Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we pr...
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Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.
Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks,...
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Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks, this paper employs a potential game for the vertex cover problem, designs a novel cost function for network vertices, and proves that the solutions to the minimum value of the potential function are the minimum vertex covering(MVC) states of a general complex network. To achieve the optimal(minimum) covering states, we propose a novel distributed time-variant binary log-linear learning algorithm,and prove that the MVC state of a general complex network is attained under the proposed optimization algorithm. Furthermore, we estimate the upper bound of the convergence rate of the proposed algorithm,and show its effectiveness and superiority using numerical examples with representative complex networks and optimization algorithms.
作者：
Ouyang, JinhuaChen, XuMechatronics
Automation and Control Systems Laboratory Department of Mechanical Engineering University of Washington SeattleWA98195 United States Mechatronics
Automation and Control Systems Laboratory Department of Mechanical Engineering University of Washington SeattleWA98195 United States
We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input...
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This paper develops and investigates a dual unscented Kalman filter (DUKF) for the joint nonlinear state and parameter identification of commercial adaptive cruise control (ACC) systems. Although the core functionalit...
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In this paper, a synchronous control strategy based on super-twisting sliding mode algorithm is proposed to enhance the tracking accuracy and robustness of H-type linear motor systems. Such systems are widely utilized...
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Smart packaging transcends traditional packaging’s basic functions by meeting emerging consumer expectations for quality and sustainability. COVID-19 has notably increased take-away orders, spotlighting the need for ...
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UAVs are becoming increasingly prevalent in a wide range of fields, including surveillance, photography, agriculture, transportation, and communications. Hence, research institutions have developed a range of linear a...
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We show that a recent dissipativity approach to feedback stability analysis of potentially open-loop unstable systems, which encompasses the classical soft integral quadratic constraint (IQC) theorem, may be recovered...
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In this work,as an extension of previous machine learning studies,three novel techniques,namely local inter-pretable model-agnostic explanations(LIME),neural network pattern recognition and association rule mining(ARM...
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In this work,as an extension of previous machine learning studies,three novel techniques,namely local inter-pretable model-agnostic explanations(LIME),neural network pattern recognition and association rule mining(ARM)were utilized for proton exchange membrane(PEM)and anion exchange membrane(AEM)electrolyzer database for hydrogen *** main goal of LIME was to determine the positive or negative effects of a variety of descriptor variables on current density,power density and *** this technique,it was possible to uncover rules or paths that lead to high current density,low power density and low *** provided the dominant rules leading to high current density such as using ELAT as the cathode gas diffusion layer,using pure Pt on the cathode surface and using pure carbon as the cathode *** addition,LIME and neural network pattern recognition successfully uncovered the importance of catalytic materials such as cath-ode/anode support/surface elements,operational variables like K_(2)CO_(3) or KOH concentration in the electrolyte,certain membrane types,gas diffusion layers,and applied potential on current *** was then concluded that machine learning can help determine the ideal conditions for developing a PEM and AEM electrolyzer to maximize hydrogen generation,which can also guide future research.
We present a method for optimal phase control of limit-cycle oscillators using strong inputs. Based on the phase-amplitude reduction, which provides a concise representation of the oscillator dynamics, we design an op...
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