bluuebunny/crossref_metadata_2025 huggingface dataset:
Classical analogies for the force acting on an impurity in a Bose–Einstein condensate
Authors: Rønning Jonas , Skaugen Audun , Hernández-García Emilio , Lopez Cristobal , Angheluta Luiza
Published: May 2020

We study the hydrodynamic forces acting on a small impurity moving in a two-dimensional Bose–Einstein condensate at non-zero temperature. The condensate is modelled by the damped-Gross Pitaevskii (dGPE) equation and the impurity by a Gaussian repulsive potential coupled to the condensate. For weak coupling, we obtain analytical expressions for the forces acting on the impurity, and compare them with those computed through direct numerical simulations of the dGPE and with the corresponding expressions for classical forces. For non-steady flows, there is a time-dependent force dominated by inertial effects and which has a correspondence in the Maxey–Riley theory for particles in classical fluids. In the steady-state regime, the force is dominated by a self-induced drag. Unlike at zero temperature, where the drag force vanishes below a critical velocity, at low temperatures the impurity experiences a net drag even at small velocities, as a consequence of the energy dissipation through interactions of the condensate with the thermal cloud. This dissipative force due to thermal drag is similar to the classical Stokes’ drag. There is still a critical velocity above which steady-state drag is dominated by acoustic excitations and behaves non-monotonically with impurity’s speed.
Tubular/helical architecture construction based on rolled-up AlN nanomembranes and resonance as optical microcavity
Authors: Yang Jinyu , Wang Yang , Wang Lu , Tian Ziao , Di Zengfeng , Mei Yongfeng
Published: May 2020

Aluminum nitride (AlN) has attracted a great amount of interest due to the fact that these group III–V semiconductors present direct band gap behavior and are compatible with current micro-electro-mechanical systems. In this work, three dimensional (3D) AlN architectures including tubes and helices were constructed by rolling up AlN nanomembranes grown on a silicon-on-insulator wafer via magnetron sputtering. The properties of the AlN membrane were characterized through transmission electron microscopy and X-ray diffraction. The thickness of AlN nanomembranes could be tuned via the RIE thinning method, and thus micro-tubes with different diameters were fabricated. The intrinsic strain in AlN membranes was investigated via micro-Raman spectroscopy, which agrees well with theory prediction. Whispering gallery mode was observed in AlN tubular optical microcavity in photoluminescence spectrum. A postprocess involving atomic layer deposition and R6G immersion were employed on as-fabricated AlN tubes to promote the Q-factor. The AlN tubular micro-resonators could offer a novel design route for Si-based integrated light sources. In addition, the rolled-up technology paves a new way for AlN 3D structure fabrication, which is promising for AlN application in MEMS and photonics fields.
An Efficient Hybrid Compact-WENO Scheme for Computational Aerodynamics
Authors: Luo Xin , Wu Songping
Published: May 2020

The hybrid compact-WENO scheme is an effective method to construct a high-resolution shock-capturing scheme for computational aerodynamics. Ren et al. (J. Comput. Phys. 192:365-386, 2003) presented a characteristic-wise hybrid compact-WENO scheme, which combines the high-resolution property of the compact scheme with the shock-capture ability of the WENO scheme. However, it needs to deal with the block-tridiagonal system of linear equations when applied to solve systems of hyperbolic conservation laws (such as Euler equations). Therefore, the scheme is computationally expensive, which limits its application in engineering practice. In this paper, we present a new hybrid compact-WENO scheme. This new scheme also uses the characteristic-wise method. However, it avoids solving the block-tridiagonal system of linear equations by cleverly utilizing the monotone-preserving limiter and the higher dissipative Lax-Friedrich flux splitting. As a result, the scheme has a much higher efficiency than Ren’s scheme. A series of numerical experiments verify the high-resolution property, excellent shock-capture ability, and high efficiency of the presented scheme. Therefore, it is suitable for engineering applications.
Memory-Based Reinforcement Learning for Trans-Domain Tiltrotor Robot Control
Authors: Huo Yujia , Li Yiping , Feng Xisheng
Published: May 2020

Aiming at the problems of motion control with high precision for a new type of air-water trans-domain tiltrotors, a deep reinforcement learning controller is applied to these conditions. Reinforcement learning algorithm with memory capability allows the robot to learn from dynamic information collected in the past. In this paper, the trans-domain tiltrotors are supposed operating as a quad-rotors with fixed-wing in the air. Moreover, simulation is based on ROS and Gazebo platform for training the reinforcement learning repeatedly and the results demonstrate this algorithm gets better accuracy and effectiveness compared with other non-current methods in the conditions of the tiltrotors control task.
Finite Element Simulation of Ball Joint under Hot Forging Process
Authors: Jantepa N. , Suranuntchai S.
Published: May 2020

