An early recognition algorithm for BitTorrent traffic based on improved K-means

In response to the deficiencies of BitTorrent, the concept of density radius was proposed, and the distance from the maximum point of radius density to cluster center as a cluster radius was taken to solve the too large cluster radius resulted from the discrete points and to reduce the false positive rate of early recognition algorithms. Simulation results show that in the actual network environment, the improved algorithm, compared with K-means, will reduce the false positive rate of early identification algorithm from 6.3% to 0.9% and has a higher operational efficiency.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.

Target recognition algorithm for passive sonar system with high generalization ability

A new algorithm based on a Supervised Self-Organizing neural network for the pas sive sonar target recognition was proposed. Because of the incompleteness of the passive sonar exemplar set, the algorithm introduced a Multi-Activation-function structure and Supervised Self-Organizing competitive learning algorithm into the classic feed-forward neural networks,and obviously improved the generalization ability in target recognition. Besides, it can effi ciently reduce the learning time and avoid the local optimum. The recognition experiments of realistic passive sonar signals show that this new algorithm has good generalization ability and high recognition rate

Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

Online fault recognition of electric power cable in coal mine based on the minimum risk neural network

Firstly,the concepts of the traveling wave entropy and the feature function of traveling wave entropy were defined.Then the statistic characters of the traveling wave entropy feature function,mean value and variance were analyzed after the zero-order component of the traveling wave of online cable was selected to serve as the observed object.Finally,the new recognition algorithm of minimum risk neural network was pre- sented.The simulation experiments show that the recognitions of the early fault states can be completed correctly by using the proposed recognition algorithm.The classes of cable faults include in 1-phase ground faults,and the 2-phase short circuit faults or ground faults and the 3-phase short circuit faults or ground faults,open circuit.The fault resistance range is 1×10^(-1)～1×10~9Ω.

Posterior probability calculation procedure for recognition rate comparison

This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.

CHARACTER DETECTION AND RECOGNITION SYSTEM OF BEER BOTTLES

An optical imaging system and a configuration characteristic algorithm are presented to reduce the difficulties in extracting intact characters image with weak contrast, in recognizing characters on fast moving beer bottles. The system consists of a hardware subsystem, including a rotating device, CCD, 16 mm focus lens, a frame grabber card, a penetrating lighting and a computer, and a software subsystem. The software subsystem performs pretreatment, character segmentation and character recognition. In the pretreatment, the original image is filtered with preset threshold to remove isolated spots. Then the horizontal projection and the vertical projection are used respectively to retrieve the character segmentation. Subsequently, the configuration characteristic algorithm is applied to recognize the characters. The experimental results demonstrate that this system can recognize the characters on beer bottles accurately and effectively; the algorithm is proven fast, stable and robust, making it suitable in the industrial environment.

Application of Multivariate Reinforcement Learning Engine in Optimizing the Power Generation Process of Domestic Waste Incineration

Garbage incineration is an ideal method for the harmless and resource-oriented treatment of urban domestic waste.However,current domestic waste incineration power plants often face challenges related to maintaining consistent steam production and high operational costs.This article capitalizes on the technical advantages of big data artificial intelligence,optimizing the power generation process of domestic waste incineration as the entry point,and adopts four main engine modules of Alibaba Cloud reinforcement learning algorithm engine,operating parameter prediction engine,anomaly recognition engine,and video visual recognition algorithm engine.The reinforcement learning algorithm extracts the operational parameters of each incinerator to obtain a control benchmark.Through the operating parameter prediction algorithm,prediction models for drum pressure,primary steam flow,NOx,SO2,and HCl are constructed to achieve short-term prediction of operational parameters,ultimately improving control performance.The anomaly recognition algorithm develops a thickness identification model for the material layer in the drying section,allowing for rapid and effective assessment of feed material thickness to ensure uniformity control.Meanwhile,the visual recognition algorithm identifies flame images and assesses the combustion status and location of the combustion fire line within the furnace.This real-time understanding of furnace flame combustion conditions guides adjustments to the grate and air volume.Integrating AI technology into the waste incineration sector empowers the environmental protection industry with the potential to leverage big data.This development holds practical significance in optimizing the harmless and resource-oriented treatment of urban domestic waste,reducing operational costs,and increasing efficiency.

Classifying Heart Disease in Medical Data Using Deep Learning Methods

Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition (NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and Whale Optimization which are consolidated and proposed another strategy Optimal Deep Neural Network (ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values.

