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In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.
Wasiq Ali; Yaan Li; Muhammad Asif Zahoor Raja; Wasim Ullah Khan; Yigang He. State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing. Entropy 2021, 23, 1124 .
AMA StyleWasiq Ali, Yaan Li, Muhammad Asif Zahoor Raja, Wasim Ullah Khan, Yigang He. State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing. Entropy. 2021; 23 (9):1124.
Chicago/Turabian StyleWasiq Ali; Yaan Li; Muhammad Asif Zahoor Raja; Wasim Ullah Khan; Yigang He. 2021. "State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing." Entropy 23, no. 9: 1124.
The research community of environmental economics has had a growing interest for the exploration of artificial intelligence (AI)-based systems to provide enriched efficiencies and strengthened human knacks in daily live maneuvers, business stratagems, and society evolution. In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs). The governing relations of EESs were presented in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters. The reference datasets of EESs were assembled using the Adams numerical solver for different EES scenarios and were used as targets of IBNs-BR to find the approximate solutions. Comparative studies based on convergence curves on the mean square error (MSE) and absolute deviation from the reference results were used to verify the correctness of IBNs-BR for solving EESs, i.e., MSE of around 10−9 to 10−10 and absolute error close to 10−5 to 10−7. The endorsement of results was further validated through performance evaluation by means of error histogram analysis, the regression index, and the mean squared deviation-based figure of merit for each EES scenario.
Adiqa Kausar Kiani; Wasim Ullah Khan; Muhammad Asif Zahoor Raja; Yigang He; Zulqurnain Sabir; Muhammad Shoaib. Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems. Sustainability 2021, 13, 9537 .
AMA StyleAdiqa Kausar Kiani, Wasim Ullah Khan, Muhammad Asif Zahoor Raja, Yigang He, Zulqurnain Sabir, Muhammad Shoaib. Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems. Sustainability. 2021; 13 (17):9537.
Chicago/Turabian StyleAdiqa Kausar Kiani; Wasim Ullah Khan; Muhammad Asif Zahoor Raja; Yigang He; Zulqurnain Sabir; Muhammad Shoaib. 2021. "Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems." Sustainability 13, no. 17: 9537.
Agricultural diversification efforts towards sustainable agriculture generates environmental and economic benefits. Climate change and agricultural production are characterized by a complex cause-effect relationship. In the present study, the primary dataset is collected through an interview-based survey from 410 farmers in 3 districts located in different agro-ecological zones of Punjab, Pakistan. Detailed analysis is conducted by employing the Gaussian treatment effects approach. Results of the study show that the farmers who adopted agricultural diversification to mitigate the impact of climate change were less and insignificantly benefited e.g., on an average of RS 95,260 (US $635) per annum whereas non-adopted farmers lost their farm income on an average of RS 115,750 (US $772) per annum if they had practiced the agricultural diversification. Moreover, determinants of agricultural diversification such as demographic and institutional indicators were significant and larger effects to adopt as compared to social indicators. This study suggests that policies should be designed in the regional context particularly related to the improvement in demographic characteristics and institutional factors such as providing subsidies, training, and awareness to the farmers, particularly to those who practice agricultural diversification. These measures will help to raise the farmers’ adaptive capacity for the adoption of agricultural diversification, and it will enable them to generate tangible benefits by increasing income through adopting sustainable agricultural livelihood.
Adiqa Kausar Kiani; Asif Sardar; Wasim Ullah Khan; Yigang He; Abdulbaki Bilgic; Yasemin Kuslu; Muhammad Asif Zahoor Raja. Role of Agricultural Diversification in Improving Resilience to Climate Change: An Empirical Analysis with Gaussian Paradigm. Sustainability 2021, 13, 9539 .
AMA StyleAdiqa Kausar Kiani, Asif Sardar, Wasim Ullah Khan, Yigang He, Abdulbaki Bilgic, Yasemin Kuslu, Muhammad Asif Zahoor Raja. Role of Agricultural Diversification in Improving Resilience to Climate Change: An Empirical Analysis with Gaussian Paradigm. Sustainability. 2021; 13 (17):9539.
