Адаптивная обработка нестационарных временных рядов на основе нечеткого подхода

На основе объединения нечеткого пакетного способа обработки и сегментации временных рядов с рекуррентными процедурами обработки текущих значений, предложен онлайн метод сегментации многомерных временных рядов, который применим для обнаружения однородных сегментов в реальном режиме времени на основе потоковых данныхBy combining the fuzzy batch mode processing and segmentation of time series with recurrent processing procedures current values offered online segmentation method of multivariate time series, which is useful for the detection of homogeneous segments in real time based on the data strea

Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

By focusing on the issue of rolling element bearing (REB) performance degradation assessment (PDA), a solution based on variational mode decomposition (VMD) and Gath-Geva clustering time series segmentation (GGCTSS) has been proposed. VMD is a new decomposition method. Since it is different from the recursive decomposition method, for example, empirical mode decomposition (EMD), local mean decomposition (LMD), and local characteristic-scale decomposition (LCD), VMD needs a priori parameters. In this paper, we will propose a method to optimize the parameters in VMD, namely, the number of decomposition modes and moderate bandwidth constraint, based on genetic algorithm. Executing VMD with the acquired parameters, the BLIMFs are obtained. By taking the envelope of the BLIMFs, the sensitive BLIMFs are selected. And then we take the amplitude of the defect frequency (ADF) as a degradative feature. To get the performance degradation assessment, we are going to use the method called Gath-Geva clustering time series segmentation. Afterwards, the method is carried out by two pieces of run-to-failure data. The results indicate that the extracted feature could depict the process of degradation precisely

Разработка категорной модели реализации жизненого цикла процесса выбора мероприятий

Рассматриваются методы формирования категорных моделей сложных объектов. В категорно-функторном представлении формализованы показатели эффективности, бизнес-процессы и мероприятия, позволяющие формализовать формирование списка мероприятий для оперативного управления БПThe methods of forming categorical models of complex objects is considered. In the representation of category and functor the performance of indicators, business processes and activities are formalized This make it possible to formalize the generation of a list of activities for the operational management of business processe

Computational Modeling Approaches For Task Analysis In Robotic-Assisted Surgery

Surgery is continuously subject to technological innovations including the introduction of robotic surgical devices. The ultimate goal is to program the surgical robot to perform certain difficult or complex surgical tasks in an autonomous manner. The feasibility of current robotic surgery systems to record quantitative motion and video data motivates developing descriptive mathematical models to recognize, classify and analyze surgical tasks. Recent advances in machine learning research for uncovering concealed patterns in huge data sets, like kinematic and video data, offer a possibility to better understand surgical procedures from a system point of view. This dissertation focuses on bridging the gap between these two lines of the research by developing computational models for task analysis in robotic-assisted surgery. The key step for advance study in robotic-assisted surgery and autonomous skill assessment is to develop techniques that are capable of recognizing fundamental surgical tasks intelligently. Surgical tasks and at a more granular level, surgical gestures, need to be quantified to make them amenable for further study. To answer to this query, we introduce a new framework, namely DTW-kNN, to recognize and classify three important surgical tasks including suturing, needle passing and knot tying based on kinematic data captured using da Vinci robotic surgery system. Our proposed method needs minimum preprocessing that results in simple, straightforward and accurate framework which can be applied for any autonomous control system. We also propose an unsupervised gesture segmentation and recognition (UGSR) method which has the ability to automatically segment and recognize temporal sequence of gestures in RMIS task. We also extent our model by applying soft boundary segmentation (Soft-UGSR) to address some of the challenges that exist in the surgical motion segmentation. The proposed algorithm can effectively model gradual transitions between surgical activities. Additionally, surgical training is undergoing a paradigm shift with more emphasis on the development of technical skills earlier in training. Thus metrics for the skills, especially objective metrics, become crucial. One field of surgery where such techniques can be developed is robotic surgery, as here all movements are already digitalized and therefore easily susceptible to analysis. Robotic surgery requires surgeons to perform a much longer and difficult training process which create numerous new challenges for surgical training. Hence, a new method of surgical skill assessment is required to ensure that surgeons have adequate skill level to be allowed to operate freely on patients. Among many possible approaches, those that provide noninvasive monitoring of expert surgeon and have the ability to automatically evaluate surgeon\u27s skill are of increased interest. Therefore, in this dissertation we develop a predictive framework for surgical skill assessment to automatically evaluate performance of surgeon in RMIS. Our classification framework is based on the Global Movement Features (GMFs) which extracted from kinematic movement data. The proposed method addresses some of the limitations in previous work and gives more insight about underlying patterns of surgical skill levels

