An odor predictive model for rendering applications

The Baihetan Hydropower Station, located in the lower reaches of Jinsha River in southwest China, is the second largest hydropower station in the world. The site is characteristic of extremely complex geological and hydrogeological conditions, with fractures, faults, and shear zones that make water tables wedge-shaped. A large-scale seepage control system that contains grouting curtains covering an area of 780,000 m2 and 12,300 drainage holes, together with concrete plugs, were designed to control the seepage in the dam foundation and in the underground powerhouse areas as the reservoir water level will be raised by 225 m during operation. How to assess the performance and optimize the design of seepage control system becomes a key technological issue in the design and construction of this project. In this study, a three-dimensional finite element model that well represents the site conditions is established. The drainage holes are modelled with a substructure technique and the seepage field is solved with the variational inequality method of Signorini’s type. A procedure is proposed to optimize the layout of the seepage control system by taking the amount of leakage, the distribution of uplift pressure, the risk of seepage erosion and cost into account. Numerical results yield the appropriate parameters for the seepage control system, including the extension and depth of grouting curtains, and the spacing of drainage holes. With these optimized parameters, the seepage control system is effective in lowering the phreatic surface and limiting the amount of leakage at the dam site.

According to the development status of large-scale PV power station, this paper expatiates the requirement of supervisory control and data acquisition (SCADA) system of large-scale PV power station, and presents the design method and application technique of the system based on fieldbus and industrial ethernet. The system is flexible and reliable, and will promote the efficiency of operation and maintenance of large-scale PV power station.KeywordsFieldbusIndustrial EthernetPV Power StationSCADA

In this paper, the authors propose an optimal IMC-PID controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the Enhanced Differential Evolution (EDE) algorithm to determine an optimal Reduced Order Model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal IMC design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, PID controller gains are acquired by least square model matching with the optimal IMC transfer function. The proposed IMC-PID controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal–thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

Measurement-driven dashboards provide a unifying mechanism for understanding, evaluating, and predicting the development, management, and economics of large-scale systems and processes. Dashboards enable interactive graphical displays of complex information and support flexible analytic capabilities for user customizability and extensibility. Dashboards commonly include software requirements and design metrics because they provide leading indicators for project size, growth, and stability. This paper focuses on dashboards that have been used on actual large-scale projects as well as example empirical relationships revealed by the dashboards. The empirical results focus on leading indicators for requirements and design of large-scale systems. In the first set of 14 projects focusing on requirements metrics, the ratio of software requirements to source-lines-of-code averaged 1:46. Projects that far exceeded the 1:46 requirements-to-code ratio tended to be more effort-intensive and fault-prone during verification. In the second set of 16 projects focusing on design metrics, the components in the top quartile of the number of component internal states had 6.2 times more faults on average than did the components in the bottom quartile, after normalization by size. The components in the top quartile of the number of component interactions had 4.3 times more faults on average than did the components in the bottom quartile, after normalization by size. When the number of component internal states was in the bottom quartile, the component fault-proneness was low even when the number of component interactions was in the upper quartiles, regardless of size normalization. Measurement-driven dashboards reveal insights that increase visibility into large-scale systems and provide feedback to organizations and projects.

: Controls research under this program concentrated on the development of a network thermodynamic stabilization framework for hybrid control design of large-scale and multiagent aerospace systems. In particular, we concentrate on hybrid control, hierarchical control, impulsive dynamical systems, nonnegative dynamical systems, compartmental systems, large-scale systems, nonlinear switching control, cooperative control, and adaptive control. Application areas include large flexible interconnected space structures, spacecraft stabilization, cooperative control of unmanned air vehicles, network systems, swarms of air and space vehicle formations, and pharmacological systems.

This paper investigates the problem of distributed piecewise filtering for discrete-time large-scale nonlinear systems. The considered large-scale system is composed of a number of nonlinear subsystems and exchanges its information through communication network. Each nonlinear subsystem is described by a Takagi-Sugeno (T-S) model, and data-packet dropouts happen intermittently in communication network, and its stochastic variables are assumed to satisfy the Bernoulli random-binary distribution. Our objective is to design a distributed piecewise filter such that the filtering error system is stochastically stable with an performance. Based on a piecewise Lyapunov function and some convexifying techniques, less conservative results are developed for the distributed piecewise filtering design of the considered system in the form of linear matrix inequalities (LMIs). The effectiveness of the proposed method is validated by two examples.

In recent years, research on speed cruise control for high-speed trains has primarily focused on control design based on multi-body models, leading to significant achievements in various aspects. However, these control methods adopt a single-system approach for the entire train, referred to as the typical method in this paper. Yet, a malfunction in one car can impact the control laws and controller design of other cars. To address this limitation, this paper proposes a large-scale system control, which decouples the control laws and controller design for each car. The control law of an individual car is based solely on its own position and velocity, with separate processes for solving the controllers for each car. Additionally, the speed cruise control for high-speed trains considers both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal {H}}_{2}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal {H}}_{\infty } $ </tex-math></inline-formula> performance in the presence of uncertain parameters. This study validates the feasibility of such decoupled methods in this domain through simulation-based comparative analysis. This study aims to verify the effectiveness of large-scale system control methods in normal train operations and to lay the foundation for more complex fault-tolerant and model-switching challenges. In these areas, large-scale system control has greater potential for application and offers significant advantages.

