Reliability Evaluation of Base-Metal-Electrode (BME) Multilayer Ceramic Capacitors for Space Applications

This paper reports reliability evaluation of BME ceramic capacitors for possible high reliability space-level applications. The study is focused on the construction and microstructure of BME capacitors and their impacts on the capacitor life reliability. First, the examinations of the construction and microstructure of commercial-off-the-shelf (COTS) BME capacitors show great variance in dielectric layer thickness, even among BME capacitors with the same rated voltage. Compared to PME (precious-metal-electrode) capacitors, BME capacitors exhibit a denser and more uniform microstructure, with an average grain size between 0.3 and approximately 0.5 micrometers, which is much less than that of most PME capacitors. The primary reasons that a BME capacitor can be fabricated with more internal electrode layers and less dielectric layer thickness is that it has a fine-grained microstructure and does not shrink much during ceramic sintering. This results in the BME capacitors a very high volumetric efficiency. The reliability of BME and PME capacitors was investigated using highly accelerated life testing (HALT) and regular life testing as per MIL-PRF-123. Most BME capacitors were found to fail with an early dielectric wearout, followed by a rapid wearout failure mode during the HALT test. When most of the early wearout failures were removed, BME capacitors exhibited a minimum mean time-to-failure of more than 10(exp 5) years. Dielectric thickness was found to be a critical parameter for the reliability of BME capacitors. The number of stacked grains in a dielectric layer appears to play a significant role in determining BME capacitor reliability. Although dielectric layer thickness varies for a given rated voltage in BME capacitors, the number of stacked grains is relatively consistent, typically between 10 and 20. This may suggest that the number of grains per dielectric layer is more critical than the thickness itself for determining the rated voltage and the life expectancy of the BME capacitor. Since BME capacitors have a much smaller grain size than PME capacitors, it is reasonable to predict that BME capacitors with thinner dielectric layers may have an equivalent life expectancy to that of PME capacitors with thicker dielectric layers

Physical and Electrical Characterization of Aluminum Polymer Capacitors

Conductive polymer aluminum capacitor (PA capacitor) is an evolution of traditional wet electrolyte aluminum capacitors by replacing liquid electrolyte with a solid, highly conductive polymer. On the other hand, the cathode construction in polymer aluminum capacitors with coating of carbon and silver epoxy for terminal connection is more like a combination of the technique that solid tantalum capacitor utilizes. This evolution and combination result in the development of several competing capacitor construction technologies in manufacturing polymer aluminum capacitors. The driving force of this research on characterization of polymer aluminum capacitors is the rapid progress in IC technology. With the microprocessor speeds exceeding a gigahertz and CPU current demands of 80 amps and more, the demand for capacitors with higher peak current and faster repetition rates bring conducting polymer capacitors to the center o( focus. This is because this type of capacitors has been known for its ultra-low ESR and high capacitance. Polymer aluminum capacitors from several manufacturers with various combinations of capacitance, rated voltage, and ESR values were obtained and tested. The construction analysis of the capacitors revealed three different constructions: conventional rolled foil, the multilayer stacking V-shape, and a dual-layer sandwich structure. The capacitor structure and its impact on the electrical characteristics has been revealed and evaluated. A destructive test with massive current over stress to fail the polymer aluminum capacitors reveals that all polymer aluminum capacitors failed in a benign mode without ignition, combustion, or any other catastrophic failures. The extraordinary low ESR (as low as 3 mOMEGA), superior frequency independence reported for polymer aluminum capacitors have been confirmed. For the applications of polymer aluminum capacitors in space programs, a thermal vacuum cycle test was performed. The results, as expected, show no impact on the electrical characteristics of the capacitors. The breakdown voltage of polymer capacitors has been evaluated using a steady step surge test. Initial results show the uniform distribution in the breakdown voltage for polymer aluminum capacitors. Polymer aluminum capacitors with a combination of very high capacitance, extraordinary low ESR, excellent frequency stability, and non-ignite benign failure mode make it a niche fit in space applications for both today and future. Polymer capacitors are apparently also the best substitutes of the currently used MnO2-based tantalum capacitors in the low voltage range. However, some critical aspects are still to be addressed in the next phase of the investigation for PA capacitors. These include the long term reliability test of 125 C dry life and 85 C/85%RH humidity, the failure mechanism and de-rating, the radiation tolerance, and the high temperature performance. All of the above requires the continuous NEPP funding and support

