Rosgen stream classification and fluvial processes of the Shiyang River,China

The Shiyang River is an important ecological pillar in northwest China,sustaining Minqin oasis and its surrounding society.However,the basin has long been plagued by water scarcity and ecological fragility.Although the river classification is critical for understanding the complexity,diversity,and ecological functions of rivers,and the foundation of river management and watershed ecological restoration,it has not received adequate attention in this region.To obtain a deeper and comprehensive understanding of the Shiyang River,this study utilizes the Rosgen stream classification system to assess the river morphology,geomorphic features,and hydrologic processes.The results showed that seven first-level and fourteen second-level river types can be identified along 53 river sections of the Shiyang River.Further comparison analysis on the hydrologic parameters for each river type demonstrated a strong positive correlation between discharge and all river parameters.As discharge increased,channels with moderate to high width/depth ratios experienced significant lateral adjustments.A consistent channel gradient,coupled with higher discharge,facilitated the transition from single to multiple channels.Braiding tendencies were more pronounced in rivers where riverbeds were wider and shallower with higher stream power.Additionally,water-flow shear stress decreased with the increase in the width/depth ratio.This study offered critical insights into the Shiyang River’s forms and processes and for the river management and ecological restoration practices.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

Machining Process Classification Based on Carbon Footprint Analysis

Despite spending considerable effort on the development of manufacturing technology during the production process,manufacturing companies experience resources waste and worse ecological influences. To overcome the inconsistencies between energy-saving and environmental conservation,a uniform way of reporting the information and classification was presented. Based on the establishment of carbon footprint( CFP) for machine tools operation,carbon footprint per kilogram( CFK) was proposed as the normalized index to evaluate the machining process.Furthermore,a classification approach was developed as a tracking and analyzing system for the machining process. In addition,a case study was also used to illustrate the validity of the methodology. The results show that the approach is reasonable and feasible for machining process evaluation,which provides a reliable reference to the optimization measures for low carbon manufacturing.

Classification and Analysis of Tube Hydroforming Processes with Respect to Adaptive FEM Simulations

Tube hydroforming process is a relative new process f or production of structural parts of low weight and high rigidity. The successfu lness of the process depends largely on the a proper selection of loading path w hich is axial feeding distance as related to the applied internal pressure. Due to the complicated nature of plastic deformation, a optimum loading path which w ill guarantee good hydroformed parts free of winkle and fracture has often to be determined by finite element analysis. In order to save trials and errors, adap tive FEM simulation method has been developed. To effectively apply the adaptive simulation method, we have to know the applicability of the method. In this pap er, a classification of tube hydroforming (THF) processes based on sensitivity to loading parameters has been suggested. Characteristics of the classification have been analyzed in terms of failure mode, dominant loading parameters and th eir working windows. It was discussed that the so called pressure dominant THF p rocess is the most difficult process for both simulation in FEM analysis and pra ctical operation in real manufacturing situation. To effectively find out the op timum loading path for pressure dominant THF process, adaptive FEM simulation st rategies are mostly needed.

Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the Information Content Criterion

The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy.

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

Empirical Study of Classification Process for Two-stage Turbo Air Classifier in Series

The suitable process parameters for a two-stage turbo air classifier are important for obtaining the ultrafine powder that has a narrow particle-size distribution, however little has been published internationally on the classification process for the two-stage turbo air classifier in series. The influence of the process parameters of a two-stage turbo air classifier in series on classification performance is empirically studied by using aluminum oxide powders as the experimental material. The experimental results show the following: 1) When the rotor cage rotary speed of the first-stage classifier is increased from 2 300 r/min to 2 500 r/min with a constant rotor cage rotary speed of the second-stage classifier, classification precision is increased from 0.64 to 0.67. However, in this case, the final ultrafine powder yield is decreased from 79% to 74%, which means the classification precision and the final ultrafine powder yield can be regulated through adjusting the rotor cage rotary speed of the first-stage classifier. 2) When the rotor cage rotary speed of the second-stage classifier is increased from 2 500 r/min to 3 100 r/min with a constant rotor cage rotary speed of the first-stage classifier, the cut size is decreased from 13.16 μm to 8.76 μm, which means the cut size of the ultrafine powder can be regulated through adjusting the rotor cage rotary speed of the second-stage classifier. 3) When the feeding speed is increased from 35 kg/h to 50 kg/h, the 'fish-hook' effect is strengthened, which makes the ultrafine powder yield decrease. 4) To weaken the 'fish-hook' effect, the equalization of the two-stage wind speeds or the combination of a high first-stage wind speed with a low second-stage wind speed should be selected. This empirical study provides a criterion of process parameter configurations for a two-stage or multi-stage classifier in series, which offers a theoretical basis for practical production.

