bluuebunny/crossref_metadata_2025 huggingface dataset:
Using different combination strategies to combine features for audio scene classification based on Convolutional Neural Networks
Authors: Lin Chan , Leng Yan , Wang Rongyan , Lu Zhaoli
Published: March 2020

Audio scene classification (ASC) which can make good use of audio information for classification is currently a hot topic. In this paper, taking Convolutional Neural Networks (CNN) as the classification model, we have designed three combination strategies to combine different kinds of features for classification. In the first combination strategy, the multi-channel CNN is used to combine different features; in the second combination strategy, different kinds of features are concatenated, and the concatenated features are used as the input of the CNN; in the third combination strategy, new features are first extracted from each kind of feature through CNN, and then these new features are concatenated for classification. Experiments are done on the DCASE2017 datasets to test the effectiveness of the combination strategies, and the experimental results show that the second combination strategy performs best, also the experimental results show that on the whole, combining more features would get better classification results.
Real-time Traffic Sign Text Detection Based on Deep Learning
Authors: Peng Ximing , Chen Xianqiao , Liu Chang
Published: March 2020

Aiming at the task of traffic sign text detection in natural scenes, a two-stage cascade detection model based on deep learning is proposed. The proposed model first locates the regions of interest (RoI) of text-based traffic sign by applying improved SSD (Single Shot MultiBox Detector) network. Then a rotation-based text detection network is used to detect text strings in the located RoI. Moreover, the lightweight convolutional neural network MobileNetV2 is combined with the deep learning component in two stages, which reduces network parameters and improves detection speed of the model. On the one hand, the proposed approach takes full advantage of the information of traffic signs. By this way the search area of text detection can be reduced, which makes it possible to simplify the text detector. On the other hand, it can obtain more complete text detection results by using rotation-based text detector. The experimental results show that the proposed method can perform well on different data sets, it can not only keep a high accuracy of detection, but also meets the realtime requirements.
Comparative Study on III-V MOSFET and Si-MOSFET Model Parameters based on BP Neural Networks Algorithm
Authors: Dai Jingjing , Li Chong , Lan Tian , Wang Zhiyong
Published: March 2020

MOSFET is the basic unit of modern integrated circuit, and main basic characteristics are: current-voltage (IV) characteristics, capacitor-voltage (CV) characteristics and source-drain contact characteristics. The introduction of group III-V semi-conductor, high k- metal date and SBSD allows MOSFET device characteristic device may keep downsizing based on Moore’s Law. However, as II-V MOSFET devices keep downsizing, the short-channel effect and quantum effect are more obvious so that it is more complicate to calculate and extract characteristic parameters of III-V MOSFET. This paper proposes a kind of III-V MOSFET characteristic parameter modeling method based on BP neutral networks algorithm. Compared to other semi-empirical models, this method needs not to calculate characteristic parameters of devices. In stead, it calculates current and voltage output characteristics and transfer characteristics of devices through BP neutral network models according to test data. Through verification, the trained and predicted output relative error is within 5%. The model has short operation time, high calculation precision and good stability. The models established may be applied extensively to other types of transistor, and feasible for practical engineering application.
The spectral conjugate gradient method in variational adjoint assimilation for model terrain correction I: Theoretical frame
Authors: Tao Sulin , Li Yuhong , Mugume Isaac , Shen Shuanghe
Published: March 2020

A spectral conjugate gradient (SCG) method is proposed within the mathematical framework of a variational adjoint assimilation system to correct the bottom terrain of a shallow-water equations model. The formulation of this method is described from the mathematical point of view with determination of the descent direction by using Andrei’s limited-memory form and the step length by solving the tangent linear model. It is proved to benefit from (i) the iterative regularization strategy, (ii) the inverse Hessian approximation involving the second-order information from Broyden-Fletcher-Goldfarb-Shanno class (BFGS), and (iii) the optimal step length. The regularization term introduced to the cost function will allow the incorporation of the known information about the desired bottom terrain and guarantee the uniqueness of the optimal solution.
Sub-pixel Center Extraction Method for Line structured Light Stripe
Authors: Liu Yuanjiong , Song Jian , Yuan Wenxin , Xue Hailang , Li Sanfeng
Published: March 2020

The extraction of line structured light center is a key technology in line structured light measurement system. The precision of light stripe extraction affects the accuracy of the measurement system. Due to the differences of the materials and quality of the laser device, the light stripes is variable in different position that the energy distribution and morphological characteristics are easily changed. It is difficult to extract the center of the stripes by using traditional light stripe extraction methods accurately. Our study proposes an extraction algorithm for the light stripe center. First of all, separating a single stripe from the image according to the morphological characteristics of the light stripe, and the effective region of the light stripe was initially determined. Then, using the grayscale attribute method as well as the grayscale threshold method to determine the region of interest in the center of the light stripe. Finally, the sub-pixel center of the light stripe in the regions of interest was calculated by the grayscale barycenter method. The experimental results showed that the algorithm in the study can be used to extract light stripes with changeable characteristics. It is more accurate and stable comparing with the traditional extraction algorithm of light stripe center.
Research on System Engineering Method of Equipment Software Quality Management
Authors: Wang Qiang , Li Qiang , Shang Jingwei
Published: March 2020

