A multi-source information fusion method for tool life prediction based on CNN-SVM

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

Structural damage detection method based on information fusion technique

Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.

DuFNet:Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology.Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis.Semantic segmentation is also a challenging technology for image understanding and scene parsing.We focused on the challenging task of real-time semantic segmentation in this paper.In this paper,we proposed a novel fast architecture for real-time semantic segmentation named DuFNet.Starting from the existing work of Bilateral Segmentation Network(BiSeNet),DuFNet proposes a novel Semantic Information Flow(SIF)structure for context information and a novel Fringe Information Flow(FIF)structure for spatial information.We also proposed two kinds of SIF with cascaded and paralleled structures,respectively.The SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature fusionmodule.Features from previous stages usually contain rich low-level details but high-level semantics for later stages.Themultiple convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational cost.The FIF consists of a pooling layer and an upsampling operator followed by projection convolution layer.The concise component provides more spatial details for the network.Compared with BiSeNet,our work achieved faster speed and comparable performance with 72.34%mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.

Enhancing train position perception through Al-driven multi-source information fusion

This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams

Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term service.Current analysis methods used to obtain safety monitoring data are suited only to single survey point data.Unreliable or even paradoxical results are inevitably obtained when processing large amounts of monitoring data,thereby causing difficulty in acquiring precise conclusions.Therefore,we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high dams.In addition,we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high dams.Data metrics reduction is achieved using information fusion at the data level.A Bayesian information fusion is then conducted at the decision level to obtain a comprehensive diagnosis.Early-warning outcomes can be released after sorting analysis results from multi-positions in the dam according to importance.A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey points.It can likewise obtain precise real-time results and export comprehensive early-warning outcomes from multi-positions of high dams.

An information-volume-based distance measure for decision-making

D-S evidence theory,as a general framework for reasoning with uncertainty,allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy measure.However,the mass assignments given by unknown information sources are disordered.How to measure the difference between the mass assignments has aroused people’s interest.In this paper,inspired by the information volume,a novel distance-based measure is proposed to measure the difference between mass assignments.The method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass assignments.At the same time,it is verified that the measure not only meets the properties of distance,but also proves the superiority of the proposed Information Volume Distance(IVD)through simulation experiments.Meanwhile,in the process of information fusion,the reliability of each source could be quantified through IVD.Therefore,based on IVD,a new multi-source information algorithm is proposed to solve the problem of multi-source information fusion.Moreover,algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.

Intelligent early-warning platform for open-pit mining: Current status and prospects

As the profundity of open-pit mining operations has increased,so has the frequency of geological disasters.The complex interaction of factors causing these disasters presents technical challenges for early warning and control systems.However,emergent technologies such as the internet,5G networks,and artificial intelligence provide new opportunities for constructing integrated digital early warning platforms that synthesise multifaceted monitoring data to predict and mitigate open-pit mine hazards.Using efficient Internet-mediated information integration,data from various sources can be consolidated for enhanced disaster management.This paper reviews the current state of digital early warning platforms for open-pit mines using a Web of Science database search for pertinent literature.The framework,data layer,technology layer,and application layer of these platforms are investigated in order to identify associated technologies and obstacles.Important results include:(1)Inconsistent data formats and monitoring software diminish platform workflow efficiency.Robust data exchange protocols and feature-rich software could increase efficiency.(2)Platforms rely on limited data types as opposed to intelligent algorithms that integrate diverse monitoring inputs into global disaster predictions.The underutilization of advanced technologies such as artificial intelligence,the internet of things,and cloud computing.Mining calamity mechanisms and rock mechanics require additional study.

Mixed Aleatory-epistemic Uncertainty Modeling of Wind Power Forecast Errors in Operation Reliability Evaluation of Power Systems

As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors(WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method(MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element(EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally,a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.