Nowadays, the finite element method (FEM) has been widely used to forecast the metal forming process and finding the parameter optimized. This work studied the use of FEM as a tool to design and process optimize a hot forging die for producing an automotive part named Ball Joint that part was made from carbon steel grade S45C. The objective of the study was to increase the efficiency of production and extend the life cycle of the machine by using load not exceeding the machine capability. To achieve this objective, the new parameters must produce workpieces without any defects. The defects regularly found in the forging workpieces are the thickness out of specification, the underfilling, and the crack. The parameter of the rougher and finisher process to studies and optimized that cover is the die gap and forming load. The die gap of 4, 3 and 2 millimeters and the forming load not exceeding 85% of machine capability were used in the hot forging simulation. From FEM simulation results, it was found that the die gap of 3 millimeters of the rougher and finisher process was the best to form workpieces without any defects, workpieces thickness within specification and the forming load not exceeding of 85%. In summary, the simulation and experimental results were compatible.
Structural Modelling and Optimization of the Double-Ducted Fan UAV
Authors: Yongjie Zhang , Hao Dong , Xinzhe Chang , Yafeng Wang , Xiaochuan Liu
Published: May 2020

This paper aims to model and optimize the structure of a double-ducted fan UAV (unmanned aerial vehicle). The structural model of the UAV is established based on main technical requirements. Aluminum alloy is selected in UAV structure. The static analysis of UAV is performed using finite element method. The section thicknesses of UAV are optimized by a feasible direction method. The optimized structure weight of the UAV is reduced by 18%. This paper provides some useful reference for the structural modelling and optimization of double-ducted fan UAV.
Application of knowledge engineering in spacecraft overall design
Authors: Zhao Hanmo , Sun Zhaowei , Zhang Hongzhu
Published: May 2020

This paper proposes an advanced technology in spacecraft overall design based on knowledge engineering. Knowledge engineering is a branch of artificial intelligence. Applying knowledge engineering technology to improve the efficiency and quality of overall design process and enhance the automation of overall design, moreover focusing on solving overall spacecraft design knowledge representation, knowledge evaluation, knowledge acquisition, knowledge inference and related issues. Unlike the existing methods, this paper uses ontology technology to build a pint-sized spacecraft overall design knowledge base under protégé software environment, which achieves a better performance. Finally, simulations for the designed power subsystem based on the knowledge base are performed, and the simulation results validate the effectiveness, reliability, of the power subsystem designed by the method of spacecraft overall design based on knowledge engineering.
Numerical Simulation on Cold-Flow Impact of the Soft Pulse Separation Device in Dual Pulse Solid Rocket Motor
Authors: Liu Wanli , Chen Xiong , Li Yingkun
Published: May 2020

In this paper, the fluid solid coupling in the cold-flow impact process of the soft pulse separation device of dual pulse solid rocket motor is numerically simulated by using ANSYS Workbench software. This paper mainly analyzes the flow characteristics of the flow field near the interlayer, deformation and stress change of interlayer under the cold-flow impact, and obtain the time of the interlayer failure. Finally, the numerical simulation results are compared with the experimental results of cold-flow impact. Finally, at 8.41 ms, the stress at the prefabricated defect of the interlayer reaches the tensile strength, and the pressure at breaking is 0.745 MPa.
Probabilistic Airport Traffic Demand Prediction Incorporating the Weather Factors
Authors: Tian Wen , Song Jinjin , Guo Yixing , Yang Fan
Published: May 2020

With the development of air transport industry in China, the congestion problem in the terminal areas of busy airports has become increasingly serious. In order to alleviate the increasingly frequent air traffic congestion, it is necessary to accurately and objectively predict traffic flow. Traditionally, most predicted methods are based on the number of aircrafts flight in the terminal area to obtain deterministic traffic flow data, without considering the impact of uncertain factors on the prediction results. Based on the uncertainty of demand, this paper uses a probability density prediction method based on quantile regression neural network and kernel density estimation, to analyse the variation of traffic flow at different quantiles according to the obtained continuous conditional quantile function. Predicting the probability density of traffic flow on a certain day, and then comparing the point prediction value corresponding to the peak value, which consider the weather factor and the conditional probability density prediction curve without considering the weather factor, it is concluded that considering the weather factor can make the traffic flow prediction more accurate.
Feasibility Study of a New Security Verification Process Based on Face Recognition Technology at Airport
Authors: Zhu Taotao , Wang Lijun
Published: May 2020

In order to optimize the mode of manual security identity verification at security checkpoints in the domestic airports in China, this paper proposes a new security identity verification process based on face recognition technology. This paper also presents the carried out practical tests on the feasibility and effectiveness of the proposed process. The new process includes two important steps: self-service centralized verification and automatic face recheck of passengers. The test results show that the process is effective and cost efficient. The technology significantly reduces the rate of human error, dramatically relieves the workload of the security staff and shorten the passenger’s time spending at identity check in the airport.
A Network Selection Algorithm Based on Vehicle Trajectory Prediction and AHP
Authors: Ding Qi , Zhang Dengyin , Zhang Zhen
Published: May 2020