Taking the limitations and challenges of current equipment software quality management into account, this paper proposes a system engineering method for equipment software quality management based on the characteristics and development trends of equipment software. Based on the modified Hall model, this paper defines the system engineering of equipment software quality, analyzes and studies the elements, relationships, boundaries and environment of the system from the perspective of software reliability engineering. Finally, the system engineering level of equipment software quality management is quantitatively evaluated by system stability and system effectiveness.
Target detection and tracking based on improved HOG-color feature fusion
Authors: Dai Tianle , Hao Ziqiang , Wang Zhongyuan , Zhan Weida , Tang Yanfeng
Published: March 2020

In the process of constructing the target appearance model, the traditional HOG feature does not take into account the relationship between adjacent regions when the image is segmented in the feature extraction process, resulting in the regional aliasing effect, and the single feature is inefficient in target detection and tracking. An improved HOG-color feature fusion method for target detection and tracking is proposed. Firstly, HOG feature and color feature were extracted from the target samples respectively. During the HOG feature extraction process, trilinear interpolation was used to eliminate regional aliasing effect. Secondly, Bhattacharyya distance is used to select the HOG feature after interpolation, and the appropriate feature is selected as the HOG feature. Then combine the HOG feature after selection with the color feature; Finally, the filter is obtained by learning the kernel correlation filter, and the image is correlated detected with the filter, and the response output is obtained. The experimental results show that this method is superior to the traditional HOG-color feature fusion target detection and tracking method in both tracking speed and tracking efficiency.
Application of Railway Passenger Flow Statistics Based on Mask R-CNN
Authors: Meng Di , Xu Shangzhi
Published: March 2020

This paper discusses and implements the application of Mask R-CNN in railway passenger flow statistics. Mask R-CNN is a target detector based on candidate region, combining deep learning, neural network and image recognition technology, and has a wide range of application value in the field of intelligent monitoring.The paper focuses on the algorithm named Mask-Flow that Mask R-CNN uses the same color for tracking on the same target, and counts the number of pedestrians. And the passenger flow statistics are compared with Retina-Net in the railway scene of the dense crowd, which proves the accuracy of the method in the paper.
Research on Two-dimension pattern based on shear cutting Problems
Authors: Ji Jun , Zhang Saikun , Li Yuhang , Shi Ning
Published: March 2020

The cutting problem is a practical engineering problem. It is classified according to the two-dimensional pattern problem. According to the requirement of blanking technology, a blanking method based on homogeneous block is proposed, and the blanking characteristics of this kind of blanking method are pointed out.
Development of Parametric Drawing System of Corrugated Boxes Based on SolidWorks
Authors: Mao Wenwu , Zhang Xinggao , Tantai Sunxiao , Shen Shujing , Zhang Zewen , Wang Jiahui
Published: March 2020

Corrugated box is a widely used means of transportation packaging, there are various box types such as slotted type, nested type, folding type, slide type, fixing type, automatic type and other types of boxes, special standards on the type and size of corrugated box is formulated by Chinese national standards. However, when drawing corrugated boxes with various CAD software now, we need to model and edit according to the box shape, specific size of corrugated boxes and the drawing efficiency is low. In this paper, on the basis of the established corrugated box access database, we use the Visual Basic to develop corrugated box parametric drawing system on SolidWorks platform. The users only need to select the desired corrugated box carton and the key parameters, then can get the 2D expansion diagrams and 3D stereoscopic diagrams in accordance with the national standard, which significantly improve the efficiency of the corrugated box drawing.
Two implementation methods of handwritten numbers recognition
Authors: Zhang Hui , Fan Yani
Published: March 2020

In order to realize the recognition of handwritten numbers in daily life by artificial intelligence, to save labor cost and time cost, a machine learning KNN (K-nearest neighbor) algorithm and CNN algorithm (convolution neural network) in deep learning are proposed. According to the calculation, the recognition accuracy of more than 95% can be achieved. In the actual verification, the recognition accuracy of 98.77% and 98.69% are obtained respectively. Practice has proved that these are effective artificial intelligence recognition methods.
An Image Classification Approach based on Deep Learning and Transfer Learning
Authors: Han Kaixu , He Jinxin , Wang Yongzhi , Xiong Yue , Zhang Chi
Published: March 2020

