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As an important supplement to traditional airborne synthetic aperture radar (SAR), multirotor unmanned aerial vehicle (UAV) SAR has the advantages of low cost, high flexibility, and strong survival ability. However, due to the complex motion and flight characteristics of the multirotor UAV platform, multirotor UAV SAR faces challenges, including spatially variant low-frequency (LF) errors and severe high-frequency (HF) errors. To deal with these problems, an improved motion compensation approach is proposed for multirotor UAV SAR imaging, which is implemented through two processing steps. 1) The LF errors are eliminated by an improved two-step MoCo approach, which takes into account the spatial variations of both envelope and phase. 2) The HF errors are estimated and corrected by an extended phase gradient autofocus scheme. Different from conventional solutions, our approach can effectively remove the complex motion errors of multirotor UAV SAR step-by-step with high robustness even in high-resolution scenarios. Computer simulation and experimental results verify the effectiveness of our approach.

Nonvolatile computing-in-memory (nvCIM) exhibits high potential for neuromorphic computing involving massive parallel computations and for achieving high energy efficiency. nvCIM is especially suitable for deep neural networks, which are required to perform large amounts of matrix-vector multiplications. However, a comprehensive quantization algorithm has yet to be developed, which overcomes the hardware limitations of resistive random access memory (ReRAM)-based nvCIM, such as the number of I/O, word lines (WLs), and ADC outputs. In this article, we propose a quantization training method for compressing deep models. The method comprises three steps: input and weight quantization, ReRAM convolution (ReConv), and ADC quantization. ADC quantization optimizes the error sampling problem by using the Gumbel-softmax trick. Under a 4-bit ADC of nvCIM, the accuracy only decreases by 0.05% and 1.31% for the MNIST and CIFAR-10, respectively, compared with the corresponding accuracies obtained under an ideal ADC. The experimental results indicate that the proposed method is effective for compensating the hardware limitations of nvCIM macros.

Soft material robots are developing rapidly benefited from their inherent flexibility, adaptability and safety compared to rigid-bodied robots. However, most soft robots are unable to offer high force/strength due to the low rigidity of soft materials that they are composed of. Absence of position feedback is another problem for soft robots. In this research, we aim to address these two challenges in a novel designed soft smart robotic hand. The design of this soft hand is also delicately considered to make it 3D printable, which shortens the design cycle and reduces the fabrication time. This hand consists of five fingers and a palm, all of which can be actuated independently. The finger is designed with two chambers: one air-tight active chamber which can be actuated by compressed air, one passive chamber filled with loosely arranged particles. During bending actuation, particles inside the passive chamber are squeezed by pressurized air, which causes passive jamming. As a result, the stiffness of the finger is strengthened during bending, which endows the hand with larger force output and load-holding capability. Furthermore, position feedback modules made of conductive elastomers are integrated and co-printed with the finger during fabrication. In this research, a hand prototype is manufactured and several experiments regarding to its characteristics and performance are conducted for evaluation. From experimental results, the soft hand achieves maximum holding weight of 1.452kg with particles and 0.8425kg without particles at same actuation pressure of 450kPa.

Streaming media has been largely used by millions of users every day. The number of customers and programs, e.g., TV series, movies, and various shows, are still growing fast. However, the demand for video transcoding for various personal terminal devices results in the shortage of computing resources and the prolongation of processing delay in centralized video transcoding systems. To solve this issue, we propose a blockchain, especially, smart contract based scheme that can achieve decentralized and on-demand crowdsourcing for video transcoding, which remarkably mitigates the transcoding overhead. Specifically, our scheme consists of four key components such as employers, workers, task allocation, and payment. An employer initializes the smart contract, releases the task, and initiates the smart contract. Workers bid for the task, and the successful bidder will obtain the task and execute the task. The task allocation mechanism and the payment mechanism can guarantee the profits of both and encourage both as well. Moreover, the smart contract consists of the bidding contract and the task execution contract. The extensive analysis of our proposed scheme justified the feasibility, security for defending against typical threats, applicability in realistic situations, and portability for most multimedia such as videos and audios.

The joint planning of spare parts and maintenance workers in a multiple echelon inventory system with lateral and cross-echelon transshipment can reduce the total cost of the system. However, partial pooling is not extensively considered in the joint optimization problem of maintenance resources. In this paper, the objective is to determine the optimal inventory of spare parts and the number of maintenance workers to minimize the total cost of the system considering partial pooling. First, a greedy heuristic is used to obtain the initial inventory. Then, the cat swarm optimization algorithm is formulated to produce nearly optimal results, which can solve larger instances with a faster computation time. Furthermore, a maintenance system with 4 local warehouses, 3 central warehouses and 1 plant serving 5 machine groups is analyzed, in which each machine consists of 5 key components that can breakdown independently. The results verify the effectiveness of the proposed optimization approach. Finally, the total cost for different resource provision scenarios, failure rates and system parameters are discussed.