This paper proposes a network selection algorithm based on Vehicle Trajectory Prediction and AHP (VMAP). The Markov chain is used to predict the running trajectory of the vehicle. The predicted trajectory is used to calculate the dwell time of the vehicle in the network. A network in which the dwell time is greater than a preset threshold is selected as a candidate network. Then, using the Analytic Hierarchy Process (AHP) to determine the weights of each attribute of the candidate network and obtain the comprehensive evaluation quality index of each candidate network to select the optimal network. This algorithm can not only minimize the ping-pong effect generated, but also enable the vehicle to access the optimal network stably.
Research on Time Triggered Ethernet Scheduling Planning Method
Authors: Wei Aodi , Zhang Guoqun , Zhang Tao
Published: May 2020

Time-triggered Ethernet adds a time-triggered traffic type based on traditional event-triggered best-effort traffic, which extends data traffic into three communication formats, namely time-triggered (TT) messages and rate-constrained (RC) messages and the best-effort (BE) messages. Aiming at the time-triggered Ethernet message scheduling planning problem, this paper first introduces the principle of the SMT solver and the idea of the solver algorithm, and designs constraints for the message scheduling problem. Based on this, the paper introduces a load balancing strategy and a step-by-step iterative conflict backtracking method, designs and completes a time-triggered Ethernet message scheduling algorithm, and then completes scheduling planning for time-triggered traffic and optimization of scheduling results. For the planning results, the paper proposes verification indicators for network load balancing and algorithm solution efficiency, designs a test set for algorithm verification, completes algorithm analysis and verification based on the test set solution results, and determines the algorithm optimization effect.
Research on Operational Risk Index of Distribution Network with Distributed Generation
Authors: Li Ping , Zhu Yu , Li Shenghui , Gu Cailian , Zhang Yizhong
Published: May 2020

This paper analyses the distributed generation’s influence on failure severity index, introduces the index which can represent the probability of islanding and derive the distribution networks’ failure severity index with the connection of distributed generation. This paper analyses how the distributed generation influence distribution networks when the wide-area distributed generation connect with networks, then deriving the distribution networks’ failure severity index with the connection of wide-area distributed generation. Through analysing the same IEEE 33 node network and calculating traditional distribution networks’ reliability index and failure severity index, we can obtain the system’s risk index. By comparing with every index in 3 different conditions, we can find that when the distributed generation connect with distribution networks, the networks’ risk tolerance has been improved, we can also find the significant improvement of distribution networks’ risk index when the wide-area distributed generation connect with distribution networks.
Face Attribute Manipulation Based on Self-Perception GAN
Authors: Tu X G , Luo Y , Zhang H S , Ai W J , Ma Z , Xie M
Published: May 2020

Manipulating human facial images between two domains is an important and interesting problem in computer vision. Most of the existing methods address this issue by applying two generators or one generator with extra conditional inputs to generate face images with manipulated attribute. In this paper, we proposed a novel self-perception method based on Generative Adversarial Networks (GANs) for automatic face attribute inverse, where giving a face image with an arbitrary facial attribute the model can generate a new face image with the reversed facial attribute. The proposed method takes face images as inputs and employs only one single generator without being conditioned on other inputs. Profiting from the multi-loss strategy and modified U-net structure, our model is quite stable in training and capable of preserving finer details of the original face images. The extensive experimental results have demonstrated the effectiveness of our method on generating high-quality and realistic attribute-reversed face images.
Research on Gait Recognition and Prediction of Exoskeleton Robot Based on Improved DTW Algorithm
Authors: Motao Wang , Zhijun Li , Qing Lei , Meng Wang , Rui Zhang
Published: May 2020

In order to realize the follow-up control of the exoskeleton robot better, the gait phase of the human body should be accurately identified and the human body motion intention should be matched in real time. In this paper, a set of gait data measurement system is used to collect the gait information of the human body during the movement process. Then, the gait recognition of the six models is carried out by the support vector machine through the plantar pressure information. Then the human movement intention is divided into five kinds and the improved DTW algorithm was used to complete the work of matching human motion intentions. Ultimately, the BP neural network model was designed to accurately predict the gait data. The experimental results show that the exoskeleton robot can accurately realize the three functions of recognition, matching and prediction.
An Eye Gaze Tracking Method of Virtual Reality Headset Using A Single Camera and Multi-light Source
Authors: Lu Shengfu , Li Richeng , Jiao Jinan , Kang Jiaming , Zhao Nana , Li Mi
Published: May 2020

