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Accurate forecasting is the key factor in promoting wind power consumption and improving the stable operation of power systems. A short-term wind power forecasting (WPF) and uncertainty analysis method based on whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and nonparametric kernel density estimation (NPKDE) was proposed in this paper. The advantages of WOA (fast convergence speed and high convergence accuracy) were used to optimize the penalty factor and kernel function width of the LSSVM model, and the calculation speed and forecasting accuracy of the LSSVM model were improved. The training sample set is classified according to the wind speed interval, and the WOA-LSSVM forecasting model is trained by subclass after classification to further improve the accuracy of short-term WPF. The NPKDE method is used to accurately calculate the probability density distribution characteristics of the forecasting error of wind power, and the confidence interval of the WPF is accurately calculated based on the probability density distribution characteristics. The calculation results show that the forecasting accuracy of the WOA-LSSVM model is higher than those of the LSSVM, long short-term memory (LSTM), and particle swarm optimization and least squares support vector machine (PSO-LSSVM) models, and the forecasting accuracy of the WOA-LSSVM model can be further improved after classifying the training sample set. The coverage of the confidence intervals in different time scales is higher than the corresponding confidence level, indicating that the NPKDE method can accurately describe the probability density distribution characteristics of the WPF errors.

Magnetic resonance imaging (MRI) is one of the most important diagnostic imaging methods, which is widely used in diagnosis and image-guided therapy, especially imaging diagnosis of the brain. However, MRI images have the characteristics of low resolution, and there are limitations such as long imaging time and noise. Super-resolution techniques have been studied on three-dimensional MRI images using three-dimensional convolutional neural network. Based on some related techniques of super-resolution reconstruction of two-dimensional MRI slices, we evaluated the capability of several super-resolution technologies. We utilize the super-resolution algorithm based on generative adversarial network ESRGAN to realize super-resolution reconstruction of two-dimensional MRI slices, and then we further demonstrate that frequent details can be obtained from ESRGAN. In the aspect of two-dimensional to three-dimensional reconstruction, we use the technique of two-dimensional super-resolution on slices from three different latitudes. We rebuild reconstructed two-dimensional images into a three-dimensional form. Then based on the principle of linear interpolation, we use the surrounding effective pixel values to interpolate the null value of each slice, and realize the reconstruction of three-dimensional brain MRI.

A new scheme which generates multi-frequency terahertz (THz) waves from planar waveguide by the optimized cascaded difference frequency generation (OCDFG) is proposed. A THz wave with frequency ω T1 is generated by the OCDFG with two infrared pump waves, and simultaneously a series of cascaded optical waves with a frequency interval ω T1 is generated. The THz wave with a frequency of M-times ω T1 is generated by mixing the m-th-order and the (m + M)-th-order cascaded optical wave. The phase mismatch distributions of cascaded difference frequency generation (CDFG) are modulated by changing the thickness of planar waveguide step by step, thereby satisfying the phase-matching condition from first-order to high-order cascaded Stokes process step by step. As a result, the intensity of THz wave can be enhanced and modulated by controlling the cascading order of OCDFG.

As we enjoy these emerging technologies, we also face the risk of information being leaked. The information security problem has been plaguing us all the time. This is also the era of big data. Due to the continuous development of science and technology, the society has also caused the strong wind of the Internet of Things, and it is increasingly understood by ordinary consumers. As a consumer, it is necessary and necessary to understand the related security issues of the Internet of Things. According to this situation, this article makes a general analysis of the security holes in the Internet of Things, and proposes some prevention and control measures in light of the current network conditions. Based on the analysis of data processing of Internet of Things technology and logistics unified information system, and according to the characteristics of Internet of Things technology and unified information, an improved Shell ranking optimization algorithm in data processing of logistics unified information system is proposed.

