Relevant bibliographies by topics / AI-motor

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'AI-motor'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "AI-motor"

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Irawan, Denny, and Pressa Perdana SS. "Kontrol Motor Brushless DC (BLDC) Berbasis Algoritma AI - PID." Jurnal Teknik Elektro dan Komputasi (ELKOM) 2, no. 1 (March 31, 2020): 41–48. http://dx.doi.org/10.32528/elkom.v2i1.3146.

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Popularitas motor DC brushless (BLDC) semakin meningkat karena memiliki kepadatan energi yang tinggi, biaya pembuatan bahan magnet permanen seperti Samarium Cobalt (Sm-Co) dan Nd-Fe-B yang semakin murah dan kemajuan dalam desain inovasi semakin efisien [7]. Kelebihan motor BLDC yang memberikan respons lebih cepat, membutuhkan pengontrol untuk pengaturan kecepatan yang cepat dan akurat. Kontroler PID yang telah banyak digunakan dalam peralatan kontrol industri dapat diandalkan dengan menyetel oleh algoritma AI untuk menentukan Kp, Ki dan Kd. Dalam penelitian ini, motor BLDC akan dikontrol kecepatan menggunakan algoritma Artifical Intelligence (AI) untuk tuning PID, yang dijalankan dengan mikrokontroler STM 32 F4 Discovery. Popularitas motor DC brushless (BLDC) semakin meningkat karena memiliki kepadatan energi yang tinggi, biaya pembuatan bahan magnet permanen seperti Samarium Cobalt (Sm-Co) dan Nd-Fe-B yang semakin murah dan kemajuan dalam desain inovasi semakin efisien [7]. Kelebihan motor BLDC yang memberikan respons lebih cepat, membutuhkan pengontrol untuk pengaturan kecepatan yang cepat dan akurat. Kontroler PID yang telah banyak digunakan dalam peralatan kontrol industri dapat diandalkan dengan menyetel oleh algoritma AI untuk menentukan Kp, Ki dan Kd. Dalam penelitian ini, motor BLDC akan dikontrol kecepatan menggunakan algoritma Artifical Intelligence (AI) untuk tuning PID, yang dijalankan dengan mikrokontroler STM 32 F4 Discovery.
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Vovk, M., and O. Kutsiak. "AI-Technology of Motor Functions Diagnostics after a Stroke." Kibernetika i vyčislitelʹnaâ tehnika 2021, no. 2(204) (June 1, 2021): 84–100. http://dx.doi.org/10.15407/kvt204.02.084.

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Romanova, Victoria, and Sergey Khromov. "Effect of asymmetry of supply voltages on asynchronous motor operation modes." E3S Web of Conferences 58 (2018): 03013. http://dx.doi.org/10.1051/e3sconf/20185803013.

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Effect of asymmetry of supply voltages on asynchronous motor operation modes was investigated using the computer-aided simulation experiments, the software complex Matlab and the package program Simulink. Publication relevance is determined by the necessity for performance of comparative analysis of the level of effect of asymmetry of supply voltages on asynchronous motors of different series and power capacities. Six motors of three different power capacities (5.5, 55, 160 kW) and of two different series (4A, AI) were modeled for investigation. The value of asymmetry of supply voltages was changed from 0 to 5%. The motor shaft load was changed from 0 to 120% of the motor rated load. Comparison of dependencies of the phase currents on the voltage unbalance ratio by reverse sequence (K2U) of asynchronous motors with short-circuited rotor series 4A and AI was made. The analysis and discussion of obtained investigation results were performed, recommendations on operation of asynchronous motors series 4A and AI in conditions of asymmetry of supply voltages were given.
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Sibley, Krista G., Christine Girges, Ehsan Hoque, and Thomas Foltynie. "Video-Based Analyses of Parkinson’s Disease Severity: A Brief Review." Journal of Parkinson's Disease 11, s1 (July 16, 2021): S83—S93. http://dx.doi.org/10.3233/jpd-202402.