Chicago/Turabian StyleAdiqa Kausar Kiani; Asif Sardar; Wasim Ullah Khan; Yigang He; Abdulbaki Bilgic; Yasemin Kuslu; Muhammad Asif Zahoor Raja. 2021. "Role of Agricultural Diversification in Improving Resilience to Climate Change: An Empirical Analysis with Gaussian Paradigm." Sustainability 13, no. 17: 9539.
Distributed generators providing auxiliary service are an important means of guaranteeing the safe and economic operation of a distribution system. In this paper, considering an energy storage system (ESS), switchable capacitor reactor (SCR), step voltage regulator (SVR), and a static VAR compensator (SVC), a two-stage multi-period hybrid integer second-order cone programming (SOCP) robust model with partial DGs providing auxiliary service is developed. If the conic relaxation is not exact, a sequential SOCP is formulated using convex–concave procedure (CCP) and cuts, which can be quickly solved. Moreover, the exact solution of the original problem can be recovered. Furthermore, in view of the shortcomings of the large computer storage capacity and slow computational rate for the column and constraint generation (CCG) method, a method direct iteratively solving the master and sub-problem is proposed. Increases to variables and constraints to solve the master problem are not needed. For the sub-problem, only the model of each single time period needs to be solved. Then, their objective function values are accumulated, and the worst scenarios of each time period are concatenated. As an outcome, a large amount of storage memory is saved and the computational efficiency is greatly enhanced. The capability of the proposed method is validated with three simulation cases.
Jian Zhang; Mingjian Cui; Yigang He. Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services. Energies 2021, 14, 4911 .
AMA StyleJian Zhang, Mingjian Cui, Yigang He. Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services. Energies. 2021; 14 (16):4911.
Chicago/Turabian StyleJian Zhang; Mingjian Cui; Yigang He. 2021. "Multi-Period Fast Robust Optimization for Partial Distributed Generators (DGs) Providing Ancillary Services." Energies 14, no. 16: 4911.
This research concerns the heat transfer and entropy generation analysis in the MHD axisymmetric flow of Al2O3-Cu/H2O hybrid nanofluid. The magnetic induction effect is considered for large magnetic Reynolds number. The influences of thermal radiations, viscous dissipation and convective temperature conditions over flow are studied. The problem is modeled using boundary layer theory, Maxwell’s equations and Fourier’s conduction law along with defined physical factors. Similarity transformations are utilized for model simplification which is analytically solved with the homotopy analysis method. The h-curves up to 20th order for solutions establishes the stability and convergence of the adopted computational method. Rheological impacts of involved parameters on flow variables and entropy generation number are demonstrated via graphs and tables. The study reveals that entropy in system of hybrid nanofluid affected by magnetic induction declines for β while it enhances for Bi, R and λ. Moreover, heat transfer rate elevates for large Bi with convective conditions at surface.
Nabeela Parveen; Muhammad Awais; Saeed Awan; Wasim Khan; Yigang He; Muhammad Malik. Entropy Generation Analysis and Radiated Heat Transfer in MHD (Al2O3-Cu/Water) Hybrid Nanofluid Flow. Micromachines 2021, 12, 887 .
AMA StyleNabeela Parveen, Muhammad Awais, Saeed Awan, Wasim Khan, Yigang He, Muhammad Malik. Entropy Generation Analysis and Radiated Heat Transfer in MHD (Al2O3-Cu/Water) Hybrid Nanofluid Flow. Micromachines. 2021; 12 (8):887.
Chicago/Turabian StyleNabeela Parveen; Muhammad Awais; Saeed Awan; Wasim Khan; Yigang He; Muhammad Malik. 2021. "Entropy Generation Analysis and Radiated Heat Transfer in MHD (Al2O3-Cu/Water) Hybrid Nanofluid Flow." Micromachines 12, no. 8: 887.
