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Journal articles on the topic 'Neural protection'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Stephan, Jane Jaleel, Sahab Dheyaa Mohammed, and Mohammed Khudhair Abbas. "Neural Network Approach to Web Application Protection." International Journal of Information and Education Technology 5, no. 2 (2015): 150–55. http://dx.doi.org/10.7763/ijiet.2015.v5.493.

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2

Uthayabalan, Sukanthatulsee. "Examining estradiols neuroprotective abilities and mechanisms of action in cerebrovascular accidents and neurodegenerative conditions." University of Ottawa Science Undergraduate Research Journal 1 (August 23, 2018): 42–46. http://dx.doi.org/10.18192/osurj.v1i1.3699.

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Estrogens are known for playing essential roles in the body. These hormones exert crucial protective actions when faced with neural damage. Numerous studies have provided a deep understanding of these unique actions that go far beyond the scope of reproduction and reproductive regulation. Through various mechanisms, delivery routes, dosage levels, and with the age and health status of the individuals receiving the treatment in mind, the hormone can be used in protecting against neural death. This review examines the discoveries that comprise the current body of knowledge regarding estrogen as

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3

Wenskay, Donald L. "Intellectual property protection for neural networks." Neural Networks 3, no. 2 (1990): 229–36. http://dx.doi.org/10.1016/0893-6080(90)90092-y.

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4

Huang, Shu-Hsiang, Sheng-Ting Fang та Yi-Cheng Chen. "Molecular Mechanism of Vitamin K2 Protection against Amyloid-β-Induced Cytotoxicity". Biomolecules 11, № 3 (2021): 423. http://dx.doi.org/10.3390/biom11030423.

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The pathological role of vitamin K2 in Alzheimer’s disease (AD) involves a definite link between impaired cognitive functions and decreased serum vitamin K levels. Vitamin K2 supplementation may have a protective effect on AD. However, the mechanism underlying vitamin K2 protection has not been elucidated. With the amyloid-β (Aβ) cascade hypothesis, we constructed a clone containing the C-terminal fragment of amyloid precursor protein (β-CTF/APP), transfected in astroglioma C6 cells and used this cell model (β-CTF/C6) to study the protective effect of vitamin K2 against Aβ cytotoxicity. Both c

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5

Sofroniew, Michael V. "Reactive Astrocytes in Neural Repair and Protection." Neuroscientist 11, no. 5 (2005): 400–407. http://dx.doi.org/10.1177/1073858405278321.

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6

Chen, Cheng-I., Chien-Kai Lan, Yeong-Chin Chen, Chung-Hsien Chen, and Yung-Ruei Chang. "Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid." Energies 13, no. 4 (2020): 1007. http://dx.doi.org/10.3390/en13041007.

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To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupl

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7

Zhu, Min, Wei Dong Liu, and Wen Song Hu. "Application of Fuzzy Neural Network in Relay Protection." Advanced Materials Research 181-182 (January 2011): 434–38. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.434.

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This paper present a relay protection algorithm based on fuzzy neural network theory. FZZ makes full use of the strong structural knowledge express ability of fuzzy logic as well as self-learning and direct quantitative data processing ability of neural network. Hence the robustness and self-learning of FZZ are improved. At the end of this paper, we use a relay protection working case to certify the application.

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8

Abramov, Nikolai, and Vitaly Fralenko. "Neural network data protection system for computer systems." Program Systems: Theory and Applications 8, no. 4 (2017): 197–207. http://dx.doi.org/10.25209/2079-3316-2017-8-1-197-207.

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9

Abramov, Nikolai, and Vitaly Fralenko. "Neural network data protection system for computer systems." Program Systems: Theory and Applications 8, no. 4 (2017): 197–207. http://dx.doi.org/10.25209/2079-3316-2017-8-4-197-207.

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10

Sahani, Nilesh A., Joseph F. Horn, Geoffrey J. J. Jeram, and J. V. R. Prasad. "Hub Moment Limit Protection Using Neural Network Prediction." Journal of the American Helicopter Society 51, no. 4 (2006): 331. http://dx.doi.org/10.4050/jahs.51.331.

