Accurate power load forecasting is of great significance to ensure the safety, stability, and economic operation of the power system. In particular, short-term power load forecasting is the basis for ...grid planning and decision making. In recent years, machine learning algorithms have been widely used for short-term power load forecasting. Specifically, long short-term memory (LSTM) and gated recurrent unit (GRU) are tailored to time series data. In this study, a multi-layer bidirectional recurrent neural network model based on LSTM and GRU is proposed to forecast short-term power load and is validated on two data sets. The experimental result shows that the proposed method is superior to the competition winner in the precision of forecasting on the European Intelligent Technology Network competition data. On power company data in Chongqing, considering the differences of the seasonal load, the hourly peak load of different types of load data is used for experiments. The authors separately forecast the seasonal load and compare it with LSTM, support vector regression and back propagation models. The results of the comparison show the priority of the proposed method in terms of forecasting accuracy as compared to the adopted models.
Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain's intentions. Convolutional ...Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What's more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG signals' advanced temporal and spatial features. Additionally, we design an online recognition system, which contributes to the further development of the BCI system.
This paper proposes a robust finite-time control scheme for the high-precision tracking problem of (FJRs) with various types of unpredictable disturbances. Specifically, based on a flatness dynamic ...model, a finite-time disturbance observer (FTDO) with only link-side position measurements is firstly developed to estimate the lumped unknown time-varying disturbance and unmeasurable states. Then, through the information of the states and disturbances provided by the FTDO, a robust output feedback controller is constructed, which can accomplish the tasks of disturbance suppression and trajectory tracking in finite time. Moreover, a rigorous stability analysis of the closed-loop system based on a finite-time bounded (FTB) function is conducted. Finally, the simulation results validate the feasibility and superiority of the proposed control scheme against other existing control results.
Electroencephalography (EEG) and surface electromyography (sEMG) have been widely used in the rehabilitation training of motor function. However, EEG signals have poor user adaptability and low ...classification accuracy in practical applications, and sEMG signals are susceptible to abnormalities such as muscle fatigue and weakness, resulting in reduced stability. To improve the accuracy and stability of interactive training recognition systems, we propose a novel approach called the Attention Mechanism-based Multi-Scale Parallel Convolutional Network (AM-PCNet) for recognizing and decoding fused EEG and sEMG signals. Firstly, we design an experimental scheme for the synchronous collection of EEG and sEMG signals and propose an ERP-WTC analysis method for channel screening of EEG signals. Then, the AM-PCNet network is designed to extract the time-domain, frequency-domain, and mixed-domain information of the EEG and sEMG fusion spectrogram images, and the attention mechanism is introduced to extract more fine-grained multi-scale feature information of the EEG and sEMG signals. Experiments on datasets obtained in the laboratory have shown that the average accuracy of EEG and sEMG fusion decoding is 96.62%. The accuracy is significantly improved compared with the classification performance of single-mode signals. When the muscle fatigue level reaches 50% and 90%, the accuracy is 92.84% and 85.29%, respectively. This study indicates that using this model to fuse EEG and sEMG signals can improve the accuracy and stability of hand rehabilitation training for patients.
A robust disturbance rejection control scheme is addressed for the trajectory tracking problem of a flexible-joint robot (FJR). The system is always severely affected by various types of unknown ...disturbances including model errors, couplings, changing working environments as well as unmodeled dynamics. These disturbances on the link and actuator side will deteriorate the control performance of FJR. By considering all the disturbances as an unknown lumped time-varying disturbance, a flatness description of FJR is developed. Then, a new output feedback controller is constructed through the estimates of unmeasurable states and unknown lumped disturbance provided by a generalized proportional integral observer (GPIO). The stability of the closed-loop system with the driven of the proposed control scheme is guaranteed under some mild assumptions. Compared with the conventional linear active disturbance rejection control (LADRC) scheme, test results are presented to demonstrate the feasibility and efficacy of the proposed control approach.
The importance of conducting potential analysis of load data and ensuring the effectiveness of feature selection cannot be overstated when it comes to enhancing the accuracy of short-term power load ...forecasting. Bisecting K-Means Algorithm is adopted for cluster analysis of the load data, the similarity data is categorized into the same cluster, and then the load data is decomposed into several Intrinsic Mode Functions (IMFs) by Ensemble Empirical Mode Decomposition (EEMD) in this study. Then the candidate features are selected by calculating Pearson correlation coefficient, and finally the forecasting input is constructed. A hybrid neural network forecasting model based on Deep Belief Network (DBN) and Bidirectional Recurrent Neural Network (Bi-RNN) is proposed. The method adopts unsupervised pre-training and supervised adjustment training methods and is verified on two different datasets. Compared with the forecasting results of other methods, it shows that the method can effectively improve the accuracy of load forecasting.
Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image ...fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.
The prediction of silicon content in hot metal has been a major study subject as one of the most important means for the monitoring state in ferrous metallurgy industry. A prediction model of silicon ...content is established based on the support vector regression (SVR) whose optimal parameters are selected by chaos particle swarm optimization. The data of the model are collected from No. 3 BF in Panzhihua Iron and Steel Group Co. of China. The results show that the proposed prediction model has better prediction results than neural network trained by chaos particle swarm optimization and least squares support vector regression, the percentage of samples whose absolute prediction errors are less than 0.03 when predicting silicon content by the proposed model is higher than 90%, it indicates that the prediction precision can meet the requirement of practical production.
Flexible strain sensors, when considering high sensitivity and a large strain range, have become a key requirement for current robotic applications. However, it is still a thorny issue to take both ...factors into consideration at the same time. Here, we report a sandwich-structured strain sensor based on Fe nanowires (Fe NWs) that has a high GF (37–53) while taking into account a large strain range (15–57.5%), low hysteresis (2.45%), stability, and low cost with an areal density of Fe NWs of 4.4 mg/cm2. Additionally, the relationship between the contact point of the conductive network, the output resistance, and the areal density of the sensing unit is analyzed. Microscopically, the contact points of the conductive network directly affect the sensor output resistance distribution, thereby affecting the gauge factor (GF) of the sensor. Macroscopically, the areal density and the output resistivity of the strain sensor have the opposite percolation theory, which affects its linearity performance. At the same time, there is a positive correlation between the areal density and the contact point: when the stretching amount is constant, it theoretically shows that the areal density affects the GF. When the areal density reaches this percolation threshold range, the sensing performance is the best. This will lay the foundation for rapid applications in wearable robots.
Most existing multi-focus color image fusion methods based on multi-scale decomposition consider three color components separately during fusion, which leads to inherent color structures change, and ...causes tonal distortion and blur in the fusion results. In order to address these problems, a novel fusion algorithm based on the quaternion multi-scale singular value decomposition (QMSVD) is proposed in this paper. First, the multi-focus color images, which represented by quaternion, to be fused is decomposed by multichannel QMSVD, and the low-frequency sub-image represented by one channel and high-frequency sub-image represented by multiple channels are obtained. Second, the activity level and matching level are exploited in the focus decision mapping of the low-frequency sub-image fusion, with the former calculated by using local window energy and the latter measured by the color difference between color pixels expressed by a quaternion. Third, the fusion results of low-frequency coefficients are incorporated into the fusion of high-frequency sub-images, and a local contrast fusion rule based on the integration of high-frequency and low-frequency regions is proposed. Finally, the fused images are reconstructed employing inverse transform of the QMSVD. Simulation results show that image fusion using this method achieves great overall visual effects, with high resolution images, rich colors, and low information loss.
PDF