Water production in gas wells is very complex in Daqing Oilfield on account of the volcanic reservoirs. Foam drainage is the most widely used dewatering technology. However, as to the conventional foaming, it is also known that manual workload is heavy and the accuracy and the timeliness of foaming agent injection are not effective, due to the influence of bad weather, scattered distribution of the remote wells and lack of in-time adjustment of agent injection rules. In order to solve these problems, an integrated foam drainage technology was developed to ensure intelligent, unattended, accurate and continuous foam dewatering. The integrated technology mainly consists of an online monitoring system, in which an automatic liquid level monitor and a gas-liquid two-phase flow monitor were invented, an intelligent controlling system, composed of specially developed hardwares and embedded intelligent decision softwares, and an automatic foaming agent injection device. Based on online monitoring of downhole liquid level and wellhead production parameters, such as tubing and casing pressures, gas and water production rates etc., the intelligent controlling system can quantitatively analyze the downhole loading liquid and production change, optimize the rules of foaming agent injection in real time, without human intervention. A pilot field trial was then taken, using the technology. The result showed the whole system operated normally. The average tubing-casing pressure difference reduced from 3.1 MPa to 0.4 MPa, the average production rate per day increased 1.1 × 104 m3. Compared with the conventional manual foam drainage technology, the foaming agent usage reduced 25%, the maintenance workload over 70%, which testified the new technology was feasible and highly effective. Therefore, it is of great significance for intelligent and digital liquid unloading in gas wells.

The current chaotic encryption system generally uses a fixed scrambling and diffusion key for encryption, which leads to low efficiency and security. This paper designs a fast image encryption method based on Linear Diophantine equation by constructing a dynamic diffusion mechanism. The coefficients of the Diophantine model are estimated by using the sequence output from the segmented chaotic map, and the Diophantine equation is solved according to the coefficients. A large number of scrambling keys and diffusion keys are generated by the index sorting method, and the scrambling and diffusion of sub images are processed respectively, thus greatly improving the security of the ciphertext. The test data shows that compared with other image encryption schemes, the proposed method has higher security and efficiency, and significantly reduces the time consumption.

We propose a high-efficiency scheme to generate THz waves combined with optimized cascaded difference frequency generation (OCDFG) and optical parametric oscillator (OPO) consisting of a periodic poled lithium niobate (PPLN) crystal or an aperiodic poled lithium niobate (APPLN) crystal at 100 K. The phase mismatches of the OCDFG are decreased in cascaded Stokes processes and increased in cascaded anti-Stokes processes respectively by setting desirable poling periods along the APPLN crystal length. By setting the reflectance of the OPO cavity mirrors, pump energies are transferred to the cascaded Stokes waves rather than the cascaded anti-Stokes waves. As a result, the cascaded Stokes processes are greatly enhanced and the cascaded anti-Stokes processes are greatly weakened simultaneously through the combination of OCDFG and OPO. Theoretical calculations show that optical-to-THz energy conversion efficiencies in excess of 40 % can be achieved when the pump intensity is 10 MW/cm2 at 100 K. The scheme can be applied to the generation of pulsed, quasi-continuous or continuous THz waves.

The photonic crystal fiber (PCF) sensor based on surface plasmon resonance (SPR) technology has flexibility in birefringence, negative dispersion, effective area and nonlinearity and has become a widely studied new fiber. However, there are many problems in the practical application of the sensor, such as complex structure design, not easy to prepare, the low sensitivity of sensing detection, narrow range of refractive index detection of analytes, which greatly limits the application range and functions of PCF sensors. To solve the above problems, this paper adopts a structure with a longer vertical distance between the D-shaped large air hole channel and the core. The energy of the core conduction mode is better limited by the cladding to transmit in the core, and the plasma mode is enhanced, which can effectively increase the wavelength sensitivity. In this paper, the hexagonal double-clad air hole structure and the D-type structure are combined to ensure a simple structure and facilitate manufacturing and production, while the wavelength sensitivity is also greatly improved. The wavelength sensitivity of the dual sample channel can reach up to 16200 nm/RIU and 15800 nm/RIU, which has broad application prospects in the field of high-sensitivity detection.