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Remote and objective assessment of the motor symptoms of Parkinson’s disease is an area of great interest particularly since the COVID-19 crisis emerged. In this paper, we focus on a) the challenges of assessing motor severity via videos and b) the use of emerging video-based Artificial Intelligence (AI)/Machine Learning techniques to quantitate human movement and its potential utility in assessing motor severity in patients with Parkinson’s disease. While we conclude that video-based assessment may be an accessible and useful way of monitoring motor severity of Parkinson’s disease, the potential of video-based AI to diagnose and quantify disease severity in the clinical context is dependent on research with large, diverse samples, and further validation using carefully considered performance standards.
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Shanthi, L. Jessi Sahaya, R. Arumugam, M. Nilesh Tawri, and S. Prem Kumar. "Comparison of AI Based Position Estimation Techniques for Switched Reluctance Motor." International Journal of Computer Applications 1, no. 26 (February 25, 2010): 44–50. http://dx.doi.org/10.5120/481-791.

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Beno, M. Marsaline, and N. S. Marimuthu. "AI Based Rotor Position Estimation Techniques for Switched Reluctance Motor Drives." i-manager's Journal on Electrical Engineering 1, no. 3 (March 15, 2008): 58–63. http://dx.doi.org/10.26634/jee.1.3.423.

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Filippetti, F., G. Franceschini, C. Tassoni, and P. Vas. "Recent developments of induction motor drives fault diagnosis using AI techniques." IEEE Transactions on Industrial Electronics 47, no. 5 (2000): 994–1004. http://dx.doi.org/10.1109/41.873207.

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Liu, Yawu, Jari O. Karonen, Juho Nuutinen, Esko Vanninen, Jyrki T. Kuikka, and Ritva L. Vanninen. "Crossed Cerebellar Diaschisis in Acute Ischemic Stroke: A Study with Serial SPECT and MRI." Journal of Cerebral Blood Flow & Metabolism 27, no. 10 (February 21, 2007): 1724–32. http://dx.doi.org/10.1038/sj.jcbfm.9600467.

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This study evaluated the relationship between crossed cerebellar diaschisis (CCD) and (1) lesion volume and location in the acute phase and 1 week after stroke onset and (2) clinical outcome. Twenty-two patients with cerebral ischemic stroke underwent single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI) within 48 h and on day 8 from onset. Interhemispheric asymmetric indices (AI) on SPECT were calculated for medial, intermediate, and lateral zones of the cerebellum. Lesion volumes and locations were obtained from diffusion-weighted MRI. Neurological status and 3-month clinical outcome were evaluated. Within 48 h, lesion locations in the temporal association cortex and pyramidal tract of the corona radiata were independent determinants for the AI of the medial zone ( R2 = 0.439). Lesion locations in the primary, premotor, and supplementary motor cortices, primary somatosensory cortex, and anterior part of the posterior limb of the internal capsule were determinants for the AI of the intermediate zone ( R2 = 0.785). Lesions in the primary motor cortex, premotor, and supplementary motor cortices and in the genu of the internal capsule were determinants for the AI of the lateral zone ( R2 = 0.746). On day 8, the associations were decreased. The AIs of the intermediate and lateral zones and lesion location in the parietal association cortex were independently associated with the 3-month clinical outcome ( R2 > 0.555). Acute CCD is a result of functional deafference, while in the subacute phase, transneuronal degeneration might contribute to CCD. CCD in the intermediate and later zones is a better indicator than that in the medial zone.
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Hanumanthakari, Sudheer, S. F. Kodad, and Sarvesh Botlaguduru. "Sensorless Direct Torque Control of Induction Motor Using AI Based Duty Ratio Controllers." International Review on Modelling and Simulations (IREMOS) 9, no. 5 (October 31, 2016): 339. http://dx.doi.org/10.15866/iremos.v9i5.10034.

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Reñones, Anibal, and Marta Galende. "F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 9, no. 4 (December 15, 2020): 83–94. http://dx.doi.org/10.14201/adcaij2020948394.

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Practical research in AI often lacks of available and reliable datasets so the practitioners can try different algorithms. The field of predictive maintenance is particularly challenging in this aspect as many researchers don't have access to full-size industrial equipment or there is not available datasets representing a rich information content in different evolutions of faults. In this paper, it is presented a dataset with evolution of typical faults (commutator, winding and brush wear) in inexpensive DC motors under extensive monitoring (vibration, temperature, voltage, current and noise). These motors exhibit a particularly short useful life when operating out of nominal conditions (from 30 minutes to 6 hours) which make them very interesting to test different signal processing algorithms and introduce students and researchers into signal processing, fault detection and predictive maintenance. The paper explains in detail the experimentation and the structure of the real, un-processed, dataset published in the AI4EU platform with the aim of complying with the FAIR principle so the dataset is Findable, Accessible, Interoperable and Reusable.
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Dissertations / Theses on the topic "AI-motor"

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Åström, Emil. "AI-motor : Artificiell intelligens för spel." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-22234.

