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Thematische Bibliographien / Detection/location / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Detection/location“

Autor: Grafiati

Veröffentlicht am 26. Juni 2021

Zuletzt aktualisiert am 8. Februar 2022

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1

Baidari, Dr Ishwar, und S. P. Sajjan. „Location Based Crime Detection Using Data Mining“. Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (31.10.2016): 208–12. http://dx.doi.org/10.9756/bijsesc.8279.

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2

Decker, Brooke K., und Tara N. Palmore. „Waterborne Pathogen Detection More than Just “Location, Location, Location…”“. Infection Control & Hospital Epidemiology 35, Nr. 2 (Februar 2014): 130–31. http://dx.doi.org/10.1086/675067.

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3

Yinsen Luan, Yinsen Luan, Bing Xu Bing Xu, Ping Yang Ping Yang und and Guomao Tang and Guomao Tang. „Optic flaws detection and location based on a plenoptic camera“. Chinese Optics Letters 15, Nr. 4 (2017): 041102–41106. http://dx.doi.org/10.3788/col201715.041102.

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4

Astley, Kenneth Richard. „Bearing anomaly detection and location“. Journal of the Acoustical Society of America 122, Nr. 3 (2007): 1313. http://dx.doi.org/10.1121/1.2781411.

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5

Raheja, J. L., Ankit Chaudhary und Shobhit Maheshwari. „Hand gesture pointing location detection“. Optik 125, Nr. 3 (Februar 2014): 993–96. http://dx.doi.org/10.1016/j.ijleo.2013.07.167.

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6

Hassoubah, Rawan S., Suhare M. Solaiman und Manal A. Abdullah. „Intrusion Detection of Hello Flood Attack in WSNs Using Location Verification Scheme“. International Journal of Computer and Communication Engineering 4, Nr. 3 (2015): 156–65. http://dx.doi.org/10.17706/ijcce.2015.4.3.156-165.

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7

Ko, Daijin, und Judith E. Zeh. „Detection of Migration Using Sound Location“. Biometrics 44, Nr. 3 (September 1988): 751. http://dx.doi.org/10.2307/2531589.

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8

Schieferdecker, Dennis. „Location-Free Detection of Network Boundaries“. ACM Transactions on Sensor Networks 11, Nr. 4 (23.12.2015): 1–40. http://dx.doi.org/10.1145/2795232.

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9

Ray, S., D. Starobinski, A. Trachtenberg und R. Ungrangsi. „Robust Location Detection With Sensor Networks“. IEEE Journal on Selected Areas in Communications 22, Nr. 6 (August 2004): 1016–25. http://dx.doi.org/10.1109/jsac.2004.830895.

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10

Ray, S., Wei Lai und I. C. Paschalidis. „Statistical location detection with sensor networks“. IEEE Transactions on Information Theory 52, Nr. 6 (Juni 2006): 2670–83. http://dx.doi.org/10.1109/tit.2006.874376.

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11

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

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12

Martin, K. F., und 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, Nr. 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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13

Cheng Xu, Chao Mi, Chao Chen und Zhibang Yang. „Road Detection Based on Vanishing Point Location“. Journal of Convergence Information Technology 7, Nr. 6 (30.04.2012): 137–45. http://dx.doi.org/10.4156/jcit.vol7.issue6.17.

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14

M. Ramo, Ramadan. „Using Genetic Algorithm For Eye Location Detection“. JOURNAL OF EDUCATION AND SCIENCE 25, Nr. 3 (01.09.2012): 110–22. http://dx.doi.org/10.33899/edusj.2012.59197.

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15

Amin, Md Syedul, Mamun Bin Ibne Reaz und Salwa Sheikh Nasir. „Integrated Vehicle Accident Detection and Location System“. TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, Nr. 1 (01.03.2014): 73. http://dx.doi.org/10.12928/telkomnika.v12i1.13.

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16

Amin, Md Syedul, Mamun Bin Ibne Reaz und Salwa Sheikh Nasir. „Integrated Vehicle Accident Detection and Location System“. TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, Nr. 1 (01.03.2014): 73. http://dx.doi.org/10.12928/telkomnika.v12i1.1787.

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17

Harrison, Christopher H. „Target detection and location with ambient noise“. Journal of the Acoustical Society of America 123, Nr. 4 (April 2008): 1834–37. http://dx.doi.org/10.1121/1.2872516.

