bluuebunny/crossref_metadata_2025_split huggingface dataset:
3D Densely Connected Convolution Neural Networks for Pulmonary Parenchyma Segmentation from CT Images
Authors: Zhao Liang 
Published: September 2020

Abstract
Lung cancer is one of the deadliest diseases in the world today. It kills many people every year. For the diagnosis and treatment of lung cancer, accurate segmentation of lung tissue from CT images is an important process. It is necessary to design a fast and accurate segmentation method to accomplish this task. In the traditional computer-aided diagnosis system, the segmentation of Lung parenchyma is very complex, and the segmentation result depends on the performance of the parameters set in the previous stage. In order to solve these problems, we propose a 3D densely connected convolution neural network which based on deep learning. It has three densely connected blocks and three deconvolution layers. The experimental data set was taken from the public LIDC-IDRI database. A total of 888 samples with slice thickness less than 2.5 mm were selected in the experiments. And the number of samples of training set, test set and validation set is 708, 90 and 90 respectively. In addition, the experimental results show that our method is more accurate than 3D-Unet, but it requires less training parameters.
Research on pH Value Control System of Haematococcus Pluvialis Algae Based on Fuzzy PID Algorithm
Authors: Cui Shigang , Cai Xingzong , Zhang Yongli , Wu Xingli , He Lin 
Published: September 2020

Abstract
In recent years, inducing the production of astaxanthin by Haematococcus pluvialis, the application of extracting astaxanthin from it has become more and more widespread. The pH of the algae fluid has an important influence on the rate of accumulation of astaxanthin by Haematococcus pluvialis. However, the pH control system of Haematococcus pluvialis algae fluid has many shortcomings, such as non-linearity, time-varying, severe lag, large inertia and uncertain mathematical model. Therefore, to address these shortcomings, a method of pH control based on fuzzy PID is proposed. In the Matlab simulation software, using traditional PID control system adjust and control the pH value of Haematococcus pluvialis. And then using the fuzzy PID control system adjust and control the Haematococcus pluvialis. Finally, comparing the simulation results of the two systems. According to the Simulink results, we finally get the following conclusion: the fuzzy PID control system has smaller overshoot, no vibration, and the control effect is better.
Multi-point Mooring PID Control Based on Kalman Filtering-Genetic Algorithm-Slow Disturbance Observer Compensation
Authors: Zhang Guichen , Lu Run , Chen Mengwei 
Published: September 2020

Abstract
An adaptive PID control technique based on the Kalman Filtering (KF) Genetic Algorithm (GA) and Slow Disturbance Observer Compensation (SDOC) is proposed for multi-point mooring system in this paper. The multi-point mooring model, which is simplified to two-point mooring, is established, its self-tuning PID mooring controller based on KF-GA-SDOC is designed. The multi-point mooring model is updated on-line with the KF and SDOC to filter and observe time-varying signal; the PID mooring control gains are adjustable parameters by selection, crossover and mutation operator of GA; the mooring control system based on KF-GA-SDOC PID is optimized. The simulation results show that the designed KF-GA-SDOC PID mooring controller can maintain good control performance under different conditions.
Multi-threshold Image Segmentation by Improved Lion Swarm Optimization Algorithm
Authors: Li Xiaogang , Jiang Mingyan 
Published: September 2020

Abstract
In this paper we propose an improved Lion Swarm Optimization (ILSO) algorithm for multi-threshold image segmentation. We introduce the global search method of Artificial Bee Colony optimization (ABC) algorithm and revise the updating function of LSO algorithm to improve the global and local search performance of LSO algorithm. We introduce a sign to record the number of times an individual falls into the local optimum, and attenuate it so that the algorithm can jump out of the local optimum more quickly in the early stage and accelerate the convergence speed in the later stage. The maximum inter-class variance criterion is selected as the fitness function to solve the Multi-threshold image segmentation problem by ILSO. Experiment results show this algorithm can obtain ideal image segmentation result. And when the dimension of the problem is higher, the advantage of the improved Lion Swarm Optimization algorithm proposed in this paper is more obvious.
DB-Net: Dual Attention Network with Bilinear Pooling for Fire-Smoke Image Classification
Authors: You Chen , Li Zhen , Li Meng , Gao Zongliang , Li Wei 
Published: September 2020

