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This paper deals with modeling human behavior routines during driving. We propose a new vision of the maximum causal entropy framework for inverse reinforcement learning to predict actions to be triggered in particular situation (lane change). We designed a plugin to enhance functionalities of the vCar platform which is presents an open source solution for the analysis and visualization of data from...
In this paper the variability of supercapacitor fractional-order model parameters are explored when extracted using a non-linear least squares optimization applied to their constant current discharging behaviour. The variability of parameters extracted 1000 different times applying the optimization process to multiple sets of simulated and experimental data are presented to validate this approach...
Multivariate time series (MTS) exist in many applications. Due to all kinds of interference factors, missing data in MTS is inevitable. Aiming at this problem, a filling method based on least squares support vector machine (LSSVM) is proposed. Firstly, for the series containing missing data, similar series are searched, and its results are viewed as the training set. Secondly, to make use of the correlation...
This paper introduces the multiple linear regression, stepwise linear regression, neural network method, and improves the neural network. Comprehensive analysis of the current prediction methods, the application principle of a detailed analysis and comparison of the various prediction methods advantages and disadvantages. Put forward to improve short-term load forecasting accuracy is not only attach...
To extract knowledge from Data bases Data mining is being used. Data mining is associated with various techniques. In those Clustering is considered to be one of the best approaches. Clustering a huge data set specifically categorical data is difficult and tedious procedure. In this context a proficient method is proposed that is focused on Rough purity for humanizing accuracy of grouping and keeping...
This paper mainly studies the relief extraction and data processing in image fusion design. To depend on plane interception and geometric detail extraction in detail shift of point model, intercepting plane is built and the segmentation between relief and base plane of relief product is realized through plane interception. Then, the extracted intermediate relief is performed the isolated sampling...
Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the machine learning and data mining community. Being difficult to distinguish from real examples, such adversarial examples could change the prediction of many of the best learning models including the state-of-the-art deep learning models. Recent attempts...
Data mining advances as a promising solution in exploring knowledge concealed in database and clustering is one its application. Clustering can be explained as the unconfirmed categorization of patterns into groups. It is the task of combining a set of objects into diverse subsets such that objects belonging to the similar cluster are extremely related to each other. Various objective functions are...
Aiming at the problem that the NAIVE algorithm which is taken to handle the similarity query based on LCSS over data stream window (SQLSW) cannot get query results until calculations on all elements in the full dynamic programming matrix are finished, the SQLSW query processing algorithm based on Possible Solution domain optimization strategy (SQLSW-PS) is proposed. It defines possible solution (PS)...
We present Rough-Fuzzy Support Vector Domain Description (RFSVDD), a novel data description algorithm that provides a rough-fuzzy characterization of a data set and shows its potential for outlier detection. Its resulting data structure is characterized by two components: a crisp lower-approximation and a fuzzy boundary. While the lower-approximation consists of those data points that lie inside the...
Many existing state-of-the-art top-N recommendation methods model users and items in the same latent space and the recommendation scores are computed via the dot product between those vectors. These methods assume that the user preference is consistent across all the items that he/she has rated. This assumption is not necessarily true, since many users can have multiple personas/interests and their...
The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the exponential growth of our digital footprints. The prevalence and potential utility of this data has led to a vast number of time-series data mining techniques,...
In this paper we propose the optimization of Rough Set method using ant colony for oil-impregnated paper bushings. Ant colony is used to discretize the training data set. The ant colony optimized rough set is compare to a rough set who's data is discretized using equal frequency bin (EFB). Ant colony optimized (ACO) rough set results show an improvement compared to the EFB. The ACO rough set has an...
A bi-objective optimization model of power and power changes generated by a wind turbine is discussed in this paper. The model involves two objectives, power maximization and power ramp rate (PRR) minimization. A new constraint for power maximization based on physics and process control theory is introduced. Data-mining algorithms were used to identify the model of power generation from the industrial...
Genetic algorithm is a programming technique that mimics biological evolution as a problem-solving strategy and being applied to a broad range of subjects. In this study hybrid genetic algorithm is used to optimize the equation for a dataset with corresponding attribute. This new approach uses local optimizer in genetic algorithm; thus, the algorithm attains more speed and accuracy. This study shows...
Computing Bayesian statistics with traditional techniques is extremely slow, specially when large data has to be exported from a relational DBMS. We propose algorithms for large scale processing of stochastic search variable selection (SSVS) for linear regression that can work entirely inside a DBMS. The traditional SSVS algorithm requires multiple scans of the input data in order to compute a regression...
In order to study the damage on weapon equipment by explosive shock wave, simulation experiments were carried out to research explosive impact damage on stiffened cantilever box girder. Data mining method of PCA was introduced to process the damage data in simulation experiments. Basing on simulation and data mining, it discussed multi-objective optimization of stiffened cantilever box girder about...
For the production of coal enterprises in new mine after the formation of a strategic alliance, the article takes into account the Union,s overall development strategy of coal resources for a certain period and the interests of the coal enterprises, establishes the alliance production planning model in pursuit of the minimum cost. The application of multi-objective genetic algorithm, each enterprise...
Extracting acronyms and their expansions from plain text is an important problem in text mining. Previous research shows that the problem can be solved via machine learning approaches. That is, converting the problem of acronym extraction to binary classification. We investigate the classification problem and find that the classes are highly unbalanced (the positive instances are very rare compared...
Data fusion approaches are nowadays needed and also a challenge in many areas, like sensor systems monitoring complex processes. This paper explores evolutionary computation approaches to sensor fusion based on unsupervised nonlinear transformations between the original sensor space (possibly highly-dimensional) and lower dimensional spaces. Domain-independent implicit and explicit transformations...
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