结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

Activity-Directed Synthesis: A Flexible Approach for Lead Generation

Activity-directed synthesis (ADS) is a structure-blind, functional-driven molecular discovery approach. In this Concept, four case studies highlight the general applicability of ADS and showcase its flexibility to support different medicinal chemistry strategies. ADS deliberately harnesses reactions with multiple possible outcomes, and allows many chemotypes to be evaluated in parallel. Resources are focused on bioactive molecules, which emerge in tandem with associated synthetic routes. Some of the future challenges for ADS are highlighted, including the realisation of an autonomous molecular discovery platform. The prospects for ADS to become a mainstream lead generation approach are discussed.     

Potential of renewable energy system utilisation at decentralised level in the state of Uttarakhand in India

ABSTRACT

Design and implementation of an effective dissemination programme for decentralised renewable energy system necessitate an accurate estimate of its utilisation potential. Hence, in this study, an attempt has been made to develop frameworks to estimate the utilisation potential of decentralised renewable energy systems in the state of Uttarakhand in India. Estimations imply large resource, technical and economic potentials of the domestic solar water heater, solar home system, solar lantern, family size biogas plant and improved biomass cookstove in Uttarakhand. With higher impact on the purchasing power of households, prevailing soft loan scheme has been found to be more appropriate than a capital subsidy for promoting the usage of decentralised renewable energy systems.     

Rogowski coil current transducer compensation method for harmonic active power error

In the harmonic active power measurement, the highest uncertainties are generally introduced by the current and voltage transducers. In a previous paper, the authors showed that the current transformer (CT) can introduce significant errors in such measurement, especially if the phase shift between voltage and current is close to ±90°. In such condition the errors on harmonic power measurement are mainly due to the CT phase displacement. This paper shows that better results can be achieved with more linear transducers, such as the Rogowski coil current transducers (RCCTs), whose metrological performance in distorted condition can be improved, by means of a proper compensation method. The proposed method for RCCTs compensation is based on the frequency response and it allows to reduce the errors on harmonic power measurement, also for phase shift close to ±90°. The study is supported by several experimental tests.     

Improved synergetic excitation control for transient stability enhancement and voltage regulation of power systems

This paper proposes decentralized improved synergetic excitation controllers (ISEC) for synchronous generators to enhance transient stability and obtain satisfactory voltage regulation performance of power systems. Each generator is considered as a subsystem, for which an ISEC is designed. According to the control objectives, a manifold, which is a linear combination of the deviation of generator terminal voltage, rotor speed and active power, is chosen for the design of ISEC. Compared with the conventional synergetic excitation controller (CSEC), a parameter adaptation scheme is proposed for updating the controller parameter online in order to improve the transient stability and voltage regulation performance simultaneously under various operating conditions. Case studies are undertaken on a single-machine infinite-bus power system and a two-area four-machine power system, respectively. Simulation results show the ISEC can provide better damping and voltage regulation performance, compared with the CSEC without parameter adaptation scheme and the conventional power system stabilizer.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

基于“天空地”一体化技术的安全监测系统建设与应用

西藏江达县白格村金沙江右岸于2018年10月11日和2018年11月3日先后发生2次大规模滑坡—堰塞湖堵江事件,溃堰洪水对下游拉哇库区不良地质体的稳定性造成不同程度的影响。为保障下游水电站建设安全,对拉哇库区主要不良地质体建立了基于星载InSAR技术、无人机技术和地面传感器实时监测的“天空地”一体化监测预警体系,以多维空间采集技术获取变形信息,通过智能监控平台对信息及时进行处理、分析和可视化呈现,利用平台、短信等方式向相关人员进行分级告警,取得了较好的应用效果。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.