Stress-dependent magnetic flux leakage: finite element modelling simulations versus experiments

Assessing the effect of defect induced stresses on magnetic flux leakage (MFL) signals is a complicated task due to nonlinear magnetomechanical coupling. To facilitate the analysis, a multi-physics finite elemental simulation model is proposed based on magnetomechanical theory. The model works by quasi-statically computing the stress distribution in the specimen, which is then inherited to solve the nonlinear magnetic problem dynamically. The converged solution allows identification and extraction of the MFL signal induced by the defect along the sensor scanning line. Experiments are conducted on an AISI 1045 steel specimen, i.e. a dog-bone shaped rod with a cylindrical square-notch defect. The experiments confirm the validity of the proposed model that predicted a linear dependency of the peak-to-peak amplitude of the normalized MFL signal on applied stress. Besides identifying the effect of stress on the induced MFL signal, the proposed model is also suitable for solving the inverse problem of sizing the defects when stress is involved

A Study of Interpolation Compensation Based Large Step Simulation of PWM Converters

Real-time online simulation based on a real-time workshop (RTW) plays a vital role in the study and application of power electronics. However, restricted by the performance of equipment and hardware, the simulators so far available in the market mainly support simulation steps over 50 μs, while large step simulation may result in the action delay of pulse-width modulating (PWM), numerical oscillation and high-level non-characteristic harmonic distortion. In view of these problems, this paper puts forward a modeling method based on integral prediction and interpolation compensation. First of all, prediction is performed one step in advance by the implicit trapezoidal method to find out the accurate time when the triangle carrier wave intersects with the modulation wave. At the same time, a mathematic model is built for the insulated gate bipolar transistor (IGBT) to output equivalent voltage waveform according to the principle of area equivalent. Next, in MATLAB/Simulink, offline simulation is performed with the three-phase AC-DC-AC converter as the subject. By comparing the control accuracy, the content of harmonic wave and the simulation time, the simulation effects of the 50 μs fixed-step interpolation prediction model are the same as that for a 5 μs fixed-step standard model. Finally, the effectiveness and high efficiency of this algorithm are verified on a real-time simulator, marking the application of offline models on real-time simulators

ResNet-AE for Radar Signal Anomaly Detection

Radar signal anomaly detection is an effective method to detect potential threat targets. Given the low Accuracy of the traditional AE model and the complex network of GAN, an anomaly detection method based on ResNet-AE is proposed. In this method, CNN is used to extract features and learn the potential distribution law of data. LSTM is used to discover the time dependence of data. ResNet is used to alleviate the problem of gradient loss and improve the efficiency of the deep network. Firstly, the signal subsequence is extracted according to the pulse’s rising edge and falling edge. Then, the normal radar signal data are used for model training, and the mean square error distance is used to calculate the error between the reconstructed data and the original data. Finally, the adaptive threshold is used to determine the anomaly. Experimental results show that the recognition Accuracy of this method can reach more than 85%. Compared with AE, CNN-AE, LSTM-AE, LSTM-GAN, LSTM-based VAE-GAN, and other models, Accuracy is increased by more than 4%, and it is improved in Precision, Recall, F1-score, and AUC. Moreover, the model has a simple structure, strong stability, and certain universality. It has good performance under different SNRs

Object Detection Based on Adaptive Feature-Aware Method in Optical Remote Sensing Images