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18

Zou, Tengtao, Chen Cao und Shangming Yang. „An Improved Location Model for Pedestrian Detection“. IOP Conference Series: Materials Science and Engineering 646 (17.10.2019): 012016. http://dx.doi.org/10.1088/1757-899x/646/1/012016.

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19

Guillaume, M., Ph Réfrégier, J. Campos und V. Lashin. „Detection theory approach to multichannel pattern location“. Optics Letters 22, Nr. 24 (15.12.1997): 1887. http://dx.doi.org/10.1364/ol.22.001887.

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20

Lustrek, Mitja, Hristijan Gjoreski, Narciso Gonzalez Vega, Simon Kozina, Bozidara Cvetkovic, Violeta Mirchevska und Matjaz Gams. „Fall Detection Using Location Sensors and Accelerometers“. IEEE Pervasive Computing 14, Nr. 4 (Oktober 2015): 72–79. http://dx.doi.org/10.1109/mprv.2015.84.

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21

Magotra, N., N. Ahmed und E. Chael. „Single-station seismic event detection and location“. IEEE Transactions on Geoscience and Remote Sensing 27, Nr. 1 (1989): 15–23. http://dx.doi.org/10.1109/36.20270.

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22

Lee, Jeong Heon, und R. Michael Buehrer. „Characterization and detection of location spoofing attacks“. Journal of Communications and Networks 14, Nr. 4 (August 2012): 396–409. http://dx.doi.org/10.1109/jcn.2012.6292246.

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23

Benkherouf, A., und A. Y. Allidina. „Leak detection and location in gas pipelines“. IEE Proceedings D Control Theory and Applications 135, Nr. 2 (1988): 142. http://dx.doi.org/10.1049/ip-d.1988.0020.

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24

Yang, XinYu, Yi Shi und HuiJun Zhu. „Detection and location algorithm against local-worm“. Science in China Series F: Information Sciences 51, Nr. 12 (27.08.2008): 1935–46. http://dx.doi.org/10.1007/s11432-008-0132-z.

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25

Inkpen, Diana, Ji Liu, Atefeh Farzindar, Farzaneh Kazemi und Diman Ghazi. „Location detection and disambiguation from twitter messages“. Journal of Intelligent Information Systems 49, Nr. 2 (31.03.2017): 237–53. http://dx.doi.org/10.1007/s10844-017-0458-3.

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26

Petrova, Olga,, Galina V. ,. Tabunshchyk und Dirk, Van Merode. „METHOD FOR LOCATION DETECTION IN INDOOR NAVIGATION SYSTEMS“. ELECTRICAL AND COMPUTER SYSTEMS 25, Nr. 101 (10.05.2017): 270–78. http://dx.doi.org/10.15276/eltecs.25.101.2017.31.

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27

QI, Yong-feng, und Yuan-lian HUO. „Color-based method for face detection and location“. Journal of Computer Applications 29, Nr. 3 (06.05.2009): 785–88. http://dx.doi.org/10.3724/sp.j.1087.2009.00785.

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28

PENG, Qi, Sidong ZHONG, Lifen TU und Tiancan MEI. „Moving Shadow Detection Based on Space Location Feature“. Robot 34, Nr. 5 (2012): 614. http://dx.doi.org/10.3724/sp.j.1218.2012.00614.

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29

Mutiara, et al. „Smart cane with location detection using RF module“. International Journal of ADVANCED AND APPLIED SCIENCES 5, Nr. 2 (Februar 2018): 65–70. http://dx.doi.org/10.21833/ijaas.2018.02.011.

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30

Tafuri, Anthony N., James J. Yezzi, Daniel J. Watts und John M. Carlyle. „LEAK DETECTION AND LEAK LOCATION IN UNDERGROUND PIPELINES1“. International Oil Spill Conference Proceedings 1997, Nr. 1 (01.04.1997): 379–81. http://dx.doi.org/10.7901/2169-3358-1997-1-379.

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ABSTRACT The use of passive acoustic techniques to detect and locate leaks in underground pipelines that carry pressurized liquids is being investigated in a joint research program between the Department of Defense (DoD), the Department of Energy (DOE), and the U.S. Environmental Protection Agency (EPA). Acoustic techniques permit more cost-effective, timely, and accurate leak detection and location than currently established techniques, such as methodologies based on volumetric changes and others based on pressure loss. An experimental facility for determining the capabilities and limitations of improved leak detection and leak location methods based on advanced acoustic principles is being developed at the EPA's research facility in Edison, New Jersey. Four representative pipeline systems will be installed and evaluated: one typical of the petrochemical industry, one typical of a navy/air force hydrant refueling system, one typical of an army central heating system, and a fourth typical of pipelines associated with low-level liquid wastes at DOE facilities. Results of acoustic experiments on an existing 2-inch-diameter test line have indicated that the fundamental theory behind passive acoustic leak detection and location is solid. Several different acoustic leak detection and location methods have been explored to date; data have been produced by all of the techniques, proving their usefulness.

