bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Investigation of Effect of Combustor Length on Combustion Characteristics of Solid Fuel Ramjet
Authors: Li Weixuan , Chen Xiong , Qiu Shuang
Published: May 2020

In this paper, the effect of solid fuel ramjet (SFRJ) combustor length on combustion characteristics with swirling turbulent reacting flow is investigated via numerical approach. An in-house code written in FORTRAN is employed to simulate the 2-dimensional turbulent reacting swirling flow in the combustor of SFRJ. The results indicate that increasing the combustor length will increase the temperature near the backward-facing step and the afterburning chamber and enhance the local heat flux at the reattachment point.
Research on Self-adaption Model of Cooling Intensity for Construction Vehicle Based on CFD and ε-NTU
Authors: Liu Peng , Li Yaogang , Wang Baozhong , Liu Jiaxin
Published: May 2020

To improve the stability of power take-off and energy conservation of cooling system, it is essential to investigate a control model of cooling system for the working engine under varied ambient temperature. A Double-drum roller from a manufacturer in China was employed as the research basis for a self-adaption model of cooling intensity. At first, a numerical investigation was performed on its engine cabin, and the results were experimentally validated. Secondly, a self-adaption model based on ε-NTU method was solved with the combination of Ferrari and Shengjin methods, and finally presented in terms of heat transfer rate, fan rotation speed and ambient temperature. At last, under a constant heat flux of the radiator, the numerical results and predicted outcomes were compared at −30°C and 20°C to confirm the accuracy of that model. The results stated that the maximum error between experimental data and simulation results is 7.89%, which validates the correctness of the implemented numerical analysis. The Ferrari and Shengjin methods are eligible to solve the derived model, which is shown with heat transfer rate, fan rotation speed and ambient temperature. The deviation between numerical results and estimated values are 0.8°C at 20°C and 0.75°C at −30°C, respectively. The conclusions of this work could provide a theory basis and reference for the relative research on the self-adaption model of cooling intensity for construction vehicles.
Design and Test Verification of Acceleration Control for the Auxiliary Power Unit Based on N-Dot Acceleration Control Law
Authors: Peng Kai , Yang Fan , Zhang Zhaorong , Jiao Chunbo
Published: May 2020

The acceleration control law based on the rotor acceleration N-Dot has been widely applied in the design of aero-engine control systems, especially in the design of transient control law. Its advantage lies in the fact that it can guarantee the consistency of the acceleration performance of engine, not affected by engine manufacturing error, component performance degradation, and so on so forth. During the development of full authority electronic control system (FADEC) for an auxiliary power unit (APU), an acceleration control law is designed based on N-Dot and corrected with turbine inlet gas temperature for APU. The modified N-Dot acceleration control law is verified through rig test and the results show that the designed control system by integrating the modified N-Dot acceleration control law with the limit protection control can effectively prevent the engine from surge and over-speed during the acceleration process, and fully take advantage of the acceleration performance of the engine.
Study on Acoustic Emission Damage Signal Denoising of 2D-C/SiC Composites
Authors: Wang Yinling , Li Huacong
Published: May 2020

In the process of damage test of carbon fiber composites, the acoustic emission signal is affected by the measurement conditions and external environment, which contains a lot of noise problems. A denoising method based on wavelet threshold is proposed. Based on the analysis of the burst signal and continuous signal of carbon fiber composite, the wavelet analysis method is theoretically studied firstly. The selection criterion of wavelet base and the selection of wavelet decomposition layer are discussed theoretically, in order to solve the threshold of wavelet threshold denoising. A high-order non-conductible problem, a soft threshold function is proposed. Finally, the denoising experiments are carried out on the burst signal and the continuous signal respectively. The effects of different basis functions and different decomposition layers on the signal-to-noise ratio are compared. Finally, the optimal wavelet basis functions and the number of decomposition layers are obtained, which makes the denoising effect optimal.
Chip Breakability in Turning of 7075 Aluminium Alloy with a High-Pressure Coolant Supply
Authors: Wada Tadahiro , Yamaguchi Takuro , Morigo Yusuke
Published: May 2020

Because of its extremely high strength to weight ratio, 7075 aluminum alloy is used for highly stressed structural parts including aircraft fittings, gears and shafts and various other commercial aircraft, aerospace and transportation equipment. In the case of cutting aluminum alloys, the chip breakability is the most important feature to ensure reliable operation in automated machining. In this study, in turning of 7075 aluminum alloy with a high-pressure coolant supply, the chip configurations, and the mass and thickness of chips were experimentally investigated. The following results were obtained:(1) In the case of a cutting speed of 5.0 m/s, a feed rate from 0.05 mm/rev to 0.50 mm/rev and a depth of cut from 0.1 mm to 3.0 mm, chips were not broken at a feed rate of 0.15 mm/rev or less with the conventional coolant supply. With a high-pressure coolant supply, the combination area of feed rate and depth of cut in which chips were broken was wider than that with the conventional coolant supply. With the high-pressure coolant supply at a coolant pressure of 7 MPa, there was a combination area of feed rate and depth of cut in which chips were not broken. However, chips were broken in all areas at a coolant pressure of 20 MPa.(2) In the case of both the high-pressure coolant supply, which has a coolant pressure of 7, 14 or 20 MPa, and the conventional coolant supply, the thickness of chip increased with the increase of the depth of cut. And, the thickness of chip did not change depending on the cutting method, namely the high-pressure coolant supply cutting method and the conventional coolant supply cutting method.
Research on Fault Diagnosis of Gearbox Based on Acoustic Emission Signal Monitoring
Authors: Yao Jun , Yin Xianming , Wang Yinling
Published: May 2020

