Relevant bibliographies by topics / Sampled-data control systems

Academic literature on the topic 'Sampled-data control systems'

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Published: 6 September 2023

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Journal articles on the topic "Sampled-data control systems"

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Al-Sunni, F. M., and S. H. Al-Amer. "Robust control of sampled data systems." IEE Proceedings - Control Theory and Applications 145, no. 2 (March 1, 1998): 236–40. http://dx.doi.org/10.1049/ip-cta:19981848.

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Chen, Xinkai. "Control of uncertain sampled-data systems." Automatica 37, no. 2 (February 2001): 322–24. http://dx.doi.org/10.1016/s0005-1098(00)00150-3.

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Gabriel, Gabriela W., and José C. Geromel. "Sampled-data control of Lur’e systems." Nonlinear Analysis: Hybrid Systems 40 (May 2021): 100994. http://dx.doi.org/10.1016/j.nahs.2020.100994.

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Voulgaris, P. "Control of asynchronous sampled data systems." IEEE Transactions on Automatic Control 39, no. 7 (July 1994): 1451–55. http://dx.doi.org/10.1109/9.299632.

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Hu, Li-Sheng, Tao Bai, Peng Shi, and Ziming Wu. "Sampled-data control of networked linear control systems." Automatica 43, no. 5 (May 2007): 903–11. http://dx.doi.org/10.1016/j.automatica.2006.11.015.

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Breeden, Joseph, Kunal Garg, and Dimitra Panagou. "Control Barrier Functions in Sampled-Data Systems." IEEE Control Systems Letters 6 (2022): 367–72. http://dx.doi.org/10.1109/lcsys.2021.3076127.

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FUJIOKA, Hisaya, and Akio TADERA. "Sampled-Data H2 Control for Symmetric Systems." Transactions of the Institute of Systems, Control and Information Engineers 10, no. 1 (1997): 33–40. http://dx.doi.org/10.5687/iscie.10.33.

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Liu, Yajuan, and Sangmoon Lee. "Sampled-data Control for Lur'e Dynamical Systems." Transactions of The Korean Institute of Electrical Engineers 63, no. 2 (February 1, 2014): 261–65. http://dx.doi.org/10.5370/kiee.2014.63.2.261.

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Hagiwara, T. "Optimal Sampled-data Control Systems[Book Reviews]." Proceedings of the IEEE 86, no. 4 (April 1998): 741–42. http://dx.doi.org/10.1109/jproc.1998.663553.

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Hagiwara, T. "Optimal Sampled-data Control Systems [Book Reviews]." Proceedings of the IEEE 86, no. 6 (June 1998): 1293–94. http://dx.doi.org/10.1109/jproc.1998.687843.

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Dissertations / Theses on the topic "Sampled-data control systems"

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Langari, Ali. "Sampled-data repetitive control systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ27986.pdf.

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Dullerud, Geir Eirik. "Control of uncertain sampled-data systems." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320110.

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Mathew, Michael Ian. "Design of nonlinear sampled-data systems." Thesis, Coventry University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.480606.

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Galloway, Peter Richard. "Direct continuous-time control of sampled-data systems." Thesis, University of Glasgow, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341740.

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Goucem, Ali. "Computer aided design of nonlinear sampled data systems." Thesis, University of Sussex, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328341.

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Cantoni, Michael William. "Linear periodic systems : robustness analysis and sampled-data control." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415264.

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Bridgett, Nicholas Arthur. "Design and analysis of nonlinear sampled-data control systems." Thesis, Coventry University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303000.

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Wu, Buzhou. "Sampled-data control of a class of nonlinear systems." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493558.

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Su, Wu-Chung. "Implementation of variable structure control for sampled-data systems /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu148785431487298.

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Zheng, Ying. "Sampling rates and the stability of nonlinear sampled-data systems." Thesis, University of Sheffield, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359483.

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Books on the topic "Sampled-data control systems"

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Ackermann, Jürgen. Sampled-Data Control Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82554-5.

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Chen, Tongwen, and Bruce Allen Francis. Optimal Sampled-Data Control Systems. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3037-6.

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Chen, Tongwen. Optimal sampled-data control systems. London: Springer, 1995.

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Chen, Tongwen. Optimal Sampled-Data Control Systems. London: Springer London, 1995.

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Langari, Ali. Sampled-data repetitive control systems. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1997.

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Dullerud, Geir E. Control of Uncertain Sampled-Data Systems. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-2440-2.

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Control of uncertain sampled-data systems. Boston: Birkhäuser, 1996.

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Ackermann, Jurgen. Sampled-data control systems: Analysis and synthesis, robust system design. Berlin: Springer-Verlag, 1985.

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Sampled-data control systems: Analysis and synthesis, robust system design. Berlin: Springer-Verlag, 1985.

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Ackermann, Jürgen. Sampled-Data Control Systems: Analysis and Synthesis, Robust System Design. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985.

