Jianwen Sun

Understanding the architecture is vital for effectively maintaining and managing large software systems. However, as software systems evolve over time, their architectures inevitably change. To keep up with the change, architects need to track the implementation-level changes and update the architectural documentation accordingly, which is time-consuming and error-prone. Therefore, many automatic architecture recovery techniques have been proposed to ease this process. Despite efforts have been made to improve the accuracy of architecture recovery, existing solutions still suffer from two limitations. First, most of them only use one or two type of information for the recovery, ignoring the potential usefulness of other sources. Second, they tend to use the information in a coarse-grained manner, overlooking important details within it. To address these limitations, we propose SARIF, a fully automated architecture recovery technique, which incorporates three types of comprehensive information, including dependencies, code text and folder structure. SARIF can recover architecture more accurately by thoroughly analyzing the details of each type of information and adaptively fusing them based on their relevance and quality. To evaluate SARIF, we collected six projects with published ground-truth architectures and three open-source projects labeled by our industrial collaborators. We compared SARIF with nine state-of-the-art techniques using three commonly-used architecture similarity metrics and two new metrics. The experimental results show that SARIF is 36.1% more accurate than the best of the previous techniques on average. By providing comprehensive architecture, SARIF can help users understand systems effectively and reduce the manual effort of obtaining ground-truth architectures.

This paper studies the vulnerability of deep reinforcement learning (DRL) models for power systems topology optimization under data perturbations and cyber-attack. DRL has recently solved many complex power system optimization problems. However, it has been practically proven that small perturbations of input data can lead to drastically different control decisions and induce danger. To evaluate and mitigate the security risks of DRL models in power systems, we propose a vulnerability assessment method for such DRL models under noisy data and cyber-attack. In specific, we assess the vulnerability of a DRL model in a way that perturbations are constructed to minimize the model's performance. Besides, several vulnerability indices are proposed to identify the characteristics of perturbations that may cause malfunction of DRL. Simulations on the 14-bus system and the IEEE 118-bus system for topology optimization are carried out to validate the effectiveness of the proposed vulnerability assessment method. The results show that the performance of DRL models for power systems can be significantly degraded under cyber-attack and data perturbations, especially when a proposed vulnerability index has abnormal values.

The model uncertainties and the heterogeneous energy states burden the effective aggregation of electric vehicles (EVs), especially coupling with the real-time frequency dynamic of the electrical grid. Integrating the advantages of deep learning and reinforcement learning, deep reinforcement learning shows its potential to relieve this challenge, where an intelligent agent fully considers the individual state of charge (SOC) difference of EV and the grid state to optimize the aggregation performance. However, existing policies of deep reinforcement learning usually provide deterministic and certain actions, and it is difficult to deal with the increasing uncertainties and randomness in modern electrical systems. In this paper, a probability-based management strategy is proposed with continuous action space based on the deep reinforcement learning, which provides fine-grained energy management and addresses the time-varying dynamics from EVs and electrical grid simultaneously. Moreover, an optimization based on the proximal policy is further introduced to clip the policy upgradation speed to enhance the training stability. The effectiveness of proposed energy management structure and policy optimization strategy are verified on various scenarios and uncertainties, which demonstrates advantageous performance in the SOC management and frequency maintenance. Besides the performance merits, the training procedure is also presented revealing the evolution reason for the proposed approach.

With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between energy cost and peak loads reduction. However, it is difficult to solve such an environment with the non-stationarity, and existing MA-DRL approaches cannot effectively give incentives for expected group behavior. In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. To mitigate the non-stationarity of the microgrid environment, a novel predictive model is proposed to measure the collective market behavior. Besides, a collective behavior entropy is introduced to reduce the high peak loads incurred by the collective behaviors of all householders in the smart grid. Empirical results show that our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization.

Transistors in three-phase voltage-source inverter often suffer from open-circuit failures due to the lifting of bonding wires caused by thermic cycling, resulting in performance degradation with ripple torque and current harmonics. Current-spectral-analysis based methods are widely applied to failure diagnosis; however, high calculation consumption and complex implementation limit their application in some real-time occasion. In this paper, a simplified Fourier series method is proposed by the product between reconstructed phase currents and reference signals. Meanwhile, a novel normalized method for DC and fundamental components of simplified Fourier series are proposed to locate twenty-one transistor open-circuit faults. Numerical results show that the proposed Fourier series method coincides with that of Fast Fourier Transform. Experimental results and the comparison with previous methods show high efficiency and merits of its application to transistor open-circuit fault location in the voltage-source inverter.