For the problem of large noise interference in gearbox fault diagnosis and inaccurate time-frequency resolution of analysis signals, a method for analyzing acoustic emission signals using Hilbert-Huang transform method was proposed. Firstly, use the acoustic emission method to collect the fault signal of the gearbox, which reduces the interference of environmental noise. Then perform domain of time analysis on the single, and the time-frequency analysis was performed by short-time Fourier transform, wavelet transform and Hilbert-Huang transform. The results show that the Hilbert-Huang transform method is the best to analyze the acoustic emission signal, and the frequency of the fault can be analyzed accurately, and the time-frequency aggregation is accurate, which verifies the superiority of the method in gear fault diagnosis.
Reduction Drag of Circular Cylinder Due to Installation of Four Flat Plates
Authors: Frolov Vladimir A. , Kozlova Anna S.
Published: May 2020

The paper presents theoretical and experimental studies on the reducing drag of a circular cylinder. The reduction drag is achieved by installing near a circular cylinder four flat plates. Flat plates installed under the meridional angle relative to the cylinder and at an angle of attack. Flat plates are symmetrically relative to the cylinder. There is a flat injection channel of constant width between the plates and the cylinder. We examine this situation of flat plates due to which the drag coefficient of the total combination reaches the smallest value. The study of reducing the drag of the cylinder was carried out in the package hydrodynamic analysis of Flow Simulation, as well as in the wind tunnel of Samara University. It has been shown that that the location of plates near the cylinder at certain angles, allows us to achieve reduction drag approximately before 40% compared with an isolated cylinder.
Two Dimensional Hypersonic Body-Intake Multi-Object Optimization with NSGA-II Algorithm
Authors: Qiao Lei , Fu Junjie , Bai Junqiang , Qu Feng
Published: May 2020

The forebody/inlet shape of the scramjet engine is significant to airplane performance. A multi-object optimization of hypersonic body-intake configuration is reported in the present work. The optimization system consists of geometry parameterization, mesh deformation, aerodynamic performance evaluation via computational fluid dynamics (CFD) and the top level driving optimization algorithm. In the present work, geometry parameterization is accomplished through B-Spline based free form deformation (FFD) method, volume mesh deformation is computed via inverse distance weight (IDW) method. Aerodynamic performance is evaluated with shear stress transportation model closed Reynolds averaged Navier-Stokes equations. At the top level, optimization is driven by the non-dominated sorting genetic algorithm II (NSGA-II) algorithm. Finally, the present optimization system is applied to a two dimensional hypersonic combined forebody-intake configuration. Numerical result shows that optimization gained improvement of 5% thrust, 9% pressure rise ratio and 7% total mass flow rate, which approved the effectiveness of the present optimization methodology.
Double Propellers Synchronous Control Based on Cross-coupling Control for Buoyancy-lifting Aircraft
Authors: Jianli Su , Hua Wang
Published: May 2020

To solve the double propellers synchronous control problem of the buoyancy-lifting aircraft, a control approach which uses cross-coupling method to control the double propellers is proposed. Firstly, the lateral-directional stability and manoeuvrability of this type of aircraft with double propellers are analyzed. Secondly, the cross-coupling control method is used to study the speed synchronization performance of the double propellers to improve the control accuracy. The simulation results show that, the double propellers speed synchronization control system based on the cross-coupling control method has a good control performance and better anti-disturbance. Finally, based on the control theory, the cross-coupling controller of the buoyancy-lifting aircraft with double propellers was designed, and ground run test and flight test are carried out. The experiment results show that the control method proposed has better performance and resistance.
Periodic Trajectory of Relative Motion Controlled by Constant Thrust
Authors: Bai Shengzhou , Han Chao , Wang Xiaohui , Sun Xiucong , Zang Jie
Published: May 2020

Formation flying has received widespread attention in recent years. However, the conventional control approaches require high demand for engine. To deal with this problem, the relative periodic trajectory can be achieved by multiple two-section constant thrust control proposed in this study. Firstly, the concept of the constant thrust control is presented. Furthermore, the strategy is presented to guarantees periodic trajectory considering J2 perturbation. Simulations are conducted to demonstrate the efficacy of the method. The proposed control approach is applied for engineering applications.
A New Information Code for Robot Localization
Authors: Yang Puguang , Chen An , Gao Hongxia , Liu Haiming , Wen Xiaoqin
Published: May 2020