Space solar cell components always suffered from the complex and hostile space environment. The reliability of space solar cell components is important to ensure the mission success of space satellites. However, the current research mostly focuses on solar cells' degradation mechanisms. There is less research based on the performance parameters of space solar cell component's degradation to analyze its reliability. Therefore, a reliability analysis method based on the method of modeling the performance degradation of space solar cell components is proposed. By combining general stress-strength interference model and Gamma process, the reliability model of space solar cell components is established. The accelerated degradation test based on the equivalent particle dose is performed to simulate the radiation influence and verify the proposed method. Compared with the displacement damage dose method, the proposed method can further develop the reliability analysis based on performance degradation analysis. Moreover, the cover glass and interconnection are also taken into account together to form a completed component analysis. Through analyzing the cover glass and interconnection experiment results, the cover glass can affect the component reliability; whereas, the interconnection has little influence at the particle radiation condition. Based on the proposed method, the reliability is easier to calculate with the degradation of output power.

In this paper, linearity improved Doherty power amplifier (DPA) is proposed for linearity improvement by modifying the input capacitor value of peaking amplifier. The non-foster circuits are substituted for the conventional λ/4 transmission line in front of peaking amplifier, which the 90° phase shifter and impedance matching can be performed by the proposed structure. Furthermore, the input capacitor value of peaking amplifier FET can be modified through modifying bias voltage and match resistance of non-foster circuits. Therefore, the AM-PM distortion of peak amplifier is suppressed, and the linearity of total DPA is improved. Compared to a conventional DPA, the third-order inter-modulation distortion of proposed DPA is reduced by 12 dB without cutting efficiency.

A complex human-machine system (CHMS) consists of heterogeneous components with extensive human-machine interactions. CHMSs are typical multistate systems with the ability to adapt to disturbances such as machine failures. These characteristics must be considered comprehensively to accurately evaluate the reliability and performance of a CHMS. However, the existing literature scarcely considers both the reliability and performance simultaneously. In this paper, we propose an agent-based approach to model and evaluate a CHMS. First, a general agent-based modeling framework for a CHMS is generated by analyzing the structure and operations of a CHMS. Then, a dual-clock mechanism is introduced to describe the behaviors of the machine failures and human errors. Two environmental disturbance modeling methods are proposed based on the state transitions of the agent and random events. The methods to model the repair and reconfiguration behaviors are presented based on the contract network. A Monte Carlo-based method is developed to evaluate the reliability and performance of the CHMS simultaneously. Finally, a deck scheduling process for an aircraft carrier is used as a case study to verify the approach. The results show that the reliability and performance of a CHMS can be effectively evaluated.

The GO methodology is an important technique for reliability assessment and evaluation of complex systems, especially for systematic timing and dynamic characteristics. However, the treatment of the shared signal and multi-state issues has increased the calculation burden in GO mode. Multi-valued decision diagrams (MDDs) have the merit of effectively performing qualitative and quantitative analysis via Boolean function forms directly; hence, in this paper, we propose a novel efficient algorithm for the multi-state GO model based on MDD, which combines the advantages of the two techniques. Moreover, the algorithm avoids the complex separate process of handling shared signals in the probability formulas method and the problem of complicated calculation in the state compounding method. In addition, the path and cut sets of the system are obtained through qualitative calculation based on the new algorithm. Finally, the correctness, simplicity, and accuracy of the new algorithm for analyzing systems that contain shared signals are verified by a case study.

Uncertainty analyses have been considered critical analysis methods for identifying the risks in reliability evaluations. However, with multi-phase, multi-state, and repairable features, this method cannot effectively and precisely display the reliability evaluation results with uncertainty for dynamic and complex systems. In this paper, uncertainty analysis has been conducted in the evaluation of safety-related risk analysis for a nuclear power plant (NPP). A GO-FLOW and dynamic Bayesian network (DBN) combination approach for the reliability evaluation with uncertainty is proposed in this paper. Based on the unified rules, the various operators can be mapped into the DBN even with the multi-phase, multi-state, and repairable characteristics. As the framework of the DBN, utilizing sensitivity analysis, this approach can provide information on those inputs that are contributing the most to the uncertainty. Next, the DBN algorithm and the Monte Carlo simulation are used to quantify the uncertainty in terms of appropriate estimates for the analysis results. Finally, the auxiliary power supply system of the pressurized water reactor in the NPP is analyzed as an example to illustrate the approach. The results of this paper show that uncertainty analysis makes the reliability evaluation more accurate compared with the results without the uncertainty analysis. Moreover, the GO-FLOW methodology can be applied easily for uncertainty analysis with its modified functions and algorithms.

