A new technique for control system design based on the Optimal Constrained Covariance control (OC 3 ) is presented. A design procedure of control system for dynamic ship positioning is proposed. In the papers publishe...
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A new technique for control system design based on the Optimal Constrained Covariance control (OC 3 ) is presented. A design procedure of control system for dynamic ship positioning is proposed. In the papers published so far the controller gain matrices were detennined using the classical Linear Quadratic Gaussian (LQG) optimal control technique, the pole-placement technique and characteristic locus design method. The main disadvantage of these techniques is their poor correspondence with the real performance requirements of control system. Using the proposed technique this disadvantage is avoided. The results of computer simulation using OC 3 control are presented.
This paper presents a parallel computation model for the time-periodic nonlinear electromagnetic field analysis in the frequency domain using harmonic balance finite element method (HBFEM). The proposed model, differe...
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This paper presents a parallel computation model for the time-periodic nonlinear electromagnetic field analysis in the frequency domain using harmonic balance finite element method (HBFEM). The proposed model, different from the traditional HBFEM technique that requires large memory and long CPU time, divides the global system matrix into a number of matrices in the frequency domain. Each computation unit has exactly the same number of elements and unknown values. The work involved in calculating the element matrices is equal, therefore the load can be well-balanced and the maximum speed-up will be M times if M processors are available (M is the number of harmonics considered in the electromagnetic field). The model is well-suited to MIMD parallel computer or multiple computers connected by local area networks.< >
Pulsewidth modulation, similar to voltage-space vector control in switches utilizing a field programmable gate array, is compared with PSpice simulation in a three switches converter is presented. The PSpice simulatio...
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Pulsewidth modulation, similar to voltage-space vector control in switches utilizing a field programmable gate array, is compared with PSpice simulation in a three switches converter is presented. The PSpice simulation is used in time domain simulation of the rectifier and inverter circuit. The PSpice PWM simulation modelled is compared with the experiment which is implemented in a field programmable gate array based on a carrier frequency of 19.2 kHz. The PWM is applied to the converter operated as a step-down converter where the output voltage can be controlled by the modulation index in the range of 0 to 1.5 V/spl circ//sub ph/. Simulation and experimental results are provided to demonstrate the effectiveness of the proposed model.
Two linear programming (LP) algorithms for the integrated state estimation (ISE) for the whole system are formulated and tested. It was found that a direct application of an ISE on a large power system suffers certain...
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Two linear programming (LP) algorithms for the integrated state estimation (ISE) for the whole system are formulated and tested. It was found that a direct application of an ISE on a large power system suffers certain limitations. This paper presents an efficient and reliable two-level state estimator (TLSE) based on LP techniques. Both the ISE and the proposed TLSE were tested under different conditions. The results are discussed and comparisons are given which show that the TLSE has many advantages over the ISE.
An approximate method of calculating the contributions of the gains in a feedback structure is produced. The discrete time, linear, quadratic state feedback problem is considered. By state and control augmentation dyn...
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An approximate method of calculating the contributions of the gains in a feedback structure is produced. The discrete time, linear, quadratic state feedback problem is considered. By state and control augmentation dynamic compensators with fixed and tunable parameters including decentralized and hierarchal structures can, also, be handled. An example is used to illustrate the usefulness of the approach in selecting good simplified feedback structures and to verify the reasonableness of the assumptions.
Hyperspectral (HS) imaging is a valuable technique for accurately classifying materials because of the abundance of spectral information and high resolution it provides. However, the characteristics of Hyperspectral i...