Target recognition of high-resolution images is an important direction of today’s classification technology, and some classification models have emerged, but there are still many technical problems to be solved. The main source of this paper is the Google Photo Collection, which includes five types of city daisy, rose, tulip, dandelion and sunflower. Using CNN (Convolutional Neural Networks) deep learning model and Google’s Inception transfer learning model to train and classify the sample images, the final accuracy of the overall test set can reach 88.3%; However, the accuracy of training without transfer learning is only 60.2%. Thus, it is more efficient to combine deep learning with transfer learning to image classifications.
Development and design of on intelligent medicine box
Authors: Chen RenYi , Wang Peng , Su Peng , Chen QiaoRui , Hu JunFeng , Li WenSong , Han Lei
Published: March 2020

This paper proposes an intelligent medicine box which can be used to provide safe medication to the elderly who are living alone. The design is based on STM32F103 micro-controller and other intelligent control components. The basic working principle is analyzed as well as the hardware structure. The proposed medicine box is full-automation, high-intelligent and multi-functional which can effectively avoid the health and safety risks caused by the memory declination of the old people.
Object Detection Based on Minimum Convex Hull and Generalized Hough Transform
Authors: Zhang Tong , Song Chenfei
Published: March 2020

In view of the low efficiency and fallibility of the feature-based matching algorithm in object detection, a new algorithm based on convex hull region and generalized hough transform is proposed.By using the region of interest to narrow the detection region and applying the key feature points to the voting elements of the generalized hough transform, the computation is greatly reduced.Firstly, the fused key feature points are extracted and filtered to generate the convex hull region which is combined with the GBR clustering algorithm to determine the region of interest. Secondly, the key feature points are taken as the voting elements in the generalized hough transform algorithm to obtain the final result. In this paper, dozens of various transformation experiments(scaling, rotation, noise, etc.) based on the proposed algorithm are compared with other similar methods, and it is verified that the method has higher efficiency and accuracy according to the theoretical basis and concrete implementation, which can meet the real-time and high-precision requirements in industrial detection.
Photovoltaic panel anomaly detection system based on Unmanned Aerial Vehicle platform
Authors: Xie Xiaoping , Wei Xiangui , Wang Xingyu , Guo Xincheng , Li Ju , Cheng Zhifeng
Published: March 2020

The emergence and rapid development of the Unmanned Aerial Vehicle (UAV) Photovoltaic inspection system have become an effective means of solving the operation and maintenance of photovoltaic power plants. In order to cooperate with the current UAV platform for photovoltaic panel anomaly detection, this paper proposes a photovoltaic infrared target anomaly detection system. In this paper, the Sobel operator is used to extract the photovoltaic slab area of the image, and the canny operator is used to obtain the photovoltaic small plate area to realize the complementary advantages. At the same time, the deep learning is applied to the algorithm to learn the discriminative characteristics of the image, and the brightness statistics are used. The method is supplemented by the method to achieve anomaly detection of the photovoltaic panel area. The effectiveness of the proposed algorithm is proved by a large number of experiments.
Research on prediction classification and compensation for silicon contents in blast furnace based on ridge regression
Authors: Sun Ling , Yin Linzi , Jiang Zhaohui , Guan Yuyin , Xu Xuemei
Published: March 2020

In blast furnace, the accurate prediction of silicon contents in hot metal plays an important role in comprehensively judging the furnace states and making correct decisions. However, traditional prediction models are easily affected by multi-collinearity among the independent variables and imbalance sample distribution in hot metal datasets. The prediction values of silicon contents would be difficult to track the real values accurately when the furnace conditions fluctuate dramatically. In this paper, a classification and compensation model for silicon contents is proposed by combining the ridge regression and random forest classification algorithm. Firstly, the ridge regression algorithm is applied to fit and optimize the prediction values outputted by a front-end prediction model. Then, the random forest technology is expected to classify and identify the furnace states. Finally, the predicted values are compensated according to classification results. In this paper, the random forest regression and BP neural network algorithm were used to predict silicon content as front-end models. The related applications show that the proposed classification and compensation model improves the accuracy of predicted values effectively.
Optimization of Random Forests Algorithm Based on ReliefF-SA
Authors: Zhou Zibo , Wang Yunfan , He Xinrui , Zhang Xuecong
Published: March 2020

As a widely used classification integration algorithm, the random forests based on decision tree combination is mostly used for classification and regression problems, but its deficiencies still need to be improved. In this paper, we propose simulated annealing algorithm, and its parameters is combined with the global search optimal solution and local optimal solution to optimize search. Then, we propose a random forests hybrid algorithm by combining the Relief algorithm and annealing algorithm, briefly introduce its thoughts and algorithm flow, and also carry out simulation experiments. According to the experimental results, the classification effect of the hybrid algorithm is generally the best, which greatly improves the overall generalization performance of the random forests.
Typical status recognition of gearbox based on big data
Authors: Zhang Lixia , Yang Zhaohong , Wang Hongyu
Published: March 2020