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Book chapters on the topic "Sampled-data control systems"

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Antsaklis, Panos J., and H. L. Trentelman. "Sampled-Data Systems." In Encyclopedia of Systems and Control, 1257–61. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_195.

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Antsaklis, P. J., and H. L. Trentelman. "Sampled-Data Systems." In Encyclopedia of Systems and Control, 1–6. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5102-9_195-1.

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Antsaklis, Panos J., and H. L. Trentelman. "Sampled-Data Systems." In Encyclopedia of Systems and Control, 2015–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_195.

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Ackermann, Jürgen. "Continuous Systems." In Sampled-Data Control Systems, 18–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82554-5_2.

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Ackermann, Jürgen. "Multivariable Systems." In Sampled-Data Control Systems, 415–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82554-5_9.

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Iqbal, Kamran. "Sampled-Data Systems." In A First Course in Control System Design, 79–94. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003336891-7.

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Keviczky, László, Ruth Bars, Jenő Hetthéssy, and Csilla Bányász. "Sampled Data Control Systems." In Advanced Textbooks in Control and Signal Processing, 351–91. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8297-9_11.

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Geromel, José C. "Sampled-Data Control Systems." In Differential Linear Matrix Inequalities, 37–72. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29754-0_3.

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Ackermann, Jürgen. "Control Loop Synthesis." In Sampled-Data Control Systems, 227–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82554-5_6.

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Ackermann, Jürgen. "Design of Robust Control Systems." In Sampled-Data Control Systems, 343–414. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82554-5_8.

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Conference papers on the topic "Sampled-data control systems"

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Bai, Er-Wei, and Soura Dasgupta. "Uncertainties in Sampled Data Systems." In 1989 American Control Conference. IEEE, 1989. http://dx.doi.org/10.23919/acc.1989.4790415.

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Luo, Jinnan, and Jianming Xiong. "Sampled-data control of fuzzy systems." In 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2022. http://dx.doi.org/10.1109/icnsc55942.2022.10004133.

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Bastug, Mert, Mihaly Petreczky, and Laurentiu Hetel. "Minimality of aperiodic sampled data systems." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7963851.

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Fujioka, Hisaya, and Shinji Hara. "Covariance Control for Sampled-Data Feedback Systems." In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792669.

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Chen, Cheng-Wu, and D. J. Chu. "Optimal Control of Multirate Sampled-Data Systems." In 1986 American Control Conference. IEEE, 1986. http://dx.doi.org/10.23919/acc.1986.4789267.

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Bernstein, Dennis S., and C. V. Hollot. "Robust Stability for Sampled-Data Control Systems." In 1989 American Control Conference. IEEE, 1989. http://dx.doi.org/10.23919/acc.1989.4790674.

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Sivashankar, N., and Pramod P. Khargonekar. "L∞-Induced Norm of Sampled-Data Systems." In 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791351.

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Zhang, Chun, and Geir E. Dullerud. "Analysis of sampled-data interconnected systems." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739466.

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Zhang, P., S. X. Ding, G. Z. Wang, and D. H. Zhou. "An FDI approach for sampled-data systems." In Proceedings of American Control Conference. IEEE, 2001. http://dx.doi.org/10.1109/acc.2001.946289.

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Christiansson, A. K., B. Lennartson, and H. T. Toivonen. "Sampled-data H∞-control for hybrid systems." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076439.

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Reports on the topic "Sampled-data control systems"

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Shieh, Leang-San, and Guanrong Chen. Robust Optimal Digital Control of Uncertain Multi-Rate Sampled-Data Systems. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada344559.

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Bainum, Peter M., Aprille J. Ericsson, and Xing Quang-qian. Dynamics and Robust Control of Sampled Data Systems for Large Space Structures. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada264192.

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Weissinger, Rebecca. Trends in water quality at Bryce Canyon National Park, water years 2006–2021. Edited by Alice Wondrak Biel. National Park Service, November 2022. http://dx.doi.org/10.36967/2294946.