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31

Tang, Yun-Lian. „Detection and location ofHelicobacter pyloriin human gastric carcinomas“. World Journal of Gastroenterology 11, Nr. 9 (2005): 1387. http://dx.doi.org/10.3748/wjg.v11.i9.1387.

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32

Misiunas, D., M. Lambert, A. Simpson und G. Olsson. „Burst detection and location in water distribution networks“. Water Supply 5, Nr. 3-4 (01.11.2005): 71–80. http://dx.doi.org/10.2166/ws.2005.0085.

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An algorithm for the detection and location of sudden bursts in water distribution networks combining both continuous monitoring of pressure and hydraulic transient computation is presented. The approach is designed for medium and large bursts that are the result of the sudden rupture of the pipe wall or other physical element in the network and are accompanied by the transient pressure wave that propagates throughout the network. The burst-induced transient wave arrival times and magnitudes measured at two or more points are used to find the location of a burst. The wave arrival times and magnitudes are detected using the modified cumulative sum (CUSUM) change detection test. Results of validation on a real network show the potential of the proposed burst detection and location technique to be used in water distribution systems.

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33

Zhou Peipei, 周培培, 丁庆海 Ding Qinghai, 罗海波 Luo Haibo und 侯幸林 Hou Xinglin. „Anomaly Detection and Location in Crowded Surveillance Videos“. Acta Optica Sinica 38, Nr. 8 (2018): 0815007. http://dx.doi.org/10.3788/aos201838.0815007.

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34

LIN Guo-jun, 林国军, 杨明中 YANG Ming-zhong, 陈明举 CHEN Ming-ju und 解. 梅. XIE Mei. „Face detection algorithm based on skin color location“. Chinese Journal of Liquid Crystals and Displays 34, Nr. 1 (2019): 70–73. http://dx.doi.org/10.3788/yjyxs20193401.0070.

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35

Liu, Xiao Lin, und Zhi Quan Li. „Aircraft Cable Fault Location Based on Detection Model“. Applied Mechanics and Materials 55-57 (Mai 2011): 332–36. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.332.

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An aircraft cable fault location method based on detection model is proposed to solve the problem of being difficult to inspect the fault for the civil aviation maintenance. In response to the condition of the experimental installation, the reference signal is designed. The fault of the cable can be located according to the reflected waveform. An aircraft cable fault location system is designed and the experimental results show that the method is rational and effective.

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36

Zhi-Xiao, Wang, Li Ze-chao, Ding Xiao-fang und Tang Jin-hui. „Overlapping community detection based on node location analysis“. Knowledge-Based Systems 105 (August 2016): 225–35. http://dx.doi.org/10.1016/j.knosys.2016.05.024.

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37

Borović, Vladan, Petar Spalević, Petar Čisar, Dejan Rančić und Srđan Jović. „Supervisory system for physical objects spatial location detection“. Physica A: Statistical Mechanics and its Applications 521 (Mai 2019): 781–95. http://dx.doi.org/10.1016/j.physa.2019.01.023.

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38

Hu, Yi, Shu Wang, Ming Zhu, Kesheng Zhang, Tingting Liu und Dongyu Xu. „Acoustic absorption spectral peak location for gas detection“. Sensors and Actuators B: Chemical 203 (November 2014): 1–8. http://dx.doi.org/10.1016/j.snb.2014.05.091.

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39

Kim, D., und Y. Kim. „Optimal sensor location for robust fault detection filter“. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 213, Nr. 1 (Januar 1999): 13–21. http://dx.doi.org/10.1243/0954410991532800.

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40

Perol, Thibaut, Michaël Gharbi und Marine Denolle. „Convolutional neural network for earthquake detection and location“. Science Advances 4, Nr. 2 (Februar 2018): e1700578. http://dx.doi.org/10.1126/sciadv.1700578.

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41

FUKUDA, Mitsuyoshi, Hiroyuki SUGAHARA, Akihiro GION, Yuto ONO, Takayasu KITANO und Toshihito SHIRAI. „Development of Train Location Detection Methods for Signalling“. Quarterly Report of RTRI 55, Nr. 2 (2014): 98–104. http://dx.doi.org/10.2219/rtriqr.55.98.

