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Combined Relay Selection Enabled by Supervised Machine Learning

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第4期70卷 3938-3943页

作者： Dang, Shuping Tang, Jiashen Li, Jun Wen, Miaowen Abdullah, Salwani Li, Chengzhong Guangxi Huanan Commun Co Ltd R&D Ctr Nanning 530007 Peoples R China King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Bangi 43600 Selangor Malaysia Guangzhou Univ Sch Elect & Commun Engn Ctr Intelligent Commun Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Peoples R China

Combined relay selection only requires two relays to forward signals transmitted on multiple subcarriers, but the optimal outage performance is almost surely achievable in the high signal-to-noise ratio (SNR) region. However, because combined relay selection involves the generation of the full set of two-relay combinations, the selection complexity of combined relay selection is much higher than that of per-subcarrier relay selection when the number of relays goes large. This drawback restricts the implementation of combined relay selection in dense networks. To overcome this drawback, we propose to enable combined relay selection by supervised machine learning (ML). Because the training procedure is off-line, the proposed implementation scheme can considerably reduce the selection complexity and the processing latency. We carry out extensive experiments on TensorFlow 2.1 over a graphics processing unit (GPU) aided computing cloud server to validate the effectiveness of the proposed scheme. The experimental results confirm that supervised ML can provide near-optimal performance with lower computing latency that well matches that provided by brute-force search and the optimal relay selection in a per-subcarrier manner.

关键词： Relays Training Indexes Signal to noise ratio Neurons Machine learning Graphics processing units Artificial neural network (ANN) back-propagation algorithm cooperative relaying multi-carrier relay selection supervised machine learning (ML)

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Interval type-2 fuzzy neural network controller for a multivariable anesthesia system based on a hardware-in-the-loop simulation

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2014年第1期61卷 1-10页

作者： El-Nagar, Ahmad M. El-Bardini, Mohammad Menoufia Univ Fac Elect Engn Dept Ind Elect & Control Engn Menoufia 32852 Egypt

Objective: This manuscript describes the use of a hardware-in-the-loop simulation to simulate the control of a multivariable anesthesia system based on an interval type-2 fuzzy neural network (IT2FNN) controller. Methods and materials: The IT2FNN controller consists of an interval type-2 fuzzy linguistic process as the antecedent part and an interval neural network as the consequent part. It has been proposed that the IT2FNN controller can be used for the control of a multivariable anesthesia system to minimize the effects of surgical stimulation and to overcome the uncertainty problem introduced by the large inter-individual variability of the patient parameters. The parameters of the IT2FNN controller were trained online using a back-propagation algorithm. Results: Three experimental cases are presented. All of the experimental results show good performance for the proposed controller over a wide range of patient parameters. Additionally, the results show better performance than the type-1 fuzzy neural network (T1FNN) controller under the effect of surgical stimulation. The response of the proposed controller has a smaller settling time and a smaller overshoot compared with the T1FNN controller and the adaptive interval type-2 fuzzy logic controller (AIT2FLC). The values of the performance indices for the proposed controller are lower than those obtained for the T1FNN controller and the AIT2FLC. Conclusion: The IT2FNN controller is superior to the T1FNN controller for the handling of uncertain information due to the structure of type-2 fuzzy logic systems (FLSs), which are able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of the FLSs. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Hardware-in-the-loop back-propagation algorithm Interval type-2 fuzzy neural network Anesthesia Analgesia Muscle relaxation

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Biologically Fe²⁺ oxidizing fluidized bed reactor performance and controlling of Fe³⁺ recycle during heap bioleaching:: an artificial neural network-based model

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BIOPROCESS AND BIOSYSTEMS ENGINEERING 2008年第2期31卷 111-117页

作者： Ozkaya, Bestamin Sahinkaya, Erkan Nurmi, Pauliina Kaksonen, Anna H. Puhakka, Jaakko A. Tampere Univ Technol Inst Environm Engn & Biotechnol FIN-33101 Tampere Finland

The performance of a biological Fe2+ oxidizing fluidized bed reactor (FBR) was modeled by a popular neural network-back-propagation algorithm over a period of 220 days at 37 degrees C under different operational conditions. A method is proposed for modeling Fe3+ production in FBR and thereby managing the regeneration of Fe3+ for heap leaching application, based on an artificial neural network-back-propagation algorithm. Depending on output value, relevant control strategies and actions are activated, and Fe3+ production in FBR was considered as a critical output parameter. The modeling of effluent Fe3+ concentration was very successful, and an excellent match was obtained between the measured and the predicted concentrations.