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The National Park Service collects water-quality samples on a rotating basis at three fixed water-quality stations in Bryce Canyon National Park (NP): Sheep Creek, Yellow Creek, and Mossy Cave Spring. Data collection began at Sheep Creek and Yellow Creek in November 2005 and at Mossy Cave in July 2008. Data on in-situ parameters, fecal-coliform samples, major ions, and nutrients are collected monthly, while trace elements are sampled quarterly. This report analyzes data from the beginning of the period of record for each station through water year 2021 to test for trends over time. Concentrations are also compared to relevant water-quality standards for the State of Utah. Overall, water quality at the park’s monitoring stations continues to be excellent, and park managers have been successful in their goal of maintaining these systems in unimpaired condition. Infrequent but continued Escherichia coli exceedances from trespass livestock at Sheep and Yellow creeks support the need for regular fence maintenance along the park boundary. High-quality conditions may qualify all three sites as Category 1 waters, the highest level of anti-degradation protection provided by the State of Utah. Minimum and maximum air temperatures at the park have increased, while precipitation remains highly variable. Increasing air temperatures have led to increasing water temperatures in Sheep and Yellow creeks. Sheep Creek also had a decrease in flow across several quantiles from 2006 to 2021, while higher flows decreased at Yellow Creek in the same period. Surface flows in these two creeks are likely to be increasingly affected by higher evapotranspiration due to warming air temperatures and possibly decreasing snowmelt runoff as the climate changes. The influx of ancient groundwater in both creek drainages helps sustain base flows at the sites. Mossy Cave Spring, which is sampled close to the spring emergence point, showed less of a climate signal than Sheep and Yellow creeks. In our record, the spring shows a modest increase in discharge, including higher flows at higher air temperatures. An uptick in visitation to Water Canyon and the Mossy Cave Trail has so far not been reflected by changes in water quality. There are additional statistical trends in water-quality parameters at all three sites. However, most of these trends are quite small and are likely ecologically negligible. Some statistical trends may be the result of instrument changes and improvements in quality assurance and quality control over time in both the field sampling effort and the laboratory analyses. Long-term monitoring of water-quality stations at Bryce Canyon NP suggests relatively stable aquatic systems that benefit from protection within the park. To maintain these unimpaired conditions into the future, park managers could consider: Regular fence checks and maintenance along active grazing allotments at the park boundary to protect riparian areas and aquatic systems from trespass livestock. Developing a springs-monitoring program to track changes in springflow at spring emergences to better understand bedrock-aquifer water supplies. These data would also help quantify springflow for use in water-rights hearings. Supporting hydrogeologic investigations to map the extent and flow paths of groundwater aquifers. Working with the State of Utah to develop groundwater-protection zones to protect groundwater aquifers from developments that would affect springs in the park. Prioritizing watershed management with proactive fire risk-reduction practices. Explicitly including watershed protection as a goal in plans for fire management and suppression. Using additional data and analyses to better understand the drivers of trends in water quality and their ecological significance. These could include higher-frequency data to better understand relationships between groundwater, precipitation, and surface flows at the sites. These could also include watershed metrics...
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Riebesell, Ulf. Comprehensive data set on ecological and biogeochemical responses of a low latitude oligotrophic ocean system to a gradient of alkalinization intensities. OceanNets, August 2022. http://dx.doi.org/10.3289/oceannets_d5.4.

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The potential biogeochemical and ecological impacts of ocean alkalinity enhancement were tested in a 5-weeks mesocosm experiment conducted in the subtropical, oligotrophic waters off Gran Canaria in September/October 2021. In the nine mesocosms, each with a volume of about 10 m3 inhabiting a natural plankton community, alkalinity enhancement was achieved through addition of a mix of sodium bicarbonate and sodium carbonate, simulating CO2-equilibrated alkalinization in a gradient from control up to twice the natural alkalinity. The response of the enclosed plankton community to the alkalinity addition was monitored in over 50 parameters which were sampled or measured in situ daily or every second day. In addition to the mesocosm experiment, a series of side experiments were conducted, focusing on individual aspects of mineral dissolution, secondary precipitation and biological responses at the primary producer level. This campaign, in which 47 scientists from 6 nations participated, generated the most comprehensive data set collected so far on the ecological and biogeochemical impacts of ocean alkalinity enhancement.
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,
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Swanson, De Los Santos, and Miller. L51539 Improved Methods for Inspecting Gas Storage Well Downhole Casing. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 1987. http://dx.doi.org/10.55274/r0010090.

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A considerable background of prior work indicates that corrosion damaged casing in gas storage wells is largely due to the development of individual corrosion pits, which occur either on the inside or outside wall of the casing. Such pits are inevitably the result of electrochemical potentials, which are established between unlike conductive materials in an electrolyte. Damage is the result of localized loss of metal, which can be assessed in terms of the size of these individual corrosion flaws. Magnetic corrosion logging has been available for at least 25 years, but has not received wide acceptance as a basis for making intelligent repair decisions. An earlier PRCI-sponsored research study concluded that the best approach to short term improvement in the performance of current corrosion logging practice was the application of modern digital data acquisition techniques to one specific type of magnetic logging, namely, flux leakage or magnetic perturbation measurements. In this research program, experimental equipment of this type has been developed and demonstrated in conjunction with a cooperative effort with one of the logging companies (Dresser-Atlas). This equipment replaces the electronics assembly in the commercial instrument with a new package which samples each of the 12 (or 24) analog flux leakage sensor signals at a rate of either 86.8 or 173.6 samples per second, depending upon whether 24 of the sensors or only 12 are being sampled. The signals are digitized at this rate under the control of a downhole microprocessor, which formatsthe digital data into a serial bit stream and transmits it to the surface over standard logging cable. The data transmission system uses Manchester encoding and performs the data transfer at a maximum rate of 40,000 bits per second.
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