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Fault prediction technology is important to avoid serious process failure. This paper is concerned with the fault prediction of dynamic industrial process with incipient faults and proposes a canonical variable trend analysis (CVTA) based fault prediction method. In the proposed method, canonical variate analysis (CVA) algorithm is firstly applied to analyze the process dynamics and extract the uncorrelated...
Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over...
According to a recent study, 30% of VMs in private cloud data centers are "comatose", in part because there is generally no strong incentive for their human owners to delete them at an appropriate time. These inactive VMs are still scheduled and executed on physical cloud resources, taking valuable access away from productive VMs. In an extreme, cloud infrastructure may deny legitimate requests...
With the rise in Air Traffic flow across the world due to advancement in technology and developments in the field of aeronautical engineering, the cases of emergency and panic situations on flights have also emerged at an exponential rate. Every single day, we hear of emergency situations in flights like fires, birdstrikes, diversions, engine failures and emergency landings etc. Across the globe,...
Cloud computing is the latest trend in business for providing software, platforms and services over the Internet. However, a widespread adoption of this paradigm has been hampered by the lack of security mechanisms. In view of this, the aim of this work is to propose a new approach for detecting anomalies in cloud network traffic. The anomaly detection mechanism works on the basis of a Support Vector...
In current enterprise environments, information is becoming more readily accessible across a wide range of interconnected systems. However, trustworthiness of documents and actors is not explicitly measured, leaving actors unaware of how latest security events may have impacted the trustworthiness of the information being used and the actors involved. This leads to situations where information producers...
A machine fault diagnosis method using industrial wireless sensor networks (IWSNs) and support vector machine (SVM) is presented in this paper as a potential low-cost and effective solution for device condition monitoring and fault diagnosis. On sensor node SVM is proposed and researched to reduce the data transmission between sensor nodes, decrease node energy consumption and increase the fault diagnosis...
The electric energy demand is dramatically growing worldwide and demand reduction emerges as an outstanding strategy; it implies detailed information about the electricity consumption, namely load disaggregation. Typical automatic methods for load disaggregation require high hardware efforts to install one sensor per appliance, whereas Non-intrusive Load Monitoring (NILM) systems diminish the hardware...
One important issue in machine vision is using automatic attention control methods for monitoring CCTV cameras, in order to enhance the security of people in public. Result of automatic methods such as crowd density estimation can alert the operator in the case of risk probability increasing. In addition to overall crowd density, other parameters such as regional crowd density and the temporal and...
Remaining useful life (RUL) is important to manage life circles of machineries and reduce maintenance cost. Support vector machine (SVM) is a promising algorithm for RUL prediction because of its advantages to deal with small size of training sets and multi-dimensional data. Recently, many methods of RUL prediction using SVM have been proposed. In this paper, a review over 60 references within the...
Accurate monitoring of urban areas using remote sensing data requires reliable change detection techniques. Nevertheless, while most of the changes are optically visible and easily detectable by an expert user, automatic processes are quite difficult to develop. That is why, the interpretation of changes has remained up-to-now visual in most operational applications in remote sensing. This paper provides...
Mouse dynamics has recently become an interesting new topic in computer security and biometrics due to its non-intrusiveness and convenience. While several pattern recognition methods have been proposed to verify a user based on characteristics of mouse dynamics, they are not applicable to continuous identity authentication and monitoring because most features adopted are statistical-based. This paper...
This paper constructed a web site supervision system to analyze the sentiment orientation of the articles (document, text) on the web sites in intranet. A scheme based on Vector Space Model (VSM) is put forward for identifying the sentiment orientation of document. Considering the feature of a document is relative to the term frequencies appears in the document and corpus, we extract the feature of...
A new method of tool wear intelligence measure based on Support Vector Machine(SVM) and Hidden Markov Models (HMM) is proposed to monitor tool wear and to predict tool failure. At first, FFT features are extracted from the model signal of the tool in cutting process, then FFT vectors are introduced to SVM-HMM for machine learning and classification. The signal of Tool wear in cutting process is introduced...
Total Order Broadcast (TOB) is a fundamental building block at the core of a number of strongly consistent, fault-tolerant replication schemes. While it is widely known that the performance of existing TOB algorithms varies greatly depending on the workload and deployment scenarios, the problem of how to forecast their performance in realistic settings is, at current date, still largely unexplored...
Large-scale geophysical monitoring systems raise the need for real-time feature extraction and signal classification. We study support vector machine (SVM) classification of hydroacoustic signals recorded by the Comprehensive Nuclear-Test-Ban Treaty's verification network. Due to constraints in the early signal processing most samples have incomplete feature sets with values missing not at random...
Recognition of appliances states is an import building block for making energy-efficiency schemes and providing energy-saving advice and performing automatic control. Several existing approaches use smart outlets or detectors to acquire the information of individual appliance and recognize the operating state. However, such approaches have to install numerous devices if they want to monitor the states...
Current monitoring method has become a new method. In this paper, we took the total load current signal of lathes as the main object, used wavelet analysis and FFT to process this signal respectively, and extracted signal features to indicate the working state. Then by constructing the Support Vector Machine (SVM) “One against One” clustering structure model the recognition of the working state can...
Tool wear monitoring is an integral part of modern CNC machine control. This paper presents a new tool wear predictive model by combination of workpiece surface texture analysis and support vector machine with genetic algorithm (SVMG). Firstly, the column projection method and the Gabor filter method are proposed to extract texture features of machined surfaces. Then, SVMG-based tool wear predictive...
In this paper, a vision-based system to detect the eyelid closure for driver alertness monitoring is presented. Similarity measures with three eye templates (open, nearly close, and close) were calculated from many different features, such as 1-D and 2-D histograms and horizontal and vertical projections, of a big set of rectangular eyes images. Two classifiers, Multi-Layer Perceptron and Support...
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