Abstract:In order to better monitor the health state of power lithium batteries. A lithium battery health state prediction method based on Improved Bidirectional Temporal Convolutional Network, Long Short Term Memory Network and Attention Mechanism is proposed. The hyperparameters of the proposed method are optimized using Crested Porcupine Optimizer. Tests were conducted on the University of Maryland lithium battery charge/discharge dataset to extract capacity-related health features, and the health features with higher correlation were screened by Pearson correlation coefficient as inputs to the neural network algorithm. The Root Mean Squard Error of the proposed method is no more than 0.020, the Mean Absolute Error is no more than 0.017, and the R-Square is above 0.995 for all battery health state predictions. Higher accuracy can be achieved in lithium battery health state prediction.

Abstract:As the internal electrode materials in multilayer ceramic capacitors (MLCCs) are increasingly being replaced by base metals such as nickel instead of precious metals, the sintering process must be conducted in a reducing atmosphere. This work investigates the properties of Mn-doped BaTiO3-based ceramics sintered under different reducing atmospheres, exploring the effect of varying H?/N? ratios on dielectric performance and reliability. BaTiO3-based ceramics were prepared using the solid-phase method under different reducing atmospheres. X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) were employed to characterize the microstructure and properties of the samples. The study found that the polarization mechanism significantly affects the dielectric performance and reliability of BaTiO3-based ceramics. Specifically, as the reducing atmosphere intensifies, the polarization mechanism transitions from short-range defect dipole polarization to long-range defect carrier polarization, impacting the dielectric constant, dielectric loss, and insulation resistance. The sample sintered under 1.5% H?/98.5% N? (S2) exhibited the highest dielectric constant, lowest dielectric loss, and best insulation resistance, demonstrating excellent overall performance. Additionally, BaTiO3-based MLCCs with thickness of 0.9 μm and the TCC of X7R were successfully fabricated. This work provides theoretical and technical guidance for improving the dielectric performance and reliability of BME-MLCCs.

Abstract:3D scene reconstruction is a critical research topic in autonomous driving, robotics, and related fields, with extensive applications in navigation mapping, environmental interaction, and virtual/augmented reality tasks. Current deep learning-based reconstruction methods can be primarily categorized into five groups from the perspectives of scene representation and core modeling techniques: cost volume-based depth estimation methods, truncated signed distance function (TSDF)-based voxel approaches, transformer architecture-based large-scale feedforward methods, multilayer perceptron (MLP)-based neural radiance fields (NeRF), and 3D Gaussian Splatting (3DGS). Each category exhibits unique strengths and limitations. The emerging 3DGS method distinguishes itself by explicitly representing scenes through Gaussian functions while achieving rapid scene rendering and novel view synthesis via efficient rasterization operations. Its most significant advantage lies in diverging from NeRF''s MLP-based scene representation paradigm - 3DGS ensures both efficient rendering and interpretable editable scene modeling, thereby paving the way for accurate 3D scene reconstruction. However, 3DGS still faces numerous challenges in practical scene reconstruction applications. Based on this analysis, this paper first provides a concise introduction to 3DGS fundamentals and conducts comparative analysis with the aforementioned four categories. Following a systematic survey of existing 3DGS reconstruction algorithms, we summarize the key challenges addressed by these methods and review current research progress on core technical difficulties through representative case studies. Finally, we prospect potential future research directions worthy of exploration.

Abstract:The study investigates the influence of different raw material particle sizes of barium titanate (BaTiO?, BT) on the dielectric properties of ceramics and aims to optimize their electrical performance through doping modifications. Ceramic samples were prepared using BT powders with particle sizes of 100 nm, 150 nm, 200 nm, and 250 nm via the solid-state ball milling method. Y?O?, Ho?O?, MgO, and SiO? were introduced as dopants to control grain growth and tailor the dielectric properties. The microstructure and dielectric characteristics of the ceramic samples were systematically analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and dielectric measurements. The results revealed that the BT-10 sample exhibited significant dopant diffusion, which hindered the formation of an ideal "core-shell" structure and reduced tetragonality. While BT-10 showed a relatively high dielectric constant, its temperature stability was poor. In contrast, the BT-15, BT-20, and BT-25 samples successfully formed a "core-shell" structure, with BT-25 demonstrating the highest tetragonality. The BT-25 sample exhibited the most favorable electrical performance, including the highest saturation polarization strength (Ps = 11.817 μC/cm2), remanent polarization strength (Pr = 1.465 μC/cm2), and superior dielectric stability under DC bias conditions. These findings indicate that the BT-25 sample offers the most balanced and optimized overall performance. Furthermore, the study highlights the challenges associated with using smaller BT particle sizes in fabricating "core-shell" structured ceramics, particularly the increased specific surface area and higher defect density of BT powders. This research provides valuable theoretical insights and technical guidance for optimizing the dielectric layers of multilayer ceramic capacitors (MLCCs).

Abstract:Hand pose estimation based on RGB images shows crucial application prospects in the fields of dynamic gesture recognition and human-computer interaction. However, existing methods face numerous challenges. For example, the high degree of self-similarity of the hand and the extremely dense distribution of key points make it extremely difficult to achieve high-precision prediction under the condition of low computational cost, which in turn leads to limitations in performance in complex scenarios.In view of this, this paper proposes a two-dimensional (2D) hand pose estimation model based on the YOLOv8 network, namely FAR-HandNet. This model ingeniously integrates the Focused Linear Attention module, the key point alignment strategy, and the regression residual fitting module, effectively enhancing the feature capture ability for small target areas (such as the hand), while reducing the adverse impact of self-similarity on the positioning accuracy of hand key points. It is worth mentioning that the regression residual fitting module uses a flow-based generative model to fit the distribution of key point residuals, which greatly improves the accuracy of the regression model.The experiments in this paper are carried out on the CMU and FreiHAND datasets. The experimental results clearly show that FAR-HandNet has obvious advantages in terms of the number of parameters and computational efficiency, and performs excellently in PCK (Percentage of Correct Keypoints) under different thresholds, showing a significant improvement compared with existing methods. In addition, the inference time of this model is only 32ms. The ablation experiments further confirm the effectiveness of each module, fully verifying the effectiveness and superiority of FAR-HandNet in the hand pose estimation task.