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11

Keerthipala, W. W. L., Chan Tat Wai, and Wang Huisheng. "Neural network based classifier for power system protection." Electric Power Systems Research 42, no. 2 (1997): 109–14. http://dx.doi.org/10.1016/s0378-7796(96)01185-6.

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12

Lu, Haoyu, Daofu Gong, Fenlin Liu, and Ping Wang. "Deep neural network-based image copyright protection scheme." Journal of Electronic Imaging 28, no. 02 (2019): 1. http://dx.doi.org/10.1117/1.jei.28.2.023021.

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13

Galdi, V., L. Ippolito, A. Piccolo, and A. Vaccaro. "Neural diagnostic system for transformer thermal overload protection." IEE Proceedings - Electric Power Applications 147, no. 5 (2000): 415. http://dx.doi.org/10.1049/ip-epa:20000519.

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14

Tripathy, M., R. P. Maheshwari, and H. K. Verma. "Probabilistic neural-network-based protection of power transformer." IET Electric Power Applications 1, no. 5 (2007): 793. http://dx.doi.org/10.1049/iet-epa:20070009.

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15

Santos, Ricardo Caneloi dos, and Eduardo Cesar Senger. "Transmission lines distance protection using artificial neural networks." International Journal of Electrical Power & Energy Systems 33, no. 3 (2011): 721–30. http://dx.doi.org/10.1016/j.ijepes.2010.12.029.

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16

Botta, Marco, Davide Cavagnino, and Roberto Esposito. "NeuNAC: A novel fragile watermarking algorithm for integrity protection of neural networks." Information Sciences 576 (October 2021): 228–41. http://dx.doi.org/10.1016/j.ins.2021.06.073.

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17

S, Nandhini. "Ransomware Protection Tool based on Recurrent Neural Network (RNN)." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 1997–2002. http://dx.doi.org/10.22214/ijraset.2020.5325.

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18

Hatata, A. Y., M. S. Kandil, M. M. I. El-Shamoty, and A. El-Saeed. "Recurrent Neural Networks Based Differential Protection of Power Transformers." ERJ. Engineering Research Journal 37, no. 3 (2014): 305–14. http://dx.doi.org/10.21608/erjm.2014.66932.

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19

Oku, Yoshitaka. "Neural Mechanisms of Swallowing and Airway Protection During Swallowing." Koutou (THE LARYNX JAPAN) 32, no. 01 (2020): 1–7. http://dx.doi.org/10.5426/larynx.32.1.

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20

Cannas, B., G. Celli, M. Marchesi, and F. Pilo. "Neural networks for power system condition monitoring and protection." Neurocomputing 23, no. 1-3 (1998): 111–23. http://dx.doi.org/10.1016/s0925-2312(98)00065-4.

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21

Hardie, D. Grahame, and Bruno G. Frenguelli. "A Neural Protection Racket: AMPK and the GABAB Receptor." Neuron 53, no. 2 (2007): 159–62. http://dx.doi.org/10.1016/j.neuron.2007.01.004.

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22

Chang, Xiaolong, Shanghe Liu, Menghua Man, Weihua Han, Jie Chu, and Liang Yuan. "Bio-Inspired Electromagnetic Protection Based on Neural Information Processing." Journal of Bionic Engineering 11, no. 1 (2014): 151–57. http://dx.doi.org/10.1016/s1672-6529(14)60030-5.

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23

Peacock, L., S. Worner, and J. Pitt. "The application of artificial neural networks in plant protection." EPPO Bulletin 37, no. 2 (2007): 277–82. http://dx.doi.org/10.1111/j.1365-2338.2007.01123.x.

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24

Orille, A. L., J. Iglesias Lorenzo, and S. Bogarra. "Neural Network and Daubechies Wavelet in Power System Protection." Renewable Energy and Power Quality Journal 1, no. 01 (2003): 149–53. http://dx.doi.org/10.24084/repqj01.329.

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25

Sanchez-Matilla, Ricardo, Chau Yi Li, Ali Shahin Shamsabadi, Riccardo Mazzon, and Andrea Cavallaro. "Exploiting Vulnerabilities of Deep Neural Networks for Privacy Protection." IEEE Transactions on Multimedia 22, no. 7 (2020): 1862–73. http://dx.doi.org/10.1109/tmm.2020.2987694.

