- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Cross-predicting the dynamics of an optically injected single-mode semiconductor laser using reservoir computing
摘要： In real-world dynamical systems, technical limitations may prevent complete access to their dynamical variables. Such a lack of information may cause significant problems, especially when monitoring or controlling the dynamics of the system is required or when decisions need to be taken based on the dynamical state of the system. Cross-predicting the missing data is, therefore, of considerable interest. Here, we use a machine learning algorithm based on reservoir computing to perform cross-prediction of unknown variables of a chaotic dynamical laser system. In particular, we chose a realistic model of an optically injected single-mode semiconductor laser. While the intensity of the laser can often be acquired easily, measuring the phase of the electric field and the carriers in real time, although possible, requires a more demanding experimental scheme. We demonstrate that the dynamics of two of the three dynamical variables describing the state of the laser can be reconstructed accurately from the knowledge of only one variable, if our algorithm has been trained beforehand with all three variables for a limited period of time. We analyze the accuracy of the method depending on the parameters of the laser system and the reservoir. Finally, we test the robustness of the cross-prediction method when adding noise to the time series. The suggested reservoir computing state observer might be used in many applications, including reconstructing time series, recovering lost time series data and testing data encryption security in cryptography based on chaotic synchronization of lasers.
关键词： reservoir computing,chaotic dynamics,cross-prediction,machine learning,semiconductor laser
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Chengdu, China (2019.5.21-2019.5.24)] 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Research on Predicting the Short-term Output of Photovoltaic (PV) Based on Extreme Learning Machine Model and Improved Similar Day
摘要： In this paper, an extreme learning machine model based on the improved similar day to predict the short-term output of photovoltaic is presented. The Pearson correlation coefficient is used to analyze the influence of various meteorological factors on the output of photovoltaic power generation, so as to find out the meteorological factors that have a greater impact on photovoltaic output. And then the prediction model is to be established with combining the fuzzy clustering method by improving the similar day selection method. The multi-day photovoltaic output data with the highest correlation about the day to be tested is used to train the extreme learning machine neural network which is then used to predict the PV output of the day to be measured. Finally, the experimental results show that the proposed method has higher prediction accuracy and shorter calculation time than the traditional prediction method, and what's more, the algorithm is simple and the prediction cost is low. It has a wide application value and research space.
关键词： extreme learning machine,similar day,PV output prediction
更新于2025-09-16 10:30:52
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Degradation prediction of a γ-ray radiation dosimeter using InGaP solar cells in a primary containment vessel of the Fukushima Daiichi Nuclear Power Station
摘要： Indium gallium phosphide (InGaP) solar cell with a superior high-radiation resistance is expected to be a powerful candidate for a dosimeter under a high-radiation dose rate environment. In this study, in order to predict the lifetime as the dosimeter using the InGaP solar cell, we clarify the effect of minority-carrier diffusion length (L) on a radiation-induced current as a dose signal in the InGaP solar cell by irradiation tests and empirical calculations. In the irradiation tests, the short-circuit current density (Jsc) as a function of the γ-ray dose rate is measured to estimate the L for the InGaP solar cell by irradiation tests. The operational lifetime as a detector using the InGaP solar cell under various dose rates is estimated by using the empirical calculations based on the relation between the L and absorbed dose. The results suggest that the dosimeter using InGaP solar cell is able to be used during more than 10 h in the primary containment vessel of the Fukushima Daiichi Nuclear Power Plant and it has a high potential of being a radiation-resistant dosimeter that would contribute to the decommissioning.