Abstract:The loss of upper limb function brings a lot of inconvenience to the life of amputees. In order to improve the life quality of upper limb amputees, it is necessary to develop a low-cost, lightweight and powerful prosthetic system. In this paper, a three-degree-of-freedom modular light-weight upper limb prosthetic arm and its multi-joint cooperative control system are designed to provide a lightweight, economical, modular and comprehensive prosthetic solution. By using the hollow structure design, the overall weight of the prosthetic arm is significantly reduced (about 2 kg), which is much lower than existing commercial prosthetic products, while ensuring the number of degrees of freedom and effectively reducing manufacturing costs, improving the comfort and suitability of the prosthetic arm. In addition, the multi-joint control system designed in this paper, combined with the precise coordination algorithm, can accurately control each joint to reach a predetermined Angle at the same time, to meet the needs of different degrees of amputees for multi-joint cooperative movement. Through precision and efficiency tests, the results show that the prosthesis performs well in terms of control accuracy, and the motion efficiency can meet most of the needs of daily life.

Abstract:Currently, with the exponential growth of data on the internet, the complexity of big data processing systems has also increased dramatically. To adapt to changes in factors such as cluster resources, datasets, and applications, big data processing systems provide adjustable configuration parameters tailored to different application scenarios. Among these systems, Spark is one of the most popular and contains over 200 configuration parameters for controlling parallelism, I/O behavior, memory settings, and compression. Incorrect configuration of these parameters often leads to severe performance degradation and stability issues. However, both ordinary users and expert administrators face significant challenges in understanding and tuning these settings for optimal performance, resulting in substantial human and time costs. In the tuning process, selecting unreasonable parameter ranges can increase time costs by fivefold, or even worse, cause operational failures in the cluster and terminate system operation—an incalculable loss for large-scale clusters serving customers.

Abstract:Electromagnetic pulse sound source (Boomer) is a commonly used explosion sound source in marine seismic exploration, and the deep-sea application of such explosion sound source needs to solve cavitation suppression problem. In this paper, a deep-sea boomer source based on pressure compensation balance is proposed. A boomer transducer with a maximum working pressure of 20MPa is developed and tested in a high-pressure anechoic tank. Through the analysis of the hydrophone outputs under different energy and pressure levels, it can be seen that an air sac with the initial pressure of 0.5MPa can effectively balance the internal and external pressure of the transducer, solve the problem of cavitation suppression, and realize the excitation of broadband pulse sound waves. The repeatability of the acoustic wave is very good, and the minimum correlation coefficient is to 0.986. With the increase of working pressure from 0.5MPa to 20MPa, the main change in acoustic characteristics is the amplitude attenuation (204.6dB to 194.2 dB) and width compression (182μs to 88μs), and the main frequency (2.3kHz as the center) slightly shifted to high frequency. Compared with the hydrophone output in the process of pressure rising and downing in the high-pressure anechoic tank, it can be seen that the repeatability of the acoustic wave is better. The higher the pressure, the better the waveform consistency, indicating that the boomer transducer based on pressure compensation balance has a more stable performance under high pressure environment.

Abstract:Cancer is a genetically related disease with multiple subtypes, each exhibiting significant differences in genetics, phenotype, and treatment response. Accurate classification of cancer subtypes is critical for personalized treatment, as it helps improve therapeutic outcomes. However, cancer subtype classification methods based on patient gene expression data often struggle to effectively distinguish rare subtypes in the presence of imbalanced samples. To address this issue, a cancer subtype classification method called MFP-VAE (Meta-learning Few-shot Prototype learning VAE) is proposed, focusing on handling datasets with imbalanced samples. This method improves the sampling strategy to ensure balanced consideration of different subtypes in meta-learning tasks. The model employs a variational autoencoder for feature extraction and classifies samples by calculating the distance between the samples and the subtype prototypes. Experimental results show that MFP-VAE outperforms existing methods on two public cancer datasets, significantly improving classification accuracy, especially under imbalanced sample conditions. Furthermore, survival analysis reveals that the distinguished cancer subtypes exhibit significant differences in clinical characteristics, providing meaningful clinical insights.

Abstract:With the rapid development of deep learning technology, autism screening based on neural signals such as Electroencephalography (EEG) is gradually emerging as a novel diagnostic approach. However, due to the complexity of EEG data acquisition, especially for children, insufficient data often poses a challenge. Data augmentation methods are commonly used to address the scarcity of real-world data, with Generative Adversarial Networks (GANs) being a frequently applied technique. However, due to the limited scale and diversity of data, current augmentation methods have yet to achieve optimal classification performance. This study introduces an improved conditional diffusion model to enhance both raw EEG signals and their corresponding functional connectivity temporal graphs. Experimental results demonstrate that this method significantly improves autism classification performance, achieving maximum classification accuracies of 84.38% and 79.01% for resting-state and task-state data, respectively. These findings validate the effectiveness of data augmentation based on the conditional diffusion model in enhancing autism screening outcomes.