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26

Chen, Shao-Yu, Michael E. Charness, Michael F. Wilkemeyer, and Kathleen K. Sulik. "Peptide-Mediated Protection from Ethanol-Induced Neural Tube Defects." Developmental Neuroscience 27, no. 1 (2005): 13–19. http://dx.doi.org/10.1159/000084528.

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27

Lian, Shiguo, and Xi Chen. "Traceable content protection based on chaos and neural networks." Applied Soft Computing 11, no. 7 (2011): 4293–301. http://dx.doi.org/10.1016/j.asoc.2010.05.033.

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28

Bukhari, Syed Basit Ali, Chul-Hwan Kim, Khawaja Khalid Mehmood, Raza Haider, and Muhammad Saeed Uz Zaman. "Convolutional Neural Network-Based Intelligent Protection Strategy for Microgrids." IET Generation, Transmission & Distribution 14, no. 7 (2020): 1177–85. http://dx.doi.org/10.1049/iet-gtd.2018.7049.

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29

Li, Zongbo, Zaibin Jiao, and Anyang He. "Knowledge-based artificial neural network for power transformer protection." IET Generation, Transmission & Distribution 14, no. 24 (2020): 5782–91. http://dx.doi.org/10.1049/iet-gtd.2020.0542.

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30

Liu, Shuqi, Mingwen Shao, and Xinping Liu. "GAN-based classifier protection against adversarial attacks." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7085–95. http://dx.doi.org/10.3233/jifs-200280.

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In recent years, deep neural networks have made significant progress in image classification, object detection and face recognition. However, they still have the problem of misclassification when facing adversarial examples. In order to address security issue and improve the robustness of the neural network, we propose a novel defense network based on generative adversarial network (GAN). The distribution of clean - and adversarial examples are matched to solve the mentioned problem. This guides the network to remove invisible noise accurately, and restore the adversarial example to a clean ex

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31

Riaz, Muhammad, Fawwad Jaskani, and Tehreem Awan. "An Artificial Neural Network Based Digital Differential Protection Scheme for Synchronous Generator Stator Winding Protection." EAI Endorsed Transactions on Creative Technologies 6, no. 18 (2019): 160837. http://dx.doi.org/10.4108/eai.30-1-2019.160837.

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32

Megahed, A. I., and O. P. Malik. "An artificial neural network based digital differential protection scheme for synchronous generator stator winding protection." IEEE Transactions on Power Delivery 14, no. 1 (1999): 86–93. http://dx.doi.org/10.1109/61.736692.

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33

Kotenko, Igor, Igor Saenko, Oleg Lauta, and Mikhail Karpov. "Methodology for Management of the Protection System of Smart Power Supply Networks in the Context of Cyberattacks." Energies 14, no. 18 (2021): 5963. http://dx.doi.org/10.3390/en14185963.

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This paper examines an approach that allows one to build an efficient system for protecting the information resources of smart power supply networks from cyberattacks based on the use of graph models and artificial neural networks. The possibility of a joint application of graphs, describing the features for the functioning of the protection system of smart power supply networks, and artificial neural in order to predict and detect cyberattacks is considered. The novelty of the obtained results lies in the fact that, on the basis of experimental studies, a methodology for managing the protecti

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34

Kalinin, Maxim, Roman Demidov, and Peter Zegzhda. "Hybrid Neural Network Model for Protection of Dynamic Cyber Infrastructure." Nonlinear Phenomena in Complex Systems 22, no. 4 (2019): 375–82. http://dx.doi.org/10.33581/1561-4085-2019-22-4-375-382.

