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System models that provide a basis for the formulation and evaluation of the performability of commercial aircraft computer system are developed. Quantitative measures of the system effectiveness are formulated. Analytic and simulation techniques for evaluation of the effectiveness and performability of a proposed or existing aircraft computer were studied.
In this presentation, the authors address topics relevant to higher order modeling using hybrid BEM/FEM formulations. The first of these is the limitation on convergence rates imposed by geometric modeling errors in the analysis of scattering by a dielectric sphere. The second topic is the application of an Incomplete LU Threshold (ILUT) preconditioner to solve the linear system resulting from the BEM/FEM formulation. The final tOpic is the application of the higher order BEM/FEM formulation to antenna modeling problems. The authors have previously presented work on the benefits of higher order modeling. To achieve these benefits, special attention is required in the integration of singular and near-singular terms arising in the surface integral equation. Several methods for handling these terms have been presented. It is also well known that achieving ~he high rates of convergence afforded by higher order bases may als'o require the employment of higher order geometry models. A number of publications have described the use of quadratic elements to model curved surfaces. The authors have shown in an EFIE formulation, applied to scattering by a PEC .sphere, that quadratic order elements may be insufficient to prevent the domination of modeling errors. In fact, on a PEC sphere with radius r = 0.58 Lambda(sub 0), a quartic order geometry representation was required to obtain a convergence benefi.t from quadratic bases when compared to the convergence rate achieved with linear bases. Initial trials indicate that, for a dielectric sphere of the same radius, - requirements on the geometry model are not as severe as for the PEC sphere. The authors will present convergence results for higher order bases as a function of the geometry model order in the hybrid BEM/FEM formulation applied to dielectric spheres. It is well known that the system matrix resulting from the hybrid BEM/FEM formulation is ill -conditioned. For many real applications, a good preconditioner is required to obtain usable convergence from an iterative solver. The authors have examined the use of an Incomplete LU Threshold (ILUT) preconditioner . to solver linear systems stemming from higher order BEM/FEM formulations in 2D scattering problems. Although the resulting preconditioner provided aD excellent approximation to the system inverse, its size in terms of non-zero entries represented only a modest improvement when compared with the fill-in associated with a sparse direct solver. Furthermore, the fill-in of the preconditioner could not be substantially reduced without the occurrence of instabilities. In addition to the results for these 2D problems, the authors will present iterative solution data from the application of the ILUT preconditioner to 3D problems.
With the development of communication and control technology, intelligent transportation systems have received increasing attention from both industry and academia. Intelligent transportation systems are supported by the Internet of Things, Cyber-Physical System, Artificial Intelligence, Cloud Computing and many other technologies, which supply fundamental information for connected and automated vehicles. Although plenty of studies have provided different formulations for intelligent transportation systems, many of them depend on Master Control Center. However, a centralized control mode requires a huge amount of data transmission and high level of hardware configuration and may cause communication delay and privacy leak. Some distributed architectures have been proposed to overcome the above problems but systematized technologies to collect and exchange information, process large amounts of data, model the dynamics of vehicles, and safely control the connected and automated vehicles are not explored in detail. In this paper, we proposed a novel distributed cyber-physical system for connected and automated vehicles in which every vehicle is modeled as a double-integrator using edge computing to analyze information collected from its nearest neighbors. The vehicles are supposed to travel along a desired trajectory and to maintain a rigid formation geometry. Related methodologies for the proposed system are illustrated and experiments are conducted showing that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study.
Differential equations and numerical methods are extensively used to model various real-world phenomena in science and engineering. With modern developments, we aim to find the underlying differential equation from a single observation of time-dependent data. If we assume that the differential equation is a linear combination of various linear and nonlinear differential terms, then the identification problem can be formulated as solving a linear system. The goal then reduces to finding the optimal coefficient vector that best represents the time derivative of the given data. We review some recent works on the identification of differential equations. We find some common themes for the improved accuracy: (i) The formulation of linear system with proper denoising is important, (ii) how to utilize sparsity and model selection to find the correct coefficient support needs careful attention, and (iii) there are ways to improve the coefficient recovery. We present an overview and analysis of recent developments on the topic.