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Artificiell intelligens (AI) är en stor del i dagens datorspel. För att få inblick i komplexiteten runt AI i spelutveckling och för att förstå delar som AI består av har detta projekt genomförts. Målet var att skapa en AI-motor från grunden med bra grundplattform som är enkel att bygga vidare på. Innan projektet startade utfördes en förundersökning där olika alternativ för kartrepresentationer och grafsökningsalgoritmer togs fram. Utvecklingen av AI-motorn har haft ett starkt beroende till projektet där en spelmotor utvecklats av Niklas Ekman och Christian Mesch. Detta projekt har utförts enligt den agila systemutvecklingsmetoden Scrum. Ett versionshanteringssystem har använts för att enkelt kunna dela källkod mellan projekten. AI-motorn har utvecklats i C++ och för operativsystemen Ubuntu och OSX. AI-motorn består av fyra huvuddelar; logik, navigering, kommunikation och AI-objekt. Logiken är hjärnan i AI-motorn, navigeringen använder sig av navmesh som kartrepresentation och A*-algoritmen är den grafsökningsalgoritm som har valts. Kommunikation sker mellan AI-motorn och spelmotorn för att kunna dela på funktionalitet. AI-objekten är främst informationsklasser som t.ex. håller reda på antalet registrerade datorstyrda spelare. Valet av metod för kartrepresentation avgjordes av att navmesh enkelt kunde genereras automatiskt med hjälp av verktyg vilket var svårare för de andra alternativen. A* valdes som grafsökningsalgoritm eftersom den gav en korrekt väg med minst antal beräkningar. AI-motorn uppfyller de krav som ställdes innan utvecklingen påbörjades och är en bra grund för att lätt kunna utöka motorn med mer avancerad funktionalitet, men det finns så klart förbättringar som kan göras.
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Yang, Chao. "Speed Sensorless Control for Induction Motor Drives with AI-Based MRAS." Thesis, University of Newcastle Upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525087.

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Tello, Rivera José Alberto. "Investigación de los procesos que originan las rajaduras en las cámaras de combustión del motor AI-20D serie 5 y desarrollo del proceso tecnológico para la aplicación de un recubrimiento de barrera térmica a las cámaras de combustión del motor AI-20D serie 5." Universidad Nacional de Ingeniería. Programa Cybertesis PERÚ, 1998. http://cybertesis.uni.edu.pe/uni/1998/tello_rj/html/index-frames.html.

