基于物联网和模糊聚类的风力发电设备故障诊断系统及方法

为了提高风力发电设备故障诊断的准确性,构建了基于物联网和模糊聚类的风力发电设备的故障诊断系统。首先提出了风力发电设备故障诊断系统的拓扑结构,并介绍了其涉及的关键技术;然后给出了该系统的所采用的数据分析和故障诊断方法,并进行了应用实例分析。结果表明：该风力设备故障诊断系统可监控风力发电设备的运行状态,并能准确地诊断出风力发电设备的故障类型。     

Performance evaluation of HDAF relaying systems for pilot and data symbol burst transmission over quasi‐static Rayleigh fading channels

This paper analyzes averaged symbol error probabilities of burst transmission consisting of pilot and data symbols for hybrid adaptive decode‐or‐amplify‐forward (HDAF) relaying systems. Under the assumption of quasi‐static Rayleigh fading channels with independent and non‐identically distribution, we consider a channel estimation scheme based on pilot symbols and show how channel estimation error affects received signal‐to‐noise ratio (SNR) and symbol error probability (SEP). Firstly, all the possible detection error‐events are presented for all the relay nodes, and their probabilities are derived as forms related with data symbol burst transmission. For the given error event, we analyze the conditional SEP as an exact form and then, the averaged SEP (ASEP) is approximately derived as a closed‐form. The simulation results verify that our derived ASEP expression is accurate over all the regions of SNR. Utilizing the proposed expressions, we can evaluate ASEP performance of HDAF relay systems easily and fast. Copyright © 2015 John Wiley & Sons, Ltd.     

Two-stage multinomial logit model

We suggest a two-stage multinomial logit model (TMLM) for incorporating and interpreting both the interaction and main effects in the model for multi-categorized responses. TMLM combines the robustness of multinomial logit model (MLM) with the good properties of decision tree (DT), which makes it possible to cluster homogeneous subjects and thus to incorporate the interaction effects of explanatory variables in MLM. In the first step of TMLM, DT is applied to determine the most influential interaction effects and to create a cluster variable that represents categories with best splits for optimal tree. In the second step, the cluster variable is involved in MLM as an explanatory variable. With TMLM, it is possible to interpret not only the interactions among explanatory variables, but also the main effects. It is also possible to cluster and characterize homogeneous subjects; these would not be possible with MLM. This model also improves the accuracy rate in multi-classification for multi-categorized responses. We apply TMLM to the national pension data of disability pensioners in Korea and compare the results with two types of MLM models. TMLM is suggested as a statistical model for characterizing both the interaction and main effects of explanatory variables and also for improving accuracy rates comparing to MLM.     

Feature fusion within local region using localized maximum-margin learning for scene categorization

In the field of visual recognition such as scene categorization, representing an image based on the local feature (e.g., the bag-of-visual-word (BOVW) model and the bag-of-contextual-visual-word (BOCVW) model) has become popular and one of the most successful methods. In this paper, we propose a method that uses localized maximum-margin learning to fuse different types of features during the BOCVW modeling for eventual scene classification. The proposed method fuses multiple features at the stage when the best contextual visual word is selected to represent a local region (hard assignment) or the probabilities of the candidate contextual visual words used to represent the unknown region are estimated (soft assignment). The merits of the proposed method are that (1) errors caused by the ambiguity of single feature when assigning local regions to the contextual visual words can be corrected or the probabilities of the candidate contextual visual words used to represent the region can be estimated more accurately; and that (2) it offers a more flexible way in fusing these features through determining the similarity-metric locally by localized maximum-margin learning. The proposed method has been evaluated experimentally and the results indicate its effectiveness.     

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

We present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over wireless LAN with the quadrocopter. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion, and a PID controller. In this paper, we (1) propose a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter’s flight dynamics, and (2) present a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors. We extensively evaluated our system in terms of pose estimation accuracy, flight accuracy, and flight agility using an external motion capture system. Furthermore, we compared the convergence and accuracy of our scale estimation method for an ultrasound altimeter and an air pressure sensor with filtering-based approaches. The complete system is available as open-source in ROS. This software can be used directly with a low-cost, off-the-shelf Parrot AR.Drone quadrocopter, and hence serves as an ideal basis for follow-up research projects.     

Online adaptive radial basis function networks for robust object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of visual tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. In this paper, we present a novel online adaptive object tracker based on fast learning radial basis function (RBF) networks. Pixel based color features are used for developing the target/object model. Here, two separate RBF networks are used, one of which is trained to maximize the classification accuracy of object pixels, while the other is trained for non-object pixels. The target is modeled using the posterior probability of object and non-object classes. Object localization is achieved by iteratively seeking the mode of the posterior probability of the pixels in each of the subsequent frames. An adaptive learning procedure is presented to update the object model in order to tackle object appearance and illumination changes. The superior performance of the proposed tracker is illustrated with many complex video sequences, as compared against the popular color-based mean-shift tracker. The proposed tracker is suitable for real-time object tracking due to its low computational complexity.     