In this paper, the experimental data of five different working states of the gearbox during the normal operation and the failure of the gearbox are taken as the research object, the python-based data analysis tools Numpy and Pandas are used to establish a fault detection model, by collecting time-domain and frequency-domain data of several faults such as fracture, crack and wear of transmission gear, and analyzing the characteristic parameters of the data, the classification of transmission faults is realized, thus laying a foundation for the application of big data tools in fault diagnosis and analysis.
Abnormal target tracking and localization algorithm for UAV PV inspection scenarios
Authors: Xie XiaoPing , Wei Xiangui , Wang Xingyu , Guo Xincheng , Li Ju , Cheng Zhifeng
Published: March 2020

In order to adapt to the development of UAV inspection technology and achieve accurate positioning of abnormal targets of photovoltaic panels, this paper applies deep learning to abnormal target location, and proposes abnormal target tracking and localization algorithm for UAV PV inspection scenarios. The advantage of deep learning lies in the ability to learn the general expression of the target. In order to obtain an efficient deep network, this paper uses a large amount of data to train the deep network, so that the features extracted by the network can achieve accurate representation of the tracking target. By testing in the abnormal target location test database, it is proved that the proposed algorithm can accurately locate the abnormal target.
Adaptive streaming algorithm based on reinforcement learning
Authors: Feng Suliu , Wang Caihong , Jiang Xiuhua
Published: March 2020

As video streaming occupies a large part of the Internet traffic, and DASH gradually becomes the dominant standard for video transmission recent years, a lot of researches about client-side bitrate adaptive algorithms are constantly emerging.This paper mainly adopts the DRL(Deep Reinforcement learning) algorithm that combines Deep Learning and Reinforcement Learning techniques to optimize the Quality of Experience (QoE) of DASH.This method learns to make decisions by observing the network environment and adjust the strategy to get more reward based on feedback,which doesn’t rely on pre-programmed models or assumptions about the environment. After experiments on the real network trace datasets, we can find that the algorithm this paper adopted can obtain higher quality of user experience than the existing researches.Moreover, the experimental results show that less buffer underflow occurs and smoother video playback is guaranteed.
Lightweight Neural Network for Component Recognition and Fault Detection of Large-Scale Transmission Line
Authors: Zhao Guowei , Wang Qiang , Zhang Juan , Zhao Rui , Zhao Jielun , Dong Hongyue , Zhang Xingzhong
Published: March 2020

Recognition and fault detection of transmission line components is a basic problem that puzzles the development of smart grid. However, due to the limitation of current datasets, it is difficult to carry out large-scale model training and mobile reasoning based on artificial intelligence technology. Although the current standard datasets for the classification of components in transmission line scenarios contain many different types of objects, they are all smaller datasets based on a certain category. This paper constructs and annotates 10 226 standard image Power Database which contain 6 component categories (including 3 fault component categories). In addition, aiming at the problem that the general target detection algorithm infers too slowly or can not run in the real-time inspection process of transmission line mobile terminal equipment, a model MSFF-KCD (Multi-scale feature fusion in key component detection) is presented, which combines the multi-scale feature fusion method with the detection of transmission line key components. Experiments on real-time component identification and fault detection by deploying the model to mobile devices show that the proposed method establishes a new performance bound and this mothed suitable for mobile terminal detection of key components of transmission lines. We also compared the models of recognition and detection for large and small targets on Power Database, which provided enlightenment for the identification and fault detection of power components.
On Error Performance and Concatenated Coding of Polar Codes in AWGN Channels
Authors: Jin Jing , Deng Rui , Liu Tiansheng , Li Liping
Published: March 2020

In additive white Gaussian noise (AWGN) channels, construction of polar codes is needed for every operating signal-to-noise ratio (SNR). Recently, the proposal of the design-SNR reduces the computation effort in constructing polar codes. In this paper, we prove that although the BER performance of the design-SNR construction is not affected, the packet-error-rate (PER) performance is degraded compared with the point-by-point construction. Therefore, a concatenation scheme is proposed to improve the degraded PER performance. Results show the validity of the proposed concatenation scheme when employing the design-SNR construction.
Design of Airport Wireless Bird Repellent Monitoring System
Authors: Yang Xiufang , Wang Chi , Chen Zhuo , Wang Dian
Published: March 2020

At present, the status detection of the bird driving equipment in the airport is completed by manual periodic inspection. This method can’t grasp the status of the bird repelling equipment in real time and cost human resources. In view of this situation, a remote monitoring system based on STM32 is proposed. The system uses the embedded STM32 as the control and data processing core to collect the signals of the temperature sensor, pressure sensor, power supply voltage and sound sensor of the bird-repelling equipment. It is uploaded to the host computer through ZigBee technology to analyze the working conditions of the bird-repelling equipment and the situation of the gas gun. Through the test of the design system, the results show that the detection accuracy of the system can meet the actual need, the correct rate of detecting sound signal is 100%. In the environment of two concrete buildings, the wireless transmission distance is within 800m, and the error rate is 0.
Passive positioning method and error analysis of UAV based on TDOA
Authors: Zhang Di , Li Changgeng , Zhou Chao , Ma Chao
Published: March 2020