For the problems the hardware configuration of multi-camera and multi-light source is complicated and the eye image of little auxiliary light source is too dark in the eye gaze tracking system, A new eye gaze tracking system based on two-dimensional mapping model is proposed, which achieved the transformation from two-dimensional coordinates to three-dimensional coordinates combined attitude angle of virtual reality headset. In this paper, the mapping relationship between visual features and eye gaze points is established by improving the method of pupil image recognition and eye gaze estimation based pupil-corneal, which the accuracy of eye gaze estimation is further improved. The experimental results show that the error of eye gaze tracking system is less than 1.1 °, which achieved the good effect of eye gaze tracking.
Multi-modal Non-linear Continuous 3D Presentations
Authors: Rodin Daniil , Elber Gershon
Published: May 2020

Computer presentations are an integral part of our lives, yet the contemporary presentation tools have not changed for about three decades, still emulating old overhead slide projectors. Combined with the rapid evolution of computer graphics technologies, this results in contemporary tools being ill-suited for many modern needs where linear sequences of fixed-sized 2D slides consisting of text and images is not nearly sufficient. In this work, we propose a presentation system aimed at overcoming the limitations of the contemporary presentation software. To achieve this goal, we abandon the usual concept of a presentation as a 2D slide sequence, and instead treat them as continuous automatically created 3D scenes of non-linear structure with multi-modal content, all without requiring any professional skills from the end user.
End-To-End Rotational Motion Deblurring Method Combining with Motion Information
Authors: Qiu Yudan , Zhang Cheng
Published: May 2020

We propose an end-to-end rotational motion deblurring method based on conditional generation adversarial networks. The proposed method calculates the blur path value of each pixel on the rotational motion blurred image to provide a priori information of its blur degree, and then connects it to the blurred image as the input of the network. In addition, a rotational motion blurred image dataset is produced, which contains different degrees of rotational motion blurred images, as an evaluation dataset for the method to the effect of rotational motion deblurring. Experiments show that the proposed method is superior to existing end-to-end deblurring methods in both qualitative and quantitative analysis when dealing with different degrees of rotational motion blur.
Application of a Novel and Improved VGG-19 Network in the Detection of Workers Wearing Masks
Authors: Xiao Jian , Wang Jia , Cao Shaozhong , Li Bilong
Published: May 2020

In order to work and travel safely during the outbreak of COVID-19, a method of security detection based on deep learning is proposed by using machine vision instead of manual monitoring. To detect the illegal behaviors of workers without masks in workplaces and densely populated areas, an improved convolutional neural network VGG-19 algorithm is proposed under the framework of tensorflow, and more than 3000 images are collected for model training and testing. Using VGG-19 network model, three FC layers are optimized into one flat layer and two FC layers with reduced parameters. The softmax classification layer of the original model is replaced by a 2-label softmax classifier. The experimental results show that the precision of the model is 97.62% and the recall is 96.31%. The precision of identifying the workers without masks is 96.82%, the recall is 94.07%, and the data set provided has a high precision. For the future social health and safety to provide favorable test data.
Using Darts to Improve Mold ID Recognition Model Based on MASK R-CNN
Authors: He Wa , Wu Yuting , Liang Peng , Hao Gang
Published: May 2020

Mask R-CNN model is an excellent image segmentation model, but cannot perform well on the task of mold ID recognition. To address the aboved problem, this paper improves the classification branch of Mask R-CNN model by applying DARTs, making the improved classification branch more suitable for mold ID recognition task. Firstly, DARTs is employed to obtain network cell structure. Secondly, the network cell structure is used to improve the classification branch. Moreover, we make the model converge under 2GPU days based on transfer learning. Finally, the experimental results show that the improved model can achieve higher accuracy with IOU of 0.5 (increased by 1.02%) and 0.75 (increased by 2.42%).
Research on Intelligent Vehicle Damage Assessment System Based on Computer Vision
Authors: Qianqian Zhu , Weiming Guo , Ying Shen , Zihao Zhao
Published: May 2020

At present, under the guidance of the new generation of information technology, the rapid accumulation of data, the continuous improvement of computing power, the continuous optimization of algorithm models, and the rapid rise of multi-scene applications have made profound changes in the development environment of artificial intelligence. In this paper, based on the demand of automobile insurance claims and intelligent transportation, combined with abundant basic data and advanced machine vision algorithm, an intelligent damage determination system of ‘Artificial Intelligence + Vehicle Insurance’ is constructed. This paper first introduces the functions of the intelligent damage assessment system. Secondly, it discusses the realization path of each functional module in detail, and finally puts forward the vision for the future.
Non-Global Shared Recursive Network for Image Super-Resolution
Authors: Zheng Bing , Ma Lihong
Published: May 2020