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La Seguridad de los vuelos depende de la confiabilidad y durabilidad de los elementos constructivos de la aeronave, de las partes y conjuntos del motor, de sus accesorios y sistemas durante el proceso de explotación, entiéndase por explotación a la operación» el mantenimiento, almacenamiento y transporte del material aeronáutico. E1 tiempo de vida útil de los motores depende de la durabilidad y confiabilidad de sus componentes, accesorios y sistemas. Un componente fundamental da los motores de turbina de gas y turbohélice es la cámara de combustión, su operatividad y durabilidad determina en gran medida la confiabilidad del motor y en consecuencia la segundad da los vuelos, por lo que se le confiere desde el diseño una serie de propiedades y exigencias técnicas muy rígidas tales como alta resistencia al desgaste, alta resistencia térmica y alta resistencia al fuego, a la corrosión, etc. Una propiedad que limita el funcionamiento de la cámara de combustión es la resistencia térmica, la qua a su vez determina las propiedades físico-mecánicas del material. Durante el proceso de explotación en el Perú de los motores AI-20D serie 5 que equipan los aviones AN-32, se observa con mucha frecuencia rajaduras prematuras en luí cámaras de combustión, convirtiéndose en el principal defecto qua aquejan a dichas cámaras, otros tipos de defectos la afectan en menor grado y no son determinantes para la inoperatividad del motor. Tales rajaduras se presentan antes de que el motor alcance su tiempo entre reparaciones T.B.O. (Time Batwaen Overhaul), establecido en 3000 horas. La estadística de fallas muestra que ésta defecto se inicia con mayor frecuencia entre las 1000 y 1500 horas de operación, detectadas durante las inspecciones boroscópicas, empiezan con pequeñas fisuras que luego van desarrollándose hasta alcanzar valores no permisibles, dichas rajaduras presentan distintas características; ubicación arbitraría, diferentes dimensiones y diferentes velocidades de crecimiento. En la mayoría de los casos los motores son retirados de la explotación en forma prematura entre las 2000 y 2500 horas de operación, en algunos casos con desprendimiento del material; éste desprendimiento puede ocasionar severos daños sobre los elementos de la turbina, tales como guiñaduras, abolladuras y deformaciones incluyendo la rotura, haciendo que se incrementen los costos de reparación Del total del parque de motores AI-20D serie 5 utilizados por la Fuerza Aérea del Perú desde la adquisición de la aeronave, un 35 % de ellos fueron retirados de la explotación en forma prematura por presentar rajaduras fuera de límites en las cámaras de combustión y un 20 % de ellos presentan actualmente rajaduras permisibles en proceso de crecimiento. Cabe indicar que las cámaras de combustión de los motores AI-20D serie 5 que operan en Rusia y en la India que han alcanzado las 3000 horas no presentan este tipo de defecto. En la fabricación de las cámaras de combustión se utiliza una gran variedad de materiales y recubrimientos especiales de distintas composiciones y propiedades, büi embargo, Los problemas para descubrir nuevos materiales con mejores propiedades térmicas, así como los problemas en la investigación de nuevos materiales metálicos y compuestos para ser utilizados como recubrimientos de barrera térmica en dichas cámaras durante el overhaul del motor, del todo no han sido resueltos. La búsqueda de nuevos materiales para la fabricación de las cámaras de combustión, el desarrollo de nuevos materiales para ser utilizados como recubrimientos de barrera térmica, el desarrollo de nuevos métodos de aplicación de dichos recubrimientos, y el desarrollo de nuevos métodos para la evaluación de sus propiedades siguen siendo un tema de actualidad.
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Ahlrichs, Claas [Verfasser], Michael [Akademischer Betreuer] Lawo, and Albert [Akademischer Betreuer] Samà. "Development and Evaluation of AI-based Parkinson's Disease Related Motor Symptom Detection Algorithms / Claas Ahlrichs. Gutachter: Michael Lawo ; Albert Samà. Betreuer: Michael Lawo." Bremen : Staats- und Universitätsbibliothek Bremen, 2015. http://d-nb.info/1075609321/34.

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Lennartsson, Alexander, and Martina Blomberg. "Fault Detection in Permanent Magnet Synchronous Motors using Machine Learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-84909.

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In the aviation industry, safety and robustness are the number one priorities, which is why they use well-tested systems such as hydraulic actuators. However, drawbacks such as high weight and maintenance have pushed the industry toward newer, electrical, actuators that are more efficient and lighter. Electrical actuators, on the other hand, have some reliability issues. In particular, short circuits in the stator windings of Permanent-Magnet SynchronousMotors (PMSMs), referred to as Inter-Turn Short Faults (ITSFs), are the dominating faults, and is the focus of this thesis. ITSFs are usually challenging to detect and often do not become noticeable until the fault has propagated, and the motor is on the verge of being destroyed. This thesis investigates the possibility of detecting ITSFs in a PMSM, at an early stage when only one turn is shorted. The method is limited to finding the faults using ML algorithms. Both an experiential PMSM and a simulated model of the experimental PMSM, with the ability to induce an ITSF, were used to collect the data. Several Machine Learning (ML) models were developed, and then trained and tested with the collected data. The results show that four of the tested ML models, being: Random Forest, Gaussian SVM, KNN, and the CNN, all achieve an accuracy exceeding 95%, and that the fault can be found at an early stage in a PMSM with three coils connected in parallel in each phase. The results also show that the ML models are able to identify the ITSF when the simulated data is downsampled to the same frequency as the experimental data. We conclude that the ML models, provided in this study, can be used to detect an ITSF in a simulated PMSM, at an early stage when only one turn is shorted, and that there is great potential for them to detect ITSFs in a physical motor as well.
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Mahal, Zeenat, and Zeenat Mahal. "Formative Assessment: Documenting Motor Vehicle Crashes and Local Perceptions with the Hualapai Tribe to Inform Injury Prevention Recommendations." Diss., The University of Arizona, 2020. http://hdl.handle.net/10150/626754.