Recognition of practical speech emotion using improved shuffled frog leaping algorithm

Due to the drawbacks in Support Vector Machine（SVM）parameter optimization,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA）was proposed,and the learning ability in practical speech emotion recognition was improved.Firstly,we introduced Simulated Annealing（SA）,Immune Vaccination（Iv）,Gaussian mutation and chaotic disturbance into the basic SFLA,which bManced the search efficiency and population diversity effectively.Secondly,Im-SFLA Was applied to the optimization of SVM parameters,and an Im-SFLA-SVM method Was proposed.Thirdly,the acoustic features of practical speech emotion,such aS ridgetiness,were analyzed.The pitch frequency,short-term energy,formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories,and we constructed a 144-dimensional emotion feature vector for recognition and reduced to 4-dimension by adopting Linear Discriminant Analysis（LDA） Finally,the Im-SFLA-SVM method Was tested on the practical speech emotion database,and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM（SFLA-SVM）method,Particle Swarm Optimization algorithm optimization-SVM（PSo-SVM） method,basic SVM,Gaussian Mixture Model（GMM）method and Back Propagation（BP）neural network method.The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.

Full Metal Species Quantification of Metal Supported Catalysts Through Massive TEM Images Recognition

For a practical high-loading single-atom catalyst,it is prone to forming diverse metal species owing to either the synthesis inhomogeneity or the reaction induced aggregation.The diversity of this metal species challenges the discerning about the contributions of specific metal species to the catalytic performance,and thus hampers the rational catalyst design.In this paper,a distinct solution of dispersion analysis based on transmission electron microscopy imaging specialized for metal-supported catalysts has been proposed in the capability of full-metal-species quantification(FMSQ)from single atoms to nanoparticles,including dispersion densities,shape geometry,and crystallographic surface exposure.This solution integrates two image-recognition algorithms including the electron microscopy-based atom recognition statistics(EMARS)for single atoms and U-Net type deep learning network for nanoparticles in different shapes.When applied to the C_(3)N_(4)-and nitrogen-doped carbon-supported catalysts,the FMSQ method successfully identifies the specific activity contributions of Au single atoms and particles in butadiene hydrogenation,which presents remarkable variation with the metal species constitution.This work demonstrates a promising value of our FMSQ strategy for identifying the activity origin of heterogeneous catalysis.

Multi-state autonomous drilling for lunar exploration

Due to the lack of information of subsurface lunar regolith stratification which varies along depth, the drilling device may encounter lunar soil and lunar rock randomly in the drilling process. To meet the load safety requirements of unmanned sampling mission under limited orbital resources, the control strategy of autonomous drilling should adapt to the indeterminable lunar environments. Based on the analysis of two types of typical drilling media （i.e., lunar soil and lunar rock）, this paper proposes a multi-state control strategy for autonomous lunar drilling. To represent the working circumstances in the lunar subsurface and reduce the complexity of the control algo- rithm, lunar drilling process was categorized into three drilling states： the interface detection, initi- ation of drilling parameters for recognition and drilling medium recognition. Support vector machine （SVM） and continuous wavelet transform were employed for the online recognition of drilling media and interface, respectively. Finite state machine was utilized to control the transition among different drilling states. To verify the effectiveness of the multi-state control strategy, drilling experiments were implemented with multi-layered drilling media constructed by lunar soil simulant and lunar rock simulant. The results reveal that the multi-state control method is capable of detecting drilling state variation and adjusting drilling parameters timely under vibration interferences. The multi-state control method provides a feasible reference for the control of extraterrestrial autonomous drilling.

Automated detection of parasitized Cadra cautella eggs by Trichogramma bourarachae using machine vision

Cadra(Ephestia)cautella(Walker)is a moth that attacks dates from ripening stages while on tree,throughout storage,and until consumption,causing enormous qualitative and quantitative damages,resulting in economic losses.Image-processing algorithms were developed for detecting and differentiating between three Cadra egg categories based on the success of Trichogramma bourarachae(Pintureau and Babaul)parasitization.These categories were parasitized(black and dark red),unparasitized fertile unhatched(yellow),and unparasitized hatched(white)eggs.Color,light intensity,and shape information was used to develop detection algorithms.Two image processing methods were developed based on three randomly selected images and were tested on a larger validation image set of 40 images:(i)segmentation and extractions of color and morphological features followed by Watershed delineation,and is referred to as Algorithm 1(ALGO1),(ii)finding circular objects by Hough Transformation followed by convolution filtering,and is referred to as Algorithm 2(ALGO2).ALGO1 and ALGO2 achieved correct classification rates(CCRs)for parasitized eggs of 92%and 96%,respectively.Their CCRs for unhatched eggs were 48%and 94%,and for hatched eggs were 42%and 73%,respectively.Regarding parasitized eggs,both methods performed satisfactorily,but,in general,ALGO2 outperformed ALGO1.These results ensure automatic evaluation of the efficiency of biological control of Cadra cautella by the egg parasitoid Trichogramma bourarachae by quantifying the rate of parasitization.The developed detection methods can be used by producers of biocontrol agents for online monitoring of Trichogramma and similar insect natural enemies during mass production and before release against crop pests.Moreover,with few adjustments these methods can be used in similar applications such as detecting plant diseases.