In Super-Resolution(SR) reconstruction, some recursive networks use shared parameters to keep lightweight. But these methods lack flexibility while use shared parameters to process different feature maps. We propose a non-global shared recursive network that consists of recursive units and channel attention modules. Firstly, we propose multi-scale channel attention module to learn the inter-channel interdependence and spatial information within the channel. The multi-scale channel attention module is composed of a multi-scale pooling layer, fully connected layers and activation functions. In order to flexibly process different feature maps in different layers, the attention module parameters are not shared, but only a small number of parameters are used. Secondly, residual dense recursive unit is used to accelerate convergence and feature reuse. In order to further reduce parameters, bottleneck layer is added to the dense connection. Based on the above mechanism, we propose a lightweight and efficient network structure. Experiments show that the proposed method has better performance than other lightweight networks in the standard dataset.
Improved IMM Algorithm based on RNNs
Authors: Deng Lichuan , Li Da , Li Ruifang
Published: May 2020

The Interactive Multi-Model (IMM) algorithm uses multiple motion models to simultaneously track the target, which effectively solves the problem of model mismatch when a single model tracks the maneuvering target, and is widely used in maneuvering target tracking tasks. However, the Interactive Multi-Model recognition motion model is not accurate enough, and there is a certain delay in the maneuver recognition of the target, which leads to a reduction in tracking accuracy. To solve this problem, considering that deep neural networks are very good at processing classification tasks, we introduce it into target tracking tasks, combining the respective of deep neural networks and traditional tracking filtering methods for maneuvering target tracking. we use the Recurrent Neural Networks to identify the motion model of the target and propose an improved LSTM-IMM model algorithm based on the interactive multi-model algorithm. Finally, we compare the traditional interactive multi-model algorithm and verify the algorithm using Monte Carlo simulation. The results show that the proposed algorithm has improved the recognition accuracy and recognition speed of the model, and the tracking accuracy has been improved.
X-ray Power Fault Detection Method Based on Feature Spectrum Reconstruction and Convolutional Neural Network
Authors: Zhang Jianlong , Cui Mengying , Wang Bin
Published: May 2020

The high frequency of X-ray high-voltage power supply (XHPS) leads to conspicuous parasitic effect of power components. And this will transform the equipment into a time-varying and nonlinear complex system. By applying the combination of convolutional neural network (CNN) and traditional methods, this paper proposes a fault detection method based on 2-D feature spectrum reconstruction and CNN. Firstly, the multi-wavelet transform is utilized to decompose the 1-D high-voltage power signal to obtain the coefficients of each frequency band. Secondly, the inverse Zigzag scan reconstructs the multi-wavelet coefficients into a feature spectrum that satisfies the input form of VGG-16, and then cascades the deep features obtained by VGG-16 with the multi-wavelet features. Finally, the final fault detection result is obtained by the support vector machine (SVM). The simulation results show that the proposed method has better fault detection performance and could provide a workable idea for fault prediction and avoidance.
Defect Detection Algorithm of Printing Roller Based on Visual Salience
Authors: Li Bilong , Cao Shaozhong , Wang Jia , Xiao Jian
Published: May 2020

Aiming at the complex surface condition of the printing roller and the requirement of high precision and high efficiency of the surface defect detection of the printing roller, a detection algorithm based on visual salience is proposed. Firstly, the dodging processing is used to eliminate the uneven illumination of the printing roller image; secondly, the non local means denoising algorithm is used to weaken the surface texture based on the redundant information commonly existing in the printing roller image; secondly, the spectral residual salience algorithm is used to calculate the salience of defects in the image and obtain the saliency map; finally, Sobel is used to detect the saliency map of defects and the manually labeled The defect images were compared and analyzed. Experimental results show that the algorithm has high recognition rate and accuracy, and can meet the needs of surface defect detection of printing roller.
Predicting the Rise and Fall of Stock Prices based on the modified BP_AdaBoost
Authors: Li Lu , Xu Shizhan , Liu Yanhong , Yang Xiaorong
Published: May 2020

In the big data era, the studies of the quantitative stock selection strategy based on machine learning are becoming more and more popular. Most of existing studies focus on short-term strategies, and few on the medium-term or long-term strategies. Moreover, many scholars tend to transform the problem of predicting changes of stock prices into the binary classification problem, which makes it difficult to earn steady abnormal returns. Therefore, it is extraordinary meaningful to study effective quantitative investment strategies. In this article, we propose the modified BP neural network combining AdaBoost algorithm (the modified BP_AdaBoost) and apply it into the quantitative stock selection. We carry out empirical studies about medium-term and long-term price changes in the A share market of our country, construct the factor pool and check the performances of the modified BP_AdaBoost.
Noise Removal from Medical Images Using Hybrid Filters of Technique
Authors: Aslam Muhammad Aqeel , Munir Muhammad Asif , Cui Daxiang
Published: May 2020