At present, the phenomenon of "black fly” and "flooding” of drones frequently occurs, which brings various security risks to the country, society and individuals. Aiming at the passive positioning problem of UAV, this paper proposes a method based on TDOA for direct closure to solve the target position.The four-station time difference method is analyzed. The position coordinates of UAV are expressed as a function of the radial distance between the UAV and the main station.Then solve the quadratic equation of the radial distance between the UAV and the main station.Finally, obtain the position coordinates of the UAV by radial distance.According to the distribution of the solution of the quadratic equation, the unique solution zone, the fuzzy solution zone and the non-solution zone are analyzed, and the fuzzy solution is solved. The method of defuzzification is proposed in the area.The positioning error is studied, and three factors affecting the positioning error are discussed: the distance between base stations, the height of the main station and the measurement accuracy of distance difference.The research conclusion has important guiding significance for the station design.
A Dual-mode Communication Power Internet of Things Routing Algorithm Based on RPL Protocol
Authors: Ye Fangbin , Zhao Ling , Jiang Chi , Sun Jianqiao
Published: March 2020

Medium frequency (less than 12 MHz) Power Line Communication (PLC) and micro-power wireless Radio Frequency (RF) communication dual-mode communication technology can realize the depth and breadth coverage of the power Internet of Things (IoT) and enhance the robustness of the network. But dual-mode communication also brings difficulties to the application of the RPL protocol. According to the characteristics of PLC and RF dual-mode communication technology, the routing communication delay is the optimization objective function, and a routing metric algorithm of RPL routing protocol is proposed. The RPL protocol is applied to the dual-mode communication in power Internet of Things. Experiments show that the proposed algorithm has higher communication efficiency and can meet the needs of dual-mode communication hybrid networking and operation. It is a feasible method.
Experimental Study on Doppler Performance of Satellite Emergency Communicator Based on LoRa System
Authors: Zhang Yonggang , Chen Jianyun , liu Sili
Published: March 2020

Aiming at the propagation loss and Doppler frequency shift problems faced by LEO satellite in realizing Internet of Things, the LEO satellite emergency communication machine is tested. The emergency communication machine adopts SX1280 chip as the transceiver unit, analyzes the bit error rate and Doppler frequency shift index of LoRa signal generated by SX1280, and proposes a test scheme combining satellite channel simulator and emergency communication machine. In the range of Doppler frequency shift less than 53 kHz, changing Doppler frequency and its change rate has less than 4 dB effect on signal communication sensitivity. Reducing signal transmission rate and bandwidth can improve communication sensitivity. It proves that LoRa signal generated by SX1280 chip can be effectively used in LEO satellite Internet of Things.
Probabilistic hesitant triangular fuzzy power aggregation operator and its application
Authors: Qiu Wenjing , Qiu Jinfei
Published: March 2020

Based on the hesitant triangular fuzzy set, the probabilistic hesitant fuzzy set and the power aggregation operator, the probabilistic hesitant triangular fuzzy multi-attribute decision problem with the correlation between the aggregated data is studied. Firstly, the probabilistic hesitant triangular fuzzy set (PHTFS) is defined, and the algorithm, scoring function and distance measure of the probabilistic hesitant triangular fuzzy element (PHTFE) are proposed. Then, according to the PHTFS and power aggregation operator, the power aggregation operator of PHTFS is proposed. Finally, a multi-attribute decision making model is constructed based on the probabilistic hesitant triangular fuzzy power aggregation operator, and the validity of the model is verified by a case.
Accelerate fine-scale geological mapping with UAV and convolutional neural networks
Authors: Zhan Liang , Liu Bin , Sang Xuejia , Xue Linfu
Published: March 2020

We propose a new fine-scale mapping process, which UAV and CNNs to distinguish the rock mass. Studies have shown that with UAV high-resolution images, comparing with traditional classification methods (52.92%~67.11%), the CNNs method has a much higher classification accuracy rate (86.54%). Although they can’t completely replace ground work, UAV and CNNs, together with appropriate field geological survey work, can quickly fill fine-scale geological maps. This is significant for harsh areas.
An adaptive hierarchical transmission mechanism of 3D models for mobile terminals
Authors: Wei Chen , Shang Yanlei
Published: March 2020

