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Social networking sites such as Twitter and Facebook attracts millions of users across the world and their interaction with social networking has affected their life. This popularity in social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content. This situation can...
Twitter is one of the most popular microblog sites developed in recent years. Feelings are analysed on the messages shared on Twitter so that users ideas on the products and companies can be determined. Sentiment analysis helps companies to improve their products and services based on the feedback obtained from the users through Twitter. In this study, it was aimed to perform sentiment analysis on...
Restricted Boltzmann Machines (RBMs) have received special attention in the last decade due to their outstanding results in number of applications, such as face and human motion recognition, and collaborative filtering, among others. However, one of the main concerns about RBMs is related to the number of hidden units, which is application-dependent. Infinite RBM (iRBM) was proposed as an alternative...
This work investigates an approach to combining accurate lithium-ion battery (LIB) dynamic modeling and effective state-of-charge (SOC) prediction at various operating conditions using a structured recurrent neural network (RNN). The RNN model is trained with drive cycle data so that model parameters do not have to be determined with characterization tests, as is typically necessary for an equivalent...
Face recognition is a mature domain with lots of different techniques proposed in the literature. Convolutional neural networks have been the most successful approach to face recognition problem recently. In this work, performance of three different face recognition models are compared. Features are extracted using a pre-trained convolutional neural network. The first model is trained using the available...
In dynamic manipulation, robots can manipulate objects without grasping by utilizing inertia effect. However, the trajectory planning for dynamic manipulation is a difficult issue due to dynamic constraint. Trajectory deformation considering dynamic constraint after original trajectories are generated is necessary for the issue. To realize such deformation methods, we introduce on sequence-to-sequence...
In this paper, we propose a modified architecture of a Pi-Sigma Neural Network (PSNN) based on two modifications: extension of the activation function and adding delays to neurons in the hidden layer. These new networks are called respectively Activation Function Extended Pi-Sigma (AFEPS) and Delayed Pi-Sigma (DPS) are obtained first by adding an activation function to all hidden neurons and secondly...
Accurate prediction of the future locations of the host vehicle as well as that of the surrounding objects is one of the key challenges in improving road traffic safety. The traditional approach for this task has been using physics-based motion models such as kinematic and dynamic models, the result of which is not reliable for long-term prediction. In this paper, we present simulation results demonstrating...
Parameter prediction with high precision is of great importance for real-time condition monitoring and fault diagnosis of the thermal system during variable load process. This paper presents a performance enhancement scheme for the extreme learning machine (ELM) to predict the operating parameters of the thermal system using particle swarm optimization (PSO). ELM is a feed-forward neural network with...
This paper presents a support vector machine (SVM) based model predictive control (MPC) strategy to manage the engine speed to the set-point of idle speed. The predictive model is trained by SVM due to its accuracy of learning nonlinear process, simple training program and no over-fitting nature. To reduce the computational burden of controller and retain the dynamic information of system, the instantaneous...
The correct analysis of power system transient stability is of great significance to the safe and stable operation of power system and the construction of smart grid. Based on the basic theory of Extreme Learning Machine (ELM), this paper studies the transient stability of power system. Firstly, the simulation model is built to simulate power system for obtaining data sets. Then, the implementation...
In this paper, because of infinite-dimensional feature and complex nonlinearities of the distributed parameter systems, a new data-driven modeling method has been proposed. The temporal-spatial output of the system is measured at a finite number of spatial locations. At the same time it is assumed that the input of the system is a temporal variable. Firstly, Karhunen-Loève(KL) decomposition is used...
In data classification mining, the decision tree method is a key algorithm. ID3 (Iterative Dichotomiser 3) algorithm which was presented by Quinlan is a famous decision tree algorithms, but ID3 has some shortcomings such as high complex computation in computing the information entropy expression, multivalue bios problem in the process of selecting an optimal attribute, large scales, etc. In order...
How to develop an intelligent ventilator and control it well to provide a better experience and treatment effect for respiratory patients is still a difficult task needed to be solved. The existing problems focus on the control algorithm and the mechanical structure. Dedicated to these two problems, the paper proposes a design of CPAP ventilator based on the ANN algorithm. Firstly, the paper introduces...
Creativity and imagination are the most interesting and peculiar of all properties of human consciousness that are not yet fully understandable for scientists. “Could creativity or imagination ever be defined?”, “Will humans lose their creativity in the rapid development of technology?” or “Could machines ever be creative and how this will effect on humanity?”. In this paper, we will address these...
Application of ANNs as a tool in proposed techniques has been developed. ANNs model of desired region's border allows to get more available information on the performance indices' behavior in the vicinity of the border. Enclosed in ANNs-model output calculation of recommended value for scanning step norm and of the performance indices gradient enables the use of ANNs-model as a source of important...
This article explores the problems of automated retail systems, which named are vending machines. The main problem is the formation of an assortment of a vending machine, the realization of which will bring maximum profit. As a modern analysis tool of consumer demand in retail trade artificial intelligence is regarded. Attention is focused on one of the methods of constructing artificial intelligence...
The method of approximating a discriminant functions of the training set is proposed. The sign of the discriminant functions allows us to classify the point in one or another class. The approximation is constructed with greater precision in the neighborhood of zero values of the discriminant function. To estimate a posterior probability of a class of a point two methods are proposed: based on a series...
In this paper, a reinforcement learning approach is proposed to detect unexpected faults, where the noise-to-signal ratio of the data series is minimized for achieving robustness. The model parameter is taken as a special action of the reinforcement learning, and the policy valuation and policy improvement are utilized to find the parameters, which can make the estimated model consistent to the real-time...
Automatic grading systems, such as WebWork, are becoming much more widely used as they relieve the instructor from needing to grade student work, provide students with automatic feedback, and can allow for immediate resubmission. They have also been shown to improve the effectiveness of teaching and learning. In this paper, we apply Item Response Theory (IRT) to a large WebWork Calculus homework dataset...
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