Negative Emotion Recognition Algorithm of Network Catchwords Based on Language Feature Dimension

The traditional negative emotion recognition algorithm has a limited language feature dimension, which leads to the inaccuracy of negative emotion recognition. In order to improve the identification and analysis of emotion in network buzzwords, the back propagation of error (BP) and the restricted Boltzmann machine (RBM) algorithms are adopted to effectively solve the problem of insufficient data for emotion analysis in different contexts. First, a method is proposed to identify negative emotions, and a deep neural network (DNN) model is constructed. Then, experiments were carried out, which used manually labeled data sets and divided them into different emotion categories, and which compared the BP algorithm, Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) for negative emotion recognition of online buzzwords. The experimental results show that the DNN model performs well in the recognition of anger, sadness, fear and disgust, with the accuracy reaching 93.56%, 93.58%, 89.84% and 88.53% respectively, which is obviously superior to the other three methods. The designed DNN model has a potential application prospect in the negative emotion recognition of online buzzwords, which can be further popularized in the future

Impedance interaction modeling and analysis for bidirectional cascaded converters

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.For the cascaded converter system, the output impedance of source converter interacts with the input impedance of load converter, and the interaction may cause the system instability. In bidirectional applications, when the power flow is reversed, the impedance interaction also varies, which brings more uncertainty to the system stability. An investigation is performed here for showing that the forward and reverse interactions are prominently different in terms of dynamics and stability even though the cascaded converter control remains unchanged. An important guideline has been drawn for the control of the cascaded converter. That is when voltage mode converter working as the load converter; the constant power mode converter as the source converter, the system is more stable. The concluded findings have been verified by simulation and experimental results

Impedance coordinative control for cascaded converter in bidirectional application

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Two stage cascaded converters are widely used in bidirectional applications, but the negative impedance may cause system instability. Actually the impedance interaction is much different between forward power flow and reversed power flow, which will introduce more uncertainty to the system stability. This paper proposes a control method for the constant power controlled converter in cascaded system, and consequently it can change the negative impedance of constant power converter into resistive impedance, which will improve the cascaded system stability, as well as merge the impedance difference between forward and reversed power flow. This paper addresses the analysis with the topology of cascaded dual-active-bridge converter (DAB) with inverter, and the proposed control method can also be implemented in unidirectional applications and other general cascaded converter system. The effectiveness has been validated by both simulation and experimental results

Active Power and DC Voltage Coordinative Control for Cascaded DC–AC Converter With Bidirectional Power Application

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other worksTwo stage-cascaded converters are widely used in dc–ac hybrid systems to achieve the bidirectional power transmission. The topology of dual active bridge cascaded with inverter DABCI) is commonly used in this application. This paper proposes a coordinative control method for DABCI and it is able to reduce the dc-link voltage fluctuation between the DAB and inverter, then reduce the stress on the switching devices, as well as improve the system dynamic performance. In the proposed control method, the DAB and inverter are coordinated to control the dc-link voltage and the power, and this responsibility sharing control can effectively suppress the impact of the power variation on the dc-link voltage, without sacrificing stability. The proposed control method is also effective for DABCI in unidirectional power transmission. The effectiveness of the propose control has been validated by both simulations and experiments

Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data

Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests

DC-Link Voltage Coordinated-Proportional Control for Cascaded Converter with Zero Steady-State Error and Reduced System Type

copyright 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Cascaded converter is formed by connecting two sub-converters together, sharing a common intermediate DC-link voltage. Regulation of this DC-link voltage is frequently realized with a Proportional-Integral (PI) controller, whose high gain at DC helps to force a zero steady-state tracking error. Such precise tracking is however at the expense of increasing the system type, caused by the extra pole at the origin introduced by the PI controller. The overall system may hence be tougher to control. To reduce the system type while preserving precise DC-link voltage tracking, this paper proposes a coordinated control scheme for the cascaded converter, which uses only a proportional DC-link voltage regulator. The resulting converter is thus dynamically faster, and when compared with the conventional PI-controlled converter, it is less affected by impedance interaction between its two sub-converters. The proposed scheme can be used with either unidirectional or bidirectional power flow, and has been verified by simulation and experimental results presented in the paper

GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting

Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93\%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/zyj-111/GCformer