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Background：Given the increasing trend of using wind energy in cities, the utilization of distributed wind energy in cities has been widely concerned by researchers. The related research on the micro-site selection of wind turbines, a sub-project of the Task27 project of the International energy agency, was continued in this paper.Methods：The wind speed data of an observation station near Hohhot, Inner Mongolia, with a range of 10-19 m were collected. The evaluation included wind direction, Weibull parameter characteristics, and turbulence intensity. The potential energy output in 10 different heights was estimated using commercial horizontal and vertical axis wind turbines of the same power.Results：The three-parameter Weibull distribution model can well describe the statistical properties of the wind speed in this site. The wind speed distribution model constructed from extrapolation parameters reflects the wind speed statistical properties out of detection positions to a certain extent.Conclusions：The wind energy density of the vertical axis wind turbine is slightly lower than that of the horizontal axis wind turbine. Furthermore, more power can be generated from March to May.

This paper discusses the result of the exergy efficiency analysis of direct-drive wind turbines by obtaining the exergy loss and destruction characteristics as well as the law of influence of every...

Object tracking method based on Siamese Network has shown great performance, which expressed the tracking problem as dependency relationship between target template x and search template z. How to extract the characteristics of the target is a crucial problem. We explicitly model channels interdependencies within modules and adaptively calibrate the feature response between channels to enhance the representation of the entire network. To enable the network to perform feature recalibration, global information is learned to selectively enhance useful features and suppress few less useful features. We proposed a Siamese Network tracker based on SE-ResNet-50. In the training phase, meta-learning was introduced to solve the problem of lack of training data, so that the model can achieve a better result on an extremely small amount of data. Our method overcame the limitations of Siamese Network through weighted cross-correlation. Experimental results illustrate our algorithm is much better than some popular trackers in solving the problems of occlusion, illumination change and object deformation.

This paper uses computer simulation to study the effect of torque load on the temperature distribution of tapered roller bearings. Taking the widely used 31308 bearing in engineering as an example, the heat source of each contact part of the bearing under different torque load is solved first, and then a three-dimensional computer model of tapered roller bearing is established, which is imported into the steady-state thermal analysis module in ANSYS workbench and the relevant parameters are set, and the influence of torque load on the temperature distribution of tapered roller bearings is simulated. The general law of the influence of torque load on the temperature distribution of tapered roller bearings is obtained.

In order to address the problem of ghosting and gap in the field of image stitching, we proposed a novel image stitching method based on seed region growth algorithm and Poisson fusion. Firstly, PSO (Particle Swarm Optimization) is used to improve the accuracy of image registration. Then we combine the improved seed region growth algorithm with Poisson fusion to implement image stitching. Experimental results illustrate outstanding performance of our method by comparing with one famous image stitching tool: PTGui and one existing image stitching method. Our method can effectively solve the problem of ghosting and gap in the process of image stitching, and avoid objects distortion.

Tool wear is a key factor in the cutting process, which directly affects the machining precision and part quality. Accurate tool wear prediction can make proper tool change at an early stage to reduce downtime and enhance product quality. However, traditional methods can not meet the high requirements of the intelligent manufacturing. Therefore, a novel method based on deep learning is proposed to improve the prediction accuracy of tool wear. The multi-scale feature fusion was implemented by the developed parallel convolutional neural networks. The channel attention mechanism combined with the residual connection was developed to consider the weight of the different feature map to enhance the performance of the model. The different tool wear prediction experiments were implemented to verify the superiority of the developed method, and the prediction results of tool wear are more robust and accurate than current methods. Finally, a tool wear monitoring system was developed and applied to the tapping process of the engine cylinder to ensure the quality of the engine cylinder and the stability of the machining process.

Although the optimization of wind turbine blade aerodynamic performance has achieved fruitful results, whether airfoil concavity, an important method for preventing flow separation, is also feasible for improving the aerodynamic performance has not been confirmed scientifically. Thus, we selected the blade of a small horizontal-axis wind turbine as a research model and proposed an optimization method based on airfoil concavity near the trailing edge of the blade suction surface. The experimental results showed that airfoil concavity improved blade aerodynamic performance by 3–15%. Subsequently, its effects on the sound pressure level within the wake flow field were investigated using an acoustic array, and the results suggested that the sound pressure level was reduced by 9.6–15.8%. Lastly, a modal test of the rotor blade was conducted. Although the natural frequencies of the 1st and 2nd order vibrations had hardly changed, their vibrational stiffness were increased by 7 and 4.9%, respectively, which indicated that airfoil concavity significantly improved structural robustness.