关键词： Fe3+ production precipitate neural network back-propagation algorithm FBR

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A neural network model for environmental predication: Case study for China

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COMPUTERS & INDUSTRIAL ENGINEERING 1996年第3-4期31卷 879-883页

作者： Sun, LY Wang, Y Management School of Man Jiaotong University Xian Shaanxi P.R.China 710049

In this paper, the popular training method back-propagation (BP) of Nueral Networks (NN) is modified by introducing two parameters into the procedure in order to speed learning and improve the convergence. The modified algorithm is named as BPGM and is successfully used in forecasting the future pollution generation and the investment demand for the environment protection in China. Based on the predication of NN model, the future situation of environment of China is analyzed and the investment in environment protection is compared with the average level of the world. Some policies suggestions are proposed.

关键词： neural network back-propagation algorithm environmental predication

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Artificial neural network for predicting nuclear power plant dynamic behaviors

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NUCLEAR ENGINEERING AND TECHNOLOGY 2021年第10期53卷 3275-3285页

作者： El-Sefy, M. Yosri, A. El-Dakhakhni, W. Nagasaki, S. Wiebe, L. McMaster Univ Dept Civil Engn NSERC CREATE Program Canadian Nucl Energy Infrastruct R Hamilton ON L8S 4L7 Canada McMaster Univ Dept Civil Engn Inst Multihazard Syst Risk Studies INTERFACE Hamilton ON L8S 4L7 Canada McMaster Univ Dept Civil Engn NSERC CaNRisk CREATE Program Hamilton ON L8S 4L7 Canada McMaster Univ Teh INTERFACE Inst Hamilton ON L8S 4L7 Canada McMaster Univ Dept Engn Phys Hamilton ON L8S 4L7 Canada

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different sys-tems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to pro -vide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require signifi-cant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Un-certainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC

关键词： Data-driven models Artificial intelligence Artificial neural network Nuclear power plant back-propagation algorithm

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An Efficient Quality Inspection of Food Products Using Neural Network Classification

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JOURNAL OF INTELLIGENT SYSTEMS 2020年第1期29卷 1425-1440页

作者： Ali, Syed Sumera Ershad Dildar, Sayyad Ajij Dr Babasaheb Ambedkar Marathwada Univ Chhatrapati Shahu Maharaj Shikshan Sanstha Chh Shahu Coll Engn Dept Elect & Telecommun Engn Aurangabad Maharashtra India MIT Coll Engn Dept Elect & Telecommun Engn Aurangabad Maharashtra India

Currently, there is a necessity for the expansion of precise, rapid, and intentional quality assurance with respect to the character of food and horticultural food items, because it is difficult to maintain and organize food products in an elevated quality and secure manner for the increasing population. In this article, we propose a procedure to resolve difficulties and to categorize food as either a broken or quality product. Therefore, the proposed process encompasses four segments, such as preprocessing, segmentation of broken division, feature extraction, and classification. At the first stage, the preprocessing method is used to remove all unnecessary noises. After that, modified region expansion-related segmentation is undertaken to segment the broken division of the food product. Then, feature extraction is used to remove the distinctive attributes of each food product to categorize their evaluation. Finally, the neural network classification procedure is used to examine the food quality. The proposed method is executed in the operational platform of MATLAB, and the consequences are examined by using obtainable methods.

关键词： Histogram equalization modified region growing segmentation color histogram features gray level co-variance matrix features artificial neural network classifier back-propagation algorithm

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Statistical and evolutionary optimisation of operating conditions for enhanced production of fungal L-asparaginase

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CHEMICAL PAPERS 2011年第6期65卷 798-804页

作者： Baskar, Gurunathan Renganathan, Sahadevan Anna Univ Alagappa Coll Technol Dept Chem Engn Madras 600025 Tamil Nadu India St Josephs Coll Engn Dept Biotechnol Madras 600119 Tamil Nadu India

A three-level central composite design of the Response Surface Methodology and the Artificial Neural Network-linked Genetic algorithm were applied to find the optimum operating conditions for enhanced production of L-asparaginase by the submerged fermentation of Aspergillus terreus MTCC 1782. The various effects of the operating conditions, including temperature, pH, inoculum concentration, agitation rate, and fermentation time on the experimental production of L-asparaginase, were fitted to a second-order polynomial model and non-linear models using Response Surface Methodology and the Artificial Neural Network, respectively. The Artificial Neural Network model fitted well, achieving a higher coefficient of determination (R-2 = 0.999) than the second-order polynomial model (R-2 = 0.962). The L-asparaginase activity (38.57 IU mL(-1)) predicted under the optimum conditions of 32.08 degrees C, pH of 5.85, inoculum concentration of 1 vol. %, agitation rate of 123.5 min(-1), and fermentation time of 55.1 h was obtained using the Artificial Neural Network-linked Genetic algorithm in very close agreement with the activity of 37.84 IU mL(- 1) achieved in confirmation experiments. (C) 2011 Institute of Chemistry, Slovak Academy of Sciences