Machine Learning-Driven Classification for Enhanced Rule Proposal Framework

In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.

A Multi-feature Fusion Apple Classification Method Based on Image Processing and Improved SVM

In order to achieve accurate classification of apple, a multi-feature fusion classification method based on image processing and improved SVM was proposed in this paper. The method was mainly divided into four parts, including image preprocessing, background segmentation, feature extraction and multi-feature fusion classification with improved SVM. Firstly, the homomorphic filtering algorithm was used to improve the quality of apple images. Secondly, the images were converted to HLS space. The background was segmented by the QTSU algorithm. Morphological processing was employed to remove fruit stem and surface defect areas. And apple contours were extracted with the Canny algorithm. Then, apples’ size, shape, color, defect and texture features were extracted. Finally, the cross verification method was used to optimize the penalty factor in SVM. A multi-feature fusion classification model was established. And the weight of each index was calculated by Fisher. In this study, 146 apple samples were selected for training and 61 apple samples were selected for testing. The test results showed that the accuracy of the classification method proposed in this paper was 96.72%, which can provide a reference for apple automatic classification.

Image Processing System for Air Classification Using Linear Discriminant Analysis

An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wastes. In these factories, operators manually adjust the air flow rate while checking the wastes discharged from the separator outlet. However, the adjustments are basically done by trial and error, and it is difficult to do them appropriately. In this study, we tried to develop the image processing system that calculates the ratio of copper (Cu) product and polyvinyl chloride (PVC) in the wastes as a substitute for the operator’s eyes. Six colors of PVC (white, gray, green, blue, black, and red) were used in the present work. An image consists of foreground and background. An image’s regions of interest are objects (Cu particles) in its foreground. However, the particles having a color similar to the background color are buried in the background. Using the difference of two color backgrounds, we separated particles and background without dependent of background. The Otsu’ thresholding was employed to choose the threshold to maximize the degree of separation of the particles and background. The ratio of Cu to PVC pixels from mixed image was calculated by linear discriminant analysis. The error of PVC pixels resulted in zero, whereas the error of Cu pixels arose to 4.19%. Comparing the numbers of Cu and PVC pixels within the contour, the minority of the object were corrected to the majority of the object. The error of Cu pixels discriminated as PVC incorrectly became zero percent through this correction.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Importance-Weighted Transfer Learning for Fault Classification under Covariate Shift

In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.

Gender Classification from Fingerprint Using Hybrid CNN-SVM

Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business profiling.Although biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable qualities.This research employs fingerprints to classify gender,with the intention of being relevant for future studies.Several methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based classifiers.Although these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running time.In this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main components.The main goal of this study is to use CNN to extract fingerprint information.These features are then sent to an SVM classifier to determine gender.The hybrid model’s performance measures are examined and compared to those of the conventional CNN model.Using a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.

Efficient pipelined flow classification for intelligent data processing in IoT

The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have tried to develop hardware-based solutions for the classification of Internet packets.Due to higher throughput and shorter delays,these solutions are considered as a major key to improving the quality of services.Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput.The proposed architectures,however,cannot reach a compromise among power consumption,memory usage,and throughput rate.In view of this,the architecture proposed in this paper contains a pipelinebased micro-core that is used in network processors to classify packets.To this end,three architectures have been implemented using the proposed micro-core.The first architecture performs parallel classification based on header fields.The second one classifies packets in a serial manner.The last architecture is the pipeline-based classifier,which can increase performance by nine times.The proposed architectures have been implemented on an FPGA chip.The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput.The architecture has a power consumption of is 1.294w,and its throughput with a frequency of 233 MHz exceeds 147 Gbps.