A novel scheme for high-efficiency terahertz (THz) wave generation based on optimized cascaded difference frequency generation (OCDFG) with planar waveguide is presented. The phase mismatches of each-order cascaded difference frequency generation (CDFG) are modulated by changing the thickness of the waveguide, resulting in a decrement of phase mismatches in cascaded Stokes processes and an increment of phase mismatches in cascaded anti-Stokes processes simultaneously. The modulated phase mismatches enhance the cascaded Stokes processes and suppress the cascaded anti-Stokes processes simultaneously, yielding energy conversion efficiencies over 25% from optical wave to THz wave at 100 K.

Photonic crystal fiber (PCF) sensors based on surface plasmon resonance (SPR) have the characteristics of high sensitivity, flexible structure, high nonlinear effect and high-quality transmission. However, in the application of PCF sensors, there are generally problems such as low detection efficiency, difficulty in coating air holes and complicated operations of microfluidic filling. These limitations fundamentally limit the development and application of this technology. In this paper, a double samples synchronous detection sensor based on up-core PCF is proposed and the area of the center large air hole can be increased effectively by moving the core upward, which can reduce its coating difficulty. Furthermore, the outer surface coating of the PCF filled with the liquid to be measured has the advantages of simple operation and easy implementation compared with the inner coating of the air hole. The sensor can simultaneously detect two kinds of microfluidics, thus improving the detection efficiency. This structure does not have the characteristics of rotational symmetry, which greatly reduces the fiber confinement loss and has high sensitivity. The simulation results show that the maximum sensitivity of the designed sensor is up to 8300 nm/RIU, and the minimum resolution of the spectrometer is assumed to be 0.01 nm, at this time, the resolution of the sensor can reach 1.20 × 10-6 RIU. The proposed structure has important scientific significance and application prospects for promoting the practical development of sensing technology and improving the efficiency of microfluidic detection.

In this work, we propose a high-efficiency terahertz (THz) wave-generation approach by cascaded difference frequency generation (CDFG) with an aperiodically poled lithium niobate (APPLN) crystal at cryogenic temperature. The APPLN crystal with desirable poling periods along the crystal length determines phase mismatches of each-order CDFG, resulting in a decrement of phase mismatches in cascaded Stokes processes and an increment of phase mismatches in cascaded anti-Stokes processes simultaneously. This is in contrast with previous works on CDFG, where the THz wave was generated with irreversible phase mismatches in cascaded Stokes and anti-Stokes processes. The variations of phase mismatches enhance the evolution of optical spectra in cascaded Stokes processes and restrain the evolution of optical spectra in cascaded anti-Stokes processes, yielding unprecedented energy efficiencies in excess of 30% from optical waves to THz waves with APPLN. The unprecedented energy efficiencies in this work are theoretical results without THz wave absorption at a temperature of 10 K. Compared with the maximum THz intensity from traditional CDFG using periodically poled lithium niobate, the maximum THz intensities are enhanced by 2.5 and 2.8 times using APPLN with stepwise changing and gradually changing poling periods, respectively.

A high-efficiency terahertz (THz) wave amplification approach by coupled cascaded optical parametric processes (CCOPP) with orientationally patterned gallium phosphide (OP-GaP) is introduced. The CCOPP comprises two cascaded optical parametric processes (COPP) which are intensely coupled by THz waves and cascaded optical waves via setting a precise periodic vector of OP-GaP. A simulation model that solves for the interactions of THz and cascaded optical waves is employed. The influences of cascading orders, phase mismatches, pump intensities, red-shifted and blue-shifted processes, nonlinear coefficients and absorption coefficients, the initial THz intensities are considered in the calculations. Simulation results predict that more than 7 THz photons are produced from a pump photon by 12 orders CCOPP.

In this paper, an improved ViBe background subtraction algorithm is proposed for dealing with ghost problem during the process of moving object detection. The ghost areas in image can be detected based on the theory that the histogram distributions of ghost areas have similarity distribution characteristics. However, the histogram distributions of real moving objects change with the real objects moving. The influence of ghost areas on moving object detection is eliminated. The improved ViBe, the original ViBe and the Gauss mixture model are compared and analyzed, The results show that the improved ViBe can effectively eliminate ghost areas, and has high real-time.