Localization is the basic problem of mobile robot. A new approach for information coding and decoding for robot localization is proposed in this paper. information codes are used as landmarks to provide a global attitude reference. The label and location information is stored in the information code and strategically placed in the operating environment. The mobile robot is equipped with a camera that can read the information code for localization purposes Firstly, the current situation of the coding methods used for robot localization is analyzed, and the exposed problems and defects in application are summarized. In view of these problems, a new code method which is more suitable for robot location is designed. Then, the coding and decoding methods of the design are elaborated in detail. Finally, relevant experiments are designed in combination with practical application scenarios. Compared with the traditional QR code decoding method, the effect is better than the traditional QR code.
MatchRec: A User-item Matching Method for Sequential Recommendation
Authors: Luo Lv , Chen Tiankai , Qiu Rong
Published: May 2020

Sequential recommendation is based on the sequence of items that users interact with to predict the items that users will interact with next time. The existing methods usually consider the hidden sequential patterns in the user’s action sequence, but modeling candidate item features is ignored. To solve this problem, in this paper, we use the two-tower network structure to model the user’s action sequence and candidate item features respectively, then recommend items to users according to the matching score of user interest and item attribute. Specifically, we use self-attention mechanism to mine the information in the user’s action sequence, and use Factorization machines to automatically construct the cross features of item. We named this method MatchRec. The experiments on the public datasets demonstrated that MatchRec has a significant improvement in recommendation accuracy compared with that of various baseline methods.
An Attention Mechanism Based Algorithm of Identifying Vulnerable Spots of the Internet Data Center Power Systems
Authors: Liu Xinpei , Kang Chunjian , Jiang Zhuo , Zhao Tiezheng
Published: May 2020

With the rapid development of Mobile Internet and Big Data in recent years, the demand for data centers is increasing. The number of super large data centers (more than 1000 counters) has increased. This paper focus on the distribution system, who is responsible for providing a continuous, stable and uninterrupted power supply for the data center. In the event of a large-scale power outage, all business in the data center will be interrupted. Therefore, identifying which electrical equipment in the data center paly the key role in a large-scale power outage is extremely important. Considering the computational complexity of the algorithm, the validity of the vulnerable nodes, the intelligence of the algorithm, this paper proposes a two-layer fragile node identification method based on attention mechanism. The algorithm selects the topology structure and electrical parameters of the data center power distribution system, and considers the flow load of the nodes around each electrical device to form an expression vector. After the vulnerability ranking of the candidate node expression vectors, the k most vulnerable nodes are selected. The outline of the two-layer node identification method is as follows, the first layer calculates an alternative node sequence through a variety of graph-based fragile node identification methods, and the second layer re-calibrates the expression vector of each candidate node based on the attention mechanism and then sorts all candidate nodes. Through the simulation test of a real data center power distribution system, we can see that the proposed algorithm in this paper identifies the effectiveness of the vulnerable nodes in the data center power distribution system.
Research on Defect Priority Classification of Crowdsourcing Testing for Mobile Applications
Authors: Liu Ying , Ma Chunyan , Dong Zhanwei , Zhang Tao , Cheng Jing , Zhang Jie
Published: May 2020

Crowdsourcing testing technology has developed in recent years with the development of software testing, which can speed up releasing cycle and improve the quality of testing. It is of great practical value to study the priority classification and cause analysis of defect reports by using the potential information of crowdsourcing test defect reports. This paper combines the research of mobile application crowdsourcing test defect report with machine learning data analysis technology, studies the priority classification of mobile application crowdsourcing test defect report, and then carries out defect cause analysis on the basis of defect priority classification. Defect classification is an intuitive reflection of defect research. This paper takes defect priority classification as the breakthrough point of defect report research, uses σ – AdaBoostSVM classification algorithm to classify defect reports, and then carries out cause analysis after defect report classification, which is conducive to the faster location and repair of defects. The experimental verification results demonstrate the effectiveness of the proposed method.
Reliability Analysis of Distribution Network with Distributed Power Supply
Authors: Li Shenghui , Bai Xue , Zhang Xiaotong , Gu Cailian , Chen Zixuan
Published: May 2020

In this paper, the reliability index calculation method of power equipment, load and system dispatching is studied. According to the location of the circuit breaker and disconnecting switch, the fault is partitioned, and the load reliability index is the same in each minimum distribution area, so that the reliability evaluation of the distribution network can improve the efficiency. On this basis, the partition coding table is also established, and the calculation of the minimum distribution area can be directly transformed into the calculation of the corresponding coding area. Combined with monte carlo method, the correctness of the method used in this paper is proved by comparing the example analysis with other literatures.
Analysis and Optimization of the Admission Capability of Distribution Network to Distributed Power Sources
Authors: Sun Feng , Li Shenghui , Jin Ying , Gu Cailian , Shu Xinlei
Published: May 2020

