this paper presents a new concept for modeling of linear fractional-order dynamical systems. the proposed model is based on specific basis functions, the so called Fractional-order Difference Basis Functions, which ar...
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ISBN:
(纸本)9781728109336
this paper presents a new concept for modeling of linear fractional-order dynamical systems. the proposed model is based on specific basis functions, the so called Fractional-order Difference Basis Functions, which are a generalization of the delayed filters used in the FIR model. In the paper, we show elementary properties of the model and present a method for model implementation. Simulation example shows that the model can be effective in modeling of a class of dynamical systems.
the stability problem of fractional discrete-time linear systems with delays has been analysed. the state-space model with a time shift in the difference has been considered. New necessary and sufficient conditions fo...
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ISBN:
(纸本)9781728109336
the stability problem of fractional discrete-time linear systems with delays has been analysed. the state-space model with a time shift in the difference has been considered. New necessary and sufficient conditions for the asymptotic stability and the practical stability have been established. the systems with only one matrix have been also analysed. It has been shown that such systems are asymptotically (practically) stable if all eigenvalues of the state matrix lie in the stability region of the complex plane.
In the paper the problem of action recognition withthe help of Markov models is considered. We propose an algorithm aiming at reduction of the false positive rate. We state a hypothesis that these errors are caused b...
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ISBN:
(纸本)9781728109336
In the paper the problem of action recognition withthe help of Markov models is considered. We propose an algorithm aiming at reduction of the false positive rate. We state a hypothesis that these errors are caused by learning gesture sequences representing rare movements and not that containing popular ones. Our algorithm translates the sequence of gestures into the corresponding Markov models which are used for a preliminary classification. the obtained evaluation coefficients (numbers of proper classifications) are then used to determine the best weights in a combined model composed of these models. In order to find a compromise between the number of true positives and the number of false positives, the power function of weights is examined.
In the paper the parameters identification problem for a new, non integer order, state space model of heat transfer process is presented. the proposed model uses Atangana-Baleanu derivative operator. the analytical fo...
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ISBN:
(纸本)9781728109336
In the paper the parameters identification problem for a new, non integer order, state space model of heat transfer process is presented. the proposed model uses Atangana-Baleanu derivative operator. the analytical formula of step response is proposed. the parameters of the model are estimated via numerical minimization of the Mean Square Error (MSE) cost function. Finally the proposed model is compared to fractional order models using Caputo (C) and Caputo-Fabrizio (CF) operators. Results of numerical tests show, that the accuracy of the proposed model is better than accuracy of CF model, but worse, than accuracy of the C model.
the paper presents selected methods for improving the accuracy of classification of headlights and taillights of the vehicles. the methods include analyzing blob properties and locations of the detections. A new featu...
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ISBN:
(纸本)9781728109336
the paper presents selected methods for improving the accuracy of classification of headlights and taillights of the vehicles. the methods include analyzing blob properties and locations of the detections. A new feature for describing binary blob shape has been proposed. Moreover, data augmentation technique has been used to improve the results of the classification. the referenced system is based on convolutional neural networks (CNNs). New solutions have been tested with comprehensive set of video sequences (of total duration exceeding ten hours) under various weather conditions and from different road types.
In standardized sectors such as the automotive, the cost-benefit ratio of automation solutions is high as they contribute to increase capacity, decrease costs and improve product quality. In less standardized applicat...
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ISBN:
(纸本)9781728109336
In standardized sectors such as the automotive, the cost-benefit ratio of automation solutions is high as they contribute to increase capacity, decrease costs and improve product quality. In less standardized application fields, the contribution of automation to improvements in capacity, cost and quality blurs. the automation of complex and unstructured tasks requires sophisticated, expensive and low-performing systems, whose impact on product quality is oftentimes not directly perceived by customers. As a result, the full automation of process chains in the general manufacturing or the logistic sectors is often a sub-optimal solution. Taking the distance from the false idea that a process should be either fully automated, or fully manual, this paper presents a novel heuristic method for design of lean human-robot interaction, the Quality Interaction Function Deployment, withthe objective of the "right level of automation". Functions are divided among human and automated agents and several automation scenarios are created and evaluated with respect to their compliance to the requirements of all process' stakeholders. As a result, synergies among operators (manual tasks) and machines (automated tasks) are improved, thus reducing time losses and increasing productivity.
the paper presents a risk-based model to coordinate the preventive-predictive maintenance process of gantry cranes in a container terminal with vessels high demand. the model coordinates the preventive-predictive main...