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Background: This research was designed to understand factors influencing Motor Vehicle Crashes (MVCs) on or near the Hualapai Tribe’s reservation in northwestern Arizona. The goal is to enable the Tribe to develop and implement a locally relevant MVC intervention program. The specific aims were to: i) compile and analyze 2010-2016 MVC data from Hualapai and federal sources to assess distributions of frequency, rates, high risk-locales, causes, days, times, age and sex of the drivers, in addition to assessing related conditions; ii) document local perceptions of environmental, social, and behavioral barriers to safe driving practices, and knowledge of MVC risk factors and existing tribal laws; and iii) provide evidence-based recommendations using the results from quantitative and qualitative data analyses. Approach/Methods: The research process applied a Community-Based Participatory Research approach and mixed methods using: a) secondary data analyses of records from six tribal programs and Indian Health Service (IHS), and b) qualitative analyses of data from two focus groups and field documents. Sensitivity analyses were conducted of IHS and Tribal Driving Under the Influence (DUI) data, using the 2010 U.S. Census as the denominator after adjusting for an undercount of up to 25%. Descriptive statistics, Fisher’s Exact Test, and linear and logistic regressions were used to examine significance. MVCs per 10 miles per year were estimated for State Route 66 and Diamond Bar Road/Grand Canyon West on or near the reservation due to higher numbers of crashes. Statistical process control charts, especially g-charts monitoring time between events, were plotted to examine the stability in the number of MVCs over time for each road. NVivo11Pro© was used to code and analyze the focus group data, guided by both inductive and deductive theories. Results: Driver’s seatbelt use in the Hualapai community increased from 2010 to 2012 (p < .0005), and reported DUIs decreased from 2010 to 2016 (p = .027). Similarly, car/booster seat use improved from 2014 to 2015 (p < .0005). Two hundred and fifty (N = 250) MVC-related injuries were registered at IHS facilities for Hualapai community members between 2006 and 2015. The highest rate, 22.4%, was observed in 15- to 24-year-olds, followed by 21.6% in 55- to 64-year-olds. For several combinations of numerators and denominators, sensitivity analysis of the IHS data shows a clear disparity between the Tribe’s MVC rate compared to the 2008 U.S. rate of 771.4 nonfatal injuries per 100,000 persons and the national goal for Healthy People 2020 (694.3 nonfatal injuries/100,000 persons). The major themes emerged from qualitative analyses of the focus groups were: i) unsafe traffic infrastructures, ii) DUI, iii) repeated DUI offenders driving on the reservation, and iv) a perception of lenient tribal traffic laws and enforcement on the reservation. Using study results, five Public Service Announcements were co-developed to inspire community-members to continue the trends noted from 2010-2016 and be aware of continued risks. Conclusions: MVCs are a multidimensional issue needing communitywide awareness of the range of risk factors. An intervention that addresses human and structural risks requires an alliance of tribal programs and external partners (e.g., IHS, university, federal, and state). Recommendations: Local recommendations include providing school- and institution-based education about alcohol/alcoholism and DUI consequences, and ongoing culturally and locally relevant communitywide education through the local newsletter and radio station.
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Books on the topic "AI-motor"

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ill, Maikexiaokui, ed. Qi che qi che ai wan shui. Beijing Shi: Zhongguo shao nian er tong chu ban she, 2017.

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Boden, Margaret A. 1. What is artificial intelligence? Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0001.

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Artificial intelligence (AI) seeks to make computers do the sorts of things that minds can do, which involves psychological skills such as perception, association, prediction, planning, and motor control. Intelligence is a richly structured space of diverse information-processing capacities. Accordingly, AI uses many different techniques, addressing many different tasks. ‘What is Artificial Intelligence?’ explains the two main aims of AI: one being technological and the other scientific. It looks at AI’s influence on the life sciences and philosophy. Can any AI system possess real intelligence, creativity, or life? It also considers how AI began, virtual machines, the major types of AI, and cybernetics.
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Book chapters on the topic "AI-motor"

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Phanden, Rakesh Kumar, Rahuldev Gupta, Md Talib Rajpoot, and Tushar Chaudhary. "Sensorless Speed Regulation Through AI Controller in Induction Motor." In Lecture Notes in Mechanical Engineering, 177–84. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9956-9_18.