This paper presents a reliability- and robustness-based formulation for robust control synthesis for systems with probabilistic uncertainty. In a reliability-based formulation, the probability of violating design requirements prescribed by inequality constraints is minimized. In a robustness-based formulation, a metric which measures the tendency of a random variable/process to cluster close to a target scalar/function is minimized. A multi-objective optimization procedure, which combines stability and performance requirements in time and frequency domains, is used to search for robustly optimal compensators. Some of the fundamental differences between the proposed strategy and conventional robust control methods are: (i) unnecessary conservatism is eliminated since there is not need for convex supports, (ii) the most likely plants are favored during synthesis allowing for probabilistic robust optimality, (iii) the tradeoff between robust stability and robust performance can be explored numerically, (iv) the uncertainty set is closely related to parameters with clear physical meaning, and (v) compensators with improved robust characteristics for a given control structure can be synthesized.
Digital computer program based on matrix formulation for analysis and design of linear state variable feedback systems
The industrial engineer is often required to design and implement control systems and organization for manufacturing and service facilities, to optimize quality, delivery, and yield, and minimize cost. Despite progress in computer science most such systems still employ human operators and managers as real-time control elements. Manual control theory should therefore be applicable to at least some aspects of industrial system design and operations. Formulation of adequate model structures is an essential prerequisite to progress in this area; since real-world production systems invariably include multilevel and multiloop control, and are implemented by timeshared human effort. A modular structure incorporating certain new types of functional element, has been developed. This forms the basis for analysis of an industrial process operation. In this case it appears that managerial controllers operate in a discrete predictive mode based on fast time modelling, with sampling interval related to plant dynamics. Successive aggregation causes reduced response bandwidth and hence increased sampling interval as a function of level.
A discrete modern control formulation is used to study the nature of the transient behavior of the learning process during repetitions. Several alternative learning control schemes are developed to improve the transient performance. These include a new method using an alternating sign on the learning gain, which is very effective in limiting peak transients and also very useful in multiple-input, multiple-output systems. Other methods include learning at an increasing number of points progressing with time, or an increasing number of points of increasing density.
We provide a distributed coordinated approach to the stability analysis and control design of largescale nonlinear dynamical systems by using a vector Lyapunov functions approach. In this formulation the large-scale system is decomposed into a network of interacting subsystems and the stability of the system is analyzed through a comparison system. However finding such comparison system is not trivial. In this work, we propose a sum-of-squares based completely decentralized approach for computing the comparison systems for networks of nonlinear systems. Moreover, based on the comparison systems, we introduce a distributed optimal control strategy in which the individual subsystems (agents) coordinate with their immediate neighbors to design local control policies that can exponentially stabilize the full system under initial disturbances.We illustrate the control algorithm on a network of interacting Van der Pol systems.
Data systems concepts that would be needed to implement the objective of the global crop production forecasting in an orderly transition from experimental to operational status in the 1985 time frame were examined. Information needs of users were converted into data system requirements, and the influence of these requirements on the formulation of a conceptual data system was analyzed. Any potential problem areas in meeting these data system requirements were identified in an iterative process.
This paper presents the formulation of a class of optimization problems dealing with selecting, at each instant of time, one measurement provided by one out of many sensors. Each measurement has an associated measurement cost. The basic problem is then to select an optimal measurement policy, during a specified observation time interval, so that a weighted combination of prediction accuracy and accumulated observation cost is optimized. The current analysis is limited to the class of linear stochastic dynamic systems and measurement subsystems. The problem of selecting the optimal measurement strategy can be transformed into a deterministic optimal control problem. It is shown that the optimal measurement policy and the associated matched Kalman-type filter can be precomputed.
NMC811 (LiNi 0.8 Co 0.1 Mn 0.1 O 2 ) and other high-Ni chemistries are promising cathode candidates for high-performance electric vehicles, owing to their high energy density and reduced cobalt content. However, their long-term cycling stability is hindered by surface degradation, particularly when paired with conventional electrolytes and a lithium metal anode. Electrolyte additives represent a practical approach to enhance interfacial stability and improve overall battery performance by promoting the formation of a robust electrolyte–electrode interphase (EEI). In this study, we revisit the effects of vinylene carbonate (VC) and tris(trimethylsilyl)borate (TMSB) additives on single-crystal SC-NMC811||Li cells. While TMSB only increases the open-circuit voltage and initial overpotential, it delivers superior capacity retention at C/3 compared to cells containing only VC or a dual additive system (VC and TMSB). Notably, under fast-charging conditions (1C, 2C, and 5C), the dual-additive system significantly outperforms other formulations, achieving markedly enhanced long-term capacity retention. Comprehensive electrochemical and spectroscopic analyses reveal that the VC/TMSB dual-additive system suppresses surface transition in NMC811, mitigates structural degradation by forming a thin, LiF-deficient cathode-electrolyte interface (CEI) layer. Moreover, they promote smooth and dense Li deposition and generate a LiF-deficient solid-electrolyte interphase (SEI). Consequently, the synergistic stabilization of both the CEI and SEI effectively limits the overall cell impedance growth during extended cycling. These findings provide key insights into co-additive strategies for engineering stable interfaces in high-energy Ni-rich Li-metal batteries.
