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oe1(光电查) - 科学论文

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出版时间
  • 2018
研究主题
  • Conditional Random Fields (CRF)
  • Convolutional Neural Network (CNN)
  • Fine Classification
  • Airborne hyperspectral
应用领域
  • Optoelectronic Information Science and Engineering
机构单位
  • Wuhan University
  • Central South University
  • Hubei University
404 条数据
?? 中文（中国） ▾

  • Improved cooperative artificial neural network <scp>a??</scp> particle swarm optimization approach for solar photovoltaic systems using maximum power point tracking

    摘要： Photovoltaic (PV) energy represents one of the most important renewable energies, but its disadvantage resides in its maximum power point, which varies according to meteorological changes that make the efficiency low. Intelligent techniques, using the maximum power point tracking (MPPT) method, can achieve an efficient real-time tracking of this point in order to ensure optimal functioning of the system. The output power of the PV system is removed from solar irradiation and cell temperature of the PV panel type SOLON 55W. Therefore, it is essential to harvest the generated power of the PV system and optimally exploit the collected solar energy. For this objective, this work treats on a new artificial neural network-particle swarm optimization approach (ANN-PSO). The ANN is used to predict the solar irradiation level and cell temperature followed by PSO to optimize the power generation and optimally track the solar power of the PV panel type SOLON 55W based on various operation conditions under changes in environmental conditions. The simulation results of the proposed approach give a minimum error with a relevant efficiency, that is, the power provided by ANN-PSO approach is optimal and closer to the PV power. Consequently, this novel approach ANN-PSO shows its major capability to extract the optimal power with excellent efficiency up of 97%. For this objective, this work treats a new hybrid ANN-PSO approach.

    关键词： photovoltaic system,particle swarm optimization,maximum power point tracking,artificial neural network

    更新于2025-09-23 15:21:01

  • Identification of Gravesa?? ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method

    摘要： Diagnosis of the Graves’ ophthalmology remains a significant challenge. We identified between Graves’ ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves’ ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves’ ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves’ ophthalmology.

    关键词： support vector machine (SVM),linear discriminant analysis (LDA),Graves’ ophthalmology,laser-induced breakdown spectroscopy (LIBS),k-nearest neighbor (kNN),generalized regression neural network (GRNN)

    更新于2025-09-23 15:21:01

  • [IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - A Multi-Copy Approach to Quantum Entanglement Characterization

    摘要： Automatic speech recognition (ASR) systems are used daily by millions of people worldwide to dictate messages, control devices, initiate searches or to facilitate data input in small devices. The user experience in these scenarios depends on the quality of the speech transcriptions and on the responsiveness of the system. For multilingual users, a further obstacle to natural interaction is the monolingual character of many ASR systems, in which users are constrained to a single preset language. In this work, we present an end-to-end multi-language ASR architecture, developed and deployed at Google, that allows users to select arbitrary combinations of spoken languages. We leverage recent advances in language identification and a novel method of real-time language selection to achieve similar recognition accuracy and nearly-identical latency characteristics as a monolingual system.

    关键词： Automatic speech recognition (ASR),multilingual,deep neural network (DNN),language identification (LID)

    更新于2025-09-23 15:21:01

  • [IEEE 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Karachi, Pakistan (2020.3.26-2020.3.27)] 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Soft Computing Technique based Nonlinear Sliding Mode Control for Stand-Alone Photovoltaic System

    摘要： Energy production capability of a photovoltaic (PV) system is extensively depends upon the ambient temperature (T) and solar irradiance (Ee). In order to adapt the ever increasing interest in energy, the PV array must be operated at the maximum power point (MPP). However, due to varying climatic conditions, there is a low energy ef?ciency problem. In this research article, a robust and ef?cient nonlinear sliding mode control (SMC) based maximum power point tracking (MPPT) technique is designed to extract maximum power from the PV array. This study uses arti?cial feed-forward neural network (AFNN) to generate the reference voltage for MPPT using non-inverting DC-DC Buck-Boost converter. Asymptotically convergence is ensures using Lyapunov stability criteria. The MATLAB/SIMULINK platform is used to design, simulate and test the performance of the proposed technique. To further validate the proposed control technique in terms of ef?ciency, tracking speed and robustness, results are compared with the non-linear backstepping (B) technique under continuous conditions of environment, faults and parametric uncertainties.