Due to the constant changes of the environment and load, the insulated-gate bipolar transistor (IGBT) module is subjected to a large amount of junction temperature (Tj) fluctuations, which often leads to damage to the bond wires. The monitoring parameters of IGBTs are often coupled with Tj, which increases the difficulty of monitoring IGBTs’ health status online. In this paper, based on the collector current (Ic) and collector-emitter on-state voltage (Vce_on) online monitoring circuit, an online monitoring method of IGBT bond wire aging against interference is proposed. First, the bond wire aging model is established, and the Vce_on is selected as the monitoring parameter. Secondly, taking a three-phase inverter circuit as an example, the Vce_on and Ic waveforms of the IGBT module are monitored in real time, and the process of online monitoring is introduced accordingly. Finally, the experimental results output by RT-LAB indicate that the method proposed in this paper can accurately identify the aging state of IGBT bond wires under different conditions.
Chuankun Wang; Yigang He; Yunfeng Jiang; Lie Li. An Anti-Interference Online Monitoring Method for IGBT Bond Wire Aging. Electronics 2021, 10, 1449 .
AMA StyleChuankun Wang, Yigang He, Yunfeng Jiang, Lie Li. An Anti-Interference Online Monitoring Method for IGBT Bond Wire Aging. Electronics. 2021; 10 (12):1449.
Chicago/Turabian StyleChuankun Wang; Yigang He; Yunfeng Jiang; Lie Li. 2021. "An Anti-Interference Online Monitoring Method for IGBT Bond Wire Aging." Electronics 10, no. 12: 1449.
The requirements for accuracy of piezoresistive pressure sensors in modern society are becoming increasingly high. Besides, a wide application range of sensor is required. However, due to the influence of material properties, many piezoresistive pressure sensors have high temperature coefficient, which limits their application temperature range. With the change of ambient temperature, the response characteristic of the sensor has strong nonlinearity. To solve the above-mentioned crucial problems, a dynamic chaos quantum-behaved particle swarm optimization optimized multiple kernel relevance vector machine (DCQPSO-MKRVM) algorithm is presented in this article. First, a basic theory of temperature effect is given and a new idea of temperature compensation is proposed. Second, the multi-kernel relevance vector machine (MKRVM) is adopted to estimate the bias values of input pressure. Through heterogeneous kernel learning method, the kernels of MKRVM maintain diversity to obtain higher estimation accuracy. Third, dynamic chaos quantum-behaved particle swarm optimization (DCQPSO) is employed to optimize the optimal sparse weights of kernel functions in MKRVM. Moreover, the dynamic parameter is applied for the boundary of chaos search between original quantum-behaved particle swarm optimization (QPSO) swarm and the chaos swarm. The experimental result indicates the complex nonlinear relationship of temperature effect, and the method proposed in this article can effectively and accurately estimate the bias of input pressure fast to achieve temperature compensation goal. The mean relative accuracy (MRA) of estimation results achieves 99.5%. It proves that the method proposed in this article is applicable and effective for industrial applications.
Yi Ruan; Lifen Yuan; Weibo Yuan; Yigang He; Li Lu. Temperature Compensation and Pressure Bias Estimation for Piezoresistive Pressure Sensor Based on Machine Learning Approach. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -10.
AMA StyleYi Ruan, Lifen Yuan, Weibo Yuan, Yigang He, Li Lu. Temperature Compensation and Pressure Bias Estimation for Piezoresistive Pressure Sensor Based on Machine Learning Approach. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-10.
Chicago/Turabian StyleYi Ruan; Lifen Yuan; Weibo Yuan; Yigang He; Li Lu. 2021. "Temperature Compensation and Pressure Bias Estimation for Piezoresistive Pressure Sensor Based on Machine Learning Approach." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-10.
