- 标题
- 摘要
- 关键词
- 实验方案
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[IEEE 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) - Paris, France (2019.9.1-2019.9.6)] 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) - Realizing Asymmetric Boundary Conditions for Plasmonic THz Wave Generation in HEMTs
摘要： As a key component in the wind turbine system, the power electronic converter and its power semiconductors suffer from complicated power loadings related to environment, and are proven to have high failure rates. Therefore, correct lifetime estimation of wind power converter is crucial for the reliability improvement and also for cost reduction of wind power technology. Unfortunately, the existing lifetime estimation methods for the power electronic converter are not yet suitable in the wind power application, because the comprehensive mission profiles are not well specified and included. Consequently, a relative more advanced approach is proposed in this paper, which is based on the loading and strength analysis of devices and takes into account different time constants of the thermal behaviors in power converter. With the established methods for loading and lifetime estimation for power devices, more detailed information of the lifetime-related performance in wind power converter can be obtained. Some experimental results are also included to validate the thermal behavior of power device under different mission profiles.
关键词： power semiconductor device,lifetime prediction,thermal cycling,wind power,IGBT,mission profiles
更新于2025-09-19 17:13:59
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[IEEE 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Shenzhen, China (2019.11.25-2019.11.27)] 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Research on a Smart LED Lighting Based on Improved Flyback Driver
摘要： In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
关键词： genetic algorithm,forecasting,PCA,echo state network,Time-series,smart grid,electric load prediction,dimensionality reduction
更新于2025-09-19 17:13:59
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[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) - Disorder-Resistant Helical Waveguiding in One Dimension
摘要： In this paper, we proposed a decentralized cooperative lane-changing decision-making framework for connected autonomous vehicles, which is composed of three modules, i.e., state prediction, candidate decision generation, and coordination. In other words, each connected autonomous vehicle makes cooperative lane-changing decision independently. In the state prediction module, we employed existing cooperative car-following models to predict the vehicles’ future state. In the candidate decision generation module, we proposed incentive based model to generate a candidate decision. In the candidate decision coordination module, we proposed an algorithm to avoid candidate lane-changing decision that may lead to a vehicle collision or traf?c deterioration to be ?nal decision. Moreover, the effects of decentralized cooperative lane-changing decision-making framework on traf?c stability, ef?ciency, homogeneity, and safety are investigated in a numerical simulation experiment. Some stability, ef?ciency, homogeneity, and safety indicators are evaluated and show the high potential of our proposed framework in traf?c dynamics.
关键词： candidate decision generation module,decentralized cooperative lane-changing decision-making framework,Connected autonomous vehicles,candidate decision coordination module,state prediction module
更新于2025-09-19 17:13:59
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A Non-Isolated Step-up DC-AC Converter With Reduced Leakage Current for Grid-Connected Photovoltaic Systems
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： Emission measurement,sampling approach,prediction,unintentional electromagnetic radiator,maximum directivity
更新于2025-09-19 17:13:59
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Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells
摘要： Perovskite solar cells (PSCs) have recently received considerable attention due to the high energy conversion efficiency achieved within a few years of their inception. However, a machine learning (ML) approach to guide the development of high-performing PSCs is still lacking. In this paper ML is used to optimize material composition, develop design strategies, and predict the performance of PSCs. The ML models are developed using 333 data points selected from about 2000 peer reviewed publications. These models guide the design of new perovskite materials and the development of high-performing solar cells. Based on ML guidance, new perovskite compositions are experimentally synthesized to test the practicability of the model. The ML model also shows its ability to predict underlying physical phenomena as well as the performance of PSCs. The PSC model matches well with the theoretical prediction by the Shockley and Queisser limit, which is almost impossible for a human to find from an ensemble of data points. Moreover, strategies for developing high-performing PSCs with different bandgaps are also derived from the model. These findings show that ML is very promising not only for predicting the performance, but also for providing a deeper understanding of the physical phenomena associated with the PSCs.