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The paper considers a combination of modern artificial neural networks (ANN) that solves the security relative task of intrusion prevention and vulnerabilities detection in cybernetic infrastructure with dynamic network topology. Self-organizing networks, WSN, m2m networks, IIoT, mesh networks are faced with the cyberthreats of specific character: dynamic routing failures, node isolation, DDoS attacks, traffic lack, etc. Most of them are caused by cybersecurity weaknesses: the software vulnerabilities and architectural features of dynamically reconfigured network. The existing methods of binar

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35

Hosny, A., and M. Safiuddin. "Adaptive protection for series-compensated transmission lines using neural networks." International Conference on Electrical Engineering 6, no. 6 (2008): 1–13. http://dx.doi.org/10.21608/iceeng.2008.34526.

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36

Zhang, Nan, and Mladen Kezunovic. "Transmission Line Boundary Protection Using Wavelet Transform and Neural Network." IEEE Transactions on Power Delivery 22, no. 2 (2007): 859–69. http://dx.doi.org/10.1109/tpwrd.2007.893596.

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37

Pihler, J., B. Grcar, and D. Dolinar. "Improved operation of power transformer protection using artificial neural network." IEEE Transactions on Power Delivery 12, no. 3 (1997): 1128–36. http://dx.doi.org/10.1109/61.636919.

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38

Coury, D. V., and D. C. Jorge. "Artificial neural network approach to distance protection of transmission lines." IEEE Transactions on Power Delivery 13, no. 1 (1998): 102–8. http://dx.doi.org/10.1109/61.660861.

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39

Tripathy, M., R. P. Maheshwari, and H. K. Verma. "Power Transformer Differential Protection Based On Optimal Probabilistic Neural Network." IEEE Transactions on Power Delivery 25, no. 1 (2010): 102–12. http://dx.doi.org/10.1109/tpwrd.2009.2028800.

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40

Lahiri, U., A. K. Pradhan, and S. Mukhopadhyaya. "Modular Neural Network-Based Directional Relay for Transmission Line Protection." IEEE Transactions on Power Systems 20, no. 4 (2005): 2154–55. http://dx.doi.org/10.1109/tpwrs.2005.857839.

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41

Feilat, E. A., and K. N. Al-Tallaq. "An Artificial Neural Network Approach for Three-Zone Distance Protection." International Journal of Modelling and Simulation 25, no. 4 (2005): 291–98. http://dx.doi.org/10.1080/02286203.2005.11442342.

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42

El Safty, S., H. El Dessouki, and M. El Sawaf. "Artificial Neural Network Fault Detection For Transmission Line Protection.(Dept.E)." MEJ. Mansoura Engineering Journal 29, no. 3 (2021): 15–18. http://dx.doi.org/10.21608/bfemu.2021.140365.

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43

Tanaka, Shinji, Chikara Abe, Stephen B. G. Abbott, et al. "Vagus nerve stimulation activates two distinct neuroimmune circuits converging in the spleen to protect mice from kidney injury." Proceedings of the National Academy of Sciences 118, no. 12 (2021): e2021758118. http://dx.doi.org/10.1073/pnas.2021758118.

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Acute kidney injury is highly prevalent and associated with high morbidity and mortality, and there are no approved drugs for its prevention and treatment. Vagus nerve stimulation (VNS) alleviates inflammatory diseases including kidney disease; however, neural circuits involved in VNS-induced tissue protection remain poorly understood. The vagus nerve, a heterogeneous group of neural fibers, innervates numerous organs. VNS broadly stimulates these fibers without specificity. We used optogenetics to selectively stimulate vagus efferent or afferent fibers. Anterograde efferent fiber stimulation

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44

Wen, Sheng, Quanyong Zhang, Xuanchun Yin, Yubin Lan, Jiantao Zhang, and Yufeng Ge. "Design of Plant Protection UAV Variable Spray System Based on Neural Networks." Sensors 19, no. 5 (2019): 1112. http://dx.doi.org/10.3390/s19051112.

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Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambie

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45

Tripathy, Manoj. "Power transformer differential protection using neural network Principal Component Analysis and Radial Basis Function Neural Network." Simulation Modelling Practice and Theory 18, no. 5 (2010): 600–611. http://dx.doi.org/10.1016/j.simpat.2010.01.003.

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46

Nelub, Vladimir, Andrey Gantimurov, and Alexey Borodulin. "Economic analysis of data protection in systems with complex architecture using neural network methods." Economic Annals-ХХI 185, no. 9-10 (2020): 178–88. http://dx.doi.org/10.21003/ea.v185-17.