In this paper noise removal from the medical images using the hybrid filter of technique is presented. From the last couple of decades, medical image processing and analysis techniques based on computing algorithms acquired prominence as an alternate skillset for medical experts in disease diagnosis and prevention. As the number of patients are increasing yearly, doctors don’t have enough time to calculate the actual information from the medical images, as most of the medical images are affected by the noise. Medical images contain a different kind of noises because several machines are operating for data acquisition and transmission, so in order to reduce the complexity from the radiologist point of view we were very much interested in the design of an algorithm which can be beneficial and useful at the convenient level. Image Processing has become a very prominent technique in medical image analysis and medical image processing. The proposed architecture is the amalgamation of morphological operations, A modified form of Median Filters and Wiener Filters. The boundary and the shape of the image is extracted through Morphological operation. For noise removal and enhancement purpose modified median filter and wiener filters were used. The parameters like Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Root Mean Square Error (RMSE) are determined through proposed algorithm. Overall results indicate that the enhancement quality is performing well in proposed technique.
Feature Extraction of Radar Echo Image Based on Improved Convolutional Neural Network
Authors: Chenhao Zhang , Hongquan Li , Xun Feng , Jingmian Tang
Published: May 2020

Feature extraction of radar echo image is an important part of identification and recognition of air targets. In recent years, with the rapid development of deep learning, new solutions are provided for it. In this paper, the convolution neural network (CNN) is applied to feature extraction. Based on the classic CNN model, Adam is used to update the model parameters, dropout is used to prevent over fitting, and an improved CNN model is constructed. Then, the radar echo image data set is used to train the model, so as to extract target features and classify them. Simulation results show that the accuracy of the improved model is 99%, and the training speed is greatly improved. Different from the traditional extraction method which relies on manual experience, the improved CNN can improve the efficiency of feature extraction of radar echo image and lay a solid foundation for further research and identification of air targets.
High-Precision Portrait Classification Based on MTCNN and Its Application on Similarity Judgement
Authors: Du Juan
Published: May 2020

Portrait classification is a complex course including at least face detection, recognization and compare each of which contains multi-tasks, facing plenty of various challenging questions due to askew poses, illuminations, occlusions, image blurring and small scale face in the pictures. Though deep learning methods, such as Convolutional Neural Network (CNN) family and You Only Look Once (YOLO) series, had boomed a large number of areas on object detection and accelerated the solving of these difficulties on image processing, they are not specially designed for the image classification and may require a great deal of resource, expensive computation and taxing annotation. In 2016, an innovative face detection model named Multi-task convolutional neural network (MTCNN) arose and triggered viral and wide spread. Its high efficient and accurate performance on both face detection and face alignment tasks, real time effect based on lightweight CNN as well as effective conducting online hard sample mining, all contribute to significant improvement to the challenges above. This paper introduces the MTCNN algorithm and applies it to the similarity judgement with two industrial real problems together with FaceNet model. In addition, some effective practical methods on increase precision of classification are also proposed to gain better effect.
A Blind Watermarking Algorithm for Digital Image Based on DWT
Authors: Tan Linglong , He Yihong , Wu Fengzhi , Zhang Dong
Published: May 2020

In order to improve the robustness of digital image watermarking algorithms, the digital image blind watermarking algorithm proposed in this paper embeds watermark information in a reasonable position. First, scramble the watermark information and embed the random signal into the low-frequency domain of the image. On the one hand, it can effectively prevent attacks on the watermark system and enhance the security of the system, disperse as much as possible. In the watermark embedding process, a method based on the wavelet transform domain is used. The original image is not required to participate in the watermark extraction process, and the blind watermark function is implemented. In order to verify the effectiveness of the algorithm, the watermarked image is subjected to filtering, scaling, noise, cropping and rotation attacks, and the peak signal-to-noise ratio and normalized correlation coefficient are used to quantitatively evaluate the watermarked image. Experimental results show that the algorithm can resist a variety of attacks, and has good imperceptibility and robustness.
Dynamic Scene Deblurring Based on Semantic Information Supplement
Authors: Liu Yiming , Li Junhui , Huang Wenzhuo , Tang Kang , Xu Dahong
Published: May 2020

To solve the problem of semantic information dilution in network propagation, a semantic information supplement mechanism (SIS) is proposed to improve the performance of dynamic scene deblurring algorithm. Based on GANs structure, our generator is to recycle the semantic information and features spanning across multiple receptive scales to restore a sharp image, when a blur image is given. What’s more, in order to better integrate semantic information with the latent-feature and solve the problem of training difficulty in very-deep network, we put forward a long and short skip-connection method. Extensive experiments show that our Semantic Information Supplement network (SIS-net) achieves both qualitative and quantitative improvements against state-of-the-art methods.
Pattern Recognition and Its Application in Image Processing
Authors: Feng Zhao , Hua Xu
Published: May 2020