Absence of fault samples of power equipment is a big bottleneck that limits the development of diagnostic method. This paper proposes a Space Hybridization Theory for dealing with diagnostic data insufficiency. First, the experimental datasets of transformer winding failure are obtained, constituting the real space. Then, the transformer digital space models are constructed similarly to obtain the simulation datasets, constituting the virtual space. After that, the features of the samples in two spaces are extracted through windowing feature calculation, and the real-virtual space is obtained through feature hybridization. The final samples in the space are taken as the auxiliary dataset during the intelligent diagnosis process. The diagnostic accuracy with the proposed method increased by several percent than without. While the MAPE between the real space and the virtual space is less than 9.4%, it can comprehensively improve the diagnostic effect.
Jiajun Duan; Yigang He; Xiaoxin Wu. A space hybridization theory for dealing with data insufficiency in intelligent power equipment diagnosis. Electric Power Systems Research 2021, 199, 107363 .
AMA StyleJiajun Duan, Yigang He, Xiaoxin Wu. A space hybridization theory for dealing with data insufficiency in intelligent power equipment diagnosis. Electric Power Systems Research. 2021; 199 ():107363.
Chicago/Turabian StyleJiajun Duan; Yigang He; Xiaoxin Wu. 2021. "A space hybridization theory for dealing with data insufficiency in intelligent power equipment diagnosis." Electric Power Systems Research 199, no. : 107363.
Insulated Gate Bipolar Transistor (IGBT) is the core power switch device in electrical field. IGBT's reliability is the basis of safe and stable operation for electrical system or equipment. At present, the aging analysis of IGBT mostly focuses on a single failure mode. This paper proposes a new aging parameter based on Miner's linear fatigue cumulative theory. Then this parameter is used to characterize the aging state of IGBT when multiple failure modes act simultaneously. The aging analysis method of IGBT considering composite failure mode is proposed. Firstly, the bonding wire and solder layer are analyzed by finite element method. Then analyzed according to the load, the master S-N curve method is used to calculate the fatigue accumulation. The larger value is taken as the overall aging degree of IGBT module. Finally, the aging analysis method of IGBT based on Miner's linear fatigue cumulative theory under composite failure mode is verified by data analysis. This method can not only be used for aging analysis and lifetime prediction of IGBT, but also can predict the final failure mode of IGBT to a certain extent.
Lie Li; Yigang He; Lei Wang; Chenyuan Wang; Chuankun Wang; Xiaoxin Wu. The aging analysis method of IGBT with composite failure mode based on Miner linear fatigue cumulative theory. Microelectronics Reliability 2021, 122, 114165 .
AMA StyleLie Li, Yigang He, Lei Wang, Chenyuan Wang, Chuankun Wang, Xiaoxin Wu. The aging analysis method of IGBT with composite failure mode based on Miner linear fatigue cumulative theory. Microelectronics Reliability. 2021; 122 ():114165.
Chicago/Turabian StyleLie Li; Yigang He; Lei Wang; Chenyuan Wang; Chuankun Wang; Xiaoxin Wu. 2021. "The aging analysis method of IGBT with composite failure mode based on Miner linear fatigue cumulative theory." Microelectronics Reliability 122, no. : 114165.
In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.
Wasiq Ali; Wasim Khan; Muhammad Raja; Yigang He; Yaan Li. Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target. Entropy 2021, 23, 550 .
AMA StyleWasiq Ali, Wasim Khan, Muhammad Raja, Yigang He, Yaan Li. Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target. Entropy. 2021; 23 (5):550.
Chicago/Turabian StyleWasiq Ali; Wasim Khan; Muhammad Raja; Yigang He; Yaan Li. 2021. "Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target." Entropy 23, no. 5: 550.
Pilot contamination resulting from pilot reuse seriously restricts the communication performance of the cell-free massive multiple-input multiple-output (MIMO) networks. To deal with this problem, an efficient pilot assignment scheme using a weighted graphic framework is proposed in this correspondence paper. Specifically, a novel metric is first introduced for capturing the severity of potential mutual pilot contamination in cell-free topology. On this basis, the weighted pilot contamination graph is constructed to depict the dynamic interference relationship corresponding to the network. Then, the pilot assignment optimization is mapped to the Max k-Cut problem, and a heuristic algorithm is exploited to realize pilot decontamination. Numerical simulation results reveal the superior performance of the proposed scheme, which is capable of achieving a significant improvement in throughput with low complexity.