This paper establishes a model for the ability of the distribution network to adopt distributed power sources, and proposes adoption constraints that consider the safe operation of the distribution network. The optimized assessment method of the ability of the distribution network to adopt distributed power sources proposed by analysis of simulation examples can help distribution network planners to ascertain the main limiting factors affecting the ability of the distribution network to adopt distributed power sources. According to the identified main limiting factors, distribution network planners can take appropriate measures to improve the ability of the distribution network to adopt distributed power sources.
Design and Implementation of Traffic Signal Control System Based on Parallel Simulation Technology
Authors: Tan Linglong , Zhou Xinliang , Su Yu , Wu Fengzhi
Published: May 2020

A traffic signal control system based on parallel simulation technology is designed in this article. Based on the second-level acquisition of intersection data, this system builds a control algorithm matching rule base based on offline algorithm adaptive analysis. In addition, the system can select the signal control algorithm that matches the real-time traffic status online, and realize the online evaluation of the control algorithm through the online parallel simulation and control algorithm evaluation platform, and provide a decision basis for further implementation of the algorithm. This system improves some existing deficiencies in signal control systems, and also provides decision support for the application of complex control algorithms. The system is applied to the control of actual signalized intersections. Practice shows that the system can better adapt to the actual situation of signalized intersections in District, improve the capacity of intersections and the efficiency of the entire road network.
Research on Experience Design Optimization of Mobile Reading Based on Continuous Using Behaviour
Authors: Lan Ma , Jianping Su
Published: May 2020

[Purpose / Meaning] The rich tools and media forms of mobile reading, while improving the convenience of reading, are also accompanied by the difficulty of maintaining the user’s concentration, and tend to be superficial reading experiences. How to use interaction design to improve the user’s reading experience and then cultivate its deep reading ability is of great significance to promote the quality of mobile reading services.[Methods / Processes]Guided by promoting users’ continuous use behaviours, this paper adopts an empirical research method in the form of questionnaires and conducts relevant regression analysis on the recovered questionnaire data to explore the influencing factors and influencing path of mobile reading applications on user cognitive experience and behavioural results. [Results / Conclusions] The factors extracted by factor analysis are classified into three categories: independent variables (content, function, interaction, vision), and intermediate variables (perceived usefulness, flow experience, reading social experience, perceived value) and outcome variables (adoption and continuous using behaviour). Furthermore, via the path analysis, this paper builds the influential factor path model on mobile reading behaviour. Based on the final empirical research results, this paper proposes specific interactive optimization strategies for mobile reading application design around Internet product design.
Skeleton-Based Pedestrian Abnormal Behavior Detection with Spatio-Temporal Model in Public Places
Authors: Wu Xiaojun , Zhao Wuyao , Yuan Sheng
Published: May 2020

In computer vision, there is growing interest in the recognition of pedestrian abnormal behaviors. The abnormal behavior of a person could be the sign of some dangerous activities. However, it’s still challenging to extract the discriminative spatial and temporal features effectively faced with video data. In this paper, we propose skeleton-based pedestrian abnormal behavior detection models. The base model is consisting of ResNet as a spatial feature extractor, LSTM as a global temporal feature extractor, and the ResNet network that use the dual-stream network to extract local temporal features. The proposed model is an improvement of all ResNet into Conv1x1_ResNet, and added a layer of Conv1x1_ResNet after dual-stream Conv1x1_ResNet to extract more accurate global space features. The proposed model achieved the highest accuracy of 89.29%, and the averaged get batch time is 0.3399 ms. The base model achieved 88.12% accuracy, and the averaged get batch time is 0.3174 ms less than the time taken by other models. Both models reach speed of 80 frames/sec. Compared with the models made in previous work, the base model has the shortest training time, and the proposed model provides the highest accuracy in the field of pedestrian detection.
Research on Multi-feature Adaptive Fusion Face Tracking Algorithm
Authors: Lei Qing , Li Zhijun , Wang Motao , Feng Jun , Zhang Rui
Published: May 2020

Under the premise of fixed computer performance, it is necessary to take into account the accuracy and real-time of face tracking. For this detection requirement, the image processing method of skin color segmentation is incorporated into AdaBoost’s face detection algorithm to accurately and quickly locate the face position. However, in the previous stage of face detection, there is a case where the real-time detection does not respond fast enough and fails the detection. In this case, a particle filter tracking algorithm based on multi-feature adaptive fusion is proposed, which uses the face area detected in the first frame as the tracking target, and achieves face detection by self-adjusting CS-LBP and the weight of how the skin color influences tracking effect, in which way the computational efficiency of face detection between frames is improved when the detection accuracy is maintained. And it is also robust to various complex external factors. It has been proved by experiments.
Research on Natural Human-Computer Interaction in Virtual Roaming
Authors: Li Fuyun , Feng Jianping , Fu Mousong
Published: May 2020