    关键词： Buck-Boost converter,Neural Network,MPPT,Photovoltaic,SMC

    更新于2025-09-23 15:21:01

  • [IEEE 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Karachi, Pakistan (2020.3.26-2020.3.27)] 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Artificial Intelligence based Nonlinear Integral Back-stepping Control Approach for MPPT of Photovoltaic System

    摘要： The energy demand of the world has been intensively increased since last two decades. The need of energy is forcing the think tanks of the developed countries to move towards the alternative energy resources. Solar energy is the most suitable solution to overcome the energy crises. In this regard, this article presents the nonlinear integral back-stepping (IB) control approach for maximum power extraction of stand-alone photovoltaic (PV) system. The proposed control strategy gives robustness against constantly varying conditions of environment. Non-inverting case of buck-boost DC-DC converter is used as interface between load and PV array. Radial basis function neural network (RBFNN) is generated the reference (Vref ) under different climatic conditions for the tracking of the developed control scheme. IB control technique is also checked under faulty conditions. The Simulations are preformed in the environment of MATLAB/Simulink. Moreover, the proposed technique results are compared with perturb and observe (P&O) maximum power point tracking (MPPT) technique.

    关键词： Neural network,MPPT,Solar energy,IB

    更新于2025-09-23 15:21:01

  • Estimating Solar Insolation and Power Generation of Photovoltaic Systems Using Previous Day Weather Data

    摘要： Day-ahead predictions of solar insolation are useful for forecasting the energy production of photovoltaic (PV) systems attached to buildings, and accurate forecasts are essential for operational efficiency and trading markets. In this study, a multilayer feed-forward neural network-based model that predicts the next day’s solar insolation by taking into consideration the weather conditions of the present day was proposed. The proposed insolation model was employed to estimate the energy production of a real PV system located in South Korea. Validation research was performed by comparing the model’s estimated energy production with the measured energy production data collected during the PV system operation. The accuracy indices for the optimal model, which included the root mean squared error, mean bias error, and mean absolute error, were 1.43 kWh/m2/day, ? 0.09 kWh/m2/day, and 1.15 kWh/m2/day, respectively. These values indicate that the proposed model is capable of producing reasonable insolation predictions; however, additional work is needed to achieve accurate estimates for energy trading.

    关键词： neural network,energy production,photovoltaic systems,solar insolation,forecasting

    更新于2025-09-23 15:21:01

  • [IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Nonlinear Acoustic Dynamics in Nanoelectromechanical Waveguides

    摘要： A novel adaptive radial basis function neural network H-in?nity control strategy with robust feedback compensator using linear matrix inequality (LMI) approach is proposed for micro electro mechanical systems vibratory gyroscopes involving parametric uncertainties and external disturbances. The proposed system is comprised of a neural network controller, which is designed to mimic an equivalent control law aimed at relaxing the requirement of exact mathematical model and a robust feedback controller, which is derived to eliminate the effect of modeling error and external disturbances. Based on the Lyapunov stability theorem, it is shown that H-in?nity tracking performance of the gyroscope system can be achieved, all variables of the closed-loop system are bounded, and the effect due to external disturbances on the tracking error can be attenuated effectively. Numerical simulations are investigated to demonstrate that the satisfactory tracking performance and strong robustness against external disturbances can be obtained using the proposed adaptive neural H-in?nity control strategy with robust feedback compensator by LMI technique.