Abstract
In recent years, with the help of deep learning, image classification has been significantly improved. More and more researchers are seeking fire alarm solutions based on these artificial intelligence algorithms. However, this raises new challenges in an effective method of classification for fire-smoke images. In this paper, we propose a Dual Attention Network with Bilinear Pooling of three steps to accomplish the task of fire-smoke classification. We first extract features of fire pictures or smoke ones by using ResNet-50 as a basic net. Then we attach two kinds of attention modules, channel attention and spatial attention, which are called dual attention, to extract key information ‘what’ and ‘where’ from pictures. Finally, we merge them by using the bilinear pooling module which has been shown to be effective at improving our classification rate. Results show that our most accurate model can reach 90.11% per-image accuracy, which is improved by 4.81% compared to the traditional ResNet-50.
Research on Main Steam Temperature Prediction Model Based on Improved LSTM Algorithm
Authors: Zheng Ling , Ye Xin , Chen Fei 
Published: September 2020

Abstract
In order to improve the reliability and accuracy of the main steam temperature trend prediction, a main steam temperature prediction model based on improved LSTM is proposed. Firstly, uses the grey correlation analysis method to select the important influencing factors. Then, a linear structure is introduced into the LSTM structure to construct a main steam temperature prediction model. Finally, based on the historical operating data of the thermal power unit, a simulation experiment is performed to compare the prediction error of the output of the RNN model, the LSTM model, and the improved LSTM model. The results show that the method has higher prediction accuracy for the main steam temperature. At the same time, compared with other traditional methods, this method has better fitting effect, which can be well applied in practical engineering.
Human Activity Recognition Using Gaussian Naïve Bayes Algorithm in Smart Home
Authors: Shen Jiajie , Fang Hongqing 
Published: September 2020

Abstract
Human activity recognition (HAR), as an important research issue, aims to identify human activities in smart homes. In this paper, we apply Gaussian Naive Bayes (GNB) algorithm to HAR and evaluate the model based on smart environment sensor data. Experimental results show that the effective selection and processing of features are helpful to improve the accuracy of activity recognition of the model. Compared with NB whose accuracy rate is 82.7%, GNB has a better accuracy rate of 89.5% and even has a higher recognition accuracy in almost every category of activities. Selecting the feature variables as good and useful as possible to get a better model in the process of activity recognition is conducive to the correct classification of samples by machine learning algorithm and improves the classification performance of the model.
A Novel Spectral Clustering Algorithm Based on Randomly State Changed Particle Swarm Optimization
Authors: Chen Hao , Guo Dechun , Yang Shengzhi , Hou Xiaochen 
Published: September 2020

Abstract
Spectral clustering algorithm is a method of clustering which allows one piece of data to belong to two or more clusters. In this paper, a novel spectral clustering algorithm based on randomly state changed particle swarm optimization is proposed. The initial population was classified by considering global and local optimal functions, the evolutionary state of each particle is considered through the comparison of cost functions, and the evolutionary state of the particles was subdivided. The Weight mode added the previously optimal local particles and global particles. According to the rule that newer particles have greater weights, particles speed was updated to reduce the possibility of falling into a local optimal state and to expand the search range of particles. The classification accuracy of the clustering algorithm was presented. Finally, by using the UCI datasets for comparison, it was found that the algorithm proposed in this paper increase the performances by 3% to 28% for different datasets, comparing with the known clustering algorithms such as particle swarm optimization algorithm, Fuzzy C-Means algorithm and conventional Spectral Clustering.
Research on a Novel Influence Maximization Algorithm Based on Community Structure
Authors: Chen Jing , Liu Jiangchuan 
Published: September 2020

Abstract
With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model.
Application of Practical Astronomical Timing Algorithm in the Lighting Control of Residential Street
Authors: Dong Jie 
Published: September 2020

Abstract
With the development of this society, the street lighting system has become a part of the community infrastructure construction. Most of the traditional street lighting methods use simple area control and timing switches, it can’t change the switch time of street lighting intelligently according to the change of actual application scene. In order to overcome this deficiency, this paper has designed an intelligent astronomical timing algorithm and software control process, which can change the street lighting switch time manually or automatically, also which is programmed in the controller and monitored through the TD200 LCD display panel. In addition, the automatic switching on/off on holidays is also added in the street lighting. Practice of using this system has proven that this intelligent timing algorithm can effectively improve the efficiency of the lighting control system and save energy.
An Improved A* Algorithm Applying to Path Planning of Games
Authors: Zhang Cheng , Ao Lei , Yang Junsheng , Xie Wenchuan 
Published: September 2020

Abstract
Pathfinding is the core issues in the artificial intelligence field of games, and how to establish an effective method of pathfinding is still focused on. A* algorithm is the most widespread type of game pathfinding at present but the problems still exist such as the curve path and the time-consuming. Based on the analysis and research of traditional A* algorithm, this paper proposes an A* algorithm based on region search. Firstly, the algorithm divides the game map into several connected regions where the role can move freely and without barriers. Then the algorithm uses region search instead of node search. It reduces the search path and the search time. According to the portal between regions, the optimal feasible path is extracted from the region paths. It can effectively solve the zigzag path problem. Experimental simulation results show that the algorithm has shorter search time and can generate smoother path and meet the real-time and real experience requirements of pathfinding in the game.
Wheat Powdery Mildew Spore Images Segmentation Based on U-Net
Authors: Liang Xinshen , Wang Botao 
Published: September 2020

