Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

Modeling and monitoring of nonlinear multi-mode processes based on similarity measure-KPCA

A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.

An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

On the Self-Similarity in Biological Processes

We show that the processes described by Avrami functions are self-similar. A comparative function characterizes a self-similar process by a certain Avrami exponent. We define the self-similar categories of some well-known biological processes. The method to determine the Avrami exponent by choosing the comparative function is demonstrated on the diffusion model of the growth of nuclei. We generalize the results.

The Exact Measure Functions of the Images for a Class of Self-Similar Processes

Let X= (Ω, ■, ■_t, X_t,, θ_t, p~x) be a self-similar Markov process on (0,∞) with non-decreasing path. The exact Hausdorff and Packing measure functions of the image X([0,t] ) are obtained.

ON THE HITTING PROBABILITY AND POLARITY FOR A CLASS OF SELF-SIMILAR MARKOV PROCESSES

The anthem investigate the hitting probability, polarity and the relationship between the polarity and Hausdorff dimension for self-similar Markov processes with state space (0, infinity) and increasing path.

Some Results about the Sample Path Properties of Markov Processes with Independent Self-Similar Components

This paper considers a special class of operator self-similar processes Markov processes {X（t）, t≥0} with independent self-similar components, that is, X （ t ） =（X^1（t）,…,X^d（t））, where {X^i（t）,t≥0}, i=1,2,…,d are d independent real valued self-similar Markov processes. By means of Brel-Cantelli lemma, we give two results about asymptotic property as t→∞ of sample paths for two special classes of Markov processes with independent self-similar components.

MOMENTS OF PASSAGE TIMES AND ASYMPTOTIC BEHAVIOR OF INCREASING SELF-SIMILAR MARKOV PROCESSES

By using Lamperti＇s bijection between self-similar Markov processes and L@vy processes~ we prove finiteness of moments and asymptotic behavior of passage times for increasing self-similar Markov processes valued in （0, ~）. We Mso investigate the behavior of the process when it crosses a level. A limit theorem concerning the distribution of the process immediately before it crosses some level is proved. Some useful examples are given.

CRITERIA OF STRONG TRANSIENCE FOR OPERATOR-SELF-SIMILAR MARKOV PROCESSES

Yamamuro in [1] defines strong and weak transience of Markov processes; gives a criterion for strong transience of Feller processes; and further, discusses strong and weak transience of Ornstein-Uhlenbeck type processes. In this article, the authors weaken the Feller property of the result in [1] to weak Feller property and discuss the strong transience of operator-self-similar Markov processes.

A Study on Process Model of Computing Similarity Between Product Features and Online Reviews

Review similarity computing is used to judge whether the content of online reviews is related to the products.It is an important prerequisite to judge the usefulness of reviews,and it is also an important basis for the classification and sorting of product reviews.This paper combines the VSM,TF-IDF algorithm and cosine similarity algorithm to build the model of similarity computing between the product online reviews and product features,and to build the process framework of review similarity computing for enterprises.Besides,this paper also verifies the model’s effectiveness and correctness based on real online review data of E-business.The experiment results show that the process model can be used to quantify the similarity between reviews and product features,and the similarity results also have a good effect on the application of the review sorting.

On use of the alpha stable self-similar stochastic process to model aggregated VBR video traffic

The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.

Similarity identification method on formational interfaces and application in general granite

An instrumented drilling system can be applied for the acquisition of drilling process parameters. The system can provide continuous and huge data for geotechnical engineering. However, due to the complexity of ground strata, the variation in the drilling parameters with stratigraphical characteristics is great and the correlation between likely comparable parameters is not high, which limits the use of conventional correlation approaches in this field. How to use the data for engineering and how to get a reasonable interpretation for the relationships among the drilling parameters, as well as between a drilling parameter and formational characteristics, become a technical choke point for the development and application of the instrumented drilling system. Based on similarity criteria, the extraction of sample data and characteristics, the pretreatment of data and feature matching algorithms have been analyzed and an approach of slope coefficient searching identification has been established. A case study was carried out for the similarity between the rotational speed of the drill bit, flushing pressure, and effective thrust force graphics in general weathered granite. The result shows that the similarity coefficients between the rotational speed of the drill bit, flushing pressure, and effective thrust force are 0.72 and 0.83, respectively. Although there are differences between the distances of the graphics, the curves of both rotational speed and flushing pressure agree with the effective thrust curve in shape, which provides a possible method for the identification of various formations by use of the similarity between feature drilling parameters.

How do stream processes affect hazard exposure on alluvial fans? Insights from an experimental study

Alluvial fans are among the most privileged settlement areas in many mountain regions. These landforms are particularly dynamic being episodically affected by distributary processes generated by extreme flood events. Addressing risk assessment entails determining hazard exposure and unravelling how it might be related to process loading and to process dynamics once the flow becomes unconfined on the surface of alluvial fans. By following a ‘similarity of process concept’, rather than by attempting to scale a real-world prototype, we performed a set of 72 experimental runs on an alluvial fan model. Thereby, we considered two model layouts, one without a guiding channel and featuring a convex shape and the other one with a guiding channel, a bridge, and inclined but planar overland flow areas as to mirror an anthropic environment. Process magnitude and intensity parameters were systematically varied, and the associated biphasic distributary processes video recorded. For each experiment, the exposure was detected by mapping the exposed area in a GIS, thereby discerning between areas exposed to biphasic flows and the associated depositional phenomena or to the liquid flow phase only. Our results reveal that total event volume, sediment availability and stream power in the feeding channel, as well as depositional effects, avulsion, and channelization on the alluvial fan concur to determine the overall exposure. Stream process loading alone, even when rigorously defined in terms of its characterizing parameters, is not sufficient to exhaustively determine exposure. Hence, further developing reliable biphasic simulation models for hazard assessment on settled alluvial fans is pivotal.

Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm

With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.

Novel evidence from Taxus fuana forests for niche-neutral process assembling community

Background:Understanding the mechanisms underlying community assembly is helpful for conservation and restoration of communities, particularly those that contain rare and endangered species like Taxus fuana, which are endemic to the Western Himalayas. The niche (limiting similarity) vs. neutral (randomness) assembly of the T.fuana forest community in Gyirong County, Tibet, China, was investigated. The net relatedness index (NRI) was calculated using a phylogenetic tree. The phylogenetic characteristics of the community and its relationships with environment were analyzed.Results:The value of the mean NRI at the community level was less than-1.96, indicating that the phylogenetic structure was overdispersed;whereas majority of the NRIs at the tree, shrub, and herb layers were within-1.96 to1.96, indicating random dispersion. Environmental factors accounted for 44.38%, 46.52%, 24.04%, and 14.07%of the variation at the community level, tree, shrub, and herb layer, respectively. The phylogenetic structure at the community level and tree layer were significantly influenced by both topographic and soil factors, while shrub and herb layers tended to be affected by a single environmental factor.Conclusions:Community assembly of the T. fuana forest was simultaneously affected by niche and neutral processes, and their variations were closely related to the environment. Neutral process dominated community assembly in the shrub and herb layers. However, the interaction of limiting similarity and randomness played a dominant role at the community level and tree layer;and contributed to maintenance of biodiversity stability. The synergy of multiple environmental factors had a more obvious influence on community assembly than individual environmental factors, especially at the community level. These findings would help to understand the conservation of rare and endangered tree species, such as T. fuana, in the native community;and highlight the importance of random and non-random processes in assembly and biodiversity maintenance of alpine plant communities.

Experimental studies of focal mechanism based on the dynamic process of energy release

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Less is More:Data Processing with SVM for Intrusion Detection

To improve the detection rate and lower down the false positive rate in intrusion detection system, dimensionality reduction is widely used in the intrusion detection system. For this purpose, a data processing （DP） with support vector machine （SVM） was built. Different from traditiona/ly identifying the redundant data before purging the audit data by expert knowledge or utilizing different kinds of subsets of the available 41-connection attributes to build a classifier, the proposed strategy first removes the attributes whose correlation with another attribute exceeds a threshold, and then classifies two sequence samples as one class while removing either of the two samples whose similarity exceeds a threshold. The results of performance experiments showed that the strategy of DP and SVM is superior to the other existing data reduction strategies （ e. g. , audit reduction, rule extraction, and feature selection）, and that the detection model based on DP and SVM outperforms those based on data mining, soft computing, and hierarchical principal component analysis neural networks.

Semantic Sentence Similarity Using Finite State Machine

In this paper, a finite state machine approach is followed in order to find the semantic similarity of two sentences. The approach exploits the concept of bi-directional logic along with a semantic ordering approach. The core part of this approach is bi-directional logic of artificial intelligence. The bi-directional logic is implemented using Finite State Machine algorithm with slight modification. For finding the semantic similarity, keyword has played climactic importance. With the help of the keyword approach, it can be found easily at the sentence level according to this algorithm. The algorithm is proposed especially for Nepali texts. With the polarity of the individual keywords, the finite state machine is made and its final state determines its polarity. If two sentences are negatively polarized, they are said to be coherent, otherwise not. Similarly, if two sentences are of a positive nature, they are said to be coherence. For measuring the coherence (similarity), contextual concept is taken into consideration. The semantic approach, in this research, is a totally contextual based method. Two sentences are said to be semantically similar if they bear the same context. The total accuracy obtained in this algorithm is 90.16%.

Automating Transfer Credit Assessment-A Natural Language Processing-Based Approach

Student mobility or academic mobility involves students moving between institutions during their post-secondary education,and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student.In general,this process involves domain experts comparing the learning outcomes of the courses,to decide on offering transfer credits to the incoming students.This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity.The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing(NLP)to effectively automate this process.Given the unique structure,domain specificity,and complexity of learning outcomes(LOs),a need for designing a tailor-made model arises.The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs.The similarity between LOs is further aggregated to form course to course similarity.Due to the lack of quality benchmark datasets,a new benchmark dataset containing seven course-to-course similarity measures is proposed.Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different levels of leniency.While providing an efficient model to assess the similarity between courses with existing resources,this research work also steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.

Self Similarity Analysis of Web Users Arrival Pattern at Selected Web Centers

The paper focuses on measuring self-similarity using few techniques by an index called Hurst index which is a self-similarity parameter. It has been evident that Internet traffic exhibits self-similarity. Motivated by this fact, real time web users at various centers considered here as traffic and it has been examined by various methods to test the self-similarity. The results from the experiments carried out verify that the traffic examined in the present study is self similar using a new method based on some descriptive measures;for example percentiles have been applied to compute Hurst parameter which gives intensity of the self-similarity. Numerical results and analysis we discussed and presented here play a significant role to improve the services at web centers in the view of quality of service (QOS).