Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

Automatic recognition of defects in plasma-facing material using image processing technology

Observing and analyzing surface images is critical for studying the interaction between plasma and irradiated plasma-facing materials.This paper presents a method for the automatic recognition of bubbles in transmission electron microscope(TEM)images of W nanofibers using image processing techniques and convolutional neural network(CNN).We employ a three-stage approach consisting of Otsu,local-threshold,and watershed segmentation to extract bubbles from noisy images.To address over-segmentation,we propose a combination of area factor and radial pixel intensity scanning.A CNN is used to recognize bubbles,outperforming traditional neural network models such as Alex Net and Google Net with an accuracy of 97.1%and recall of 98.6%.Our method is tested on both clear and blurred TEM images,and demonstrates humanlike performance in recognizing bubbles.This work contributes to the development of quantitative image analysis in the field of plasma-material interactions,offering a scalable solution for analyzing material defects.Overall,this study's findings establish the potential for automatic defect recognition and its applications in the assessment of plasma-material interactions.This method can be employed in a variety of specialties,including plasma physics and materials science.

Research on PCA and KPCA Self-Fusion Based MSTAR SAR Automatic Target Recognition Algorithm

This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.

Dynamics analysis on transverse cutting mechanism of straw checkerboard paving robot

The transverse cutting mechanism of the checkerboard paving robot, which directly affects the robot＇s operation, is regarded as the most important part in a transverse pavement system This research aims at designing a new system which is appropriate for desert wofidng en- vironments and finishing cutting the rope of straw. In this paper, computer emulating technology is applied, and with three-dimensional modeling by Pro/ENGINEER software, can observe whether or not interference exists. Also, the speed and displacement parameters of the main parts of the cutting system are given by the dynamic emulation by Automatic Dynamic Analysis of Mechanical System （ADAMS）, which theoretically assures the feasibility and appropriateness of the cutting mechanisrn~ Finally, results of the experinaent shows that the cutting mechanism can cut the straw strand as designed.

Dynamic Simulation Analysis of the Flexible Swap Device

With the development of space science and technology,the on-orbit servicing technologies of spacecraft get more and more attention.According to the design criterion of existing spacecraft in orbit module replacement technology,the flexible swap device is designed and the dynamics simulation of institutions by the automatic dynamic analysis of mechanical systems(ADAMS)simulation software is analyzed.Throughout the analysis process,this paper studies the effect of collision force of flexible mechanism and provides a basis for the optimization of flexible plug agencies.

滚-跳多运动模式球形机器人的动力学仿真研究

复杂的探索环境对探测机器人的通行能力提出了更高要求,具有多种运动模式的球形机器人是探测机器人领域的研究热点之一。通过对球形机器人内部构件质量分布的调节,设计了一种基于单摆驱动式的滚-跳多运动模式球形机器人,其具有滚动、转向、爬坡与跳跃4种基本运动能力;随后,又根据球形机器人的设计方案建立了球形机器人4种基本运动动力学方程。利用Automatic Dynamics Analysis of Mechanical Systems(ADAMS)软件构建该球形机器人虚拟样机并对其进行了滚动、转向、爬坡与跳跃的动力学仿真,最后以此为依据计算滚动、转向、爬坡与跳跃运动性能的仿真值,并制作实验室样机验证了动力学仿真的正确性。

LAME:Layout-Aware Metadata Extraction Approach for Research Articles

The volume of academic literature,such as academic conference papers and journals,has increased rapidly worldwide,and research on metadata extraction is ongoing.However,high-performing metadata extraction is still challenging due to diverse layout formats according to journal publishers.To accommodate the diversity of the layouts of academic journals,we propose a novel LAyout-aware Metadata Extraction(LAME)framework equipped with the three characteristics(e.g.,design of automatic layout analysis,construction of a large meta-data training set,and implementation of metadata extractor).In the framework,we designed an automatic layout analysis using PDF Miner.Based on the layout analysis,a large volume of metadata-separated training data,including the title,abstract,author name,author affiliated organization,and keywords,were automatically extracted.Moreover,we constructed a pre-trainedmodel,Layout-Meta BERT,to extract the metadata from academic journals with varying layout formats.The experimental results with our metadata extractor exhibited robust performance(Macro-F1,93.27%)in metadata extraction for unseen journals with different layout formats.

