Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection. In this paper, a new method is presented to capture the contour edge, texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm. More abundant texture information is extracted. Cloud images can be obtained a multiscale and multidirection decomposition. The coefficient matrix from Contourlet transform of ground nephogram is calculated. The energy, mean and variance characteristics calculated from coefficient matrix are composed of the feature information. The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis. Then Support Vector Machines (SVM) classifier is used to classify according to the frequency information of the trend graph of data series. It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.     

Formation of radioactive cesium microparticles originating from the Fukushima Daiichi Nuclear Power Plant accident: characteristics and perspectives

ABSTRACT

Following identification of radioactive Cs microparticles (CsMPs) in aerosol samples from the Fukushima Daiichi Nuclear Power Plant (FDNPP), numerous reports on CsMPs have been published. This paper reviews recent progress in the measurement and characterization of CsMPs by advanced analytical techniques, including advanced transmission electron microscopy (TEM) and synchrotron X-ray analysis. These analyses revealed that the CsMPs contained Si, Fe, Zn, Cs, and minor quantities of U together with some fission products. Uranium in the CsMPs was identified as being in the form of uraninite and (U,Zr)O₂. Detailed advanced TEM analysis has clarified some of the processes resulting in the alteration of constituents of the nuclear fuels and containment vessel materials during this severe accident. In addition, a detailed report on the elemental compositions and structures of the fuel debris fragments collected inside and outside of the primary containment vessel²⁵ highlighted the fact that the fuel debris fragments contained nanoparticles with the U and Zr components having similar structures to that of the CsMPs. This similarity in structures has stimulated further research on the structure and elemental constituents, especially for U and Zr in the CsMPs, and has opened up new avenues for studying the chemical characteristics of the fuel debris.     

Weighted power means of q-rung orthopair fuzzy information and their applications in multiattribute decision making

Weighted power means with weights and exponents serving as their parameters are generalizations of arithmetic means. Taking into account decision makers' flexibility in decision making, each attribute value is usually expressed by a $q$-rung orthopair fuzzy value ($q$-ROFV, $q\ge 1$), where the former indicates the support for membership, the latter support against membership, and the sum of their $q$th powers is bounded by one. In this paper, we propose the weighted power means of $q$-rung orthopair fuzzy values to enrich and flourish aggregations on $q$-ROFVs. First, the $q$-rung orthopair fuzzy weighted power mean operator is presented, and its boundedness is precisely characterized in terms of the power exponent. Then, the $q$-rung orthopair fuzzy ordered weighted power mean operator is introduced, and some of its fundamental properties are investigated in detail. Finally, a novel multiattribute decision making method is explored based on developed operators under the $q$-rung orthopair fuzzy environment. A numerical example is given to illustrate the feasibility and validity of the proposed approach, and it is shown that the power exponent is an index suggesting the degree of the optimism of decision makers.     

Event-triggered distributed receding horizon control for tracking and formation of homogeneous multi-agent systems

This paper proposes an event-triggered distributed receding horizon control (DRHC) approach for the formation and tracking problems of homogeneous multi-agent systems. For each agent, an event-triggering condition, based on assumed predictive information of the neighbours, is derived from stability analysis. Considering the uncertain deviation between the assumed and true predictive information, we design a time-varying compatibility constraint for the individual optimization problem. In the event-triggered DRHC algorithm, each agent solves the optimization problem and communicates with its neighbours only when the event-triggering condition is satisfied, so the communication and computation burden are reduced. Moreover, guarantees for the recursive feasibility and asymptotic stability of the overall system are proved. A simulation example is provided to illustrate effectiveness of the proposed approach.     

Compound fault-tolerant attitude control for hypersonic vehicle with reaction control systems in reentry phase

In this paper, a novel compound fault-tolerant attitude control (FTC) scheme is proposed for reentry hypersonic vehicles with aerodynamic surfaces and reaction control systems (RCS) in the presence of parameter uncertainties, external disturbances and aerodynamic surfaces faults. Aerodynamic surfaces work as the primary actuators and RCS serve as auxiliary actuators. When aerodynamic surfaces cannot provide the required attitude control torque due to low dynamic pressure or faults, RCS are activated to assist aerodynamic surfaces to generate the residual torque. A nonlinear disturbance observer-based sliding mode controller is designed to calculate the required attitude control torque which can handle the parametric uncertainties and external disturbances together. The quadratic programming method is applied to obtain the optimal aerodynamic surfaces deflections from the required control torque. An innovative fuzzy rule-based decision-making system is design to solve the RCS control allocation problem, which is conceptually easy to understand and computationally efficiently compared with existing approaches. Based on quantized control theory, the closed-loop control system stability is rigorously analyzed. Simulation results are given to demonstrate the effectiveness and efficiency of developed FTC scheme.     