With the development of computer technology, human-computer interaction technology has gone through the development process of “command line interface”, “graphical user interface” and “natural user interface”. This paper explores the use of low-cost Kinect body sensor, in close to the premise of people’s habits, let users interact with the virtual scene roaming system in a natural way. Using the Kinect skeleton tracking technology, human posture is recognized through the relative position between joint points, and four kinds of self-defined postures are used to control the change of camera’s view angle to realize virtual scene roaming. The algorithm of this method is simple, it realizes the natural interaction between human and computer, and provides reference for the development of related systems in the future.
Unsupervised Person Re-identification via Discriminative Exemplar-level and Patch-level Feature Fusion
Authors: Lai Zhiping , Qi Meibin , Chen Cuiqun , Jiang Jianguo
Published: May 2020

The majority of existing person re-identification(re-ID) approaches adopt supervised learning pattern, which require large amount of labeled data to train models. However, due to the high cost of marking by hand, they are limited to be widely used in reality. On the other hand, due to the difference of the camera angle, there are many variations in pedestrian postures and illumination. It is known that Extracting discriminative features is pretty effective to solve the problem of person re-ID. Therefore, we propose to fuse exemplar-level features and patch-level features to obtain more distinguishing pedestrian image features for unsupervised person re-ID. Firstly, We carefully design exemplar-level and patch-level feature learning framework(EPFL). The skeleton frame adopts bicomponent branch, one branch is used to learn the global features of pedestrian images, the other is used to learn local features. Then, the global features at the example level and local features at the patch level are fused, thus the discriminative pedestrian image features can be obtained. Furthermore, feature memory bank (FMB) is introduced to facilitate the calculation of the similarity between pedestrian images on unlabeled dataset. We carry on our proposed method on two frequently-used datasets, namely, Market-1501 and DukeMTMC-reID dateset. Experimental results clearly demonstrate the advantage of the proposed approach for unsupervised person re-ID.
ARL-IL CNN for Automatic Facial Expression Recognition of Infants under 24 Months of Age
Authors: Yan Simeng , Zheng Wenming , Tang Chuangao , Zong Yuan , Qiu Nana , Ke Xiaoyan
Published: May 2020

Automatic facial expression coding of infants plays an important role in infants-related applications, including computer-aided ASD diagnosis, automatic intervention for ASD children and diagnosis of ADHD, etc. However, most of existing facial expression researches focused on adult facial expression analysis, the infant facial expression recognition has been less investigated. Due to an age gap between the facial expression datasets of adults and infants, a facial expression recognition model trained on adult datasets usually shows poor generalization to infants datasets. A labeled infant facial expression dataset can mitigate this problem, and hence we first collect a facial expression dataset of 30 infants under 24 months of age by recording videos of infants’ facial expression during a face-to-face mother-infant interaction. Due to infants spontaneous facial behaviors, the dataset covers multiple challenges, such as large head-poses, occlusion, facial expression intensities, etc. To develop an automatic facial expression coding system, we propose a framework consisted of adaptive region learning and island loss, i.e., ARL-IL, to self-adaptively discover facial regions with higher discriminability between different emotion classes. The framework was verified on our collected dataset, and attained a classification accuracy of 86.86%, which has shown better performance than conventional method based on hand-crafted features and some basic CNN architectures. To interpret the effectiveness of ARL-IL, we also visualize the learned features and find that the proposed framework focuses on facial regions with more emotion information compared with other hand-crafted features or learned features from basic CNN architectures. The experimental results show that our proposed framework has robustness to the large head-poses and occlusion.
Emotiongan: Facial Expression Synthesis Based on Pre-Trained Generator
Authors: Ning Xin , Xu Shaohui , Zong Yixin , Tian Weijuan , Sun Linjun , Dong Xiaoli
Published: May 2020

Since the Generative Adversarial Networks (GANs) was proposed, researches on image generation attract many scholars’ general attention and good graces. Traditional GANs generate a sample by playing a minimax game between generator and discriminator. In this paper, we propose a new method called EmotionGAN for generating facial expression. Specifically, the inverse of the generator is firstly utilized to establish the mapping between the input and feature vector. Then the Generalized Linear Model (GLM) is used to fit the changing direction of different expressions in the feature space, which provide a linear guidance to the feature vector along the expression axis, and thus spatial distribution consistence with the target feature vector is assured. Finally the generator is applied to reconstruct the facial image of the expression. By controlling the intensity of the feature vector, the generated image can be smoothly changed on a specific expression. Experiments have shown that EmotionGAN can quickly generate face images with arbitrary expressions while ensuring identity information is not changed, and the image attributes are more accurate and the resolution is higher.
Social VR : A New Form of Social Communication in the Future or a Beautiful Illusion?
Authors: Wang Minhan
Published: May 2020