Classification and exploration potential of sedimentary basins based on the superposition and evolution process of prototype basins

Classification,superimposed evolution and sedimentary filling of prototype basins are analyzed based on the Wilson cycle principle of plate theory,by dissecting the evolution history of 483 sedimentary basins around the world since the Pre-cambrian,combined with the three stress environments of tension,compression and shear.It is found that plate tectonic evo-lution controls the superimposed development process and petroleum-bearing conditions of the prototype basins in three as-pects:first,more than 85%of the sedimentary basins in the world are developed from the superimposed development of two or more prototype basins;second,the superposition evolution process of the prototype basin takes Wilson cycle as the cycle and cycles in a fixed trajectory repeatedly.In each stage of a cycle,a specific type of prototype basin can be formed;third,each prototype basin can form a unique tectonic-sedimentary system,which determines its unique source,reservoir,cap conditions etc.For hydrocarbon accumulation,the later superimposed prototype basin can change the oil and gas accumulation conditions of the earlier prototype basin,and may form new petroleum systems.Based on this,by defining the type of a current basin as its prototype basin formed by the latest plate tectonic movement,14 types of prototype basins can be classified in the world,namely,intracontinental growth rift,intr acontinental aborted rift,intercontinental rift,passive continental margin,interior craton,trench,fore-arc rift,ba ck-arc rift,back-arc de pression,back-arc small ocean,peri pheral foreland,back-arc foreland,strike-slip pull-apart,and strike-slip flexural basins.The classification scheme can ensure the uniqueness of the types of in di-vidual sedimentary basin,and make it possible to predict their oil and gas potential scientifically through analogy.

Animal Classification System Based on Image Processing &Support Vector Machine

This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.

Dynamic modeling and analysis of the closed-circuit grinding-classification process

Mathematical models of the grinding process are the basis of analysis, simulation and control. Most existent models in- cluding theoretical models and identification models are, however, inconvenient for direct analysis. In addition, many models pay much attention to the local details in the closed-circuit grinding process while overlooking the systematic behavior of the process as a whole. From the systematic perspective, the dynamic behavior of the whole closed-circuit grinding-classification process is consid- ered and a first-order transfer function model describing the dynamic relation between the raw material and the product is established. The model proves that the time constant of the closed-circuit process is lager than that of the open-circuit process and reveals how physical parameters affect the process dynamic behavior. These are very helpful to understand, design and control the closed-circuit grinding-classification process.

Modulation classification of MPSK signals based on nonparametric Bayesian inference

A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals. The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart （NIW） distribution as the prior probability of the unknown mean and covariance. Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain （MCMC） random sampling algorithm. By iterations, the density estimation of the MPSK signals can be estimated. Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR 〉5 dB and 1 600 symbols are used in this method.

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types.In the proposed algorithmic scheme,fuzzy cmeans clustering is used for image segmentation via image subtraction prior to defect detection.Arithmetic and logic operations,the circle hough transform(CHT),morphological reconstruction(MR),and connected component labeling(CCL)are used in defect classification.The algorithmic scheme achieves 100%defect detection and 99.05%defect classification accuracies.The novelty of this research lies in the concurrent use of CHT,MR,and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location,area,and nature of defects.This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process.Moreover,the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process,improve the PCB quality,and lower the production cost.

Presenting an engineering classification system for coal spontaneous combustion potential

The phenomenon of coal spontaneous combustion is one of the common hazards in coal mines and also one of the important reasons for the loss of coal in piles and mines. Based on previous researches, different types of coals have different spontaneous combustion characteristics. For coal loss prevention, a measure is necessary for prediction of coal spontaneous combustion. In this study, a new engineering classification system called ＂Coal Spontaneous Combustion Potential Index （CSCPI）＂ is presented based on the Fuzzy Delphi Analytic Hierarchy Process （FDAHP） approach. CSCPI classifies coals based on their spontaneous combustion capability. After recognition of the roles of the effective parameters influencing the initiation of a spontaneous combustion, a series of intrinsic, geological, and mining characteristics of coal seams are investigated. Then, the main stages of the implementation of the FDAHP method are studied and the weight of each parameter involved is calculated. A classification list of each parameter is formed, the CSCPI system is described, and the engineering classifying system is subsequently presented. In the CSCPI system, each coal seam can be rated by a number from 0 to 100; a higher number implies a greater ease for the coal spontaneous combustion capability. Based on the CSCPI system, the propensity of spontaneous combustion of coal can be classified into three potential levels： low, medium, and high. Finally, using the events of coal spontaneous combustion occurring in one of the Iranian coal mines, Eastern Alborz Coal Mines, an initial validation of the mentioned systematic approach is conducted. Comparison of the results obtained in this study illustrate a relatively good agreement.