Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data

The eddy covariance technique provides measurements of net ecosystem exchange (NEE) of CO₂ between the atmosphere and terrestrial ecosystems, which is widely used to estimate ecosystem respiration and gross primary production (GPP) at a number of CO₂ eddy flux tower sites. In this paper, canopy-level maximum light use efficiency, a key parameter in the satellite-based Vegetation Photosynthesis Model (VPM), was estimated by using the observed CO₂ flux data and photosynthetically active radiation (PAR) data from eddy flux tower sites in an alpine swamp ecosystem, an alpine shrub ecosystem and an alpine meadow ecosystem in Qinghai-Tibetan Plateau, China. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)) derived from the Moderate Resolution Imaging Spectral radiometer (MODIS) data and climate data at the flux tower sites, and estimated the seasonal dynamics of GPP of the three alpine grassland ecosystems in Qinghai-Tibetan Plateau. The seasonal dynamics of GPP predicted by the VPM model agreed well with estimated GPP from eddy flux towers. These results demonstrated the potential of the satellite-driven VPM model for scaling-up GPP of alpine grassland ecosystems, a key component for the study of the carbon cycle at regional and global scales.     

Modeling of the carbon dioxide capture process system using machine intelligence approaches

Improving the efficiency of the carbon dioxide (CO₂) capture process requires a good understanding of the intricate relationships among parameters involved in the process. The objective of this paper is to study the relationships among the significant parameters impacting CO₂ production. An enhanced understanding of the intricate relationships among the process parameters supports prediction and optimization, thereby improving efficiency of the CO₂ capture process. Our modeling study used the 3-year operational data collected from the amine-based post combustion CO₂ capture process system at the International Test Centre (ITC) of CO₂ Capture located in Regina, Saskatchewan of Canada. This paper describes the data modeling process using the approaches of (1) neural network modeling combined with sensitivity analysis and (2) neuro-fuzzy modeling technique. The results from the two modeling processes were compared from the perspectives of predictive accuracy, inclusion of parameters, and support for explication of problem space. We conclude from the study that the neuro-fuzzy modeling technique was able to achieve higher accuracy in predicting the CO₂ production rate than the combined approach of neural network modeling and sensitivity analysis.     

On optimal experiment design for identifying premise and conclusion parameters of Takagi-Sugeno models: Nonlinear regression case

Optimal Experiment Design (OED) is a well-developed concept for regression problems that are linear-in-the-parameters. In case of experiment design to identify nonlinear Takagi-Sugeno (TS) models, non-model-based approaches or OED restricted to the local model parameters (assuming the partitioning to be given) have been proposed. In this article, a Fisher Information Matrix (FIM) based OED method is proposed that considers local model and partition parameters. Due to the nonlinear model, the FIM depends on the model parameters that are subject of the subsequent identification. To resolve this paradoxical situation, at first a model-free space filling design (such as Latin Hypercube Sampling) is carried out. The collected data permits making design decisions such as determining the number of local models and identifying the parameters of an initial TS model. This initial TS model permits a FIM-based OED, such that data is collected which is optimal for a TS model. The estimates of this first stage will in general not be ideal. To become robust against parameter mismatch, a sequential optimal design is applied. In this work the focus is on D-optimal designs. The proposed method is demonstrated for three nonlinear regression problems: an industrial axial compressor and two test functions.     

Asserting the utility of using the Cowichan Problem Set

Parallel programming environments provide a way for programmers to reap the benefits of parallelism, while reducing the effort required to create parallel applications. The CO₂P₃S parallel programming system is one such tool that uses a pattern-based approach to express concurrency. Using the Cowichan Problems, we demonstrate that CO₂P₃S contains a rich set of parallel patterns for implementing a wide variety of applications running on shared-memory or distributed-memory hardware. An example of these parallel patterns, the Search-Tree pattern, is described and it is shown how the pattern was used to solve the Fifteen Puzzle problem. Code metrics and performance results are presented for the Cowichan applications to show the usability of the CO₂P₃S system and its ability to reduce programming effort, while producing programs with reasonable performance.     

TCSC 系统的自适应minimax 干扰抑制控制器设计

针对含有未知外部干扰和不确定参数的非线性晶闸管控制串联补偿器(TCSC) 系统, 提出一种L₂增益干扰抑制算法. 将minimax 方法引入耗散Hamilton 系统, 消除了不等式假设条件的约束; 构造检验函数, 推算出系统所能承受的最大干扰程度, 降低了传统干扰处理方法的保守性; 采用参数映射方法设计自适应律, 提高了参数跟踪效率. 最后通过机械功率和对地短路故障的仿真结果表明了所提出的控制方案能够有效改善系统的暂态性能.