Some Clustering Methods, Algorithms and their Applications

Clustering is a type of unsupervised learning [15]. When no target values are known, or "supervisors," in an unsupervised learning task, the purpose is to produce training data from the inputs themselves. Data mining and machine learning would be useless without clustering. If you utilize it to categorize your datasets according to their similarities, you'll be able to predict user behavior more accurately. The purpose of this research is to compare and contrast three widely-used data-clustering methods. Clustering techniques include partitioning, hierarchy, density, grid, and fuzzy clustering. Machine learning, data mining, pattern recognition, image analysis, and bioinformatics are just a few of the many fields where clustering is utilized as an analytical technique. In addition to defining the various algorithms, specialized forms of cluster analysis, linking methods, and please offer a review of the clustering techniques used in the big data setting

Automatic clustering with application to time dependent fault detection in chemical processes

Fault detection and diagnosis presents a big challenge within the petrochemical industry. The annual economic impact of unexpected shutdowns is estimated to be $20 billion. Assistive technologies will help with the effective detection and classification of the faults causing these shutdowns. Clustering analysis presents a form of unsupervised learning which identifies data with similar properties. Various algorithms were used and included hard-partitioning algorithms (K-means and K-medoid) and fuzzy algorithms (Fuzzy C-means, Gustafson-Kessel and Gath-Geva). A novel approach to the clustering problem of time-series data is proposed. It exploits the time dependency of variables (time delays) within a process engineering environment. Before clustering, process lags are identified via signal cross-correlations. From this, a least-squares optimal signal time shift is calculated. Dimensional reduction techniques are used to visualise the data. Various nonlinear dimensional reduction techniques have been proposed in recent years. These techniques have been shown to outperform their linear counterparts on various artificial data sets including the Swiss roll and helix data sets but have not been widely implemented in a process engineering environment. The algorithms that were used included linear PCA and standard Sammon and fuzzy Sammon mappings. Time shifting resulted in better clustering accuracy on a synthetic data set based on than traditional clustering techniques based on quantitative criteria (including Partition Coefficient, Classification Entropy, Partition Index, Separation Index, Dunn’s Index and Alternative Dunn Index). However, the time shifted clustering results of the Tennessee Eastman process were not as good as the non-shifted data. CopyrightDissertation (MEng)--University of Pretoria, 2009.Chemical Engineeringunrestricte

Clustering and Shifting of Regional Appearance for Deformable Model Segmentation

Automated medical image segmentation is a challenging task that benefits from the use of effective image appearance models. An appearance model describes the grey-level intensity information relative to the object being segmented. Previous models that compare the target against a single template image or that assume a very small-scale correspondence fail to capture the variability seen in the target cases. In this dissertation I present novel appearance models to address these deficiencies, and I show their efficacy in segmentation via deformable models. The models developed here use clustering and shifting of the object-relative appearance to capture the true variability in appearance. They all learn their parameters from training sets of previously-segmented images. The first model uses clustering on cross-boundary intensity profiles in the training set to determine profile types, and then it builds a template of optimal types that reflects the various edge characteristics seen around the boundary. The second model uses clustering on local regional image descriptors to determine large-scale regions relative to the boundary. The method then partitions the object boundary according to region type and captures the intensity variability per region type. The third and fourth models allow shifting of the image model on the boundary to reflect knowledge of the variable regional conformations seen in training. I evaluate the appearance models by considering their efficacy in segmentation of the kidney, bladder, and prostate in abdominal and male pelvis CT. I compare the automatically generated segmentations using these models against expert manual segmentations of the target cases and against automatically generated segmentations using previous models