Micromechanics-based investigation of the elastic properties of polymer-modified cementitious materials using nanoindentation and semi-analytical modeling

Cementitious materials are modified by the addition of polymers in order to improve the durability and the adhesive strength. However, polymer-modified mortars and concretes exhibit lower elastic moduli in comparison to unmodified systems. The macroscopic properties are governed by microstructural changes in the binder matrix, which consists of both cementitious and polymer components. Herein, different polymer-modified cement pastes were characterized using nanoindentation to better understand the microscopic origin of the macroscopic elastic modulus. By means of the statistical nanoindentation technique, the existence of three micromechanical phases in plain and polymer-modified cement pastes with a water-to-cement mass ratio of 0.40 is evidenced, illustrating that the polymer modification does not induce the formation of additional reaction products. Instead, the polymers adsorb on the hydration products as well as on unhydrated clinker grains and decrease the indentation moduli of the micromechanical phases. The link between the microscopic and macroscopic mechanical properties is established by means of a continuum micromechanics approach. A multiscale model aimed at the prediction of the elastic moduli of polymer-modified cementitious materials is developed with input parameters that are partially obtained from the nanoindentation tests. The comparison of the modeling results with the experimentally determined elastic (macroscopic) moduli at the scales of cement paste, mortar, and concrete is satisfactorily good, underlining the predictive capability of the modeling approach. The improvement of prediction models is essential for the application of polymer-modified cementitious materials in construction and will encourage their integration into design guidelines.     

Discrete multi-objective artificial bee colony algorithm for green co-scheduling problem of ship lift and ship lock

This paper investigates a multi-objective green co-scheduling problem of ship lift and ship lock (GCP-SL&SL) at the Three Gorges Cascade Hub (TGCH). A mathematical model of GCP-SL&SL with objectives of the average utilizations rate of the lock chamber, average waiting time and total energy consumption of vessels, is proposed by separating it into three sub-problems: the facility assignment, lockage assignment and lockage operation scheduling. To solve this problem, a discrete multi-objective artificial bee colony (DMOABC) algorithm is developed. Within the DMOABC, a two-dimensional matrix encoding scheme is designed to encode and a group right-shift decoding scheme is specifically proposed to decode each food source. Then, a novel fitness evaluation mechanism based on fuzzy relative entropy is introduced to hand this multi-objective problem. Next, the food sources are improved from three aspects: (1) the employed bee phase uses new evolutionary operators for fast local search; (2) the onlooker bee phase adopts a modified tabu search for strong global search; (3) the scout bee phase embeds chemical reaction optimization for disturbing population. Finally, extensive experiments are conducted with the real data from historical traffic at TGCH. The results demonstrate our proposed algorithm is significantly better at solving the GCP-SL&SL than other five well-known multi-objective algorithms. The effect analysis under different scenarios indicates that the average waiting time of vessels at the dam is greatly reduced because of considering the synchronous moving process.     

Dynamic material parameter inversion of high arch dam under discharge excitation based on the modal parameters and Bayesian optimised deep learning

Vibration-based dam safety monitoring methods have increasingly become a research hotspot for assessing dam safety. The accurate material parameters of a high arch dam and its foundation are critical for monitoring vibration safety. Accordingly, a dynamic material parameter inversion framework for an arch dam is developed based on modal parameters and deep learning. Firstly, an determined-order stochastic subspace identification method with adaptive variational modal decomposition is proposed based on the prototype vibration signal obtained under the discharge excitation, which can effectively identify the modal parameters. Secondly, the sensitivity of the dynamic elastic modulus (DEM) in different regions to the modal parameters of the arch dam was analysed using the orthogonal test method and variance analysis method, and it was utilised to ascertain the DEM to be inverted. Finally, a Bayesian optimised multi-output long short-term memory neural network is used to establish a nonlinear mapping relationship between the DEM and the modal parameters as an alternative to finite element calculations, and the identified modal parameters are employed as network inputs to inverse the actual DEM of each zoning. An engineering example shows that the proposed DEM inversion method for high arch dams is effective and accurate, providing a good basis for the vibration safety analysis of arch dams. This study overcomes the limitations of difficult to effectively extract modal parameters of arch dams under discharge excitation, and advances the application of deep learning technology in hydraulic engineering by combining modal parameters.