关键词： L-asparaginase optimisation operating conditions response-surface methodology back-propagation algorithm genetic algorithm

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Prediction of processing parameters for injection moulding by using a hybrid neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2001年第10期215卷 1465-1470页

作者： Yarlagadda, PKDV Queensland Univ Technol Fac Built Environm & Engn Sch Mech Mfg & Med Engn Brisbane Qld 4001 Australia

In the present work attempts have been made to determine the process parameters that could affect an injection moulding process based on governing equations of the mould-filling process. Focus is then directed to parameters that require the use of trial and error methods or other complex software to determine the process parameters. The two parameters that are predicted from the developed network are injection time and injection pressure. In this work, the training data are generated by simulation using C-MOLD flow simulation software. A total of 114 data points under different process conditions were collected out of which 94 data points were used to train the network using MATLAB and the remaining information was used for testing the network. Two algorithms are used during the training phase, namely the error back-propagation algorithm and the Levenberg-Marquardt approximation algorithm. Results showed that the latter algorithm is more suitable for this application since the Levenberg algorithm converged rapidly with fewer training cycles when compared with the error back-propagation algorithm. The accuracy of the developed network has been tested by predicting the injection pressure and injection time for few engineering components.

关键词： hybrid neural network back-propagation algorithm injection moulding Levenberg-Marquardt algorithm

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Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2021年第4期4卷 259-274页

作者： Onyelowe, Kennedy C. Iqbal, Mudassir Jalal, Fazal E. Onyia, Michael E. Onuoha, Ifeanyichukwu C. Michael Okpara Univ Agr Dept Civil Engn Umudike Nigeria Kampala Int Univ Dept Civil & Mech Engn Kampala Uganda Shangai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Civil Engn Shanghai Peoples R China Univ Nigeria Fac Engn Dept Civil Engn Nsukka Nigeria Fed Univ Technol Owerri Dept Environm Technol Owerri Nigeria

Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg-Muarquardt backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil's strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (w(L)), (plastic limit (w(P)), plasticity index (I-P), optimum moisture content (w(OMC)), clay activity (A(C)), and (maximum dry density (delta(max)). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models' accuracy.

关键词： Soft computing Artificial intelligence Artificial neural network (ANN) Machine learning in geotechnics back-propagation algorithm Levenberg– muarquardt algorithm Bayesian algorithm Conjugate gradient algorithm Sustainable construction materials

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Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems

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ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS 2019年第1期3卷 1–17页

作者： Zhang, Qingchen Yang, Laurence T. Chen, Zhikui Li, Peng Univ Elect Sci & Technol China Sch Informat & Commun Engn Beijing Peoples R China St Francis Xavier Univ Dept Comp Sci 18 Hillcrest St Antigonish NS B2G 1Z3 Canada St Francis Xavier Univ Dept Comp Sci 2323 Notre Dame Ave Antigonish NS B2G 2W5 Canada Dalian Univ Technol Sch Software Technol 321 Tuqing St Dalian 116620 Liaoning Peoples R China

With the ongoing development of sensor devices and network techniques, big data are being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission un-reliability, a large number of low-quality data, such as noisy data and incomplete data, is collected from the cyber-physical systems. Low-quality data pose a remarkable challenge on deep learning models for big data feature learning. As a novel deep learning model, the deep computation model achieves superior performance for big data feature learning. However, it is difficult for the deep computation model to learn dependable features for low-quality data, since it uses the nonlinear function as the encoder. In this article, a dependable deep computation model is proposed for feature learning on low-quality big data in cyber-physical systems. Specially, a regularity is added into the objective function of the deep computation model to obtain reliable features in the intermediate-level representation space. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the proposed model. Finally, experiments are conducted on three representative datasets and a real dataset to evaluate the effectiveness of the dependable deep computation model for low-quality big data feature learning. Results show that the proposed model achieves a remarkable result for the tasks of classification, restoration, and prediction, proving the potential of this work for practical applications in cyber-physical systems.

关键词： Cyber-physical systems big data dependable deep computation model feature learning back-propagation algorithm

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