The proceedings contain 58 papers. The topics discussed include: impact of the e-commerce on risk and export performance: evidence from Malaysian small and medium enterprises;model driven development process for a ser...
ISBN:
(纸本)9781728143064
The proceedings contain 58 papers. The topics discussed include: impact of the e-commerce on risk and export performance: evidence from Malaysian small and medium enterprises;model driven development process for a service-oriented industry 4.0 system;an analysis of particle swarm optimization of multi-objective knapsack problem;tacit knowledge based acquisition of verified machining data;point based deep learning to automate automotive assembly simulation model generation with respect to the digital factory;virtual commissioning for scalable production systems in the automotive industry: model for evaluating benefit and effort of virtual commissioning;agriculture 4.0: how use traceability data to tell food product to the consumers;and crowd behavior modeling developments through mixed integer programming: the case of airport queue management.
Softwarization of carrier networks is an unstoppable trend, driven by the possibility of instantiating Virtual Network Functions (VNFs) in carrier-owned data centers in their points-of-presence. In optical networks, V...
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Bundle adjustment plays an significant role in SLAM (simultaneous localization and mapping), which is utilized in both front-end visual odometry and global back-end optimization. In many SLAM systems, bundle adjustmen...
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In the past, many greenhouse control algorithms have been developed. However, the majority of these algorithms rely on an explicit parametric model description of the greenhouse. These models are often based on physic...
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In the past, many greenhouse control algorithms have been developed. However, the majority of these algorithms rely on an explicit parametric model description of the greenhouse. These models are often based on physical laws such as conservation of mass and energy and contain many parameters which should be identified. Due to the complex and nonlinear dynamics of greenhouses, these models might not be applicable to control greenhouses other than the ones for which these models have been designed and identified. Hence, in current horticultural practice these control algorithms are scarcely used. Therefore, the need rises for a control algorithm which does not rely on a parametric system representation but rather on input/output data of the greenhouse system, hereby establishing a way to control the system with unknown or unmodeled dynamics. A recently proposed algorithm, Data-Enabled Predictive Control (DeePC), is able to replace system identification, state estimation and future trajectory prediction by one single optimization framework. The algorithm exploits a non-parametric model constructed solely from input/output data of the system. In this work, we apply this algorithm in order to control the greenhouse climate. It is shown that in numerical simulation the DeePC algorithm is able to control the greenhouse climate while only relying on past input/output data. The algorithm is bench-marked against the Nonlinear Model Predictive (NMPC) algorithm in order to show the differences between a predictive control algorithm that has direct access to the nonlinear greenhouse simulation model and a purely data-driven predictive control algorithm. Both algorithms are compared based on reference tracking accuracy and computational time. Furthermore, it is shown in numerical simulation that the DeePC algorithm is able to cope with changing dynamics within the greenhouse system throughout the crop cycle.
Applications of signal processing and control are classically model-based, involving a two-step procedure for modeling and design: first a model is built from given data, and second, the estimated model is used for fi...
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ISBN:
(纸本)9781479981311
Applications of signal processing and control are classically model-based, involving a two-step procedure for modeling and design: first a model is built from given data, and second, the estimated model is used for filtering, estimation, or control. Both steps typically involve optimization problems, but the combination of both is not necessarily optimal, and the modeling step often ignores the ultimate design objective. Recently, data-driven alternatives are receiving attention, which employ a direct approach combining the modeling and design into a single step. In earlier work, it was shown that datadriven signal processing problems can often be rephrased as missing data completion problems, where the signal of interest is part of an incomplete low-rank mosaic Hankel structured matrix. In this paper, we consider the exact data case and the problem of simulating from a given input, an output trajectory of the unknown data generating system. Our findings suggest that, when using an adequate rescaling of the given data, the exact data-drivensimulation problem can be solved by replacing the original structured low-rank matrix completion problem by a convex optimization problem, using the nuclear norm heuristic.
Due to the promising technical feasibility of PV powered RO unit, many studies worldwide are carried out to further enhance their performance. In this simulation studies, we investigate the performance and efficiency ...
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Digital production and logistic enterprise systems are complex systems, in terms of layout variability, control strategies, business processes and system parameter. All of these aspects are not independent and even du...