Wenbo Zeng; Yigang He; Bing Li; Shudong Wang. Pilot Assignment for Cell-free Massive MIMO Systems Using a Weighted Graphic Framework. IEEE Transactions on Vehicular Technology 2021, PP, 1 -1.
AMA StyleWenbo Zeng, Yigang He, Bing Li, Shudong Wang. Pilot Assignment for Cell-free Massive MIMO Systems Using a Weighted Graphic Framework. IEEE Transactions on Vehicular Technology. 2021; PP (99):1-1.
Chicago/Turabian StyleWenbo Zeng; Yigang He; Bing Li; Shudong Wang. 2021. "Pilot Assignment for Cell-free Massive MIMO Systems Using a Weighted Graphic Framework." IEEE Transactions on Vehicular Technology PP, no. 99: 1-1.
The current study is an attempt to analytically characterize the second law analysis and mixed convective rheology of the (Al2O3–Ag/H2O) hybrid nanofluid flow influenced by magnetic induction effects towards a stretching sheet. Viscous dissipation and internal heat generation effects are encountered in the analysis as well. The mathematical model of partial differential equations is fabricated by employing boundary-layer approximation. The transformed system of nonlinear ordinary differential equations is solved using the homotopy analysis method. The entropy generation number is formulated in terms of fluid friction, heat transfer and Joule heating. The effects of dimensionless parameters on flow variables and entropy generation number are examined using graphs and tables. Further, the convergence of HAM solutions is examined in terms of defined physical quantities up to 20th iterations, and confirmed. It is observed that large
Wasim Khan; Muhammad Awais; Nabeela Parveen; Aamir Ali; Saeed Awan; Muhammad Malik; Yigang He. Analytical Assessment of (Al2O3–Ag/H2O) Hybrid Nanofluid Influenced by Induced Magnetic Field for Second Law Analysis with Mixed Convection, Viscous Dissipation and Heat Generation. Coatings 2021, 11, 498 .
AMA StyleWasim Khan, Muhammad Awais, Nabeela Parveen, Aamir Ali, Saeed Awan, Muhammad Malik, Yigang He. Analytical Assessment of (Al2O3–Ag/H2O) Hybrid Nanofluid Influenced by Induced Magnetic Field for Second Law Analysis with Mixed Convection, Viscous Dissipation and Heat Generation. Coatings. 2021; 11 (5):498.
Chicago/Turabian StyleWasim Khan; Muhammad Awais; Nabeela Parveen; Aamir Ali; Saeed Awan; Muhammad Malik; Yigang He. 2021. "Analytical Assessment of (Al2O3–Ag/H2O) Hybrid Nanofluid Influenced by Induced Magnetic Field for Second Law Analysis with Mixed Convection, Viscous Dissipation and Heat Generation." Coatings 11, no. 5: 498.
This paper proposes a virtual-real twin spatial fusion theory to deal with the problem of insufficient data for intelligent diagnosis of power equipment. First, build a transformer winding fault experiment platform, and use Frequency Response Analysis (FRA) to obtain the measured sample set that contains different fault locations, fault types and severity of the transformer to form an accurate physical space. Then, use COMSOL and matlab to build a transformer digital space model, correspondingly set the fault, obtain the simulation sample set, and form a fuzzy mirror space. Then, the sample data in the two spaces is pre-processed by feature extraction and fused with each other to obtain the virtual and real twin spaces. The fused samples are used as auxiliary training samples of intelligent fault diagnosis network. Finally, based on the proposed method, a variety of intelligent diagnostic networks are applied to diagnose the measured dataset, verifying that the proposed method can effectively improve the diagnosis and positioning effects of small sample data.
Jiajun Duan; Yigang He; Xiaoxin Wu. Assisted Diagnosis of Real-Virtual Twin Space for Data Insufficiency. International Conference on Communication, Computing and Electronics Systems 2021, 387 -395.
