location and the chosen sensor array setup. This paper presents a novel approach to assess the reliability of an SHM system enabling to quantify localization accuracy. A two-step technique is developed to combine multiple paths to generate one probability of detection (POD) curve that provides information regarding the detection capability of an SHM system at a defined damage position. Moreover, a new method is presented to analyze localization accuracy. Established probability-based diagnostic imaging using a signal correlation algorithm is used to determine the damage location. The resultant output of the localization accuracy analysis is the smallest damage size at which a defined accuracy level can be reached at a determined location. The proposed methods for determination of detection probability and localization accuracy are applied to a plate-like CFRP structure with an omega stringer with artificial damage of different sizes at different locations. The results show that the location of the damage influences the sensitivity of detection and localization accuracy for the used detection and localization methods. Localization accuracy is enhanced as it becomes closer to the array’s center, but its detection sensitivity deteriorates.

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Zhang, Di, Feng Pan, Qi Diao, Xiaoxue Feng, Weixing Li, and Jiacheng Wang. "Seeding Crop Detection Framework Using Prototypical Network Method in UAV Images." Agriculture 12, no. 1 (December 27, 2021): 26. http://dx.doi.org/10.3390/agriculture12010026.

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With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the problem of locating chili seedling crops in large-field UAV images is processed. Two problems are encountered in the location process: a small number of samples and objects in UAV images are similar on a small scale, which increases the location difficulty. A detection framework based on a prototypical network to detect crops in UAV aerial images is proposed. In particular, a method of subcategory slicing is applied to solve the problem, in which objects in aerial images have similarities at a smaller scale. The detection framework is divided into two parts: training and detection. In the training process, crop images are sliced into subcategories, and then these subcategory patch images and background category images are used to train the prototype network. In the detection process, a simple linear iterative clustering superpixel segmentation method is used to generate candidate regions in the UAV image. The location method uses a prototypical network to recognize nine patch images extracted simultaneously. To train and evaluate the proposed method, we construct an evaluation dataset by collecting the images of chilies in a seedling stage by an UAV. We achieve a location accuracy of 96.46%. This study proposes a seedling crop detection framework based on few-shot learning that does not require the use of labeled boxes. It reduces the workload of manual annotation and meets the location needs of seedling crops.

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van Moorselaar, Dirk, and Jan Theeuwes. "Spatial suppression due to statistical regularities in a visual detection task." Attention, Perception, & Psychophysics 84, no. 2 (November 12, 2021): 450–58. http://dx.doi.org/10.3758/s13414-021-02330-0.

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AbstractIncreasing evidence demonstrates that observers can learn the likely location of salient singleton distractors during visual search. To date, the reduced attentional capture at high-probability distractor locations has typically been examined using so called compound search, in which by design a target is always present. Here, we explored whether statistical distractor learning can also be observed in a visual detection task, in which participants respond target present if the singleton target is present and respond target absent when the singleton target is absent. If so, this allows us to examine suppression of the location that is likely to contain a distractor both in the presence, but critically also in the absence, of a priority signal generated by the target singleton. In an online variant of the additional singleton paradigm, observers had to indicate whether a unique shape was present or absent, while ignoring a colored singleton, which appeared with a higher probability in one specific location. We show that attentional capture was reduced, but not absent, at high-probability distractor locations, irrespective of whether the display contained a target or not. By contrast, target processing at the high-probability distractor location was selectively impaired on distractor-present displays. Moreover, all suppressive effects were characterized by a gradient such that suppression scaled with the distance to the high-probability distractor location. We conclude that statistical distractor learning can be examined in visual detection tasks, and discuss the implications for attentional suppression due to statistical learning.

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Zhang, Weixing, Chandi Witharana, Weidong Li, Chuanrong Zhang, Xiaojiang Li, and Jason Parent. "Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images." Sensors 18, no. 8 (August 1, 2018): 2484. http://dx.doi.org/10.3390/s18082484.

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Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.

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Poelman, Dieter R., Wolfgang Schulz, and Christian Vergeiner. "Performance Characteristics of Distinct Lightning Detection Networks Covering Belgium." Journal of Atmospheric and Oceanic Technology 30, no. 5 (May 1, 2013): 942–51. http://dx.doi.org/10.1175/jtech-d-12-00162.1.