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Introduction. In the United States, Europe, and Asia, there have been spikes in cyber attacks on protected and confidential information (including bank data, personal data, and confidential business information) over the period 2000-2020. Data protection in systems with complex architectures is a complex and non-trivial solution, which is suitable for flexible self-tuning and self-learning tools, such as neural networks as state modern studies. The described above state of things stipulates the importance and topicality of the direction of our research. Our research raises the question about t

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47

Ivanov, Sergey O., Aleksandr A. Lariukhin, Maxim V. Nikandrov, and Leonid A. Slavutskii. "ACCURACY ESTIMATION FOR OPERATING CHARACTERISTICS NEUROMODELING OF THE OVERCURRENT PROTECTION IN A THREE PHASE MAINS." Vestnik Chuvashskogo universiteta, no. 1 (March 25, 2021): 68–77. http://dx.doi.org/10.47026/1810-1909-2021-1-68-77.

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Modern electric power facilities-stations and high-voltage substations have become digital objects with the active use of high-speed local networks directly involved in the technological process. Management, analysis and control of information exchange in the digital substation of the power system require the development of new tools and approaches. For these purposes, machine learning methods can be used, in particular, the artificial neural networks. The paper presents the results of neural network modeling of the operation of the overcurrent protection – as a variant of the information exch

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48

Hasabe, Ramchandra P., and Anil P. Vaidya. "Development of Adaptive Distance Relay for STATCOM Connected Transmission Line with Wavelet Transform and ANN." Applied Mechanics and Materials 705 (December 2014): 237–42. http://dx.doi.org/10.4028/www.scientific.net/amm.705.237.

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A new scheme to enhance the solution of the problems associated with Transmission line protection with Statcom connected is presentedin this paper.Static Synchronous Compensator (STATCOM) is a shunt type FACTS device connected at the midpoint of the transmission line to maintain the voltage atdesired level by injecting/absorbing the reactive power. This connection affects the performance of distance protection relay during line faults. Thefault detectionis carried out byusingenergy of the detail coefficients of the phase signals and artificial neutral network algorithm used for fault distance

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49

Rushikesh Thakre, Ketaki Harne, Pradip Tekade, and Shweta Parve. "Role of Ajan Vruksha/Khandu Chakka Plant (Ehretia Laevis Roxb.) in Covid-19 Pandemic." International Journal of Research in Pharmaceutical Sciences 11, SPL1 (2020): 224–33. http://dx.doi.org/10.26452/ijrps.v11ispl1.2703.

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Ehretia Laevis Roxb. plant has many compounds useful in wound healing, fractures, UTI, aphrodisiac, headache, antihelminthics, diuretic, demulcent, expectorant, RTI, fever, fungal infections, hepato-protective , cytotoxic, insecticidal, anti-inflammatory, anti-apoptotic, anti-carcinogenic, weight gain, diabetes , muscles wasting, anti viral activity, preventing viral mutations, blood clotting, reduce the serum lipid level, immunity booster, promotes neural crest cell survival, sedation , anti-Alzheimer,antinociceptive , thyroid uptake promotion, anticoagulant, antiplatelet aggregatory, peptic

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50

Panaro, Maria Antonietta, Tarek Benameur, and Chiara Porro. "Hypothalamic Neuropeptide Brain Protection: Focus on Oxytocin." Journal of Clinical Medicine 9, no. 5 (2020): 1534. http://dx.doi.org/10.3390/jcm9051534.

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Oxytocin (OXT) is hypothalamic neuropeptide synthetized in the brain by magnocellular and parvo cellular neurons of the paraventricular (PVN), supraoptic (SON) and accessory nuclei (AN) of the hypothalamus. OXT acts in the central and peripheral nervous systems via G-protein-coupled receptors. The classical physiological functions of OXT are uterine contractions, the milk ejection reflex during lactation, penile erection and sexual arousal, but recent studies have demonstrated that OXT may have anti-inflammatory and anti-oxidant properties and regulate immune and anti-inflammatory responses. I

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