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Hyperspectral (HS) imaging is a valuable technique for accurately classifying materials because of the abundance of spectral information and high resolution it provides. However, the characteristics of Hyperspectral images (HSI), such as high-dimensional features and information redundancy, pose significant challenges to data processing. Traditional dimensionality reduction methods often have information loss, high computational complexity, and easy to ignore the strong correlation between HSI spectral bands when dealing with HSI data. Although other methods can achieve satisfactory classification performance, they do not consider the dimensionality reduction of HSI, and they focus on the model performance, which limits further improvement in classification performance. This paper proposes a transformer-based framework called “SpectrumRecombineFormer” (SRF), which is composed of two key modules, namely “Spatial Spectral ReCombination” (SSRC) and “Cross-layer Fusion” (CF). The SSRC is capable of utilizing both adjacent and non-adjacent spectrums to generate the spatial-sequential perceptive representations, which alleviates the effect of the strong correlation between HSI spectral bands. The CF can avoid the loss of information during the feed-forward procedure among layers. Extensive experiments on five existing datasets (widely-adopted Indian Pines, Houston2013, Pavia University, Salinas and KSC) demonstrate the capability of our proposed method to address the above mentioned challenges. Both quantitative and qualitative experimental ablation studies, including visualization results, reveal that the proposed SRF method can successfully and efficiently classify hyperspectral images and surpass the other state-of-the-art methods. For access to the source code, please visit https://***/kangpeilun/SRF-HSI-Classification-master.
The series Advances in Industrial control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New ...
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ISBN:
(数字)9781846283345
ISBN:
(纸本)9781852339821;9781849969895
The series Advances in Industrial control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. In some areas of manufacturing, the elements of a flexible manufacturing system form the key components of the process line. These key components are four-fold: a set of programmable robots and machines, an automated materia- handling system that allows parts to be freely routed and re-routed, a buffer storage system where parts and partly-assembled components can wait until required for further processing and assembly and finally, a supervisory control system. The technology employed to coordinate and control all these components as a working system is usually based on programmable logic controllers. The use of this automation hardware and software in manufacturing is designed to yield significant cost reductions and to enhance quality.
Teamwork is crucial in software engineering. However, recent literature concludes that software engineering graduates have underdeveloped teamwork skills. Instructors wishing to develop teamwork skills are faced with ...
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Teamwork is crucial in software engineering. However, recent literature concludes that software engineering graduates have underdeveloped teamwork skills. Instructors wishing to develop teamwork skills are faced with many teamwork models and a lack of empirical studies that examine their utility in higher education. We conducted an exploratory study to examine teamwork components of attitudinal, behavioral, and cognitive psychological facets (collectively called ABCs) in eight undergraduate software engineering teams composed of 14 to 17 members. We answered two research questions: Which teamwork components were the least developed at the beginning of the project, and Which teamwork components remained underdeveloped by the end of the project. For each team, we conducted two focus group discussions, and analyzed four sprint retrospective reports during the semester. We synthesize the gathered data to define each team by the presence of teamwork components, development of teamwork components, experienced challenges, average course grades, and project results. Teamwork components that were initially underdeveloped were Mutual performance monitoring, Shared cognitions, Leadership, Communication, Psychological safety and Trust. Two teamwork components that remained underdeveloped were Shared cognitions and Mutual performance monitoring. The answer to our first question highlights teamwork components that instructors should pay attention to in their teamwork-oriented courses. For the second question, we explain the mechanisms that we applied to develop teamwork components and highlight which components were the most challenging to develop, as well as in which period instructors should provide the most support to students. Engineering education researchers might benefit from our methodological design, measurement instruments, and raw data to conduct studies in their contexts.
Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating rese...
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Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-physical attacks, issues related to the security and privacy of grid systems are receiving much attention from researchers. In this paper, privacy-aware energy grid management systems with anomaly detection networks and distributed learning mechanisms are proposed. The anomaly detection network consists of a server and a client learning network, which collaboratively learn patterns without sharing data, and periodically train and exchange knowledge. We also develop learning mechanisms with federated, distributed, and split learning to improve privacy and use Q-learning for decision-making to facilitate interpretability. To demonstrate the effectiveness and robustness of the proposed schemes, extensive simulations are conducted in different energy grid environments with different target distributions, ORRs, and attack scenarios. The experimental results show that the proposed schemes not only improve management performance but also enhance privacy and security levels. We also compare the management performance and privacy level of the different learning machines and provide usage recommendations.
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