The further development of 5G technology provides new development possibilities for the application of VR technology in various fields. The application of VR in the social field has derived the concept of “social VR”. This paper focuses on where social VR is new and where is the future of social VR going. By means of comparing with traditional ways of socializing, this paper proposes three new features of social VR: high immersion, diverse interactive modes, and contextualized social content. We classify social VR applications and take two popular social VR games: VRChat and Facebook Horizon, as examples to illustrate the current development of social VR applications and the possible existence of social VR. Moreover, a series of problems and countermeasures are explained and further considered. This paper is of great significance in the development of social VR. It indicates that there is a good prospect for the future of social VR.
RAFM: A Real-time Auto Detecting and Fingerprinting Method for IoT devices
Authors: Cheng Weihua , Ding Zhengyang , Xu Chao , Wu Xiaohu , Xia Yuanyi , Mao Jiaming
Published: May 2020

In recent years, with the rapid development of Internet of Things (IoT) technology, a large number of Internet of things devices such as network printers, webcams and routers have emerged in the cyberspace. However, the situation of network security is increasingly serious. Large-scale network attacks launched by terminal devices connected to the Internet occur frequently, causing a series of adverse effects such as information leakage and property loss to people. The establishment of a set of fingerprint generation system for Internet of things devices to accurately identify the device type is of great significance for the unified security control of the Internet of things. We proposed a RAFM which is a detection and identification system of IoT. RAFM consists two major module including auto detection and fingerprinting. RAFM collects messages sent by different Internet of things devices by means of passive listening. Based on the differences in the header fields of different devices, it USES a series of multi-class classification algorithms to identify device types. Simulation experiments show that RAFM can achieve an average prediction accuracy of 93.75%.
Detection of Late Blight in Potato Leaves Based on Multi-Feature and SVM Classifier
Authors: Liu Wenjie , Zhang Yongjun , Fan Haisheng , Zou Yongjie , Qin Yongbin
Published: May 2020

Due to the influence of germs and viruses, plants often show various symptoms of diseases and insect pests during the growth process, which leads to a large economic loss of fruit farmers. It also brings a certain economic loss to our society, so prevent earlier and advise growers about plant diseases and insect pests have important value and significance. In this case, this paper proposes a detection method which is based on the combination of HOG, LBP and CSS features with Support Vector Machine (SVM) classifier. This method extracts the histogram of oriented gradients, texture, and color self-similar features of potato leaves, and then training samples with SVM classifier to detect late blight as early as possible in the early stages of potato growth. In addition, this paper proposes a method to increase virtual samples, that is, generating symmetrical samples according to the original samples. Due to the limitation of the number of collected samples, increasing symmetrical samples can expand the diversity of samples. The results show that this method can obtain a detection rate of 92.7%, and has better detection and recognition performance in practical application.
Infrared Image Enhancement by Multi-Modal Sensor Fusion in Enhanced Synthetic Vision System
Authors: Cheng Yue , Li Yahui , Han Wei , Liu Zuolong , Yu Guanfeng
Published: May 2020

ESVS (Enhanced Synthetic Vision System) employs multi-modal sensor fusion to provide equivalent out-of-cabin view for pilots. By using such device, pilots can identify the runway and obstacles even in low visibility weather condition during approach and landing. Previously, short-wave infrared video was used as real-time image sensor in our developed ESVS. While, compared to visible image, infrared image is not easy to understand. Moreover, the image quality of infrared degrades to a considerable extent in haze weather condition. To improve the infrared image visibility in ESVS, we proposed a multi-modal sensor fusion strategy by combining real-time infrared video and previous recorded visible video in the clear weather condition. During image fusion, considerable color detail of the visible image is injected to infrared image to ameliorate the infrared image quality. Furthermore, the proposed fusion method has two extra advantages. First, the important tagged information including runway and obstacles in visible image are transferred to infrared video, and second, the missing information in infrared vision can be complemented. To evaluate our proposed method, we use the Y-12F aircraft equipped with both visible and infrared video camera for flight test data collection. By collecting visible and infrared video in clear and haze weather conditions, as well as necessary navigation data, we carry out the final image fusion processing. Experimental results show that the fused video frame has enhanced image quality and improved readability for the pilots, which will significantly promote the pilots’ situation awareness during approach and landing. Thus, the proposed idea for fusion of multi-sensor image has the great potential to be applied to other cockpit display systems.
A Survey on Monocular 3D Object Detection Algorithms Based on Deep Learning
Authors: Wu Junhui , Yin Dong , Chen Jie , Wu Yusheng , Si Huiping , Lin Kaiyan
Published: May 2020