关键词： solar cell,operation lifetime,decommissioning,radiation-induced current,minority-carrier diffusion length,Dosimetry,InGaP compound semiconductor,degradation prediction
更新于2025-09-16 10:30:52
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Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model
摘要： Photovoltaic power generation is greatly affected by weather conditions while the photovoltaic power has a certain negative impact on the power grid. The power sector takes certain measures to abandon photovoltaic power generation, thus limiting the development of clean energy power generation. This study is to propose an accurate short-term photovoltaic power prediction method. A new short-term photovoltaic power output prediction model is proposed Based on extreme learning machine and intelligent optimizer. Firstly, the input of the model is determined by correlation coef?cient method. Then the chicken swarm optimizer is improved to strengthen the convergence. Secondly, the improved chicken swarm optimizer is used to optimize the weights and the extreme learning machine thresholds to improve the prediction effect. Finally, the improved chicken swarm optimizer extreme learning machine model is used to predict the photovoltaic power under different weather conditions. The testing results show that the average mean absolute percentage error and root mean square error of improved chicken swarm optimizer - extreme learning machine model are 5.54% and 3.08%. The proposed method is of great signi?cance for the economic dispatch of power systems and the development of clean energy.
关键词： Extreme learning machine,Model-driven method,Photovoltaic power generation,Intelligent optimizer,Power prediction
更新于2025-09-16 10:30:52
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Machine learning based temperature prediction of poly( <i>N</i> -isopropylacrylamide)-capped plasmonic nanoparticle solutions
摘要： The temperature-dependent optical properties of gold nanoparticles that are capped with the thermo-sensitive polymer: ‘poly(N-isopropylacrylamide)’ (PNIPAM), have been studied extensively for several years. Also, their suitability to function as nanoscopic thermometers for bio-sensing applications has been suggested numerous times. In an attempt to establish this, many have studied the temperature-dependent optical resonance characteristics of these particles; however, developing a simple mathematical relationship between the optical measurements and the solution temperature remains an open challenge. In this paper, we attempt to systematically address this problem using machine learning techniques to quickly and accurately predict the solution-temperature, based on spectroscopic data. Our emphasis is on establishing a simple and practically useful solution to this problem. Our dataset comprises spectroscopic absorption data from both nanorods and nanobipyramids capped with PNIPAM, measured at discretely varied and pre-set temperature states. Specific regions of the spectroscopic data are selected as features for prediction using random forest (RF), gradient boosting (GB) and adaptive boosting (AB) regression techniques. Our prediction results indicate that RF and GB techniques can be used successfully to predict solution temperatures instantly to within 1 1C of accuracy.
关键词： PNIPAM,spectroscopic data,temperature prediction,adaptive boosting,machine learning,random forest,gradient boosting,gold nanoparticles
更新于2025-09-16 10:30:52
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Evaluation of Photovoltaic Power Generation by using Deep Learning in Solar Panels Installed in Buildings
摘要： Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed methodology can be applied to evaluate photovoltaic (PV) power generation panels installed on building rooftops in Southern Taiwan. In the first phase, this study selected panels of the BP3 series, including BP350, BP365, BP380, and BP3125, to assess their PV output efficiency. BP Solar is a manufacturer and installer of photovoltaic solar cells. This study first derived ideal PV power generation and then determined the suitable tilt angle for the PV panels leading to direct sunlight that could be acquired to increase power output by panels installed on building rooftops. The potential annual power outputs for these solar panels were calculated. Climate data of 2016 were used to estimate the annual solar power output of the BP3 series per unit area. The results indicated that BP380 was the most efficient model for power generation (183.5 KWh/m2-y), followed by BP3125 (182.2 KWh/m2-y); by contrast, BP350 was the least efficient (164.2 KWh/m2-y). In the second phase, to simulate meteorological uncertainty during hourly PV power generation, a surface solar radiation prediction model was developed. This study used a deep learning–based deep neural network (DNN) for predicting hourly irradiation. The simulation results of the DNN were compared with those of a backpropagation neural network (BPN) and a linear regression (LR) model. In the final phase, the panel of module BP3125 was used as an example and demonstrated the hourly PV power output prediction at different lead times on a solar panel. The results demonstrated that the proposed method is useful for evaluating the power generation efficiency of the solar panels.