With the continuous improvement of China’s national economy and the unprecedented innovation and development of modern science and technology, artificial intelligence technology has been widely used in China’s industrial production, power transportation, aerospace and other fields. As an extension of modern artificial intelligence technology, pattern recognition technology plays an important role in modern computer image processing by virtue of its own intelligence and automation. It fundamentally guarantees the accuracy of image processing and greatly improves computer image recognition and information processing. Overall work efficiency. This paper mainly analyses the main characteristics of pattern recognition technology, and further studies the practical application of pattern recognition technology in image processing, hoping to provide corresponding reference for the future application and development of pattern recognition technology in image processing.
Software Design of High Precision Filling Test System Based on LabWindows / CVI
Authors: Li Bin , Fan Xinghang , Chen Jie , Cheng Shuo , Gou Yang
Published: May 2020

Filling technology has a large number of applications in the chemical, food, pharmaceutical and other industries. Filling repeatability is an important indicator in the field of flow measurement. Because of its powerful data analysis capabilities, virtual instrument technology is playing an increasingly important role in the field of automated detection. This paper designs the host computer software based on LabWindows / CVI for a high-precision filling test system. LabWindows / CVI combines a powerful and flexible C language platform with professional measurement and control tools so it can realize the requirements for designing filling system software. The main functions of the software are hardware communication, data acquisition and processing, and export of test results then a user-friendly interface is designed. This article explains the basic program design process, data structure and introduces program functions. The software can run stably and can provide filling test solutions for different test devices.
The Design of Service Acquisition System for Tiantong Satellite Mobile Communication Terminal
Authors: Zhang Kaihe , Wang Yanjun , Wang Liquan , Wang Chong , Lu Xiangyu
Published: May 2020

In order to maintain national security and social stability, the ground mobile communication system is monitored by security sector in some area that captures information to prevent crime and terrorist attack. Because TianTong (TT)satellite mobile communication system is run by China Telecom publicly, so it must be equipped with capture system to forbid transmission of crime information. By analyzing advantages of independent station, L1 interface and IU interface, the method of data distribution through fiber between Access Network and Core Network is adopted in this system. The data obtained from the IU interface, through the signalling processing and service processing equipment, forms the call list, analyses the SMS and location, and obtains the corresponding user voice of the call. Finally, these service data are stored in the database. The state list and hierarchical state machine (HSM) mothed is used in this design to solve the difficulty of tracking handset’s many states. This design has lots of advantages such as overall data, good expansibility, and easy implementation and so on, which can satisfy the needs of recent and further use extremely. Now, the test of this system is completed and all devices are ready to equip.
The Construction Scheme of D-Iot Cloud Master Station
Authors: Dai W , Song W , Lv Y , Xi Y Y
Published: May 2020

With the development of the Energy Internet, specific requirements are put forward for the Distribution Internet of Things technology. How to control the panoramic equipment and data through a horizontalization of all links and a secondary level of operation on the entire business cloud. And provide external platforms and applications. This article first proposes the architecture of the IoT cloud platform for distribution IoT, and discusses open cloud platform, Internet of Things access and equipment management, data storage and management technology, cloud edge collaboration, and other key technologies. Finally, it discusses application scenarios based on IoT technology, such as panoramic condition monitoring based on multi-dimensional information perception, tiered line loss hierarchical layered management, low voltage fault location. To provide technical support for the ubiquitous electric power Internet of Things in order to realize the interconnection of all things and human-computer interaction in all aspects of the power system, create a comprehensive status perception, efficient information processing, and convenient and flexible application.
Research on EA-Xgboost Hybrid Model for Building Energy Prediction
Authors: Yucong Wu , Bo Wang
Published: May 2020

Building energy forecast plays an important role in Intelligent Building. Due to its non-stationarity and uncertainty, the prediction accuracy of existing methods need to be further improved. In view of this problem, propose the EA-XGBoost model, which combines Empirical Mode Decomposition (EMD), ARIMA and XGBoost model to predict building energy consumption. First, EMD is used to decompose the consumption data into multiple Intrinsic Mode Functions(IMF). Afterwards, ARIMA model is applied for each IMF to get regression result, then sum the results and calculate the residual. Taking the residual as an input feature of XGBoost, combined with other energy-related factors such as dry and wet bulb temperature, using XGBoost after Grid-Search to predict building energy consumption data. Compared with ARIMA and XGBoost model, EA-XGBoost hybrid model performs best in forecasting building energy consumption dataset which provided by the US National Renewable Energy Laboratory. The experiment shows the feasibility and effectiveness of the new model.
Studying problems on choosing stable orbits of nanosatellites to provide passive and periodic relative trajectories
Authors: Shcherbakov M.S. , Avariaskin D.P.
Published: May 2020

