Applications of artificial intelligence in powerline communications in terms of noise detection and reduction : a review
- Shekoni, Olamide M., Hasan, Ali N., Shongwe, Thokozani
- Authors: Shekoni, Olamide M. , Hasan, Ali N. , Shongwe, Thokozani
- Date: 2018
- Subjects: Additive white Gaussian Noise (AWGN) , Artificial Intelligence (AI) , Impulsive Noise
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/364309 , http://hdl.handle.net/10210/274971 , uj:29360 , Citation: Olamide M. Shekoni, Ali N. Hasan & Thokozani Shongwe (2018) Applications of artificial intelligence in powerline communications in terms of noise detection and reduction: a review, Australian Journal of Electrical and Electronics Engineering, 15:1-2, 29-37, DOI: 10.1080/1448837X.2018.1496689
- Description: Abstract: The technology which utilizes the power line as a medium for transferring information known as powerline communication (PLC) has been in existence for over a hundred years. It is beneficial because it avoids new installation since it uses the present installation for electrical power to transmit data. However, transmission of data signals through a power line channel usually experience some challenges which include impulsive noise, frequency selectivity, high channel attenuation, low line impedance etc. The impulsive noise exhibits a power spectral density within the range of 10-15 dB higher than the background noise, which could cause a severe problem in a communication system. For better outcome of the PLC system, these noises must be detected and suppressed. This paper reviews various techniques used in detecting and mitigating the impulsive noise in PLC and suggests the application of machine learning algorithms for the detection and removal of impulsive noise in power line communication systems.
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- Authors: Shekoni, Olamide M. , Hasan, Ali N. , Shongwe, Thokozani
- Date: 2018
- Subjects: Additive white Gaussian Noise (AWGN) , Artificial Intelligence (AI) , Impulsive Noise
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/364309 , http://hdl.handle.net/10210/274971 , uj:29360 , Citation: Olamide M. Shekoni, Ali N. Hasan & Thokozani Shongwe (2018) Applications of artificial intelligence in powerline communications in terms of noise detection and reduction: a review, Australian Journal of Electrical and Electronics Engineering, 15:1-2, 29-37, DOI: 10.1080/1448837X.2018.1496689
- Description: Abstract: The technology which utilizes the power line as a medium for transferring information known as powerline communication (PLC) has been in existence for over a hundred years. It is beneficial because it avoids new installation since it uses the present installation for electrical power to transmit data. However, transmission of data signals through a power line channel usually experience some challenges which include impulsive noise, frequency selectivity, high channel attenuation, low line impedance etc. The impulsive noise exhibits a power spectral density within the range of 10-15 dB higher than the background noise, which could cause a severe problem in a communication system. For better outcome of the PLC system, these noises must be detected and suppressed. This paper reviews various techniques used in detecting and mitigating the impulsive noise in PLC and suggests the application of machine learning algorithms for the detection and removal of impulsive noise in power line communication systems.
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Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking
- Farayola, Adedayo M., Hasan, Ali N., Ali, Ahmad
- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmad
- Date: 2017
- Subjects: ANFIS , Artificial Intelligence (AI) , Curve fitting polynomials
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/232998 , uj:23778 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2017. Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking. The 8th International Renewable Energy Congress (IREC 2017).
- Description: Abstract: In this paper, an approach of designing a fast tracking MPPT is introduced using a predicted sixth order polynomial curve fitting MPPT technique. The results are compared with the lower order polynomials curve fitting MPPT and also compared with the Artificial Neuro-Fuzzy Inference System (ANFIS) results. The polynomials were generated from an offline solar data. This work was done to validate the effect of using a higher order polynomials under various weather conditions using modified CUK DC-DC converter. Findings suggest that using the 6th order polynomial curve fitting and the ANFIS techniques could track the highest maximum power point than the lower order curve techniques.
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- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmad
- Date: 2017
- Subjects: ANFIS , Artificial Intelligence (AI) , Curve fitting polynomials
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/232998 , uj:23778 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2017. Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking. The 8th International Renewable Energy Congress (IREC 2017).
- Description: Abstract: In this paper, an approach of designing a fast tracking MPPT is introduced using a predicted sixth order polynomial curve fitting MPPT technique. The results are compared with the lower order polynomials curve fitting MPPT and also compared with the Artificial Neuro-Fuzzy Inference System (ANFIS) results. The polynomials were generated from an offline solar data. This work was done to validate the effect of using a higher order polynomials under various weather conditions using modified CUK DC-DC converter. Findings suggest that using the 6th order polynomial curve fitting and the ANFIS techniques could track the highest maximum power point than the lower order curve techniques.
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Strategizing management education in response to artificial intelligence
- Van Lill, Daneel, Spowart, Jane
- Authors: Van Lill, Daneel , Spowart, Jane
- Date: 2017
- Subjects: Artificial Intelligence (AI) , Management education , Business schools
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/238560 , uj:24478 , Citation: Van Lill, D. & Spowart, J. 2017. Strategizing management education in response to artificial intelligence.
- Description: Abstract: This review informs the positioning of management education in a much changed global socio-economic context. The authors relied on scholarly articles and intellectual trusts found among the leaders of competitive industries. We set the stage where the impact of Artificial Intelligence on human agency plays out. Attention is drawn to information knowledge management and learning; the probable extinction of managers and finally, shifts in the futures of providers of management education.
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- Authors: Van Lill, Daneel , Spowart, Jane
- Date: 2017
- Subjects: Artificial Intelligence (AI) , Management education , Business schools
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/238560 , uj:24478 , Citation: Van Lill, D. & Spowart, J. 2017. Strategizing management education in response to artificial intelligence.
- Description: Abstract: This review informs the positioning of management education in a much changed global socio-economic context. The authors relied on scholarly articles and intellectual trusts found among the leaders of competitive industries. We set the stage where the impact of Artificial Intelligence on human agency plays out. Attention is drawn to information knowledge management and learning; the probable extinction of managers and finally, shifts in the futures of providers of management education.
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Use of MPPT techniques to reduce the energy pay-back time in PV systems
- Farayola, Adedayo M., Hasan, Ali N., Ali, Ahmed
- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmed
- Date: 2018
- Subjects: Artificial Intelligence (AI) , ANFIS , ANN
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274581 , uj:29302 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2018. Use of MPPT techniques to reduce the energy pay-back time in PV systems.
- Description: Abstract: Photovoltaic (PV) energy is a free-energy that is used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy pay-back time. This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened EPBT. To confirm the effectiveness of the ANFIS algorithm, its result was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT.
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- Authors: Farayola, Adedayo M. , Hasan, Ali N. , Ali, Ahmed
- Date: 2018
- Subjects: Artificial Intelligence (AI) , ANFIS , ANN
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274581 , uj:29302 , Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2018. Use of MPPT techniques to reduce the energy pay-back time in PV systems.
- Description: Abstract: Photovoltaic (PV) energy is a free-energy that is used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy pay-back time. This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened EPBT. To confirm the effectiveness of the ANFIS algorithm, its result was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT.
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