AMA StyleJiajun Duan, Yigang He, Xiaoxin Wu. Assisted Diagnosis of Real-Virtual Twin Space for Data Insufficiency. International Conference on Communication, Computing and Electronics Systems. 2021; ():387-395.
Chicago/Turabian StyleJiajun Duan; Yigang He; Xiaoxin Wu. 2021. "Assisted Diagnosis of Real-Virtual Twin Space for Data Insufficiency." International Conference on Communication, Computing and Electronics Systems , no. : 387-395.
Novel nonlinear power-law flux models were utilized to model the heat transport phe-nomenon in nano-micropolar fluid over a flexible surface. The nonlinear conservation laws (mass, momentum, energy, mass transport and angular momentum) and KKL cor-relations for nanomaterial under novel flux model were solved numerically. Computed results were used to study the shear-thinning and shear-thickening nature of nano pol-ymer suspension by considering n-diffusion theory. Normalized velocity, temperature and micro-rotation profiles were investigated under the variation of physical parame-ters. Shear stresses at the wall for nanoparticles (CuO and Al2O3 ) were recorded and dis-played in the table. Error analyses for different physical parameters were prepared for various parameters to validate the obtained results.
Muhammad Awais; Saeed Ehsan Awan; Muhammad Raja; Muhammad Nawaz; Wasim Khan; Muhammad Yousaf Malik; Yigang He. Heat Transfer in Nanomaterial Suspension (CuO and Al2O3) Using KKL Model. Coatings 2021, 11, 417 .
AMA StyleMuhammad Awais, Saeed Ehsan Awan, Muhammad Raja, Muhammad Nawaz, Wasim Khan, Muhammad Yousaf Malik, Yigang He. Heat Transfer in Nanomaterial Suspension (CuO and Al2O3) Using KKL Model. Coatings. 2021; 11 (4):417.
Chicago/Turabian StyleMuhammad Awais; Saeed Ehsan Awan; Muhammad Raja; Muhammad Nawaz; Wasim Khan; Muhammad Yousaf Malik; Yigang He. 2021. "Heat Transfer in Nanomaterial Suspension (CuO and Al2O3) Using KKL Model." Coatings 11, no. 4: 417.
A Serial Transfer Learning (STL) theory was proposed to assist fault diagnosis. At first, the Frequency Response Analysis (FRA) was conducted on a customized transformer and its simulation model respectively to obtain the experimental/simulation dataset, which were converted into images. The simulation dataset was used for the first-step of STL, and the experimental dataset for the further STL process. Considering the case of multi-monitoring points, the membership matrices were further merged using evidence theory. Deep Convolutional Neural Networks with STL and the graphical representation of data can realize intelligent fault diagnosis with a few amount of samples. Experimental results indicated that STL could make up for the data insufficiency problem, which achieved a diagnostic accuracy of 92.6% with a small amount of experimental data for 22 types of labels. It was 7.18% higher than the accuracy (86.4%) obtained by traditional Transfer Learning.
Jiajun Duan; Yigang He; Xiaoxin Wu. Serial transfer learning (STL) theory for processing data insufficiency: Fault diagnosis of transformer windings. International Journal of Electrical Power & Energy Systems 2021, 130, 106965 .
AMA StyleJiajun Duan, Yigang He, Xiaoxin Wu. Serial transfer learning (STL) theory for processing data insufficiency: Fault diagnosis of transformer windings. International Journal of Electrical Power & Energy Systems. 2021; 130 ():106965.
Chicago/Turabian StyleJiajun Duan; Yigang He; Xiaoxin Wu. 2021. "Serial transfer learning (STL) theory for processing data insufficiency: Fault diagnosis of transformer windings." International Journal of Electrical Power & Energy Systems 130, no. : 106965.
