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Many complex engineering systems have the characteristics such as difficult modeling, dangerous testing, and high cost to experiment with fault. Aim at these points, a new framework for online fault detection is presented. This framework includes a mechanism for associating each detection result with a confidence value. Based on this framework, a concrete real-time fault detection method is developed...
Ten female individuals (age = 21 ± 2 years; weight = 50 ± 2 kg; height = 160 ± 3 cm) were analyzed. For the determination of the electromyographic fatigue threshold (EMGft), four percentages of load (10, 20, 30 and 40%), in relation the maximum voluntary contraction (MVC) were used, until the subjective exhaustion. The biosignals in these percentages had been linearly adjusted in domain of the number...
Well-appreciated in statistics for its ability to select relevant grouped features (factors) in linear regression models, the group-Lasso estimator has been fruitfully applied to diverse signal processing problems including RF spectrum cartography and robust layered sensing. These applications motivate the distributed group-Lasso algorithm developed in this paper, that can be run by a network of wireless...
Fault prediction is the key technology for ensuring safe operation and scientific maintenance of large equipment. As the running of flue gas turbine has nonlinear characteristics, echo state network (ESN) was introduced to predict the condition trend of the turbine. Singular value decomposition was used to improve the linear regression algorithm of ESN, and the prediction workflow was given. Condition...
In this paper we propose a quantitative model to study user satisfaction level in online video streaming, which features great variety across video access sessions under the impact of network conditions and user-specific factors. By applying survival analysis to video session duration ratio, which in our study is regarded to be a measure of user satisfaction level, we find user satisfaction is not...
The support vector regression (SVR) framework is proposed as the basis for an echo correction technique. Its generalization capabilities in input-output knowledge discovery make the method suitable to solve the problem of the unwanted reflections in measurement systems and to recover the real values of the antenna under test. Experimental validation is also presented to show the efficiency of the...
Cases of physical and mental diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response...
We present a new method to calculate valid end-effector orientations for grasping tasks. A fast and accurate three-layered hierarchical supervised machine learning framework is developed. The algorithm is trained with a human-in-the-loop in a learn-by-demonstration procedure where the robot is shown a set of valid end-effector rotations. Learning is then achieved through a multi-class support vector...
Autonomous management of a multi-tier Internet service involves two critical and challenging tasks, one understanding its dynamic behavior when subjected to dynamic workload and second adaptive management of its resources to achieve performance guarantees. In this paper, we propose a statistical machine learning based approach to achieve session slowdown guarantees of a multi-tier Internet service...
Although many models have been developed for prediction and forecasting of time series in various engineering fields, there is no perfect model to forecast hydrologic time series. In recent decades, Artificial Neural Networks (ANNs) have been very common for prediction and forecasting of hydrologic time series because of their practicality in applications. In this paper, we propose the application...
This work presents a new approach based on support vector regression to deal with incomplete input (unseen) data and compares it to other existing techniques. The empirical analysis has been done over 18 real data sets and using five different classifiers, with the aim of foreseeing which technique can be deemed as more suitable for each classifier. Also, this study tries to devise how the relevance...
Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural...
We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feature whose addition provides the best leave-one-out cross-validation performance...
Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Although conventional methods, designed for nominal classes or regression problems, can be used to solve the ordinal data problem, there are benefits in developing models specific to this kind of data. This paper introduces a...
Human robot interaction is an emerging area of research, where human understandable robotic representations can play a major role. Knowledge of semantic labels of places can be used to effectively communicate with people and to develop efficient navigation solutions in complex environments. In this paper, we propose a new approach that enables a robot to learn and classify observations in an indoor...
This study proposed a novel HPSO-SVR model that hybridized the particle swarm optimization (PSO) and support vector regression (SVR) to improve the regression accuracy based on the type of kernel function and kernel parameter value optimization with a small and appropriate feature subset, which is then applied to forecast the monthly rainfall. This optimization mechanism combined the discrete PSO...
One of the major obstacles that hinders the application of robots to human day-to-day tasks is the current lack of flexible learning methods to endow the robots with the necessary skills and to allow them to adapt to new situations. In this work, we present a new intuitive method for teaching a robot anthropomorphic motion primitives. Our method combines the advantages of reinforcement and imitation...
In dealing with a large number of train samples, Support Vector Regression (SVR) algorithm is slow. In particular, while new sample is added, all the training samples must be re-trained. In this paper, a new SVR incremental algorithm is presented, which is based on boundary vector. The algorithm takes full advantages of the geometric information of training sample sets. The observed data of China's...
Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (ANFIS) and to draw up, by ANFIS learning mechanism, the relational rules from historical order data,...
We used the experience of spam filtering on account of Chinese short messages service spam filtering and compared the performances of typical discriminative learning model and generative model, namely naive bayesian model and logistic regression model. Overall, in Chinese short messages service spam filtering, the performance of naive bayesian model is better than logistic regression model using 1-ROCA...
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