The paper considers the passive perturbed inspection movement of one nanosatellite (NS) relative to another. The inspection movement is considered in local vertical local horizontal associated with the inspected NS moving in a near-circular low Earth orbit. NS2 has an inspection movement around NS1. Under the influence of J2 perturbation the real movement diverges from the inspection one and the relative trajectory of satellites becomes open. The paper shows that by choosing the initial conditions of both NS motion, the perturbation of the inspection motion can be reduced. As strict J2 invariant trajectory conditions exists [1] to provide quasi-periodic relative orbit [2], the perturbed inspection movement will slightly differ from the reference trajectory. Thus, NS can fulfill the target task without orbital correction for a certain time interval. Paper provides analytical relations for the conditions of quasi-periodic relative orbit and a methodology for selecting the initial conditions for the NS motion.
Azimuth correction system of an inclinometer
Authors: Konovalov S.F. , Mayorov D.V. , Rusanov P.G. , Chulkov V.E.
Published: May 2020

The paper is devoted to azimuth correction system of an inclinometer used for well drilling in high latitude areas. In these areas, ordinary types of navigation equipment (gyroscopic and magnetic sensors) are ineffective because of small horizontal component of the Earth rotation velocity and significant values of magnetic declination. In the paper, an approach to the correction system study is described. The system consisting of two vibrational sources shifted from the wellhead to a certain distance in the direction of projected drilling and spaced to same distance from the specified direction is characterized.

Special attention is given to investigation of a motion model of a flexible shaft, which is proposed as the vibrational source of the system. On the basis of experiments and calculations, it is shown in the paper that in a complex mechanical system with a flexible shaft of vibrational source, a consistent multi-resonant effect can be observed when the shaft starts rotating. Moreover, a simulation model and results of estimation of the vibrational source power are presented. A possibility of use a compact “handheld” vibrational source with flexible shaft for azimuth correction system operation is shown
Implementation of metal 3D-printing in manufacturing navigation systems: the results of practical studies
Authors: Eliseev D.P. , Malashenkov K.S.
Published: May 2020

Practical investigation results of metal 3D-printing of parts are presented. Surface roughness, dimensional repeatability, and porosity of produced specimens are measured. Machinability, tensile strength, as well as cost efficiency of parts are evaluated. Based on the obtained results, a conclusion about implementation of metal 3D-printing in navigation systems manufacturing is made.
Accuracy evaluation of SLAM algorithms
Authors: Titov R.U. , Motorin A.V.
Published: May 2020

The paper is devoted to methods for solving the problem of simultaneous localization and mapping (SLAM) for an autonomous vehicle. Accuracy evaluation of the three-dimensional SLAM approach as compared to classical two-dimensional SLAM is obtained through the simulation study
Studying the atomic gyroscope magnetic shield residual magnetization by comparing parametric resonance signals
Authors: Chalkov V.V. , Shevchenko A.N.
Published: May 2020

The paper presents an algorithm for measuring the residual magnetic field in the magnetic shield of a quantum rotation sensor, which is based on the analysis of cesium parametric resonance signal. The effect of the residual magnetic field in the magnetic shield of the passive shielding system of an atomic gyroscope on its performance is investigated. The form of cesium parametric resonance is analysed. The results of experimental verification of the algorithm are presented to confirm its effectiveness.
Verification of digital integrated circuits taking into account pad models
Authors: Nevirkovets N.N. , Chernetskaya N.M. , Kostygov D.V. , Belyaev Y.V. , Mikhteeva A.A.
Published: May 2020

The digital integrated circuit is the main part of different sensors. It performs signal processing, controlling of a system and provides interface with external devices. Timing analysis is a basic verification method of digital integrated circuits. A new technique of digital blocks timing analysis taking into account the models of contact pads is proposed. The results of the I2C interface unit development using the proposed technique are presented.
Effect of Jiuwei Jiangzhi tablet on intestinal peristalsis in mice
Authors: Geng X L , Sun J H , Li L , Huang Y L , Hao S J , Zhang Z C
Published: May 2020

This study is to observe the effect of nine-flavor lipid-lowering tablet on the peristalsis of small intestine in mice. The effect of the nine-flavor lipid-lowering tablet on the small intestinal peristalsis of mice was studied by measuring the distance of the carbon powder in the small intestine of the mouse. 60 mice,18-22 g of body weight, and half of male and female, were randomly divided into 5 groups, nine-flavor lipid-lowering tablets, medium and low-dose groups, Sanjiu Weitai suspension and the same volume of physiological saline, and each day was administered once for 7 days, with a volume of 0.2 mL/10 g of body weight. The mice were fasted prior to dosing on Day 7 (water), and after the last dose of 30 min, the mice were given the carbon-end suspension 0.2 mL/10 g of body weight for 30 min after the last dose. After 20 min, the mice were sacrificed after the cervical dislocation of the cervical spine. The abdominal cavity was cut, and the small intestine of the small intestine was isolated and the total length of the small intestine was measured (cm). And the propulsion. Compared with the blank group, Jiuwei Jiangzhi tablet low dose group could significantly promote intestinal peristalsis in mice, positive group and Jiuwei Jiangzhi tablet high dose, middle dose group also significantly increased peristalsis in mice. Jiuwei Jiangzhi tablet can promote peristalsis in small intestine of mice.