Parameter estimation of Direction of Arrival (DOA) using deterministic and stochastic computing paradigms is an enabling development for underwater acoustic signal processing beside its applications in the field of seismology, astronomy, earthquake and bio-medicine. In this work, the comparative study between state of the art deterministic and heuristics algorithms is presented for viable DOA estimation for different underwater dynamic objects. A Uniform Linear Array (ULA) of eight hydrophones is used for impinging acoustic waves from far-field targets. In order to evaluate the performance, the viability of innovative statistical indices is utilized to explain. Performance analysis of Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) is conducted with standard counterparts including MVDR, MUSIC, ESPRIT and UESPRIT for different number of targets in terms of estimation accuracy, robustness against the number of elements and noise, cumulative distribution function of Root Mean Sqaure Error(RMSE), frequency distribution of the RMSE over the monte carlo trials, the resolution ability and computational complexity in the presence of white Gaussian measurement noise. Crammer Rao Bound (CRB) based analysis is also performed for the validation assessments and results on Monte Carlo simulations depict that the Genetic Algorithm(GA) showed the outperform counterparts on precision, convergence and complexity indices.
Nauman Ahmed; Huigang Wang; Muhammad Asif Zahoor Raja; Wasiq Ali; Fawad Zaman; Wasim Ullah Khan; Yigang He. Performance Analysis of Efficient Computing Techniques for Direction of Arrival Estimation of Underwater Multi Targets. IEEE Access 2021, 9, 33284 -33298.
AMA StyleNauman Ahmed, Huigang Wang, Muhammad Asif Zahoor Raja, Wasiq Ali, Fawad Zaman, Wasim Ullah Khan, Yigang He. Performance Analysis of Efficient Computing Techniques for Direction of Arrival Estimation of Underwater Multi Targets. IEEE Access. 2021; 9 ():33284-33298.
Chicago/Turabian StyleNauman Ahmed; Huigang Wang; Muhammad Asif Zahoor Raja; Wasiq Ali; Fawad Zaman; Wasim Ullah Khan; Yigang He. 2021. "Performance Analysis of Efficient Computing Techniques for Direction of Arrival Estimation of Underwater Multi Targets." IEEE Access 9, no. : 33284-33298.
Rheology of MHD bioconvective nanofluid containing motile microorganisms is inspected numerically in order to analyze heat and mass transfer characteristics. Bioconvection is implemented by combined effects of magnetic field and buoyancy force. Gyrotactic microorganisms enhance the heat and transfer as well as perk up the nanomaterials’ stability. Variable transport properties along with assisting and opposing flow situations are taken into account. The significant influences of thermophoresis and Brownian motion have also been taken by employing Buongiorno’s model of nanofluid. Lie group analysis approach is utilized in order to compute the absolute invariants for the system of differential equations, which are solved numerically using Adams-Bashforth technique. Validity of results is confirmed by performing error analysis. Graphical and numerical illustrations are prepared in order to get the physical insight of the considered analysis. It is observed that for controlling parameters corresponding to variable transport properties c2, c4, c6, and c8, the velocity, temperature, concentration, and bioconvection density distributions accelerates, respectively. While heat and mass transfer rates increases for convection parameter and bioconvection Rayleigh number, respectively.
Muhammad Awais; Saeed Ehsan Awan; Muhammad Asif Zahoor Raja; Nabeela Parveen; Wasim Ullah Khan; Muhammad Yousaf Malik; Yigang He. Effects of Variable Transport Properties on Heat and Mass Transfer in MHD Bioconvective Nanofluid Rheology with Gyrotactic Microorganisms: Numerical Approach. Coatings 2021, 11, 231 .
AMA StyleMuhammad Awais, Saeed Ehsan Awan, Muhammad Asif Zahoor Raja, Nabeela Parveen, Wasim Ullah Khan, Muhammad Yousaf Malik, Yigang He. Effects of Variable Transport Properties on Heat and Mass Transfer in MHD Bioconvective Nanofluid Rheology with Gyrotactic Microorganisms: Numerical Approach. Coatings. 2021; 11 (2):231.