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42

Wood, MG, JET Penny und MI Friswell. „Detection and Location of Nonlinearities using Reciprocity Breakdown“. Journal of Physics: Conference Series 1106 (Oktober 2018): 012028. http://dx.doi.org/10.1088/1742-6596/1106/1/012028.

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43

Lancia, G., F. Rinaldi und P. Serafini. „A Facility Location Model for Air Pollution Detection“. Mathematical Problems in Engineering 2018 (18.10.2018): 1–8. http://dx.doi.org/10.1155/2018/1683249.

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We describe mathematical models and practical algorithms for a problem concerned with monitoring the air pollution in a large city. We have worked on this problem within a project for assessing the air quality in the city of Rome by placing a certain number of sensors on some of the city buses. We cast the problem as a facility location model. By reducing the large number of data variables and constraints, we were able to solve to optimality the resulting MILP model within minutes. Furthermore, we designed a genetic algorithm whose solutions were on average very close to the optimal ones. In our computational experiments we studied the placement of sensors on 187 candidate bus routes. We considered the coverage provided by 10 up to 60 sensors.

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44

Li, Liang, und Bruce A. Schneider. „Gap detection and location in the precedence effect“. Journal of the Acoustical Society of America 112, Nr. 5 (November 2002): 2244–45. http://dx.doi.org/10.1121/1.4808579.

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45

Eerenberg, Onno, Jeroen Kettenis und Peter de With. „Block-based detection systems for visual artifact location“. IEEE Transactions on Consumer Electronics 59, Nr. 2 (Mai 2013): 376–84. http://dx.doi.org/10.1109/tce.2013.6531120.

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46

Ferreira, Kurt J., und Alexander E. Emanuel. „A Noninvasive Technique for Fault Detection and Location“. IEEE Transactions on Power Delivery 25, Nr. 4 (Oktober 2010): 3024–34. http://dx.doi.org/10.1109/tpwrd.2010.2057455.

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47

Ji, T. Y., Q. H. Wu, L. Jiang und W. H. Tang. „Disturbance detection, location and classification in phase space“. IET Generation, Transmission & Distribution 5, Nr. 2 (2011): 257. http://dx.doi.org/10.1049/iet-gtd.2010.0254.

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48

Chen, Hanshen, Minghai Yao und Qinlong Gu. „Pothole detection using location-aware convolutional neural networks“. International Journal of Machine Learning and Cybernetics 11, Nr. 4 (12.02.2020): 899–911. http://dx.doi.org/10.1007/s13042-020-01078-7.

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49

Kavithamani, Ashok, Venugopal Manikandan und Nanjundappan Devarajan. „Analog Circuit Fault Detection Using Location of Poles“. Journal of Electronic Testing 27, Nr. 5 (11.08.2011): 673–78. http://dx.doi.org/10.1007/s10836-011-5240-9.

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50

Atarashi, H., I. Yamaguchi, H. Watanabe, R. Yamamoto, K. Ohe und K. Tanaka. „Wireless LAN Security Management with Location Detection Capability in Hospitals“. Methods of Information in Medicine 51, Nr. 03 (2012): 221–28. http://dx.doi.org/10.3414/me10-01-0002.

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SummaryObjectives: In medical institutions, unauthorized access points and terminals obstruct the stable operation of a large-scale wireless local area network (LAN) system. By establishing a real-time monitoring method to detect such unauthorized wireless devices, we can improve the efficiency of security management.Methods: We detected unauthorized wireless devices by using a centralized wireless LAN system and a location detection system at 370 access points at the University of Tokyo Hospital. By storing the detected radio signal strength and location information in a database, we evaluated the risk level from the detection history. We also evaluated the location detection performance in our hospital ward using Wi-Fi tags.Results: The presence of electric waves outside the hospital and those emitted from portable game machines with wireless communication capability was confirmed from the detection result. The location detection performance showed an error margin of approximately 4 m in detection accuracy and approximately 5% in false detection. Therefore, it was effective to consider the radio signal strength as both an index of likelihood at the detection location and an index for the level of risk.Conclusions: We determined the location of wireless devices with high accuracy by filtering the detection results on the basis of radio signal strength and detection history. Results of this study showed that it would be effective to use the developed location database containing radio signal strength and detection history for security management of wireless LAN systems and more general-purpose location detection applications.

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