This paper demonstrates a 40-unit linear slotted array for circular polarization, which is based on multi-cascaded ridged waveguide power divider. Here the required circularly polarized field is excited by parasitic dipoles above the radiation slots. The whole array structure is optimized by Ansoft HFSS, and principal results are presented. From the simulation, the −10 dB impedance bandwidth is achieved in the required band from 35.5 GHz to 37.5 GHz, and the axial ratio (AR) within this band is less than 2.6 dB. The side-lobe level at the center frequency is about −13.5 dB, showing a desired uniform distribution.

Wind power prediction of wind farm plays a decisive role in stable electric power system operation.The BP neural network’s basic principle was introduced, and the numerical weather prediction (NWP) data and power data of wind farm as the training data of BP neural network was selected and trained; a linear regression model about the sample prediction error was presented, which considers the coupling relationship between the individual sample prediction error, the individual sample prediction error of BP neural network was selected as the regression factor, the individual sample prediction result of BP neural network was modified. As the modified prediction results performing, the prediction algorithm of short-term wind power considering the sample prediction error correction, has good self-learning and adaptive ability of BP neural network. It has overcome the shortcoming that the BP neural network has only considered the overall the prediction error of training samples, but without considered the prediction error of individual samples. This has further improved the prediction accuracy of BP neural network.

Remote welding technology plays an important role in the construction of national key projects. Instead of manual welding and full-autonomous welding, it is the best way to apply remote welding technology to practical engineering by employing a special robot. This study uses the remote welding power supply cables as the carrier to construct the power line communication network. The key technical problems of the power line communication for complicated electromagnetic environment are proposed in this paper. On this basis, the welding parameters and the environment parameters are transferred to the local control unit, by the power line communication network. The influence rule on weld formation, microstructure, mechanical properties, and welding defects can be revealed by analyzing the above factors. This study explores a feasible way for the robot remote welding by the way of integration innovation. It will lay a foundation for the application of the robot remote welding technology in the construction of national key engineering.

Thermal errors are the largest contributor to the dimensional errors of a workpiece in precision machining. The error compensation technique is an effective way of reducing thermal errors. Accurate modeling of errors is a key part of error compensation. The thermal errors of a machine tool can be treated as the superposition of a series of thermal error modes. In this paper, five key temperature points of a turning center were obtained based on the thermal error mode analysis. A thermal error model based on the five key temperature points was proposed by using genetic algorithm-based back propagation neural network (GA-BPN). The GA-BPN method improves the accuracy and reduces computational cost for the prediction of thermal deformation in the turning center. A thermal error real-time compensation system was developed based on the proposed model. An experiment was carried out to verify the performance of the compensation system. The experimental results show that the diameter error of the workpiece reduced from about 27–10 μm after implementation of the compensation.

Based on China’s monthly precipitation data of 629 stations during 1950–2000, we calculated Z indices and separated them into seven Dryness and Wetness grades. Further, a drought area index was proposed to study changes in drought severity in northern China. The results revealed that the different severity of droughts all showed expanding trends in northern China’s main agricultural area. Moreover, the area coverage of droughts in different seasons and different regions displayed different trends.

Background Tortuous blood vessels are commonly seen in the cerebral arteries. The association between vertebrobasilar artery tortuosity and vascular vertigo remains obscure. Case presentation We describe two patients with vascular vertigo who had bilateral curving and spiral looping in multiple segments of the vertebral arteries and also exhibited basilar artery tortuosity. Both patients had cerebrovascular risk factors and exhibited clinical features of vertigo with high severity, slow recovery, and recurrent tendencies. Contrast enhanced magnetic resonance angiography of the neck showed bilateral tortuosity in the V2 segments and spiral twisting in the V4 segments of the vertebral arteries, and basilar artery curving. No obvious sign of atherosclerotic stenosis was found in the vertebrobasilar arteries and no abnormalities were observed in the internal carotid arteries. Transcranial Doppler ultrasound showed decreased blood flow in tortuous vertebrobasilar arteries. Brainstem auditory evoked potentials showed that the interpeak latencies (IPL) of waves III-IV were prolonged, with a ratio of IPL III-V/IPL I-III > 1. Conclusions Vertebrobasilar tortuosity in combination with cerebrovascular risk factors may lead to vascular vertigo in these patients.