Object detection is used widely in remote sensing image interpretation. Although most models used for object detection have achieved high detection accuracy, computational complexity and low detection speeds limit their application in real-time detection tasks. This study developed an adaptive feature-aware method of object detection in remote sensing images based on the single-shot detector architecture called adaptive feature-aware detector (AFADet). Self-attention is used to extract high-level semantic information derived from deep feature maps for spatial localization of objects and the model is improved in localizing objects. The adaptive feature-aware module is used to perform adaptive cross-scale depth fusion of different-scale feature maps to improve the learning ability of the model and reduce the influence of complex backgrounds in remote sensing images. The focal loss is used during training to address the positive and negative sample imbalance problem, reduce the influence of the loss value dominated by easily classified samples, and enhance the stability of model training. Experiments are conducted on three object detection datasets, and the results are compared with those of the classical and recent object detection algorithms. The mean average precision(mAP) values are 66.12%, 95.54%, and 86.44% for three datasets, which suggests that AFADet can detect remote sensing images in real-time with high accuracy and can effectively balance detection accuracy and speed

Multi-Feature Information Complementary Detector: A High-Precision Object Detection Model for Remote Sensing Images

Remote sensing for image object detection has numerous important applications. However, complex backgrounds and large object-scale differences pose considerable challenges in the detection task. To overcome these issues, we proposed a one-stage remote sensing image object detection model: a multi-feature information complementary detector (MFICDet). This detector contains a positive and negative feature guidance module (PNFG) and a global feature information complementary module (GFIC). Specifically, the PNFG is used to refine features that are beneficial for object detection and explore the noisy features in a complex background of abstract features. The proportion of beneficial features in the feature information stream is increased by suppressing noisy features. The GFIC uses pooling to compress the deep abstract features and improve the model’s ability to resist feature displacement and rotation. The pooling operation has the disadvantage of losing detailed feature information; thus, dilated convolution is introduced for feature complementation. Dilated convolution increases the receptive field of the model while maintaining an unchanged spatial resolution. This can improve the ability of the model to recognize long-distance dependent information and establish spatial location relationships between features. The detector proposed also improves the detection performance of objects at different scales in the same image using a dual multi-scale feature fusion strategy. Finally, classification and regression tasks are decoupled in space using a decoupled head. We experimented on the DIOR and NWPU VHR-10 datasets to demonstrate that the newly proposed MFICDet achieves competitive performance compared to current state-of-the-art detectors

Multi-Feature Information Complementary Detector: A High-Precision Object Detection Model for Remote Sensing Images

Remote sensing for image object detection has numerous important applications. However, complex backgrounds and large object-scale differences pose considerable challenges in the detection task. To overcome these issues, we proposed a one-stage remote sensing image object detection model: a multi-feature information complementary detector (MFICDet). This detector contains a positive and negative feature guidance module (PNFG) and a global feature information complementary module (GFIC). Specifically, the PNFG is used to refine features that are beneficial for object detection and explore the noisy features in a complex background of abstract features. The proportion of beneficial features in the feature information stream is increased by suppressing noisy features. The GFIC uses pooling to compress the deep abstract features and improve the model’s ability to resist feature displacement and rotation. The pooling operation has the disadvantage of losing detailed feature information; thus, dilated convolution is introduced for feature complementation. Dilated convolution increases the receptive field of the model while maintaining an unchanged spatial resolution. This can improve the ability of the model to recognize long-distance dependent information and establish spatial location relationships between features. The detector proposed also improves the detection performance of objects at different scales in the same image using a dual multi-scale feature fusion strategy. Finally, classification and regression tasks are decoupled in space using a decoupled head. We experimented on the DIOR and NWPU VHR-10 datasets to demonstrate that the newly proposed MFICDet achieves competitive performance compared to current state-of-the-art detectors

Progress and prospects of data-driven stock price forecasting research

With the rapid development of social economy and the continuous improvement of stock market, stock investment has become more and more widely concerned. Stock price prediction has become an important research direction in the field of cognitive computing in engineering. Data-driven stock price forecasting aims to predict future stock price trends based on historical values and textual data, which can effectively help people reduce risks and improve returns in the process of stock investment. The article reviews the literature on stock price forecasting methods, and classifies stock price forecasting methods from two different perspectives of model and feature. According to different model angles, the existing stock price prediction methods can be divided into statistical analysis methods, traditional machine learning methods and deep learning methods. According to different characteristic angles, the existing stock price prediction methods can be divided into those based on numerical data and those based on text mixed with numerical data. Finally, we summarize the research challenges faced by stock price prediction and provide future research directions