Bilinear pairing, an essential tool to construct-efficient digital signatures, has applications in mobile devices and other applications. One particular research challenge is to design cross-platform security protocols (e.g. Windows, Linux, and other popular mobile operating systems) while achieving an optimal security-performance tradeoff. That is, how to choose the right digital signature algorithm, for example, on mobile devices while considering the limitations on both computation capacity and battery life. In this paper, we examine the security-performance tradeoff of four popular digital signature algorithms, namely: CC (proposed by Cha and Cheon in 2003), Hess (proposed by Hess in 2002), BLMQ (proposed by Barreto et al. in 2005), and PS (proposed by Paterson and Schuldt in 2006), on various platforms. We empirically evaluate their performance using experiments on Windows, Android, and Linux platforms, and find that BLMQ algorithm has the highest computational efficiency and communication efficiency. We also study their security properties under the random oracle model and assuming the intractability of the CDH problem, we reveal that the BLMQ digital signature scheme satisfies the property of existential unforgeable on adaptively chosen message and ID attack. The efficiency of PS algorithm is lower, but it is secure under the standard model.

In view of the varying characteristics of material properties, environmental conditions, and loading effects randomly with long time, the random and temporal characters of different variables should take into account when the structural reliability analysis methods are adopted to estimate the reliability of ship structures. Therefore, a time-variant reliability analysis method combined the out-crossing approach and the first-order reliability method is proposed. In this paper, the research is conducted on the time-variant feature of ship structures under the corrosive action according to the environmental testing data. Furthermore, the limit state function is derived based on the time-variant feature analysis results in which the strength and stress of ship structures are both regarded as variables with respect to time. Then, the time-dependent reliability model of ship structures is established based on this combined method, and the solving process of parameters in this model is illustrated. Finally, a case of a ship grillage structure under marine environment is given to prove that the proposed method has a good application in evaluating the reliability of ship structures. The comparison analysis using different methods is also conducted to study the accuracy and efficiency of this method.

The sealing performance of rubber O-rings plays an irreplaceable role in ensuring the high reliability and safety of mechanical systems. However, traditional statistical analysis may not consider the aging process of rubber O-rings at different temperatures and the time-varying characteristics of rubber material parameters. In view of these problems, an improved reliability analysis method based on the accelerated aging test of rubber O-rings is proposed in this paper. This method is featured by a combination with the traditional accelerated model and the Gamma stochastic process. This paper first adopts the stationary Gamma process to describe the degradation of compression set which is the performance degradation indicator of rubber O-rings. Then, the reliability model is derived from the Gamma degradation model. Next, the Arrhenius model is used to represent the shape parameter of Gamma degradation model and the approach of maximum likelihood estimation is adopted to solve the parameters. Finally, the rubber O-rings of gas steering engine were selected and a series of accelerated aging tests were conducted to obtain the reliability and the storage lifetime of rubber O-rings at different temperature stresses through our improved method. Besides, the comparison with the traditional statistical method is also conducted to prove the advantage of this method.

The GO methodology, which is a success-oriented system analysis technique, is effective for evaluating the reliability of complex systems with multiple states and time-series. It is widely used in the domain of nuclear and ship industry. However, the GO methodology has some restrictions in modeling and analyzing an intricate system that contains dynamic behavior characteristics, such as function dependency, backup dependency, and load sharing. To enhance both the capacity of the modeling and the scope of applications, we proposed an extended GO methodology in this paper to describe the dependencies of the dynamic behaviors. Integrated with the dynamic Bayesian network (DBN), the dynamic behaviors can be presented in a unified way. By using mature software, the extended GO methodology proposed in this paper can be calculated conveniently. Meanwhile, based on unified rules, the multi-operator can be mapped into the DBN, followed by a complete GO model with complex characteristics that can be converted into an isomorphic DBN and analyzed easily by utilizing DBN's powerful inference capabilities. Moreover, the approach makes the extended GO model easy to analyze and intuitive for nonexperts.

A major vulnerability of mobile robots operating in more environments is their fragility in facing disturbances. A two-fold solution is proposed in this paper. First, a deformable structure was designed to reduce turbulence and to adapt to the uneven ground. Second, a novel control strategy is employed to avoid the limitations of the existing methods. In particular, among the existing solutions, the PID controller is known to be severely limited in handling disturbances and failures and the model-based designs all require detailed mathematical model, which may not be readily available, especially in the case of failures. To address these issues, a solution based on active disturbance rejection control is proposed in this paper, for its simplicity in design and tuning and its robustness against parameter variations and even failures in the pose deformation system. The proposed solution is systematically validated in the simulation, and the results are promising. The performance of the system was maintained in the presence of disturbances and uncertain dynamics, and the reliability of the robot is considerably improved when the unknown leg failures occurred.