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ISBN:
(纸本)9781728109336
the paper presents a risk-based model to coordinate the preventive-predictive maintenance process of gantry cranes in a container terminal with vessels high demand. the model coordinates the preventive-predictive maintenance process minimizing the risk of Gantry Cranes Inefficiency (GCI). the risk is estimated with a sequential Markov Chain Monte Carlo (MCMC) simulation model. In this paper, the Preventive-Predictive Maintenance Scheduling (PPMS) process of gantry cranes is non-linear stochastic optimization problem and it is efficiently solved withthe algorithms Particle Swarm Optimization (PSO). the model allows the terminal container operators to obtain a maintenance schedule that minimizes the risk of GCI, as much as possible in a container terminal;as well as establishing the desired level of risk the paper demonstrates the proposed model effectiveness with data of a real container terminal.
the wheel-type robot has found numerous applications in hospitals, restaurants, entertainment, the automation industry, etc., and shows its applicability in solving the tasks efficiently. However, it failed to achieve...
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ISBN:
(纸本)9781665409636
the wheel-type robot has found numerous applications in hospitals, restaurants, entertainment, the automation industry, etc., and shows its applicability in solving the tasks efficiently. However, it failed to achieve the same efficiency in an unstructured environment that is mostly found in the real world. thus, a biped robot can replace the wheel-type robot for better performance. the biped robot has many joints which make it a complex higher degree of freedom system. Hence, the designing of the controller, reference trajectory generation, state estimation and, filter design for feedback signal is a very cumbersome task. this paper focuses on the generation of the reference trajectories. Since human locomotion is optimal naturally, therefore, the human data is used for this study, which is collected at robotics and Machine ANalytics (RAMAN) Lab, MNIT, Jaipur, India. In the literature, various authors have implemented model-based learning methods to develop a model based on data. However, these models suffer from model bias i.e., it is assumed that learned model accurately define the real system. therefore, in this paper, the authors have proposed probabilistic models to model the human locomotion data. the reference trajectory is generated using the Bayesian ridge regression, Automatic relevance determination regression, and Gaussian process regression. the performance evaluation of developed models are based on average error, maximum error, root mean square error, and percentage normalized root mean square error.
Since Advanced Driver Assistance Systems are getting more and more complex a strong effort is put into the development of open-source autonomous driving simulators. However a robust virtual environment should not only...
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ISBN:
(纸本)9781728109336
Since Advanced Driver Assistance Systems are getting more and more complex a strong effort is put into the development of open-source autonomous driving simulators. However a robust virtual environment should not only be assessed by its realistic physics or 3D assets variety, but it also has to incorporate highly accurate and real-time capable sensor models. the research presented in this paper introduces a robust method for validating radar sensor models enabling a simple proof of their reliability in a virtual scenario. To give an overview of radar sensor modeling approaches, the solutions available in the literature are evaluated in terms of usage in virtual environments based on simple criteria. Finally, an exemplary radar sensor model integrated into the CARLA simulator is presented and also a short outline of further research is depicted.
Spreading of misinformation on the web nowadays represents a serious issue, as their influence on peoples opinions may be significant. Fake news represents a specific type of misinformation. While its detection was mo...
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ISBN:
(纸本)9781728156255
Spreading of misinformation on the web nowadays represents a serious issue, as their influence on peoples opinions may be significant. Fake news represents a specific type of misinformation. While its detection was mostly being performed manually in the past, automated methods using machine learning and related fields became more critical. On the other hand, deep learning methods became very popular and frequently used methods in the field of data analysis in recent years. the study presented in this paper deals withthe detection of fake news from the textual data using deep learning techniques. Our main idea was to train different types of neural network models using both entire texts from the articles and to use just the title text. the models were trained and evaluated on the Fake News dataset obtained from the Kaggle competition.
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