Accurate prediction of remaining useful life (RUL) is of critical significance to the safety and reliability of lithium-ion batteries, which can offer efficient early warning signals for failure. Due to the complicated aging mechanism and realistic noise operation environment, direct predicting RUL with the measured data recorded in practice is challenging. In this work, a novel hybrid approach to forecasting battery future capacity and RUL is proposed by combining the improved variational modal decomposition (VMD), particle filter (PF) and gaussian process regression (GPR). The VMD algorithm is employed to decompose the recorded battery capacity data into an aging trend sequence and several residual sequences, where the number of modal layers is produced by the proposed posterior feedback confidence (PFC) method. The prediction models of PF and GPR algorithm are then respectively established to predict the aging trend sequence and residual sequences. Future capacity and RUL prediction experiments for battery pack and battery cells are performed to verify the effectiveness of the proposed hybrid approach, and the compared experiment results demonstrate that the proposed approach offers wide generality and reduced errors.

Image encryption is an effective way to protect image data. However, existing image encryption algorithms are still unable to strike a good balance between security and efficiency. To overcome the shortcomings of these algorithms, an image encryption algorithm based on plane-level image filtering and discrete logarithmic transformation (IEA-IF-DLT) is proposed. By utilizing the hash value more rationally, our proposed IEA-IF-DLT avoids the overhead caused by repeated generations of chaotic sequences and further improves the encryption efficiency through plane-level and three-dimensional (3D) encryption operations. Aiming at the problem that common modular addition and XOR operations are subject to differential attacks, IEA-IF-DLT additionally includes discrete logarithmic transformation to boost security. In IEA-IF-DLT, the plain image is first transformed into a 3D image, and then three rounds of plane-level permutation, plane-level pixel filtering, and 3D chaotic image superposition are performed. Next, after a discrete logarithmic transformation, a random pixel swapping is conducted to obtain the cipher image. To demonstrate the superiority of IEA-IF-DLT, we compared it with some state-of-the-art algorithms. The test and analysis results show that IEA-IF-DLT not only has better security performance, but also exhibits significant efficiency advantages.

In this article, defects in the production process of silicon photovoltaic (Si-PV) cells are urgently needed to be detected due to their serious impact on the normal generation of PV system. In view of the shortcomings, such as low-defect efficiency, few detection data, and high detection error rate in the existing industrial production line, the main research purpose of this article is to complete an intelligent classification method for efficient and innovative defect detection for Si-PV cells and modules. The purpose is to improve the detection efficiency of Si-PV cell, to ensure the safety and reliability of Si-PV cell production process, to achieve large number of Si-PV cell defects detection and classification. First, the eddy current thermography system of Si-PV cells is established. Second, principal component analysis, independent component analysis, and nonnegative matrix factorization algorithms are compared for thermography sequences processing. Third, LeNet-5, VGG-16, and GoogleNet models are compared for Si-PV cell defects classification. Finally, the results show that the proposed method have successful application in Si-PV cell defects detection and classification.

Pilot contamination resulting from pilot reuse seriously restricts the communication performance of the cell-free massive multiple-input multiple-output (MIMO) networks. To deal with this problem, an efficient pilot assignment scheme using a weighted graphic framework is proposed in this correspondence paper. Specifically, a novel metric is first introduced for capturing the severity of potential mutual pilot contamination in cell-free topology. On this basis, the weighted pilot contamination graph is constructed to depict the dynamic interference relationship corresponding to the network. Then, the pilot assignment optimization is mapped to the Max k-Cut problem, and a heuristic algorithm is exploited to realize pilot decontamination. Numerical simulation results reveal the superior performance of the proposed scheme, which is capable of achieving a significant improvement in throughput with low complexity.

With the increasing popularization of a three-level neutral point clamped (NPC) inverter, realizing an accurate and fast fault diagnosis is also required. However, most methods are either not accurate enough or require a long period of time to collect signals as input. To compensate for these drawbacks, a novel 1-D convolution neural network (CNN) with the improved stochastic gradient optimization method is proposed in this article. First, the input data are segmented to ensure that the fault diagnosis can be completed if the current of a period is collected at any time in a period. Then, the feature of the waveform is extracted by the proposed network. In the meantime, the improved Adamod (IAdamod) method is implemented to better adaptively adjust the learning rate during the training process. Specifically, the learning rate is bigger at the beginning and smaller at the end. Finally, the classifier softmax is used for classification. The experimental results demonstrate the effectiveness of the proposed method in detecting and locating the faulty devices, which has shown that it perfectly realizes the fault diagnosis with 100% accuracy and can be diagnosed online with the time of less than 0.25 ms, far less than one cycle. The proposed method is also compared the traditional methods, and shows a better performance both in speed and accuracy, the loss can be less than 0.5 in iteration 220 and achieve zero in 1000.