An adaptive hierarchical transmission mechanism of 3D mesh models for mobile terminals based on WebAR technology is introduced. By proposing a vertex merging algorithm based on vertex megring degree, the original model is decomposed into multiple groups of data consist of a simplified model and a layer file for reconstruction according to the specific compression ratio. The experiments show that the initialization time of the AR scene using the simplified model as the base model is effectively reduced and the simplified model can be reconstructed to the original model losslessly with the layer file. The proposed mechanism can be used for adaptive transmission, lossless reconstruction and real-time rendering on mobile terminals.
Research on Power Load Forecasting and Visualization Method Based on Deep Neural Network
Authors: Wang Yifei , Li Xian , Mo Shuang
Published: March 2020

Short-term load forecasting (STLF) has attracted much attention in the last decade, which uses data from historical power grid operation monitor to forecast future power demand for a few days. Previous works on STLF mainly focus on features, which are widely used to capturing external factors in power system. There are also approaches based on physics, which use linear combination of active and reactive power consumption to model dynamics of power demand. However, in many real-world applications they don’t work well. In the self-service power grid, STLF is a very important part in power scheduling and planning. But STLF is a challenging task for its complex power demand and the effects of weather. In this paper, we study the problem of combination of complex and seasonality of power demand, as well as effect of external weather. Therefore, we come up with an approach, called DNNCast, it’s a model based on deep neural network, which can achieve a good accuracy and fast training speed. In the structure it uses a deep neural network to model various power loads and external weather conditions. Finally, with visualization tools we can wisely make decision about power scheduling and planning. Compared with the current STLF methods, our model has a better prediction effect, and faster in the experiments of the extended experiment on 133 real power datasets.
Research on the Design and Application of Implicit Semantic Model in Bundle Recommendation Method
Authors: Li Zhitao , Li Shuqing
Published: March 2020

The recommendation system has been studied for a long time. Data of more users and commodities has been used to improve precision of the recommendation system. However, few studies are on utilization of bundling information. The bundling information refers to the inherent connection of the commodities, and depended by objective properties of commodities. This paper adds bundling information to the recommendation method based on traditional implicit semantic model and collaborative filtering method. This paper sets the bundling coefficient to show the connection degree between commodities and recommended decisive factors. Experiment shows the precision of recommendation could be improved with bundling information in specific environment through changing bundling coefficient.
Research on Text Mining of Material Science Based on Natural Language Processing
Authors: Gao Xiang , Tan Rong , Li Guanghui
Published: March 2020

Facing large-scale and rapidly growing material science literature data, text mining has become a research hotspot of material science. In recent years, natural language processing technology and machine learning methods have become the main technical means of text mining in materials science. The main task of text mining is to transform unstructured text data into structured material data by information extraction methods such as Named Entity Recognition and entity relationship extraction. This research proposes a general solution framework for material information extraction tasks, and introduces the main concepts and processes of text processing, text annotation, entity relationship extraction, etc., and discusses the current research progress and possible future research directions.
Parameter Analysis and Simulation Experiment of Ant Colony Optimization on Small-scale TSP Problem
Authors: Yang Lin , Wang Yongjie , Zhang Jun
Published: March 2020

Ant colony optimization (ACO) is a simple and efficient bionic intelligent algorithm, which can be applied to various optimization problems. There are many controllable parameters in the ant colony optimization, so the parameter setting has a great impact on the efficiency of the algorithm. In order to further improve the efficiency of the algorithm, many researchers have proposed various improvements. However, the current improved method of ACO ignored the consideration of its original parameter settings. In order to analyze the effect of parameter setting on the efficiency of the ant colony optimization, this article uses the ant colony optimization to solve small-scale TSP problems as an example for simulation experiments. Explore the impact of three parameter pairs on the efficiency of algorithm optimization by setting variable pairs. The experimental results show that when the ant colony size is set to approximately 0.6 times the TSP problem size, a better algorithm efficiency can be obtained. Within the value range of [1, 10], setting the information heuristic factor to 1, and the expected heuristic factor to a range of [5, 10] can get a better efficiency. In the last experiments, the pheromone volatility coefficient is generally better to be set greater than 0.2, and in a small-scale TSP problem, totally randomness has little effect on the optimization accuracy of the algorithm, and the value of pheromone enhancement coefficient has little effect on the efficiency of the algorithm.
Ship Agent model for traffic flow simulation in inland waterway
Authors: Xu Wuxiong , Wu Siqing
Published: March 2020

Water traffic is a nonlinear complex system, which is difficult to be described by a mathematical model. Naturally, simulation has become an important method to study it. In the simulation system, how to establish a ship simulation model is very crucial. In this paper, according to characteristics of ship’s behaviour, the Ship Agent model for simulation in the inland waterway is constructed, and the Multi-Agent simulation system is composed of these Ship Agents. As a case, the simulation system is set up based on the Monte Carlo method. The conclusion can be drawn that this Multi-Agent system can imitate the ship’s behaviour, and achieve the basic operation, such as the ship’s free navigation, overtaking, turning, collision avoidance and so on.
Collision detection based on octree for virtual surgery system
Authors: Hu Yanrong , Yan Zhiyuan , Yin Zhenlun , Du Zhijiang
Published: March 2020