关键词： perovskite solar cells,machine learning,perovskite materials,bandgap prediction
更新于2025-09-19 17:13:59
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[IEEE 2019 7th International Japan-Africa Conference on Electronics, Communications, and Computations, (JAC-ECC) - Alexandria, Egypt (2019.12.15-2019.12.16)] 2019 7th International Japan-Africa Conference on Electronics, Communications, and Computations, (JAC-ECC) - Broadside/Endfire Switched Beam Double Ridge-Gap Waveguide H-plane Horn Antenna
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： Emission measurement,sampling approach,prediction,unintentional electromagnetic radiator,maximum directivity
更新于2025-09-19 17:13:59
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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) - A Linear Relation behind Outstanding Crystalline Silicon Solar Cells
摘要： When and why people change their mobile phones are important issues in mobile communications industry, because it will impact greatly on the marketing strategy and revenue estimation for both mobile operators and manufactures. It is a promising way to take use of big data to analyze and predict the phone changing event. In this paper, based on mobile user big data, ?rst through statistical analysis, we ?nd that three important probability distributions, i.e., power-law, log-normal, and geometric distribution, play an important role in the user behaviors. Second, the relationships between eight selected attributes and phone changing are built, for example, young people have greater intention to change their phones if they are using the phones belonging to the low occupancy phones or feature phones. Third, we veri?ed the performance of four prediction models on phone changing event under three scenarios. Information gain ratio was used to implement attribute selection and then sampling method, cost-sensitive together with standard classi?ers were used to solve imbalanced phone changing event. Experiment results show our proposed enhanced backpropagation neural network in the undersampling scenario can attain better prediction performance.
关键词： imbalance problem,attribute selection,phone changing prediction,machine learning,Mobile big data
更新于2025-09-19 17:13:59
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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) - The Luminescent Down Shifting Effect of Single-Junction GaAs Solar Cell with Perovskite Quantum Dots
摘要： We present a method for quantifying a risk for killer defects at layer level and estimating yield for substrate packages using information from design ?les. To calculate risk ranks and predicted yield, we de?ne a risk distance that is a key parameter extracted from designs using image processing techniques. In order to validate our model, we analyze two different designs, each having multiple layers, and compare with data from baseline lots. It is shown that there is an inverse correlation between risk layer ranks and yield. Estimated yield based on our model is compared with baseline yield for four layers of the second design. The model-to-baseline yield difference is less than 1% for three layers we tested.
关键词： metrology sampling,circuit analysis,assembly,yield estimation,integrated circuit packaging,Yield prediction
更新于2025-09-19 17:13:59
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[IEEE 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) - Bangkok, Thailand (2019.12.11-2019.12.13)] 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) - Impact of Correlation-based Feature Selection on Photovoltaic Power Prediction
摘要： This paper empirically presents the impact of the correlation-based feature selection on the accuracy of the photovoltaic (PV) power prediction, and then selects the weather variables that maximize prediction accuracy. To this end, the experiments are conducted using the weather dataset consisting of eighteen weather variables (i.e., features). For experiments, we first calculate a correlation coefficient of each weather variable by analyzing the correlation between PV power and each weather variable. Then, we create the subsets of weather variables considering the absolute value of correlation coefficient and generate the multiple prediction models using the created subsets. Finally, the accuracy of the generated prediction models is compared with each other to find the most accurate prediction model. The experiment results provide a reference guideline for selecting the weather variables that maximize the accuracy of PV power prediction.
关键词： Correlation coefficient,photovoltaics power prediction,weather variables,feature selection,machine learning
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW) - London, United Kingdom (2019.6.18-2019.6.21)] 2019 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW) - Impacts of Coupling Plates on Single-Switch Capacitive-Coupled WPT Systems
摘要： In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
关键词： linear prediction,Brain-computer interface,channel selection,feature extraction,orthogonal transform
更新于2025-09-19 17:13:59