An accurate and effective perception of environment is important for autonomous vehicle and robot. The perception system needs to obtain the 3D information of objects, which includes objects’ space location and pose. Camera is widely equipped on autonomous vehicle because of its price advantage. However, the monocular camera cannot provide depth information which is necessary for 3D object detection. Many algorithms based on monocular 3D object detection have been developed in recent years. Deep learning is popular for perception system which transforms image data from camera into semantic information. This paper presents an overview of monocular 3D object detection algorithms based on deep Learning and summarize the contributions and limitations of these algorithms. We also compare the performance of different algorithms on different datasets.
Ccfcnet: Channel-Communication Factorization Convnet for Real-Time Semantic Segmentation
Authors: Lin J , Hu J
Published: May 2020

In this paper, we address a challenging real-time semantic segmentation task by proposing a lightweight encoder-decoder architecture. The architecture called channel-communication factorization ConvNet (CCFCNet), in which both efficiency and accuracy are taken into account. The core of our work is channel-communication factorization convolution module (CCFC) and dense dialation-rate block (DDB). CCFC module, where split, channel communication and fuse operations are utilized to greatly save reasoning time and improve the quality of information refinement with a few additional calculations. Meanwhile, four CCFC modules with different dialation rates form a dense dialation-rate block (DDB), which can obtain denser feature refinement and enlarge receptive field to improve the segmentation accuracy of objects with different size in images. The proposed architecture, which contains only 1.25M parameters, achieves a mean IOU of 71.4% on the Cityscapes dataset and can run over to 42FPS on a single GTX 1080Ti GPU. It makes a better trade-off between accuracy and efficiency than state-of-the-art methods with comparable performance.
A Method of Text Detection and Recognition from Receipt Images Based on CRAFT and CRNN
Authors: Wang Xiaohui , Zhang Xi , Lei Shuya , Deng Hongmei
Published: May 2020

Text detection and recognition from paper bank receipts image has a lot of applications in the business field, such as in power system marketing, which is applied in verification and cancellation of electricity charges and automatic archiving. In this paper, we use a text detection method to effectively detect text area in receipt image by exploring each character and affinity between characters. Then put the text sequence into text recognizition model which is combined by DCNN and RNN and integrates feature extraction, sequence modeling and transcription. The experiments demonstrate the superiority of the proposed algorithm over the prior arts, which performs well in the task of blurred image-based bank receipt chinese text recognition.
Research on Consistency Enhancement of Wireless Channel Key Against RF Fingerprint
Authors: Zhang Mingming , Xu Chao , Li Meng , Xia Fei , Zhao Ran
Published: May 2020

The development of the physical layer key extraction technology provides a new type of encryption mechanism that uses the reciprocity and time varying of wireless channels to generate keys. This encryption mechanism can achieve informational security. However, the wireless channels in the real environment are not completely reciprocal, which results in low consistency in generating the keys. In this paper, we study the influence of RF fingerprint on channel reciprocity, and propose a calibration matrix algorithm resist the RF fingerprint effect. Based on the RF fingerprinting model established previously, we propose two calibration methods from the perspective of RF gain and I/Q gain. The superiority of the two calibration methods is proved by the average system capacity and the linear quantification error rate. Next, we extend the calibration matrix algorithm of narrowband systems to wideband OFDM systems. The applicability of the calibration matrix algorithm in OFDM systems is proved by the linear quantification error rate of the uplink and downlink channels. In order to prove the reliability of the algorithm in the real environment, we use a USRP to collect multiple sets of measured data, and analyse the data through the calibration algorithm. The final result shows that this algorithm makes the uplink and downlink channel reciprocity compensated.
Video Flame Detection Method Based on Improved Fast Robust Feature
Authors: Lingkai Zhang , Li Lu
Published: May 2020

Flame detection has important practical significance. Based on the uniqueness of the color information of the flame, using the flame’s color model to initially extract the suspected flame area to improve the accuracy of flame detection in complex environments. In order to reduce the amount of unnecessary algorithm calculation, an improved feature extraction method combing color information with acceleration robust combining features (SURF) is proposed. On this basis, the morphological features of flame, such as roundness and rectangularity, are added as auxiliary classification features, and the flame region features extracted from the image are input to support vector machine (SVM) for training and learning. The experimental results show that the flame detection method proposed in this paper is applicable to a wider range of scenes, with the advantages of high accuracy and robustness, high reliability, small calculation amount, short detection time, and still has good robustness in complex scenes with more interferences.
Factor Analysis and Optimization Strategy of Differences in Dual Channel Service Experience
Authors: Zhang Deran , Yuan Yunyun
Published: May 2020

Dual channel sales provide consumers with different service experiences. Studies have shown that “cheap” and “safety” are the most important factors for consumers in the online direct selling experience, however, with the higher pursuit of quality of life, The traditional entity sales experience is unique, and a series of real shopping experiences such as “situation consumption” and “physical experience” can bring joy to the consumers, and stimulate the consumer’s desire to purchase to a certain extent. Therefore, scientifically integrating the dual-channel supply chain, carrying out orderly competition, and complementing each other is an effective strategy to further optimize the dual-channel supply chain and better serve consumers.
Hamiltonian path problem: the performance comparison deoxyribonucleic acid computing and the branch-and-bound method
Authors: Sergeenko A.N. , Granichin O.N. , Yakunina M.V.
Published: May 2020