Cavitation Bubble Luminescence in Cone-Type Throttle Valve

In light of the light emission from cavitation bubbles under certain conditions, the phenomena of the cavitation bubble luminescence in the hydraulic cone-type throttle valve is focused in this paper. Firstly,the software of automatic dynamic incremental nonlinear analysis( ADINA) is applied to studying the flow field of the flow channel of the cone-type throttle valve. And the pressure distribution of the valve flow channel is obtained. The easyhappening area of cavitation in the cone-type throttle valve is also found out by ADINA. Then,the experimental research on the conetype throttle valve is carried out in this paper. The changing law of the hydraulic oil temperature in the corresponding region under different system pressure and the backpressure condition are experimentally researched. The relationship between the luminescence intensity and the cavitation intensity,the pressure,and the temperature are also studied. Finally,a summary of the causal relationship between the luminescence and cavitation in the cone-type throttle valve,the cavitation effect on the hydraulic oil temperature,and the method for the inhibition of cavitation bubble luminescence are presented. The results show that the light intensity increases with the increase of the cavitation intensity,and the luminescence can be inhibited by the increase of backpressure.

Feature Extraction of Radar Range Profiles Based on Normalized Central Moments

The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.

Automatic Extraction of Tongue Coatings from Digital Images:A Traditional Chinese Medicine Diagnostic Tool

In traditional Chinese medicine, the coating on the tongue is considered to be a reflection of various pathologic factors. However, the conventional method to examine the tongue lacks an acceptable standard and does not provide the means for sharing information. This paper describes a segmentation method to extract tongue coatings. First, the tongue body was extracted from the original image using the watershed transform. Then, a threshold method was applied to the image to eliminate the light from the camera flash. Finally, a threshold method using the Otsu model in combination with a splitting-merging method was used in the red, green, and blue （RGB） space to extract the thin coating. The combination of the above two methods is applied in the hue, saturation, and value （HSV） space to extract the thick coating. The feasibility of this method is tested by experiments, and the accuracy of segmentation is 95.9%.

Analysis of mobility data-A focus on Mobile Mapping Systems

The increasing availability of devices to capture the position of moving objects(and other environmental information)leads to a very large amount and variety of mobility data.In order to obtain important information about the objects,their behavior or the environment of the objects,an automatic analysis is required.This article highlights current research questions in the context of the analysis of mobility data and presents them on the basis of work carried out at the Institute of Cartography and Geoinformatics(ikg)at Leibniz University of Hannover,Germany.A focus is put on the analysis and exploitation of information from Mobile Mapping vehicles.

On-line full scan inspection of particle size and shape using digital image processing

An on-line full scan inspection system is developed for particle size analysis. A particle image is first obtained through optical line scan technology and is then analyzed using digital image processing. The system is composed of a particle separation module, an image acquisition module, an image processing module, and an electric control module. Experiments are carried out using non-uniform 0.1 mm particles. The main advantage of this system consists of a full analysis of particles without any overlap or miss, thus improving the Area Scan Charge Coupled Device （CCD） acquisition problems. Particle size distribution, roundness, and sphericity can be obtained using the system with a deviation of repeated precision of around ±1%. The developed system is shown to be also convenient and versatile for any particle size and shape for academic and industrial users.

"Weather" transit is reliable? Using AVL data to explore tram performance in Melbourne,Australia

This paper uses automatic vehicle location （AVL） records to investigate the effect of weather conditions on the travel time reliability of on-road rail transit, through a case study of the Melbourne streetcar （tram） network. The datasets available were an extensive historica; AVL dataset as well as weather observations. The sample size used in the analysis included all trips made over a period of five years （2006-2010 inclusive）, during the morning peak （7 am-9 am） for fifteen randomly selected radial tram routes, all traveling to the Melbourne CBD create a linear model Ordinary least square （OLS） regression analysis was conducted to with tram travel time being the dependent variable. An alternative formulation of the model is also compared. Travel time was regressed on various weather effects including precipitation, air temperature, sea level pressure and wind speed; as well as indicator variables for weekends, public holidays and route numbers to investigate a correlation between weather condition and the on-time performance of the trams. The results indicate that only precipitation and air temperature are significant in their effect on tram travel time. The model demonstrates that on average, an additional millimeter of precipitation during the peak period adversely affects the average travel time during that period by approximately 8 s, that is, rainfall tends to increase the travel time. The effect of air temperature is less intuitive, with the model indicating that trams adhere more closely to schedule when the temperature is different in absolute terms to the mean operating conditions （taken as 15 ℃）.