A triboelectric nanogenerator (TENG) is a kinetic energy transducer with small and time-varying internal capacitance, which increases the difficulties of extracting harvested energy. In this paper, an efficient rectifier, hybridizing synchronized electric charge extraction (SECE) and bias-flipping techniques, is proposed. The two techniques alternatively operate at opposite voltage polarities of the TENG. By taking advantage of the varying capacitance, the proposed synchronized extraction and flipping (SEF) rectifier shows significantly improved energy extraction performance. The design is implemented in a 180-nm high-voltage BCD technology, and the results show a 7.4X energy extraction enhancement, 65-V voltage tolerance, and 35-nA quiescent current.

The stringent reliability requirements of electronic packages for safety-critical automotive applications have spurred developments in real-time monitoring of electronic components. A key aspect of these advancements is the availability of physical health sensing elements and failure-predicting algorithms that can be embedded within the integrated circuit. In this paper, 4-wire resistance measurement features are embedded in Quad-flat no-leads (QFN) packages to detect physical damages at the printed circuit board (PCB)-solder interconnect interface. Additionally, several Artificial Intelligent (AI) algorithms are assessed, and the most suitable one is implemented to predict the in-situ resistance changes over time under vibration loads. The time series failure forecast from this algorithm correlates well to the experimentally determined lifetime of solder joints. This method opens avenues for investigating the physics of degradation in board level reliability.

High-power white light-emitting diodes (LEDs) have demonstrated superior efficiency and reliability compared to traditional white light sources . However, ensuring maximum performance for a prolonged lifetime use presents a significant challenge for manufacturers and end users, especially in safety–critical applications. Thus, identifying functional anomalies and predicting the remaining useful lifetime (RUL) is of enormous importance in the operational longevity of the device. To address such challenges, this study proposes a combination of distance-based Mahalanobis distance (MD), entropy generation rate (EGR), and deep learning models for improved anomaly detection and RUL prediction accuracy. Unlike conventional health indicators based on luminous flux data that are challenging to monitor relevant optical performance, the MD and EGR methods are employed to extract in-situ monitored thermal and electrical data as new health indicators. Long short-term memory recurrent neural networks (LSTM-RNN) and convolutional neural networks (CNN) are established to detect anomalies and predict the RUL. The accelerated degradation tests of 3 W high-power white LED have been conducted, and the online and offline collected experimental data are deployed for model development and performance evaluation. The performance of the proposed methods is compared against the Illuminating Engineering Society of North America (IESNA) TM-21 method. The results indicate that LSTM-RNN, when combined with either MD or EGR, can detect anomalies with significantly fewer data (70 %) than is typically required. Furthermore, a significant improvement in prediction accuracy in RUL prediction based on MD and EGR-constructed time series health indicators and employed with the LSTM-RNN model demonstrates the effectiveness of the proposed methods.

Remote center of motion (RCM) has become a rising research direction in the field of robotics. It means that a robot with a rod-shaped end-effector operates through a tiny hole on the surface. Thereinto, a key issue is the deviation of the RCM point's position, apart from the operating accuracy. In addition, considering that an RCM robotic system generally consists of a commercial robot and a specialized rod-shaped end-effector, there exist some errors in the structural information related to the attached end-effector. In this article, a remote center of cyclic motion scheme with a data-driven technology is proposed to control robots, of which end-effectors' structural parameters are inaccurate. Meanwhile, a recurrent neural network is proposed to figure out the scheme's solution, with the relevant theoretical analysis given. Furthermore, simulative and physical experiments on a FRANKA Panda robot with a rod-shaped end-effector are conducted to validate the control scheme's effectiveness distinctly.

Sports intelligence receives constant attention, especially with the development of information technology. Existing tennis-launching machines, a kind of device launching tennis balls from a fixed point, have shortcomings such as limited launching height and low control accuracy, which are lack of considerable flexibility when applied in a practical situation. In this article, a tennis-training robot based on a redundant manipulator cooperated with a tennis-launching structure is presented to realize a high-precision and flexible ball-launching task. In order to construct a control scheme of the robotic system, the physical situation of tennis launching is modeled, and further transformed into a quadratic programming problem. Then, a recurrent neural network (RNN) is built to obtain the optimal solution. Furthermore, simulative experiments based on the CoppeliaSim platform using a FRANKA EMIKA manipulator are carried out to demonstrate the realizability of the designed application scenarios.

The objective of this article is to investigate the thermal-fatigue properties of a commercially available lead-free solder alloy (SnBiAgCu) under the use of different types of potting compounds. Solder alloys with lower silver content are expected to substitute the conventional solder alloys SAC305 (Sn-3.0Ag-0.5Cu). First, the tensile behavior and creep behavior of the SnBiAgCu solder alloys were studied at three temperatures (25, 75, 125). Results show that this type of solder alloys presented higher tensile strength and creep deformation endurance than conventional SAC305 solder alloys. Second, a dynamic mechanical analysis was performed to get the storage modulus and glass transition temperature of three types of potting compounds, which were used in the thermal-fatigue simulation. Third, the experimentally determined material data was used for the averaged strain energy density increment calculated by the finite element method. This simulation approach was selected as damage metrics to evaluate solder interconnect reliability under different combinations of materials. It is found that the application of potting compounds will increase strain energy density significantly when compared with the strain energy density calculated without potting compound, which means that potting compounds will deteriorate the thermal-fatigue reliability of solder interconnects. These accurate data-driven simulation models can in the future form the basis for compact digital twins for predicting useful remaining lifetime.

In this work, we propose a deep reinforcement learning (DRL) algorithm DoubleJump which can master the game of Amazons efficiently. To address the bottleneck problem of sparse supervision signal in DRL, we split the neural network into rule network and skill network, using huge amounts of inexpensive data with game rule information and scarce data containing game skill information to train two networks respectively. Besides, we split the three sub-actions of each action into independent states during Monte-Carlo tree search (MCTS), to improve the probability of finding the global optimal state and reduce the average branching factor. The experimental results show our algorithm reaches about 130:70 in the zero-knowledge learning compared with the AlphaGo Zero algorithm, significantly improves the learning speed, and then alleviates the severe dependence on computing resources.

We aim at examining the current status of advanced control methods in spacecrafts from an engineer’s perspective. Instead of reviewing all the fancy theoretical results in advanced control for aerospace vehicles, the focus is on the advanced control methods that have been practically applied to spacecrafts during flight tests, or have been tested in real time on ground facilities and general testbeds/simulators built with actual flight data. The aim is to provide engineers with all the possible control laws that are readily available rather than those that are tested only in the laboratory at the moment. It turns out that despite the blooming developments of modern control theories, most of them have various limitations, which stop them from being practically applied to spacecrafts. There are a limited number of spacecrafts that are controlled by advanced control methods, among which H 2 / H ∞ robust control is the most popular method to deal with flexible structures, adaptive control is commonly used to deal with model/parameter uncertainty, and the linear quadratic regulator (LQR) is the most frequently used method in case of optimal control. It is hoped that this review paper will enlighten aerospace engineers who hold an open mind about advanced control methods, as well as scholars who are enthusiastic about engineering-oriented problems.