Інформаційна технологія аналізу динаміки швидкості реакції операторів

The purpose of this work is to develop the information technology for studing the dynamics of adaptive properties of the nervous system of operators. Experimental data are multi-dimensional time series that have been obtained using the reflexometric method of analyzing the reaction time of various psychophysiological types of individuals. For efficient processing of these data, better understanding the structure, finding hidden patterns, trends, some algorithms are proposed, and software (Java programming language) that implements them and provides visualization of the data and the obtained results for convenient analysis is created. The proposed technology allows to determine the similar patterns of the dynamics of reaction rates of various psychophysiological types of individuals, to classify new observation objects to the most relevant psychotype, and to analyze changes of the dynamics in the behavior of each of the investigated operators. The detection of the similarities of the dynamics series, for a more objective assessment, is carried out with the application of a set of similarity measures of different categories: Shape-, Edit-, Structure-, Model- and Compression-based (DTW, LCSS, modified LCSS, TQuest, Euclidean metrics, correlation metrics, trend, seasonality indicators,  etc., construction of dendrograms by hierarchical clusterization method). To classify a new object of observation to the most appropriate psychotype, a new method based on a multicriteria comparison with the models is proposed. Analysis of changes in the dynamics of the reaction rate of each of the investigated operators is based on the segmentation algorithm of the multidimensional series on the basis of differential evolution with the previous smoothing of data.The proposed information technology has been tested on the data of the reaction time dynamics of various psychophysiological types of individuals, but can also be applied in the analysis of the similarity in the dynamics of processes of different nature.Разработана информационная технология, которая позволяет определять похожие закономерности динамики реакции различных психофизиологических типов личностей, классифицировать новые объекты наблюдения к наиболее подходящему психотипу, а также анализировать изменения динамики в поведении каждого из исследуемых операторов.Розроблено інформаційну технологію, яка дозволяє визначати схожі закономірності динаміки швидкості реакції різних психофізіологічних типів особистостей, класифікувати нові об’єкти спостереження до найбільш відповідного психотипу, а також аналізувати зміни динаміки в поведінці кожного з досліджуваних операторів

Validation of driving behaviour as a step towards the investigation of Connected and Automated Vehicles by means of driving simulators

Connected and Automated Vehicles (CAVs) are likely to become an integral part of the traffic stream within the next few years. Their presence is expected to greatly modify mobility behaviours, travel demands and habits, traffic flow characteristics, traffic safety and related external impacts. Tools and methodologies are needed to evaluate the effects of CAVs on traffic streams, as well as the impact on traffic externalities. This is particularly relevant under mixed traffic conditions, where human-driven vehicles and CAVs will interact. Understanding technological aspects (e.g. communication protocols, control algorithms, etc.) is crucial for analysing the impact of CAVs, but the modification induced in human driving behaviours by the presence of CAVs is also of paramount importance. For this reason, the definition of appropriate CAV investigations methods and tools represents a key (and open) issue. One of the most promising approaches for assessing the impact of CAVs is operator in the loop simulators, since having a real driver involved in the simulation represents an advantageous approach. However, the behaviour of the driver in the simulator must be validated and this paper discusses the results of some experiments concerning car-following behaviour. These experiments have included both driving simulators and an instrumented vehicle, and have observed the behaviours of a large sample of drivers, in similar conditions, in different experimental environments. Similarities and differences in driver behaviour will be presented and discussed with respect to the observation of one important quantity of car-following, the maintained spacing