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Abstract This study reports results from electric field measurements coupled to high-speed camera observations of cloud-to-ground lightning to test the performance of lightning location networks in terms of its detection efficiency and location accuracy. The measurements were carried out in August 2011 in Belgium, during which 57 negative cloud-to-ground flashes, with a total of 210 strokes, were recorded. One of these flashes was followed by a continuing current of over 1 s—one of the longest ever observed in natural negative cloud-to-ground lightning. Lightning data gathered from the lightning detection network operated by the Royal Meteorological Institute of Belgium [consisting of a network employing solely Surveillance et Alerte Foudre par Interférométrie Radioélectrique (SAFIR) sensors and a network combining SAFIR and LS sensors], the European Cooperation for Lightning Detection (EUCLID), Vaisala’s Global Lightning Detection network GLD360, and the Met Office’s long-range Arrival Time Difference network (ATDnet) are evaluated against this ground-truth dataset. It is found that all networks are capable of detecting over 90% of the observed flashes, but a larger spread is observed at the level of the individual strokes. The median location accuracy varies between 0.6 and 1 km, except for the SAFIR network, locating the ground contacts with 6.1-km median accuracy. The same holds for the reported peak currents, where a good correlation is found among the networks that provide peak current estimates, apart from the SAFIR network being off by a factor of 3.

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Cheon, Minkyu, Wonju Lee, Changyong Yoon, and Mignon Park. "Vision-Based Vehicle Detection System With Consideration of the Detecting Location." IEEE Transactions on Intelligent Transportation Systems 13, no. 3 (September 2012): 1243–52. http://dx.doi.org/10.1109/tits.2012.2188630.

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M. Ramo, Ramadan. "Using Genetic Algorithm For Eye Location Detection." JOURNAL OF EDUCATION AND SCIENCE 25, no. 3 (September 1, 2012): 110–22. http://dx.doi.org/10.33899/edusj.2012.59197.

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Cheng Xu, Chao Mi, Chao Chen, and Zhibang Yang. "Road Detection Based on Vanishing Point Location." Journal of Convergence Information Technology 7, no. 6 (April 30, 2012): 137–45. http://dx.doi.org/10.4156/jcit.vol7.issue6.17.

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Lee, Jeong Heon, and R. Michael Buehrer. "Characterization and detection of location spoofing attacks." Journal of Communications and Networks 14, no. 4 (August 2012): 396–409. http://dx.doi.org/10.1109/jcn.2012.6292246.

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Lustrek, Mitja, Hristijan Gjoreski, Narciso Gonzalez Vega, Simon Kozina, Bozidara Cvetkovic, Violeta Mirchevska, and Matjaz Gams. "Fall Detection Using Location Sensors and Accelerometers." IEEE Pervasive Computing 14, no. 4 (October 2015): 72–79. http://dx.doi.org/10.1109/mprv.2015.84.

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Guillaume, M., Ph Réfrégier, J. Campos, and V. Lashin. "Detection theory approach to multichannel pattern location." Optics Letters 22, no. 24 (December 15, 1997): 1887. http://dx.doi.org/10.1364/ol.22.001887.

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Harrison, Christopher H. "Target detection and location with ambient noise." Journal of the Acoustical Society of America 123, no. 4 (April 2008): 1834–37. http://dx.doi.org/10.1121/1.2872516.

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Amin, Md Syedul, Mamun Bin Ibne Reaz, and Salwa Sheikh Nasir. "Integrated Vehicle Accident Detection and Location System." TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, no. 1 (March 1, 2014): 73. http://dx.doi.org/10.12928/telkomnika.v12i1.13.

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Amin, Md Syedul, Mamun Bin Ibne Reaz, and Salwa Sheikh Nasir. "Integrated Vehicle Accident Detection and Location System." TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, no. 1 (March 1, 2014): 73. http://dx.doi.org/10.12928/telkomnika.v12i1.1787.

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Zou, Tengtao, Chen Cao, and Shangming Yang. "An Improved Location Model for Pedestrian Detection." IOP Conference Series: Materials Science and Engineering 646 (October 17, 2019): 012016. http://dx.doi.org/10.1088/1757-899x/646/1/012016.

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Journal articles on the topic 'Detection and location'

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