A review on MRAS-type speed estimators for reliable and efficient induction motor drives

Induction machines have recently been very popular in variable-speed drives, because of their robust construction and relatively low manufacturing costs (brushless), maintenance-free and well-matured control methods. However, for high-precision control and efficiency optimization one needs the information on the rotor speed which can be measured using different speed sensors. All sensors require a mounting space and cabling, they also generate extra costs and reduce system reliability. Therefore, many of the recent research efforts have been dedicated to sensorless or encoderless electrical drives offering such considerable advantages as: lower cost, reduced size and hardware complexity of the drive system, elimination of sensor cables, lower maintenance requirements, possible operation in aggressive environment, higher noise immunity, reliable and user friendly operation. In this article all well-known sensorless techniques are shortly addressed, but the main focus is on the solutions based on the Model Reference Adaptive System (MRAS) concept. The mathematical models and schemes of all types of MRAS-type speed estimators known from the literature are gathered in this article. The comparative analysis of these speed estimators is done from the following points of view: the speed adaptation mechanism derivation based on the Lyapunov theory, stability problems near zero speed and in the regenerating operation mode, and the sensitivity of MRAS estimators to induction machine parameter changes.     

Visual detection and tracking with UAVs,following a mobile object

ABSTRACT

Conception and development of an Unmanned Aerial Vehicle (UAV) capable of detecting, tracking and following a moving object with unknown dynamics is presented in this work, considering a human face as a case of study. Object detection is accomplished by a Haar cascade classifier. Once an object is detected, it is tracked with the help of a Kalman Filter (KF), and an estimation of the relative position with respect to the target is obtained. A linear controller is used to validate the proposed vision scheme and for regulating the aerial robot's position in order to keep a constant distance with respect to the mobile target, employing as well the extra available information from the embedded sensors. The proposed system was extensively tested in real-time experiments, through different conditions, using a commercial quadcopter connected via wireless to a ground station running under the Robot Operative System (ROS). The proposed overall strategy shows a good performance even under disadvantageous conditions as outdoor flight, being robust against illumination changes, image noise and the presence of other people in the scene.     

Digital hysteresis sliding mode control for interleaved DC–DC converters

This paper presents a digital constant frequency sliding mode control (SMC) law for interleaved DC–DC converters. Constant switching frequency and interleaving are achieved by dynamically adjusting the hysteresis of the control signals generating comparators. The interleaving method neither imposes constraints on the number of required phases to obtain a specific output voltage nor uses quasi-SMC. Hence, the control scheme achieves high flexibility, robustness, and performance. Furthermore, a sliding mode observer (SMO) for reconstructing the inductor currents is proposed. Experimental results for a two-phase buck converter are reported. The control concept accomplishes an improved dynamic performance in comparison with quasi-SMC.     

计及Spark和属性权重的售电套餐推荐方法

针对电力市场用户群庞大，交易过程中售电套餐选择困难的问题，在Spark环境下设计了一种售电套餐推荐方法，同时也解决了售电套餐推荐过程中在大数据环境下的可扩展性及实时性问题。首先，计算出每个套餐属性的权重值，从而计算得到售电套餐综合相似度。然后，计及用户和套餐两方面提出一种售电套餐推荐方法，实现售电套餐的精准推荐。实验表明，提出的推荐方法能够明显提高推荐的准确度，并且在分布式环境下具有良好的推荐效率和可扩展性。     

基于PSO-ELM模型的潜油柱塞泵冲次优化方法

为改进油井后期泵效低下、能耗高等缺陷，提出了一种基于PSO-ELM模型的潜油柱塞泵冲次优化方法.采用粒子群（PSO）算法与极限学习机（ELM）相结合的方式来实现动液面软测量建模；根据动液面及潜油柱塞泵工作电流变化，以油井运行经济性最优为目的建立目标函数得到潜油柱塞泵冲次，解决了在油井生产时不能准确调节抽油机冲次问题；最后以目标函数关系建立模糊控制器模型，根据输入参数调整潜油柱塞泵冲次.实验结果表明，建立的软测量模型预测动液面精度高，模糊控制器能够更加合理地调整抽油机冲次，最终达到智能调整冲次大小、提高油井采油率及节能的目的.