Abstract
In recent years, there have been significant development in deep learning technology, which has also promoted the development of image segmentation. Wheat powdery mildew is a common crop disease, which is very harmful to crops. The segmentation task of wheat powdery mildew spore images is important for the target spores identification and spores counting. In this paper, an improved framework based on U-Net is proposed, comparing with original U-Net, we add the pyramid pooling module after the 1024-channel feature map in the down-sampling part to extract different sizes pooling feature and fuse into a global feature map, and adjust some skip connections in original U-Net to obtain more features that are effective for spore images segmentation, experiment shows that the improved U-Net structure has better segmentation performance than U-Net, and the segmentation miou (mean intersection over union) has reached 91.4% in wheat powdery mildew spore image dataset, which proves that our proposed architecture is effective and competitive in the wheat powdery mildew spore segmentation task.
Fixed Power Allocation Algorithm Based on Time-Shift Pilot in Massive MIMO System
Authors: Guo Zijiao , Xiong Rujing , Deng Honggui 
Published: September 2020

Abstract
Massive Multiple-Input-Multiple-Output (MIMO) technology can greatly improve the spectrum utilization rate and system anti-interference ability as the key of technologies in modern wireless communication. However, pilot pollution has restricted the further development of large-scale MIMO technology. In this paper, a fixed allocation algorithm based on time-shift pilot to control pilot pollution in massive MIMO systems is proposed. Firstly, signal-to-interference-plus-noise-ratio (SINR) expression of the edge terminal based on time-shift pilot is derived, and then we calculate the proportional power allocation factor through the number of terminals. Finally, the uplink data power is reallocated by combining the proposed algorithm and time-shift pilot strategy. The simulation results show that the proposed algorithm can improve the communication ability of the edge terminal effectively.
The Use of LSTM-Based RNN and SVM Models to Detect Ludian Coseismic Landslides in Time Series Images
Authors: Zhou Zeng-Guang , Chen Binbin , Li Ziyang , Li Chuanrong 
Published: September 2020

Abstract
With the increase of temporal frequency of satellite images, time series analysis based on Artificial Neural Network has become a tendency to detect land cover changes in images. We briefly introduces a case study that uses Long-Short Term Memory (LSTM), a specific Recurrent Neural Network (RNN) for time series modelling and forecasting, and Support Vector Machine (SVM) to detect landslides triggered by Ms.6.5 earthquake in Ludian, China in 2014. The study uses 72 available images with 16/30m spatial resolution from Landsat-7/8, GaoFen-1 and HJ-1A/1B satellites. Firstly, per-pixel LSTM models are trained by Normalized Difference Vegetation Index (NDVI) images before the earthquake. Secondly, the trained LSTM models are used to predict NDVI images after the earthquake. Then, anomalies or changes are detected by comparing predicted and observed NDVI images. Finally, anomalies related to landslides are separated from other changes with a SVM model which was trained by multi-spectral images in the study area. Experiment demonstrates that the recall rate and precision rate of landslides detection are 82.09% and 76.21%, respectively. The study shows a potential that the combination of LSTM and SVM models can be used to detect landslides in Landsat-like time series images with 30m resolution.
Analysis on the Vibration of Cracked Cantilever Beams with Application on Crack Detection
Authors: Zhao Di 
Published: September 2020

Abstract
In this research, the transfer matrix of a cracked beam which includes the vibration modes of both longitudinal and transverse deformation is derived based on Timoshenko beam model. The expression of the natural frequencies and mode shapes of vibration using transfer matrix method are proposed which can be effectively used for real-time crack detection. Manifold types of cracks in the cantilever beam which have been simulated by finite element method, and the solutions are compared with those from the transfer matrix method. The difference between results could be used for detecting both the size and location of the crack. Therefore, the transfer matrix method can be successful used for analysis of vibration in the cracked beam, and the errors are within the tolerant range.
Design of Pump Station Operation Condition Monitoring System Based on MATLAB Simulation
Authors: Zhang Yitong 
Published: September 2020