Chicago/Turabian StyleMuhammad Awais; Saeed Ehsan Awan; Muhammad Asif Zahoor Raja; Nabeela Parveen; Wasim Ullah Khan; Muhammad Yousaf Malik; Yigang He. 2021. "Effects of Variable Transport Properties on Heat and Mass Transfer in MHD Bioconvective Nanofluid Rheology with Gyrotactic Microorganisms: Numerical Approach." Coatings 11, no. 2: 231.
The demand for optimization design and performance evaluation of wireless communication links in a mobile Internet of Things (IoT) motivates the exploitation of realistic and tractable channel models. In this paper, we develop a novel three-dimensional (3D) multiple-antenna channel model to adequately characterize the scattering environment for mobile IoT scenarios. Specifically, taking into consideration both accuracy and mathematical tractability, a 3D double-spheres model and ellipsoid model are introduced to describe the distribution region of the local scatterers and remote scatterers, respectively. Based on the explicit geometry relationships between transmitter, receiver, and scatterers, we derive the complex channel gains by adopting the radio-wave propagation model. Subsequently, the correlation-based approach for theoretical analysis is performed, and the detailed impacts with respect to the antenna deployment, scatterer distribution, and scatterer density on the vital statistical properties are investigated. Numerical simulation results have shown that the statistical channel characteristics in the developed simulation model nicely match those of the corresponding theoretical results, which demonstrates the utility of our model.
Wenbo Zeng; Yigang He; Bing Li; Shudong Wang. 3D Multiple-Antenna Channel Modeling and Propagation Characteristics Analysis for Mobile Internet of Things. Sensors 2021, 21, 989 .
AMA StyleWenbo Zeng, Yigang He, Bing Li, Shudong Wang. 3D Multiple-Antenna Channel Modeling and Propagation Characteristics Analysis for Mobile Internet of Things. Sensors. 2021; 21 (3):989.
Chicago/Turabian StyleWenbo Zeng; Yigang He; Bing Li; Shudong Wang. 2021. "3D Multiple-Antenna Channel Modeling and Propagation Characteristics Analysis for Mobile Internet of Things." Sensors 21, no. 3: 989.
High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.
Lei Wang; Yigang He; Lie Li. A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network. Electronics 2021, 10, 255 .
AMA StyleLei Wang, Yigang He, Lie Li. A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network. Electronics. 2021; 10 (3):255.
Chicago/Turabian StyleLei Wang; Yigang He; Lie Li. 2021. "A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network." Electronics 10, no. 3: 255.
The detection of partial discharge (PD) is a crucial method to evaluate the insulation status of transformers. The main difficulties of the current localization algorithms are the complexity of the solution and sensitivity to time delay errors. This article proposes a PD localization method in transformers based on linear conversion and density peak clustering (DPC). First, to reduce the complexity of solving the localization equations, the nonlinear localization equations are transformed into linear localization equations by eliminating the second-order terms. Then, to reduce the influence of time delay errors on localization accuracy, the initial localization values are located by multiple acoustic emission (AE) sensors. Finally, the optimal PD coordinates are determined by clustering the initial location values using density peaks clustering algorithm with automatic finding centers (AFC-DPC). The experimental results show that the proposed method can improve PD localization accuracy in transformers, and the average localization error is 5.30 cm.
Shudong Wang; Yigang He; Baiqiang Yin; Wenbo Zeng; Ying Deng; Zengchao Hu. A Partial Discharge Localization Method in Transformers Based on Linear Conversion and Density Peak Clustering. IEEE Access 2021, 9, 7447 -7459.
AMA StyleShudong Wang, Yigang He, Baiqiang Yin, Wenbo Zeng, Ying Deng, Zengchao Hu. A Partial Discharge Localization Method in Transformers Based on Linear Conversion and Density Peak Clustering. IEEE Access. 2021; 9 ():7447-7459.
Chicago/Turabian StyleShudong Wang; Yigang He; Baiqiang Yin; Wenbo Zeng; Ying Deng; Zengchao Hu. 2021. "A Partial Discharge Localization Method in Transformers Based on Linear Conversion and Density Peak Clustering." IEEE Access 9, no. : 7447-7459.