A track before detect method based on data fusion of infrared and radar is proposed to increase the probability of correct track initiation and shorten initiation time. Track before detect is a new technique for dim target detection and tracking which is useful when the signal-to-noise ratio of target is low. Particle filter and dynamic programming for track before detect are currently proposed for detecting and tracking dim targets in low signal-to-noise ratio background. In this paper, we apply them in dual-module data fusion of infrared and radar. Particle Filter is applied to process the acquired data from radar. Dynamic programming is applied to process the acquired data from Infrared. Sensor receives data and generates the first stage decision. The decisions are subsequently transmitted to the fusion center where they are combined into a final decision on distributed fusion architectures. The proposed method is applied to simulate track before detect in dual module system of infrared and radar. The simulation results show that the proposed method increase the probability of correct track initiation and shorten initiation time.

The rod-fastened rotor is comprised of a series of discs clamped together by a central tie rod or several tie rods on the pitch circle diameter. The equivalent flexural stiffness of contact interfaces in the rod-fastened rotor is the key concern for accurate rotor dynamic performance analysis. Each contact interface was modeled as a bending spring with stiffness of Kc and a hinge in this study. The contact states of the contact interfaces which depend on the pre-tightening forces and bending moments (static) have effects on Kc. The approach to calculating Kc in two contact states is presented. The first contact state is that the whole zone of the contact interface is in contact, Kc is determined by the contact layer which consists of asperities of the contact surfaces. Hertz contact theory and the GW (Greenwood and Williamson) statistical model are used to calculate the equivalent flexural stiffness of the contact layer Kcc. The second contact state is that some zones of the contact interface are separated (when the bending moment is relative large), the equivalent flexural stiffness of the rotor segment Ksf (not include Kcc) decreases as the material in the separated zone has no contribution to the bending load carrying capacity of the rotor. The strain energy which is calculated by the finite element method (FEM) is used to determine Ksf. The stiffness Ksf is equivalent to the series stiffness of the discs of the rotor segment with flexural stiffness of Kd and a spring with bending stiffness of Kcf in the location of the contact interface, so Kc is equal to the series stiffness of Kcc and Kcf in the second contact state. The results of a simplified rod-fastened rotor indicate that for a fixed pre-tightening force, Kcc decreases with bending moments in the first contact state whereas increases with bending moments in the second contact state. In addition, Kcf and Kc decreases abruptly with the increase of bending moments in the second contact state when the rotor is subjected to a relatively large pre-tightening force. Finally, the multipoint exciting method was used to measure the modal parameters of the experimental rod-fastened rotor. It is found that the experimental modal frequencies decrease as the pre-tightening force decreases.Copyright © 2012 by ASME

The real-time identification of insect pests on field crops was an inevitable trend of modern plant protection. The second-generation detection device for insect pests was formed with the hardware and the software system. The hardware system included the trapping, stunning and buffering unit, the even illumination unit, the scattering and transporting unit, and the image vision unit. The software system included image enhancement, image segmentation, feature selection and recognition for insect pests. The device realized the complete automation from the collecting to the identification of insect pests. The nine species of insect pests were automatically recognized, and the correct identification ratio was over 86%. The experiment showed that the system was practical and feasible.