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ISBN:
(纸本)9781450366199
Digital production and logistic enterprise systems are complex systems, in terms of layout variability, control strategies, business processes and system parameter. All of these aspects are not independent and even due to the system dynamics the optimal solution may differ depending on the actual situation applying specific requirements. The planning, optimization and operation of these complex systems requires modern, data and simulationdriven multi criteria decision approaches. simulation based analyses can be uses through all phases on different planning and operating levels. In order to improve operative decision making in production and logistic enterprises the digital twin concept, using virtual clones of real systems or subsystems, can be applied. Due to improved network and computing power operative simulation concepts are starting now to be realized in industrial practice.
Global optimization techniques often suffer the curse of dimensionality. In an attempt to face this challenge, high dimensional search techniques try to identify and leverage upon the effective, lower, dimensionality ...
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ISBN:
(纸本)9781728132839
Global optimization techniques often suffer the curse of dimensionality. In an attempt to face this challenge, high dimensional search techniques try to identify and leverage upon the effective, lower, dimensionality of the problem either in the original or in a transformed space. As a result, algorithms search for and exploit a projection or create a random embedding. Our approach avoids modeling of high dimensional spaces, and the assumption of low effective dimensionality. We argue that effectively high dimensional functions can be recursively optimized over sets of complementary lower dimensional subspaces. In this light, we propose the novel Subspace COmmunication for optimization (SCOOP) algorithm, which enables intelligent information sharing among subspaces such that each subspace guides the other towards improved locations. The experiments show that the accuracy of SCOOP rivals the state-of-the-art global optimization techniques, while being several orders of magnitude faster and having better scalability against the problem dimensionality.
The proceedings contain 49 papers. The special focus in this conference is on Transport of the 21st Century. The topics include: A simulation-Based Approach for the Conflict Resolution Method optimization in a Distrib...
ISBN:
(纸本)9783030276867
The proceedings contain 49 papers. The special focus in this conference is on Transport of the 21st Century. The topics include: A simulation-Based Approach for the Conflict Resolution Method optimization in a Distributed Air Traffic Control System;Premises for Developing an IT Network Design for Railway Transport in Poland;modeling of Adjustable Muffler in the Exhaust System of an Internal Combustion Engine;virtual Reality Technologies in the Training of Professional Drivers. Comparison of the 2D and 3D simulation Application;evaluation of Exhaust Emissions in Real Driving Emissions Tests in Different Test Route Configurations;method of Planning the Work of Conductor Crews Taking into Account the Polish Conditions;analysis of Lean Angle Influence on Three Wheeled Vehicle Steerability Characteristics;rolling Stability Control Based on Torque Vectoring for Narrow Vehicles;driver’s Reaction Time in the Context of an Accident in Road Traffic;Alternative Method of Diagnosing CAN Communication;developing a Method for Automated Creation of Interlocking Tables for Railway Traffic Control Systems;an Influence of Design Features of Tramway Vehicles on Kinematic Extortion from Geometry of a Track;Mathematical Model of the Movement Authority in the ERTMS/ETCS System;An Analysis of Electromechanical Interactions in the Railway Vehicle Traction Drive Systems driven by AC Motors;maneuverability of Tracked Vehicle at the Border of Traction Between Tracks and Ground;application of V2X Technology in Communication Between Vehicles and Infrastructure in Chosen Area;effect of Track Structure on Dynamical Responses of a Railway Vehicle;telematics as a New Method of Transport System Safety Verification.
In machine learning, removing uninformative or redundant features from a dataset can significantly improve the construction, analysis, and interpretation of the prediction models, especially when the set of collected ...
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ISBN:
(数字)9781728194998
ISBN:
(纸本)9781728195001
In machine learning, removing uninformative or redundant features from a dataset can significantly improve the construction, analysis, and interpretation of the prediction models, especially when the set of collected features is extensive. We approach this challenge with simulationoptimization over a high dimensional binary space in place of the classic greedy search in forward or backward selection or regularization methods. We use genetic algorithms to generate scenarios, bootstrapping to estimate the contribution of the intrinsic and extrinsic noise and sampling strategies to expedite the procedure. By including the uncertainty from the input data in the measurement of the estimators' variability, the new framework obtains robustness and efficiency. Our results on a simulated dataset exhibit improvement over state-of-the-art accuracy, interpretability, and reliability. Our proposed framework provides insight for leveraging Monte Carlo methodology in probabilistic data-drivenmodeling and analysis.
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