Topics include a lab overview, testing and processing equipment, hemochromic hydrogen sensors, antimicrobial materials, wire system materials, CNT ink formulations, CNT ink dust screens, CNT ink printed circuitry, cryogenic materials development, fire and polymers, the importance of lighting, electric lighting systems, LED for plant growth, and carbon nanotube fiber filaments.
Conventional electric power systems are composed of different unidirectional power flow stages of generation, transmission, and distribution, managed independently by transmission system and distribution system operators. However, as distribution systems increase in complexity due to the integration of distributed energy resources, coordination between transmission and distribution networks will be imperative for the optimal operation of the power grid. However, coupling models and formulations between transmission and distribution is non-trivial, in particular due to the common practice of modeling transmission systems as single-phase, and distribution systems as multi-conductor phase-unbalanced. To enable the rapid prototyping of power flow formulations, in particular in the modeling of the boundary conditions between these two seemingly incompatible data models, we introduce PowerModelsITD.jl, a free, open-source toolkit written in Julia for integrated transmission-distribution (ITD) optimization that leverages mature optimization libraries from the InfrastructureModels.jl-ecosystem. The primary objective of the proposed framework is to provide baseline implementations of steady-state ITD optimization problems, while providing a common platform for the evaluation of emerging formulations and optimization problems. In this work, we introduce the nonlinear formulations currently supported in PowerModelsITD.jl, which include AC-polar, AC-rectangular, current-voltage, and a linear network transportation model. Results are validated using combinations of IEEE transmission and distribution networks.
Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.
Purpose: Image quality indices based upon model observers are promising alternatives to laborious human readings of contrast-detail images. This is especially appealing in digital mammography as limiting values for contrast thresholds determine, according to some international protocols, the acceptability of these systems in the radiological practice. The objective of the present study was to compare the signal to noise ratios (SNR) obtained with two nonprewhitening matched filter model observer approaches, one in the spatial domain and the other in the frequency domain, and with both of them worked out for disks as present in the CDMAM phantom. Methods: The analysis was performed using images acquired with the Siemens Novation and Inspiration digital mammography systems. The spatial domain formulation uses a series of high dose CDMAM images as the signal and a routine exposure of two flood images to calculate the covariance matrix. The frequency domain approach uses the mathematical description of a disk and modulation transfer function (MTF) and noise power spectrum (NPS) calculated from images. Results: For both systems most of the SNR values calculated in the frequency domain were in very good agreement with the SNR values calculated in the spatial domain. Both the formulations in the frequency domain and in the spatial domain show a linear relationship between SNR and the diameter of the CDMAM discs. Conclusions: The results suggest that both formulations of the model observer lead to very similar figures of merit. This is a step forward in the adoption of figures of merit based on NPS and MTF for the acceptance testing of mammography systems.
Using a primitive variable formulation of the finite-element method, the differences in the surface observables, such as topography and geoid, produced by single- and double-layered thermal convection, were compared. Both constant and depth-dependent viscosities have been considered. For the same Rayleigh number, larger surface perturbations are produced by single-cell convection. For the same Nusselt number, the magnitudes of the surface observables are greater for double-layered convection. For the same surface heat-flux, surface topographies have similar magnitudes, but the relative amplitudes of geoid anomalies depend greatly on the style of viscosity stratification. This difference in the geoid between the two systems increases with greater surface heat-flow, regardless of viscosity structure.
The use of information theory to formulate an informational model of the change in state of mechanical systems is considered. By that means, models of the change in state of system components may be developed for different operating conditions. In regions of the system’s state space corresponding to critical values, the probability of a fault may be determined.