关键词： solar irradiation,deep learning,photovoltaic solar energy,prediction
更新于2025-09-16 10:30:52
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Single-phase photovoltaic grid-connected inverter for predictive control method with power feed-forward
摘要： In the traditional photovoltaic grid-connected inverter control, there are problems such as slow system response speed, low stability and grid-connected current distortion. In this regard, a novel current prediction control method for grid-connected inverter with power feed-forward is proposed. The method consists of DC voltage outer loop control, current predictive control, power feed-forward and grid voltage feed-forward. The characteristic is that the current prediction control algorithm derived from the state equation of the grid-connected inverter effectively suppresses the influence of DC voltage fluctuation on the grid-connected current and enhances the robustness of the system; the introduction of power feed-forward accelerates the dynamic response of the system; The introduction of grid voltage feed-forward reduces voltage distortion or current distortion. And the traditional PI control module is not needed, the structure is simple, and the circuit is easy to implement. Matlab/Simulink simulation model and experimental results prove the effectiveness of the proposed control method, which is more practical than the traditional double loop control method.
关键词： predictive control,power feed-forward,photovoltaic grid-connected inverter,grid voltage feed-forward,current prediction control
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Room temperature photoluminescence analysis of alkali treated single-stage thin Cu(In,Ga)Se <sub/>2</sub> absorber layers
摘要： Measurement procedures for determining the radiated disturbances from electronic equipment are described in several IEC/CISPR standards and are well probed for frequencies up to 1 GHz. Above that frequency, radiation pattern of EUTs evolve complex forms so that the direction and magnitude of the maximum directivity is not known by design. Hence, standardized sampling approaches might underestimate the “true” maximum of the radiated emission. In this paper, an extension of these measurement procedures is proposed. The method uses a stochastic approach for estimating the maximum directivity based on the electrical size of the EUT. This is combined with a total radiated power measurement for a reduced sampling procedure to predict the maximum free-space, far-zone electric field. For validation purposes, an extensive 3-D scan of the radiation pattern of a generic EUT is performed. Different subsampling approaches are then investigated while the new prediction method is applied. It can be shown that the accuracy of the measurement procedures can be increased due to the proposed method.
关键词： prediction,sampling approach,unintentional electromagnetic radiator,Emission measurement,maximum directivity
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Geometry Models for the Calculation of Land Usage of PV Systems
摘要： To facilitate software quality assurance, defect prediction metrics, such as source code metrics, change churns, and the number of previous defects, have been actively studied. Despite the common understanding that developer behavioral interaction patterns can affect software quality, these widely used defect prediction metrics do not consider developer behavior. We therefore propose micro interaction metrics (MIMs), which are metrics that leverage developer interaction information. The developer interactions, such as file editing and browsing events in task sessions, are captured and stored as information by Mylyn, an Eclipse plug-in. Our experimental evaluation demonstrates that MIMs significantly improve overall defect prediction accuracy when combined with existing software measures, perform well in a cost-effective manner, and provide intuitive feedback that enables developers to recognize their own inefficient behaviors during software development.
关键词： software metrics,Defect prediction,developer interaction,software quality,Mylyn
更新于2025-09-16 10:30:52
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[IEEE 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC) - Fukuoka, Japan (2019.7.7-2019.7.11)] 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC) - How to Establish a Sustainable Ecosystem for Photonic Integrated Circuits? What are Major Hurdles to Overcome?
摘要： Short-term traf?c prediction plays a critical role in many important applications of intelligent transportation systems such as traf?c congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in traf?c data. In this paper, we present a novel short-term traf?c ?ow prediction approach based on dynamic tensor completion (DTC), in which the traf?c data are represented as a dynamic tensor pattern, which is able capture more information of traf?c ?ow than traditional methods, namely, temporal variabilities, spatial characteristics, and multimode periodicity. A DTC algorithm is designed to use the multimode information to forecast traf?c ?ow with a low-rank constraint. The proposed method is evaluated on real-world data sets and compared with other state-of-the-art methods, and the ef?cacy of the proposed approach is validated on the experiments of traf?c ?ow prediction, particularly when dealing with incomplete traf?c data.
关键词： missing data,dynamic tensor completion,Short-term traf?c ?ow prediction,multi-mode information
更新于2025-09-16 10:30:52