In the virtual surgery system, the organ is always non-convex and deforms over time which makes the collision detection problem more difficult to handle. In this case, we put forward an innovative collision detection algorithm based on octree. This algorithm can perfectly apply to deal with non-convex organs and organ deformation scenario. In addition, it can achieve high run-time efficiency.
Research on the Classification of Fish Parasitoids Based on Digital Image Processing Technology
Authors: Zhan Yekang , Geng Xiebo , Liu Yang , Zhang Ying
Published: March 2020

The traditional classification method of morphology and the combination of morphology and molecular biology have low efficiency and accuracy in the classification of fish parasitoids. In this paper, Four kinds of blepharoplasma, such as ugly round Myxobolus, Myxobolus shantungensis, Myxobolus Koi and Myxobolus dispar, were selected as the research objects. A new method based on digital image processing technology is proposed to extract the edge of a spore and pouches, and calculates various morphological parameters of the Myxobolus. Based on the obtained parameters, a classification standard is established to classify four different Myxobolus and a new classification method based on morphological criteria was proposed.
Optimization Algorithms for Tibetan-Chinese Neural Machine Translation
Authors: Jiang Jing , Li Yachao
Published: March 2020

Tibetan-Chinese neural machine translation (NMT) is facing serious resource scarcity problem. This paper compares the application effect of multiple neural network optimization algorithms under the condition of resource scarcity, and proposes an optimization method which is suitable for resource scarcity languages. Then, by which it improving the performance of the Tibetan-Chinese NMT. Experimental results show that when choosing a suitable optimization algorithm, the Tibetan-Chinese NMT can still exceed the traditional statistical machine translation (SMT) and achieve better translation performance.
Research on the Congregation of Cross-border E-commer Talents Based on Structural Equation Model
Authors: Tian Xiao , Yan Xiaogai
Published: March 2020

The competition in modern industries is the competition for talents. The booming development of cross-border e-commerce has brought new challenges to talent gathering. Based on a questionnaire survey of 241 Zhengzhou cross-border e-commerce practitioners, based on the three dimensions of regional factors, organizational factors, and personal factors, a structural equation model was used to analyze its impact path on the cross-border e-commerce talent gathering. The results of empirical research show that: organizational factors are the main factors affecting the gathering of cross-border e-commerce talents, personal factors are the important factors affecting the gathering of talents, and regional factors are the effective factors affecting the gathering of talents. Finally, corresponding suggestions are put forward.
Research on Tolerance Redistribution Method Based on Measurement Data
Authors: Fu Tianzhao , Lyu Ruiqiang , Wang Mingyang , Yang Weimin , Wang Mingyang , Xie Pengcheng
Published: March 2020

As the performance requirements of aircrafts increase, the mechanical structure of the product is more complicated and the requirements for assembly are getting higher and higher. Due to the accumulation of errors in the processing and assembly process, assembly may not meet the product design requirements. It is necessary to repair some of the processed parts before performing the assembly process. This paper proposes an assembly simulation analysis and a tolerance redistribution approach based on measurement data that is making decisions on which part should be repaired. Firstly, the processed parts and assembly fixture are measured by measuring devices and fitted into the digital model with measurement data. Secondly, the assembled digital simulation is performed to verify whether the processed parts can be assembled successfully. Finally, if the parts cannot be assembled successfully, an improved genetic algorithm is used to optimize the tolerance to form a repair scheme.
Preparation and Force Sensitive Properties of ZnO/Graphene Composite Films
Authors: Wang Jianming , Luo Zhaomin , Zheng Feng , Wang Qiaozhi , Xu Xingming
Published: March 2020

In this paper, the preparation and force sensitivity of ErGo-ZnO composite films were studied. In order to improve the application value of graphene composite film in optical devices, sensors, photocatalysis, etc., ZnO is added. In this experiment, the ErGo-ZnO film was successfully prepared by vacuum filtration. At the same time, by adjusting the self-assembled layer of the composite film and the concentration of graphene oxide in the self-assembly growth process, the optimal design parameters are further optimized. The experimental results show that the self-assembled 6 layers have the best performance of the graphene oxide concentration of 0.5mg/ml.The crystallinity of the 0.05wt% graphene oxide ErGo-ZnO composite film is obviously improved. The amount of phase in the film after axial stretching is greatly increased (from 20% before stretching to 92%). In addition, comprehensive characterization and experimental results show that the excellent mechanical properties and specific surface area of graphene oxide and the excellent compatibility with ErGo-ZnO materials are the key factors to improve the interface stress transfer efficiency. This makes the crystallinity and mechanical properties of the composite film greatly improved, and the piezoelectric properties are further enhanced, which greatly improves the application value of the graphene composite film in optical devices, sensors, photocatalysis and the like.
Design and finite element analysis of thick shell mussel shell opener
Authors: Li Zhenhua , Luo Erlin , Tan Jinling , Ni Yang , Song Zuchano , Wang Jian
Published: March 2020