    关键词： neural network control,Adaptive control,H-In?nity Control

    更新于2025-09-23 15:21:01

  • [IEEE 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) - Shenyang, China (2019.11.22-2019.11.24)] 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) - The Modeling of Tracking of Circumferential Scanning-type Satellite Laser Communication Terminals

    摘要： In the intersatellite laser communication system, the circumferential optical tracking structure has the advantages of small size and simple structure, which can be widely used. However, due to the coupling relationship between the azimuth axis and the pitching axis in the tracking of the target laser signal, and the disturbance caused by the electric machine, the satellite is used to get in and out of the earth. The vibration, which affects the tracking precision. In order to improve the tracking accuracy of the system, the matrix optical method is adopted to establish the propagation matrix of the light in the circumferential laser communication terminal, and the rough tracking algorithm based on the target light angle and the attitude information of the communication terminal is derived. Then the BP neural network is used to set the PID control on the basis of the precise motor model. Methods, the control model of communication terminal is established. Finally, the rough tracking control model is built in the Matlab-Simulink environment. The experiment validates the necessity of establishing the tracking algorithm model, and obtains better dynamic tracking error to meet the high precision rough tracking requirement of the inter satellite laser communication.

    关键词： satellite laser communication,tracking model,BP neural network,circumferential scanning-type

    更新于2025-09-23 15:21:01

  • [IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Using Artificial Neural Network for Compensation of Semiconductor Thermistor Nonlinearity

    摘要： A method for correcting the transformation function of a semiconductor thermistor using a nonlinearity compensator based on a three-layer perceptron is proposed. Using of computer simulation, the operability of the proposed method has been investigated, a comparative analysis has been carried out with polynomial compensators of nonlinearity.

    关键词： compensation of nonlinearity,learning,perceptron,artificial neural network

    更新于2025-09-23 15:21:01

  • Deep Learning-based Algorithms in Screening of Diabetic Retinopathy: A Systematic Review of Diagnostic Performance.

    摘要： Topic: Diagnostic performance of deep learning-based algorithms in screening patients with diabetes for diabetic retinopathy (DR). The algorithms were compared to the current gold standard of classification by human specialists. Clinical relevance: As DR is a common cause of visual impairment, screening is indicated to avoid irreversible vision loss. Automated DR-classification using deep learning may be a suitable new screening tool that could improve diagnostic performance and reduce manpower. Methods: For this systematic review, we aimed to identify studies that incorporated the use of deep learning in classifying full-scale DR in retinal fundus images of patients with diabetes. The studies had to provide a DR-grading scale, a human grader as a reference standard and a deep learning performance score. A systematic search on April 5, 2018 through Medline and Embase yielded 304 publications. To identify potentially missed publications, the reference lists of the final included studies were manually screened, yielding no additional publications. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used for risk of bias and applicability assessment. Results: Based on objective selection, we included 11 diagnostic accuracy studies that validated the performance of their deep learning method using either a new group of patients or retrospective datasets. Eight studies reported sensitivity and specificity of 80.28%–100.0% and 84.0%–99.0%, respectively. Two studies report accuracies of 78.7% and 81.0%. One study provides an area under the receiver operating curve (AUC) of 0.955. In addition to diagnostic performance, one study also reported on patient satisfaction, showing that 78% of patients preferred an automated deep learning model over manual human grading. Conclusion: Advantages of implementing deep learning-based algorithms in DR-screening include reduction in manpower, cost of screening and issues relating to intra- and intergrader variability. However, limitations which may hinder such an implementation particularly revolves around ethical concerns regarding lack of trust in the diagnostic accuracy of computers. Considering both strengths and limitations as well as the high performance of deep learning-based algorithms, automated DR classification using deep learning could be feasible in a real-world screening scenario.

    关键词： Diagnostic performance,Diabetic retinopathy,Automated classification,Neural network,Deep learning,Screening

    更新于2025-09-23 15:21:01