Piggeries are known for their nuisance odors, creating problems for workers and nearby residents. Chemical substances that contribute to these odors include sulfurous organic compounds, hydrogen sulfide, phenols and indoles, ammonia, volatile amines, and volatile fatty acids. In this work, daily mean concentrations of ammonia (NH3) and hydrogen sulfide (H2S) were measured by hand-held devices. Measurements were taken in several places within the facility (farrowing to finishing rooms). Hydrogen sulfide concentration was found to be 40 to 50 times higher than the human odor threshold value in the nursery and fattening room, resulting in strong nuisance odors. Ammonia concentrations ranged from 2 to 18 mL m(-3) and also contributed to the total odor nuisance. Emission data from various chambers of the pig farm were used with the dispersion model AERMOD to determine the odor nuisance caused due to the presence of H2S and NH3 to receptors at various distances from the facility. Because just a few seconds of exposure can cause an odor nuisance, a "peak-to-mean" ratio was used to predict the maximum odor concentrations. Several scenarios were examined using the modified AERMOD program, taking into account the complex terrain around the pig farm.

Extreme UltraViolet (EUV) lithography is a new technology for production of integrated circuits. In EUV lithographic machines, optical elements are heated by absorption of exposure energy. Heating induces thermoelastic deformations of optical elements and consequently, it creates wavefront aberrations. These Thermally Induced Wavefront Aberrations (TIWA) can significantly degrade the resolution of EUV lithographic machines. One of the ways to correct TIWA and consequently, to improve the resolution of EUV lithographic machines, is to use the Adaptive Optics (AO) technique and predictive control algorithms. However, the predictive control of TIWA is a challenging problem, mainly because the dynamical behavior of TIWA is described by thermoelastic Partial Differential Equations (PDEs). By discretizing the thermoelastic equations using the finite difference or finite element methods, large-scale state-space models can be obtained. A striking feature of these state-space models is that they have sparse (multi) banded matrices. Consequently, these state-space models can be interpreted as large-scale networks of local subsystems. All this implies that the problem of correcting TIWA can be placed in a much more general context of identifying, estimating and controlling large-scale interconnected (distributed) systems. However, currently used estimation and control algorithms are not computationally feasible for large-scale systems. For this reason, this thesis focuses on the development of computationally efficient identification, estimation and control algorithms for large-scale interconnected systems. In this thesis, we prove that the inverses of (finite-time) Gramians of large-scale interconnected systems, obtained by discretizing PDEs, belong to a class of off-diagonally decaying matrices. Consequently, these inverses can be approximated by sparse (multi) banded matrices with O(N) complexity, where N is the number of local subsystems. To compute the approximate inverses, the Chebyshev approximation method and the Newton iteration are used. On the basis of these theoretical results, we develop novel estimation and identification algorithms for large-scale interconnected systems. The computational and memory complexity of these methods scale with O(N). This is the main advantage over the traditional estimation and identification methods, which complexity is O(N^3). Furthermore, in this thesis we develop and experimentally verify AO control algorithms for predictive correction of TIWA. The experimental results give a good first indication for a possible implementation of the developed predictive control algorithms in EUV lithographic machines. Finally, we develop and experimentally test an iterative learning control algorithm for high performance correction of wavefront aberrations.

The absolute stability of large-scale Lurie direct control systems with time-varying coefficients is investigated. Based on the decomposition method for large-scale systems and technique of the nonsingular M-matrix, a suitable scalar Lyapunov function as a weighted sum is constructed. By estimating its total time derivative, some absolute stability criteria and practical corollaries are derived. Furthermore, the results are extended to multiple nonlinearities. The salient feature of this paper is that the criteria which we propose allow for the situation that the norms of time-varying coefficients are unbounded. The main idea of the methodology is that even if the coefficients are norm-unbounded, by restricting their relative magnitude, the problem of negative definiteness for the derivative can also be changed into the problem of stability for a constant matrix. Finally, some numerical examples are included to illustrate the effectiveness of the proposed criteria.

In this paper, we studied a class of particular interval large-scale Lurie indirect control systems by using the decompose technique of large-scale system, and get some sufficient conditions of the robust absolute stability and a series of corollaries. The superiority of this method was shown by an example.

A Component-Based Approach to the Design of Networked Control Systems

Modeling of physical systems with high accuracy results into a complex model with higher-order dynamics. This creates intricacy in analysis, simulation and controller design. So one needs to find an approximate simpler representation of such complex systems while preserving the characteristic properties of the higher order system. Reduced lower order representations provide facility in easy analysis of such systems by reduction in computational complexity and faster simulation. In the present paper, a numerically efficient approximation method such as balanced truncation has been applied to obtain the small scale linear time invariant system. Here results of approximation are then evaluated through integral square error. Finally controller has also been designed for the reduced dimension representation of large scale systems. Few numerical problems have been tested to show the effectiveness of the approach.

This paper addresses the development of a dynamic programming decomposition-coordination algorithm for the optimal control of discrete-time large-scale systems with multiple time delays. The systems under study can be converted into the corresponding augmented systems satisfying the Markov processes denning the augmented state vectors. The resulting dynamic programming can then be further decomposed into a series of subproblems which might be solved in parallel by combining the dynamic programming techniques with approaches to matrix partition. The entire system's solution is finally given by the iterative computation between the subproblems being the local control level and the coordinator. The major advantages of the proposed algorithm over others are to avoid two-point boundary-value problems of the systems with multiple time delays and to significantly reduce the computer memory and time required for such large-scale systems optimal control problems.

On Decentralized Control of Large Scale Systems