In this article different approaches to one of the most popular combinatorial problem — the Hamiltonian path problem – are illustrated and compared between each other. It is shown that it becomes inefficient to use branch-and-bound method, the most popular method which is realized on a computer, from the counted number of vertices because of its exponentially growing complexity, one more algorithm which is based on working with deoxyribonucleic acid (DNA) molecules in a laboratory is analysed. That method works parallel and has linearly growing time consumption. Due to the improvements in the biophysics methods, which are needed for DNA computing, that algorithm became much faster than it was several years ago and it is now possible to add some new stages in DNA computing, which are shown in this paper.
Underwater radiated noise model of marine object
Authors: Prokopovich V.V. , Shafranyuk A.V.
Published: May 2020

The paper is devoted to the developing a comprehensive model for underwater radiated noise of marine object in order to model and simulate sonar systems. Many works present underwater radiated noise models, but as a rule, they consider either one of the noise components in order to develop algorithms for the corresponding sonar operation mode, or one of the noise sources in order to design ship parts and assemblies. This paper combines several noise models of marine objects considering additional factors that were not taken into account in these models. The obtained comprehensive model is under verification phase, but its application will significantly expand the possibilities for developing and synthesizing classification and tracking algorithms of sonar system
Research and development of a method for determining the dimensional characteristics of sea waves with measurements made by a wave buoy
Authors: Gryazin D.G. , Gleb K.A.
Published: May 2020

The problem of determining the spatial characteristics of sea waves with incomplete measurement information obtained by modern wave buoys is discussed. Information about wave ordinates, slopes and curvature of the waves in two perpendicular directions is needed to evaluate the full directional spectrum. Since the direct measurement of wave curvature by buoys is difficult due to technical reasons, their manufacturers decided to correct incomplete spectra using mathematical methods, such as maximum entropy, maximum likelihood methods, etc. The current study shows that the curvature of the waves can be estimated by using additional information on the parameters of the wave propagation, which can be obtained using ordinary wave buoys. Such an approach will make it possible to obtain more reliable spatial spectra.
Influence of structural parameters on instrumental error of electromagnetic log sensor
Authors: Voronov A.S. , Evstifeev M.I.
Published: May 2020

Instrumental error of an electromagnetic log sensor at depths of more than one kilometer is studied. Operating principle of the electromagnetic sensor is shown and a mathematical model is developed. Analysis of sensor-registered speed dependence on external hydrostatic pressure is presented. It is demonstrated that an additional instrumental error occurs in the electromagnetic sensor due to the structure deformation during deep-sea submersion. It has been found that the value of relative error depends on design parameters and operational conditions of the sensor. It is shown that even when deep-sea vehicles travel at a low speed, the error caused by external hydrostatic pressure can make up a significant portion of the instrumental error and should be taken into account during operation. The design parameters of the sensor, affecting its instrumental error are determined. The influence of temperature on this error is studied. The ratio of design parameters at which the instrumental error does not exceed the permissible value is identified.
Analysis of indirect stabilization system errors in a sea gravimeter
Authors: Alekseenko A.S. , Staroseltsev L.P.
Published: May 2020

A system of sea gravimeter indirect stabilization is presented. Mathematical modeling is carried out, and the main errors of the indirect stabilization system are studied, using strapdown inertial navigation system output data.
Clinical effect of uterine artery embolization in the treatment of refractory postpartum hemorrhage
Authors: Wang H Y , Li M Z , Sun J H , Li K Z , Li X
Published: May 2020

Abstracts

This study is to investigate the clinical effect of uterine artery embolism in the treatment of refractory postpartum hemorrhage. Retrospective analysis of 50 patients with refractory postpartum hemorrhage admitted to PLA Group 83 Military Hospital from July 2014 to July 2018, using uterine artery embolization (observation group) and hysterectomy (control group), compared the effective rate of hemostasis, total bleeding volume, operation time and postoperative complications (fever, pain, infection) between the two groups. The effective rate of hemostasis was 96% in the observation group and 92% in the control group. There was no significant difference between the two groups (P > 0.05). The total blood loss was 2237.34 ±548.33 ml and 39.02 ±12.14 min in the observation group and 3427.55 ±667.42 ml in the control group, and 74.21 ±16.16 min in the control group. There was significant difference between the two groups (P < 0.05).The postoperative complications of the observation group were lower than that of the control group (P <0.05). The two methods are effective in the treatment of refractory postpartum hemorrhage, but uterine artery embolism has the advantages of less bleeding, short operation time and rapid recovery after operation, which is worthy of clinical application.