To realize partial discharge (PD) detection of all electrical equipment in substations, an asymmetric biconical antenna ultra-high frequency (UHF) antenna is developed as PD electromagnetic wave signal detection sensor based on vehicle-mounted PD detection system to meet the design requirements of wide band, low voltage standing wave ratio (VSWR), omnidirectional horizontal plane (H plane) and upturned vertical plane (E plane). Asymmetric biconical antenna is chosen as UHF sensor to take advantage of its good performance on characteristic radiation and characteristic impedance, and a gradient structure is applied into the antenna to reduce the antenna size. The HFSS simulation indicated that the sensors are featured by omni-direction, broadband and low VSWR, meeting the requirements for detection and location of PD sources in whole substations.

Partial discharge location can find insulation defects to avoid insulation deterioration and dielectric breakdown. Most of current PD location is implemented based on TDOA principle and the location accuracy is improved through multiple measurements. In this article, a novel location method is proposed using Monte-Carlo simulation to reduce error of TDOA which only need carry out one measurement. Result shows that located angle of PD source within 1.5 degree error can be achieved when the time difference error bound is less than 3ns by using an array of 4 UHF sensors arranged in the shape of 30m×20m rectangle.

Modal testing was used to show that the roundabout swing was a natural vibration mode of the wind wheel of a horizontal-axis wind turbine (HAWT). During the vibration, the blade root was simultaneously subjected to bending and rotary shear stresses. A method for indirect testing and determination of the dynamic frequencies of the typical vibrations of the wind wheel was developed, based on the frequency-holding characteristic of each subsignal during the transmission of the multiple mixed-vibration signals. The developed method enabled simple and accurate acquisition of the dynamic frequencies without destruction of the flow and structural fields. The dynamic vibration stress of the roundabout swing was found to be significantly stronger than those of the first- and second-order flexural vibrations of the blades. By a combination of numerical simulations and tests, it was determined that the pneumatic circumferential force was the primary determinant of the roundabout swing vibration frequencies, the relationship being quadratic. The roundabout swing vibration potentially offers new explanations and analytical pathways regarding the behavior of horizontal-axis wind turbines, which have been found to be frequently involved in fatigue-damage accidents within periods shorter than their design lives.

This paper introduces the standardization degree of Australia, the United States and South Africa in coal industry, and introduces relevant institutions and respective characteristics with constructing coal standardization. The development process and characteristics of China’s coal industry standardizations were presented in this paper. There are comparative studies on domestic and international in many aspects—standards-making process, functions and characteristics of standardization institution, degree with international standards. According to above studies, many problems were presented in process of building coal standardization, including: superficial consciousness in concept of standardization, older standards, lower degree with international standards. To solve these problems, some suggestions and solutions were put forward to promote standardization level in China’s coal industry.

Field sampling is a significant kind of application of wireless sensor networks (WSNs). In this application, multiple sensor nodes usually cooperate to sample and estimate the physical signal. A sampling scheme which only considers the sampling process of each sensor node is not suitable for the application. Cooperative sampling with multiple sensor nodes can fulfil the application. For a dense WSN in which more sensor nodes are deployed in an interested field, cooperative sampling scheme is crucial for conserving energy and improving estimation accuracy. In this paper, we present a cooperative sampling scheme for field sampling application. The basis of the scheme is to group the suitable sensor nodes and arrange the sampling time of these groups. The analysis and simulation results show that, under the restriction of the estimation accuracy, the scheme can conserve the energy of the sensor nodes and balance the energy consumption amongst the different groups.

With the development of computer technology, network security has become an important issue of concern. In view of the growing number of network security threats and the current intrusion detection system development, this paper gives a new model of anomaly intrusion detection based on clustering algorithm. Because of the k-means algorithm’s shortcomings about dependence and complexity, the paper puts forward an improved clustering algorithm through studying on the traditional means clustering algorithm. The new algorithm learns the strong points from the k-medoids and improved relations trilateral triangle theorem. The experiments proved that the new algorithm could improve accuracy of data classification and detection efficiency significantly. The results show that this algorithm achieves the desired objectives with a high detection rate and high efficiency.