Quantum Computing (QC) is a fast-growing field that has enhanced the emergence of new programming languages and frameworks. Furthermore, the increased availability of computational resources has also contributed to an influx in the development of quantum programs. Given that classical and QC are significantly different due to the intrinsic nature of quantum programs, several aspects of QC (e.g., performance, bugs) have been investigated, and novel approaches have been proposed. However, from a purely quantum perspective, maintenance, one of the major steps in a software development life-cycle, has not been considered by researchers yet. In this paper, we fill this gap and investigate the prevalence of code smells in quantum programs as an indicator of maintenance issues. We defined eight quantum-specific smells and validated them through a survey with 35 quantum developers. Since no tool specifically aims to detect quantum smells, we developed a tool called QSmell that supports the proposed quantum-specific smells. Finally, we conducted an empirical investigation to analyze the prevalence of quantum-specific smells in 15 open-source quantum programs. Our results showed that 11 programs (73.33%) contain at least one smell and, on average, a program has three smells. Furthermore, the long circuit is the most prevalent smell present in 53.33% of the programs.

Interpreting and debugging machine learning models is necessary to ensure the robustness of the machine learning models. Explaining mispredictions can help significantly in doing so. While recent works on misprediction explanation have proven promising in generating interpretable explanations for mispredictions, the state-of-the-art techniques “blindly” deduce misprediction explanation rules from all data features, which may not be scalable depending on the number of features. To alleviate this problem, we propose an efficient misprediction explanation technique named Bias Guided Misprediction Diagnoser (BGMD), which leverages two prior knowledge about data: a) data often exhibit highly-skewed feature distributions and b) trained models in many cases perform poorly on subdataset with under-represented features. Next, we propose a technique named MAPS (Mispredicted Area UPweight Sampling). MAPS increases the weights of subdataset during model retraining that belong to the group that is prone to be mispredicted because of containing under-represented features. Thus, MAPS make retrained model pay more attention to the under-represented features. Our empirical study shows that our proposed BGMD outperformed the state-of-the-art misprediction diagnoser and reduces diagnosis time by 92%. Furthermore, MAPS outperformed two state-of-the-art techniques on fixing the machine learning model's performance on mispredicted data without compromising performance on all data. All the research artifacts (i.e., tools, scripts, and data) of this study are available in the accompanying website [1].

Static analysis tools attempt to detect faults in code without executing it. Understanding the strengths and weaknesses of such tools, and performing direct comparisons of their ef-fectiveness, is difficult, involving either manual examination of differing warnings on real code, or the bias-prone construction of artificial test cases. This paper proposes a novel automated approach to comparing static analysis tools, based on producing mutants of real code, and comparing detection rates over these mutants. In addition to making tool differences quantitatively observable without extensive manual effort, this approach offers a new way to detect and fix omissions in a static analysis tool's set of detectors. We present an extensive comparison of three smart contract static analysis tools, and show how our approach allowed us to add three effective new detectors to the best of these. We also evaluate popular Java and Python static analysis tools and discuss their strengths and weaknesses.

Taking the high real-time performance, good stability and high reliability of DC-DC converter in electric vehicles as the research target, a constrained model predictive control (MPC) based on spider monkey optimization algorithm was proposed. Taking the Buck mode for an example, the equivalent circuit model is obtained under different switching states, and the state space model is established, then the prediction model is established through discretization. Define the appropriate cost function according to the control requirements and add the constraints of the control variables. The spider monkey optimization algorithm(SMO) is introduced to solve optimization problem of the constrained MPC in order to get the optimal solution more quickly and accurately. the simulation was carried out by MATLAB/Simulink, and the simulation results of PI control, MPC based on Particle Swarm Optimization(PSO-MPC) and SMO-MPC was compared. The simulation show that the converter using MPC has better control effect, and SMO is feasible and effective.

In order to solve the problem of device impact and switching speed in dynamic wireless charging device for electric vehicles (EVs), first, a new SiC MOSFET model was established based on manufacturer's model and datasheet. Combined with this model, the hard-switching and soft-switching processes of SiC MOSFETs are compared and analyzed. Although soft-switching can solve the problems of changing switching speed and reducing device impact, it needs to modify the hardware circuit, which is not practical in the changeable environment of dynamic wireless charging of EV s. Based on this question, a new multi-grade voltage drive circuit is designed to meet the various requirements of the drive circuit in the wireless charging process of EVs. Finally, the drive circuit is simulated in Pspice, and its feasibility is explored according to its waveforms.

The DC-DC circuit is widely discussed in Electric Vehicle Charging Systems (EVCS). In this paper, a high-power, high-voltage four-phase interleaved parallel Buck converter topology structure is designed, which is suitable for both contact and contactless charging systems. A small-signal mathematical model of a four-phase interleaved parallel Buck converter is established by constructing the state variables of the converter; a Proportion Integration (PI) controller is designed by analyzing the Bode diagram to compensate the system and ensure the control performance of the closed-loop system; a control strategy based on PI control and maximum current sharing control is proposed. The simulation results demonstrate that, the output ripple of this new type of converter cancels each other, the currents of each phase are balanced, and a large output power can be achieved.

Coupling system has great influence on the transmission performance of wireless charging system. Improving the mutual inductance between coupling coils can effectively improve the transmission efficiency. Using magnetic core is an effective way to enhance mutual inductance of coupling coils. The introduction of magnetic core also leads to core loss. Based on the DD coil structure and considering the magnetic core weight and coupling coefficient, a method for ferrite core design and optimization is proposed, which can reduce the core loss between transmitter and receiver and improve the average utilization ratio of the magnetic core. MAXWELL simulated and analyzed the coupling coil before and after optimization, and discussed its feasibility.

For small scale off-grid wind-solar-diesel hybrid generations with energy storage (WHGES), it is necessary to coordinate the operation of generation units and energy storage unit to maintain the voltage constant and supply the loads. This paper proposes a low cost charging scheme for off-grid WHEGES with 48V DC bus voltage. It is possible to realize the dynamic charging control of the storage unit without bidirectional converter, and a reasonable charging curve is achieved by controlling diesel generator. The scheme proposed in this paper can not only reduce hardware cost but also improve reliability of the system. Finally, a model of off-grid WHGES was built employing MATLAB/SIMULINK, the control strategy proposed in this paper is verified.

An improved deadbeat plus plug-in repetitive control scheme is proposed for grid-connected three-phase four-leg inverters to achieve fast and accurate feed-in grid current tracking. Discrete-time mathematical model of the three-phase four-leg grid-connected inverter is developed. An improved deadbeat (DB) plus plug-in repetitive control (RC) scheme is proposed to reject periodic disturbances while keeping good dynamic response. Simulation results are provided to validate the effectiveness and advantages of the proposed method.

This paper presents a neural network predictive control strategy to optimize oxygen supply for a proton exchange membrane fuel cell system. We propose using a time varying and local linearization auto-regressive moving average with exogenous (ARMAX) to model the nonlinear system, and employing recurrent neural network to estimate coefficients of the ARMAX model. Then constrained linear model predictive algorithm is presented to optimize oxygen supply of the fuel cell system, which significantly simplifies implementation and can handle multiple constraints. Study results demonstrate that the modeling and control strategy are effective.