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Gupta, Awadhesh, Lini Mathew, and S. Chatterji. "V/f-Based Speed Controllers for an Induction Motor Using AI Techniques: A Comparative Analysis." In Advances in Intelligent Systems and Computing, 1161–71. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1602-5_122.

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Yang, Chao, and John W. Finch. "A Comparison of Induction Motor Speed Estimation Using Conventional MRAS and an AI-Based MRAS Parallel System." In Lecture Notes in Electrical Engineering, 75–85. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2311-7_7.

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Muchuchuti, Jacob, and Stewart Muchuchuti. "Predicting Fraudulent Motor Vehicle Insurance Claims Using Data Mining Model." In Industry 4.0, AI, and Data Science, 1–11. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097181-1.

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Sharaf, Adel M., and Adel A. A. Elgammal. "Novel AI-Based Soft Computing Applications in Motor Drives." In Power Electronics Handbook, 1261–302. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-811407-0.00042-8.

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Sharaf, Adel M., and Adel A. A. El-Gammal. "Novel AI-Based Soft Computing Applications in Motor Drives." In Power Electronics Handbook, 993–1034. Elsevier, 2011. http://dx.doi.org/10.1016/b978-0-12-382036-5.00035-5.

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Meigal, Alexander Yu, Dmitry G. Korzun, Alex P. Moschevikin, Sergey Reginya, and Liudmila I. Gerasimova-Meigal. "Ambient Assisted Living At-Home Laboratory for Motor Status Diagnostics in Parkinson's Disease Patients and Aged People." In Tools and Technologies for the Development of Cyber-Physical Systems, 176–201. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1974-5.ch007.

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The chapter summarizes the authors' development on the concept of “at-home lab” (AHL). The concept employs the methods of artificial intelligence (AI), smart internet of things (IoT) technologies, and data mining techniques. The aim is at support for patients with Parkinson's disease and aged people to continuously monitor and evaluate their motor and cognitive status using own smartphone (in particular, IMU as wearable sensor, apps for testing cognitive status, camera for motor tracking). In addition, other devices in the IoT environment can participate in creating the information assistance support for people. This chapter presents and discuss the AHL concept as a further development step of AI in respect with human evolution (NeoNeoCortex). The focus is on evolutionary, environmental, and biological aspects of AI.
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Sharma, Aman, and Saksham Chaturvedi. "Mouse-Less Cursor Control for Quadriplegic and Autistic Patients Using Artificial Intelligence." In Advances in Medical Diagnosis, Treatment, and Care, 105–37. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7460-7.ch008.

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Artificial intelligence is a field within computer science that attempts to simulate and build enhanced human intelligence into computers, mobiles, and various other machines. It can be termed as a powerful tool that has the capability to process huge sums of information with ease and assess patterns created over a period of time to give significant results or suggestions. It has garnered focus from almost every field from education to healthcare. Broadly, AI applications in healthcare include early detection and diagnosis, suggesting treatments, evaluating progress, medical history, and predicting outcomes. This chapter discussed AI, ASD, and what role AI currently plays in advancing autistic lives including detection, analysis, and treatment of ASD and how AI has been improving healthcare and the existing medical and technology aids available for autistic people. Current and future advancements are discussed and suggested in the direction of improving social abilities and reducing the communication and motor difficulties faced by people with ASD.
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Dhanarajalu, Susitra. "MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor." In Emerging Electric Machines - Advances, Perspectives and Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96555.

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This chapter aims in presenting the methods for the accurate estimation of highly non linear phase inductance profile of a switched reluctance motor (SRM). The magnetization characteristics of a test SRM is derived from the SRDaS (Switched Reluctance Design and Simulation) simulation software. Statistical interpolation based regression analysis and Artificial Intelligence (AI) techniques are used for developing the computationally efficient inductance model. Multi Variate Non linear Regression (MVNLR) from the class of regression analysis and Adaptive Neuro Fuzzy Inference System (ANFIS) under the class of AI are implemented and tested on the simulated data. Non linear Inductance profile L(I,θ) of SRM is successfully estimated for the complete working range of phase currents (Iph). At each Iph, L(I,θ) values are estimated and presented for one cycle of rotor position (θ). Estimated inductance profile based on the two proposed methods is observed to be in excellent correlation with the true value of data.
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Pagel, J. F. "Synchronous Physiological Electrical Fields." In Exploring the Collective Unconscious in the Age of Digital Media, 107–27. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9891-8.ch004.

