Oscillation in control loops is a frequent problem faced in process industries. It deviates the process variables from their desired condition, affecting negatively plant productivity. To guarantee profitability, osci...
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Oscillation in control loops is a frequent problem faced in process industries. It deviates the process variables from their desired condition, affecting negatively plant productivity. To guarantee profitability, oscillation must be detected, diagnosed, and, finally, eliminated. Dozens of automatic detection and diagnosis techniques have been proposed over the last years. However, the application to real industrial data reveals low efficiencies, which indicates that the techniques require improvement. This work presents a new method for oscillation detection and diagnosis. The technique classifies the loops based on the shape of the PV(OP) diagram by a pattern recognition approach, where the model is trained with simulated examples that cover a large range of processes and different conditions found in industrial data, such as noise and disturbance presence. The performance of the proposed technique is compared to well-established oscillation detection and diagnosis methods, returning better results.
The paper presents a method to estimate the frequencies of a multi-harmonic signal in finite time. We use parameterization based on applying delay operators to a measurable signal. The result is a linear regression mo...
The paper presents a method to estimate the frequencies of a multi-harmonic signal in finite time. We use parameterization based on applying delay operators to a measurable signal. The result is a linear regression model with an unknown vector which depends on the signal parameters. We use Dynamic Regressor Extension and Mixing method to replace the n-th order regression model with scalar regressions. After that, we estimate the parameters separately using the standard gradient descent method. In the last step, we find algebraically the finite-time parameter estimates. The set of numerical simulations demonstrates the efficiency of the proposed approach.
Reinforcement Learning (RL) is an effective way of designing model-free linear quadratic regulator (LQR) controller for linear time-invariant (LTI) networks with unknown state-space models. However, when the network s...
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The paper presents a study on defining the perfect control stability areas for LTI MIMO discrete-time fractional-order systems in state-space. The formula for calculating the stability areas corresponds to that dedica...
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In this article authors proposed the fuzzy controller for the correct diagnosis of the breast pathological states. This mean can be used in medical practice by the cytologist as an additional way of diagnosis confirmi...
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This paper presents results of research related to estimating a fractional order of supercapacitor models governed by non-integer differential equations. The results are determined on the basis of supercapacitors resp...
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This paper presents results of research related to estimating a fractional order of supercapacitor models governed by non-integer differential equations. The results are determined on the basis of supercapacitors responses to voltage and current steps of different values, separately for charging and discharging cycles. The use of fractional order models makes it possible to reduce the complexity of the model and, at the same time, ensures very good compliance with the real system response. It is well known that the fractional order of the differential equations corresponds to the physicochemical properties of the supercapacitor. Three widely available supercapacitors are examined. The tests are carried out for five different values of voltage and current steps. The obtained results indicate an important dependence of the estimated non-integer order also on the conditions of their use.
The paper presents a new prediction algorithm for a new class of fractional-order systems, in terms of two-layer discrete-time fractional-order Laguerre-systems. The proposed algorithm is applied to extended horizon m...
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The paper presents a new prediction algorithm for a new class of fractional-order systems, in terms of two-layer discrete-time fractional-order Laguerre-systems. The proposed algorithm is applied to extended horizon model predictive control. Simulation examples confirm the effectiveness of the introduced prediction algorithm and usefulness of two-layer Laguerre models in design of the predictive controllers for discrete-time fractional-order systems.
Public educational systems operate thousands of buildings with vastly different characteristics in terms of size, age, location, construction, thermal behavior and user communities. Their strategic planning and sustai...
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ISBN:
(纸本)9781538632284
Public educational systems operate thousands of buildings with vastly different characteristics in terms of size, age, location, construction, thermal behavior and user communities. Their strategic planning and sustainable operation is an extremely complex and requires quantitative evidence on the performance of buildings such as the interaction of indoor-outdoor environment. Internet of Things (IoT) deployments can provide the necessary data to evaluate, redesign and eventually improve the organizational and managerial measures. In this work a data mining approach is presented to analyze the sensor data collected over a period of 2 years from an IoT infrastructure deployed over 18 school buildings spread in Greece, Italy and Sweden. The real-world evaluation indicates that data mining on sensor data can provide critical insights to building managers and custodial staff about ways to lower a buildings energy footprint through effectively managing building operations.
In this paper we propose an approach to embed continuous and selector cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary ...
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| The demand for sophisticated tools and approaches in heat management and control has triggered fast development of emerging fields including conductive thermal metamaterials, nanophononics, far-field and near-field ...
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