The detection of the stored-grain insects based on image recognition technology is high accuracy, cost-effective, high efficiency, no pollution and less labor. The classification of the stored-grain insects was multi-feature and multi-compound degree of various insects. The classifier design was critical for the detection system of the stored-grain insects. The optimal parameters C and g should be identified while using RBF kernels in the SVM classifier. The goal was to get the best cross-validation training model and improve the classification accuracy of the classifier. The grid search consisting of rough selection and fine selection was proposed to optimize parameters C and g by the recognition ratio of the training model. The optimal parameters were 30574 and 0.5743 after training, respectively. The fifteen species of the stored-grain insects that spoiled seriously in granary were automatically recognized by SVM classifier, and the correct identification ratio was over 94.67%. The experiment showed that it was practical and feasible.

Symbol and data are two cores of GIS and mapping systems. However, with the full sharing of geographical data, symbol information is not shared synchronously and there is a large number of low-level duplication in the process of making symbols. Based on symbol sharing problem among different systems, point symbols will be taken as a breakthrough to study symbol sharing between CAD and GIS, and to provide the mapping mechanism from CAD symbols to TrueType fonts. Experiment validates the feasibility of sharing between CAD blocks and TrueType-based GIS point symbols and offer approach for one map symbol library with multi-usage.

In order to solve the faults in usual detection of droplet distribution and motion in pesticide application and agricultural spraying field, a new method is given for the analysis of droplets characteristics and motion with PDIA (Particle/Droplet Image Analysis) and digital image processing technique. The diode laser is an illumination system that provides pulses of infrared laser light with variable pulse durations and pulse frequencies. During the analysis of the size of droplet and the velocity, images of droplets in pesticide spray field have been captured by using high-speed imager. The parameter of droplet such as size, perimeter, equivalent diameter, Shape factor and position etc, have been calculated with digital image processing technology. The trace of droplet in different frames has been tracked with the method, which is based on flag tracking and droplet neighborhood matching probability technique. The results showed this method can both realize the motion trace of droplet in different image frames and analyses the velocity of droplet. This technique can detect the droplet parameters quickly and accurately for agricultural sprays, and provide the basic way for research on flow visualization image analysis in pesticide application.

This article focuses on the issue of participant selection in supply networks risk management. Though there are many ways and applications which are introduced to selection partcipant in viewpoint of risk management, the practical effect of these methods is not considerable full and satisfactory. In this paper, we introduce a non-linear programming(NLP) method based on multiplicative and fuzzy preference relations to rank the candidates of probable participants in supply networks in viewpoint of risk management. The sound performance of discrimination power, the convience of applications and the relatively accuracy of perception expressions make this method of ranking suitable to participants selection in supply networks risk management. Numberial example and demostration are present at last.

Objective: This study aimed to compare the dose difference in 125 I seed implantation between three-dimensional (3D) print template-guided operation and traditional implantation. Materials and Methods: This study retrospectively analyzed 27 patients who underwent 125I seed implantation from August 2015 to December 2015 in Hebei General Hospital. Of these, 13 underwent seed implantation guided by a 3D print template, named the template group, and 14 underwent traditional implantation, named the traditional group. All patients underwent computed tomography (CT) scan. Then, 3D templates were printed in the template group. The implantation was guided by CT and 3D templates. The patients in the traditional group underwent implantation with free hands guided by CT scan. Postplan was performed after the operation. The dose-volume histogram, D90, and V90 were calculated. The D90 values pre- and post-operation were compared in each group. The postoperation V90 values of the two groups were also compared. Results: The mean D90 values pre- and post-operation in the template group were (87.09 ± 33.63) Gy and (85.31 ± 34.40) Gy, respectively, with no statistically significant difference. The mean D90 values pre- and post-operation in the traditional group were (86.04 ± 29.93) Gy and (74.96 ± 46.10) Gy, respectively, with a statistically significant difference. The mean V90 values postoperation in the template and traditional groups were (92.76% ± 1.89%) and (84.59% ± 7.56%), respectively, with a statistically significant difference. Conclusions: The postplan and preplan dose parameters of 3D print template-guided seed implantation were nearly consistent. The dose parameters of template group superior to the traditional group. The seeds can be implanted accurately with 3D print template.