Abstract
In this paper, through analysis of pump station operation status monitoring system, on the basis of applying wavelet packet analysis to signals processing, MATLAB was used to simulate the design of pump station operation status monitoring system. In the simulation link, through the wavelet packet decomposition and reconstruction of the collected original signal and fault signal of the pump station rotor, fault signal can be judged by comparing and analysing the characteristic vector, power spectrum and energy spectrum of the wavelet packet node. Real-time monitoring of running status of pump station unit rotor was realized, and a simple human-computer interaction interface was designed using MATLAB/GUI (Graphical User Interface), which is simple and convenient to operate. In the human-computer interaction system, the user can observe some characteristic values and spectrum of the rotor signal of the pump station through the drop-down menu, so as to quickly find the fault signal and realize real-time monitoring.
Constraint Inversion Model of Core Science Complex Network
Authors: Jia Yuzhe , Yi Junkai , Yan Shuan 
Published: September 2020

Abstract
In order to reduce the research costs, increase the efficiency of scientific researches, and improve the inversion abilities of core technologies, in the paper, a constrained inversion model aiming at tracking the source of core technology was proposed. Firstly, a complex network was built and it treated the most important product or technology as the core node vector. In the network, the social and technical attributes of the product were assigned as the elements of the core node vector. Therefore, the relationships among the core node and other nodes represented connective strength which were fairly important for calculating the path weights. Essentially, the weights were integrated attributes derive from a called score matrix. Then a constrained optimization criterion was employed to evaluate the scores of paths so that the best optimal path was determined. For reducing the number of searching and the times of calculating, all the constraints relationships of node vectors were classified into several types. The experimental results demonstrate that the proposed approach has a superior performance against the current state-of-the art methods over a real case.
Research on Fault Line Selection Method Based on Convolutional Neural Networks
Authors: Gao Jinfeng , Wang Yufeng 
Published: September 2020

Abstract
In order to improve the reliability and accuracy of Single-Phase-to-Ground Fault line selection for small current grounding systems, this paper proposes a method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses the original current signal as input and uses a wide kernel in the first convolutional layer to extract features and suppress high-frequency noise. The following convolutional layers with small convolution kernels are used for multilayer nonlinear mapping. Batch normalization layers and dropout layers are carried out for improving the generalization ability of the model. The simulation results show that the recognition rate of the proposed WDCNN can reach 99%, and it is not affected by factors such as grounding impedance, fault location and fault time.
UAV Altitude Measurement Method Based on Data Fusion and Kalman Filter
Authors: Yang Junjia , Qian Bing , Zhang Erwei , Qu Kun 
Published: September 2020

Abstract
Accurate altitude measurement of UAV is very important for its flight mission. Based on the analysis of the measurement performance of different altitude measurement sensors, this paper designs a multi-altitude measurement sensor fusion filtering structure, and then describes the fusion filtering method. This method mainly provides weighted fusion of data from various altitude measurement sensors to obtain altitude information with higher accuracy than any sensor. Through Kalman filtering of the fusion data, more accurate flight altitude data of UAV can be obtained. The method is validated by a typical example. The results show that the proposed method can solve the problem of precise measurement of altitude data in UAV flight process, and has certain theoretical significance and good application value.
Collaborative Filtering-Based Film Recommendation Technique Utilizing Time and Film Genres
Authors: Wang Huilin , Wu Jianhua 
Published: September 2020

Abstract
Due to the uncertainty of users’ preferences for different movies at different times, movie recommendation poses a huge challenge. We present an improved collaborative filtering algorithm for film recommendation whose design was motivated by a need to extract meaningful information for further recommending from film viewing applications. To this end, the method we propose utilises an improved method for analyzing user interest in film genres, which can then be subsequently used to predict users’ ratings of unwatched movies in combination with the current viewing time. In our new algorithm, we extract genre information considering time decay factor and combine it with matrix factorization collaborative filtering algorithm, and propose a novel algorithm structure, the MFTGICF algorithm. Compared with general traditional collaborative filtering algorithms and some other methods, this algorithm has more precision and higher stability. Both the theoretical analysis and extensive experiments show the improvement of the effectiveness and efficiency of the proposed method.
Research Progress of Network Public Opinion Based on Fuzzy Set from the Perspective of Big Data
Authors: Tang Jianguo , Wang Jianghua , Li Fang 
Published: September 2020

Abstract
Granular computing is a new method of intelligent information processing. It describes knowledge and simulates human thinking mode in a granular way. It can realize the rapid transformation between different granular knowledge and is suitable for the analysis and modeling of large-scale and complex data. Fuzzy set is an important model in granular computing, which describes uncertain problems through fuzzy membership function and has been widely used in many fields such as network public opinion analysis. This paper combs the application research of fuzzy sets in network public opinion analysis from the perspective of big data, and discusses the different roles of fuzzy sets in network public opinion analysis from four aspects: fuzzy comprehensive evaluation, fuzzy reasoning, fuzzy granularity and generalized fuzzy set. The characteristics of different methods are compared and the unique features of fuzzy sets in these four aspects are pointed out. The unique advantages and some problems are discussed as well, and the future trend of this field is discussed.