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Humans utilize sensory and motor systems developed genetically, physically and socially for interfacing with our external environment. We use these same systems to interface in our interactions with artificial intelligence. There are other functioning central nervous system (CNS) systems, however, involved in cognitive processing for which the function and environmental interface is less clear. The synchronous physiologic electrical field system utilizes broadcast extracellular electrical fields for a wide variety of CNS functions. The operations of this system are usually non-conscious and most apparent during sleep (especially the conscious states of sleep that include dreaming), and un-focused waking. The electrical fields of this system are altered and affected by both internal and external stimuli. These fields can be monitored and analyzed by artificial intelligence (AI) systems, and independently of human input, AI systems can utilize similar frequency based electrical potentials to convey data, communicate, supply power, and to store memory. From both human and AI perspectives, these systems have the potential to function more fully in human/machine interaction. This chapter reviews our current knowledge as to function, current interactive approaches, and interface potential for these physiological electrical fields.
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Conference papers on the topic "AI-motor"

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Srisertpol, J., A. Srikaew, and P. Jawayon. "AI-based sensorless speed estimation of 3-phase BLDC motor." In 2010 Third International Workshop on Advanced Computational Intelligence (IWACI). IEEE, 2010. http://dx.doi.org/10.1109/iwaci.2010.5585142.

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Mahamad, Abd Kadir, and Takashi Hiyama. "Fault classification performance of induction motor bearing using AI methods." In 2010 5th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2010. http://dx.doi.org/10.1109/iciea.2010.5514772.

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Miguel, Beatriz San, Aisha Naseer, and Hiroya Inakoshi. "Putting Accountability of AI Systems into Practice." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/768.

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To improve and ensure trustworthiness and ethics on Artificial Intelligence (AI) systems, several initiatives around the globe are producing principles and recommendations, which are providing to be difficult to translate into technical solutions. A common trait among ethical AI requirements is accountability that aims at ensuring responsibility, auditability, and reduction of negative impact of AI systems. To put accountability into practice, this paper presents the Global-view Accountability Framework (GAF) that considers auditability and redress of conflicting information arising from a context with two or more AI systems which can produce a negative impact. A technical implementation of the framework for automotive and motor insurance is demonstrated, where the focus is on preventing and reporting harm rendered by autonomous vehicles.
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Hossain, S., A. Gaffar, T. Roy, and S. Chattopadhyay. "Adaptive AI based Short Duration Fault Diagnosis of PMDC Motor used in Air-Circulation System of Light Motor Vehicle." In Michael Faraday IET International Summit 2020 (MFIIS 2020). Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/icp.2021.1181.

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Aspalli, M. S., and Sushma J. Patil. "Study of AI and PI controller using SVPWM technique for induction motor speed control." In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284589.

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Khairnar, S. S., and B. E. Kushare. "AI based method for diagnosis of incipient stator faults in three phase induction motor." In ICWET '10: International Conference and Workshop on Emerging Trends in Technology. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/1741906.1742131.

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Gupta, Gaurav, and Prerna Gaur. "Comparative study of-various DC-DC converters used in AI-based Solar fed PMBLDC motor drive." In 2015 Annual IEEE India Conference (INDICON). IEEE, 2015. http://dx.doi.org/10.1109/indicon.2015.7443352.

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Vas, P., A. F. Stronach, and M. Neuroth. "Application of conventional and AI-based techniques in sensorless high-performance torque-controlled induction motor drives." In IEE Colloquium on Vector Control Revisited. IEE, 1998. http://dx.doi.org/10.1049/ic:19980063.

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Preethi, I., S. Suryaprakash, and M. Mathankumar. "A State-of-Art Approach on Fault Detection in Three Phase Induction Motor using AI Techniques." In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021. http://dx.doi.org/10.1109/iccmc51019.2021.9418444.

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Vas, P. "DSP-based speed-sensorless vector controlled induction motor drives using AI-based speed estimator and two current sensors." In Seventh International Conference on Power Electronics and Variable Speed Drives. IEE, 1998. http://dx.doi.org/10.1049/cp:19980566.

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