In order to obtain fresh mussel meat, this paper comes up with a design of mechanical automatic thick-shell mussel open shell machine. According to the physiological parameters and structural characteristics of thick shell mussel, it is determined that the scheme of automatic shell opening and the related parameters of open shell cutter tool. At the same time, Using Solidworks simulation and ANSYS Workbench on the open shell tools and pneumatic cylinder and other major component of finite element analysis, which is ensure that thick-shell mussel open shell machine open shell with stable and efficient. And the relevant part of parameters are confirmed that the thick shell mussel open shell machine avoid some adverse vibration frequency in theory. The purpose of the thick-shell mussel open shell machine is to replace manual opening shell, improve the efficiency of mussel shell opening and provide further reference for the production design of the automatic thick shell mussel opening shell equipment.
Modeling and Simulation of Fuel Engine Rail Pressure System
Authors: Wang Hong
Published: March 2020

The rail pressure system of fuel engine is a nonlinear dynamic system with strong disturbance and its system modeling is especially important, the engine rail pressure system is taken as the research object in this paper, and the “pump-pipe-nozzle” mathematical model is established, the dynamic relationship among the cam speed of oil pump, the fuel injection quantity of fuel spray nozzle and the fuel pressure in the common rail pipe is explored in theory and simulation.
Optimization design of hoisting column for multi-casting system of solid rocket motor based on response surface method
Authors: Jiang Fulong , Cui Zhaoxia , Tao Yang , Zhou Le
Published: March 2020

The erection of the solid rocket motor multi-casting system is optimized. The three-dimensional parametric model of the column is established, and the static analysis is carried out to determine the total deformation and the equivalent stress distribution of the hoisting column. Take the important dimensions such as shaft diameter and length of column as design variables, mass, equivalent stress and maximum deformation as target variables. The multi-objective genetic algorithm based on response surface is used to optimize the size of the hoisting column. The results show that after optimization, the overall quality of the column is 28.931kg, which is reduced by 6.16kg, which is 17.7% lower than the original plan.
Optimum Analysis of Microporous Foaming/Mould Decoration Compound Forming Technology for Automobile Door Decoration Panel
Authors: Hu Yi , Yuan Tieqiang
Published: March 2020

All Energy conservation and environmental protection are increasingly concerned by the automotive industry. Lightweight vehicles are one of the effective ways to achieve energy conservation and environmental protection. As an important method in plastic processing, the microcellular foam injection molding process can further meet the requirements of lightweight vehicles while meeting the performance requirements of the parts. However, a large number of bubbles exist on the surface of the microcellular foamed injection molded product, and the problems such as large surface roughness, low gloss, and poor apparent quality restrict the application of the process in automotive plastic parts. In order to improve the surface defects of microcellular foamed parts, this paper establishes experimental schemes and mathematical models based on partial factor test design method, response surface design and genetic algorithm, and obtains the optimal micro-foaming/in-mold decorative composite injection molding. Process parameter plan.
Experimental Research on Physical Indices and Mechanical Properties of Sandstone under Combined Action of Chemical-Freeze-Thaw Cycles
Authors: Feng Xuezhi , Wang Yongyan , Ge Qiang , Yu Zhuoqun
Published: March 2020

In order to study the effects of chemical solution erosion and freeze-thaw cycles on the physical parameters and mechanical properties of rocks, sandstone samples from central Shandong Province were taken as the research objects. The freeze-thaw cycle test and uniaxial compression test were carried out for rock samples eroded by 0.01mol/L H2SO4 solution, 0.01mol/L NaCl solution and 0.01mol/L NaOH solution, respectively. The results show that the mass loss and porosity of rock samples eroded by different chemical solutions increase with the increase of freeze-thaw cycles, and the peak strength and elastic modulus decrease exponentially with the increase of freeze-thaw cycles. The damage degree of acidic solution to freeze-thaw cycling sandstone samples is the greatest, that of alkaline solution is the next, and that of neutral solution is the smallest. The conclusions can be used for reference in support and design of underground engineering.
Modeling and solving the dynamic problem of a porous rotational shell based by modified Boit’s theory
Authors: Hao Jin , Yu Xiang , Jing Lu
Published: March 2020

Combined the Boit’s theory and the classical elastic theory of thin shell, a new model for analyzing the vibrations of a thin fluid-saturated porous rotational shell is proposed and solved by the extended homogeneous capacity precision integration method and the precise element method. The present model is precise and closer to the real materials as it takes the fluid-solid coupling effect into account and doesn’t make any hypothesis for the fluid displacement. At the end of the paper, the accuracy of the model had been verified by experiment and FEM methods.