     

Comment on “Thermodynamic modelling of an Al2O3-MnO system using the ionic model” by A.B. Farina and F.B. Neto

The MnO-Al₂O₃ system is very important for the non-metallic inclusion controls in steelmaking and the slag chemistry for high Mn alloy production. Farina and Neto presented their recent thermodynamic assessment on the MnO-Al₂O₃ system using a two-sublattice ionic liquid model for liquid oxide phase and the Compound Energy Formalism for solid phases. However, we found several doubts and mistakes in their assessment in particular related to the Gibbs energy of MnAl₂O₄ spinel phase.     

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO₂ between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO₂ flux measurements at individual CO₂ eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO₂ flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.     

A novel data reduction method for Takagi–Sugeno fuzzy system design based on statistical design of experiment

This paper introduces a simple, systematic and effective method for designing Takagi–Sugeno (T–S) fuzzy systems utilizing a significantly smaller training data set versus existing methods. Creating proper training data is usually not an easy task and requires spending considerable time and resources. The proposed method first uses a three-level factorial design to partition the output space. Next the least square technique is used to estimate each of the partitioned output spaces. The membership functions are introduced with only three variables (min, max and number of membership functions). Fuzzy rules are generated with respect to the partitioned output surfaces and the membership functions. The proposed method is applied to two benchmark problems, controlling an inverted pendulum as well as modeling a nonlinear function. In the case of the inverted pendulum simulation results demonstrate significant improvement. In the case of nonlinear function modeling we demonstrated sufficient accuracy with only 9 training data, which represents 98% reduction in the number of training data compare to other method. Additionally, the proposed method offers extremely low computation time allowing it to be used with adaptive type systems.     

Generation of random non-overlapping dot patterns for light guides using molecular dynamics simulations with variable r-cut and reflective boundary techniques

This paper presents a molecular dynamics (MD) scheme for the automatic generation of dot patterns for the light guides used in LCD backlight modules. Several MD computational techniques are integrated with the conventional MD scheme to enable the adjustment of the dot density in specific regions of the light guide in order to create a dot distribution with a high dot density variation and a high spatial uniformity. These techniques include the cell division technique, the variable r-cut technique, the boundary smoothing technique and the reflective boundary condition. The reflective boundary condition enables a precise control of the dot density within each cell, and is instrumental in achieving a dot distribution with both a high dot density variation and a high spatial uniformity. The performance of the proposed dot generation scheme is verified by considering the practical example of the dot pattern design of a light guide with a single LED light source located in the lower-right corner. The numerical results confirm the ability of the proposed method to achieve an even luminance condition by establishing a dot pattern whose density increases concentrically with an increasing distance from the light source.     

Development of SAW-based multi-gas sensor for simultaneous detection of CO2 and NO2

A 440 MHz wireless and passive surface acoustic wave (SAW)-based multi-gas sensor integrated with a temperature sensor was developed on a 41° YX LiNbO₃ piezoelectric substrate for the simultaneous detection of CO₂, NO₂, and temperature. The developed sensor was composed of a SAW reflective delay lines structured by an interdigital transducer (IDT), ten reflectors, a CO₂ sensitive film (Teflon AF 2400), and a NO₂ sensitive film (indium tin oxide). Teflon AF 2400 was used for the CO₂ sensitive film because it provides a high CO₂ solubility, with good permeability and selectivity. For the NO₂ sensitive film, indium tin oxide (ITO) was used. Coupling of mode (COM) modeling was conducted to determine the optimal device parameters prior to fabrication. Using the parameters determined by the simulation results, the device was fabricated and then wirelessly measured using a network analyzer. The measured reflective coefficient S₁₁ in the time domain showed high signal/noise (S/N) ratio, small signal attenuation, and few spurious peaks. The time positions of the reflection peaks were well matched with the predicted values from the simulation. High sensitivity and selectivity were observed at each target gas testing. The obtained sensitivity was 2.12°/ppm for CO₂ and 51.5°/ppm for NO₂, respectively. With the integrated temperature sensor, temperature compensation was also performed during gas sensitivity evaluation process.     

Remote sensing of tundra gross ecosystem productivity and light use efficiency under varying temperature and moisture conditions

Satellite observations have shown greening trends in tundra in response to climate change, suggesting increases in productivity. To better understand the ability of remote sensing to detect climate impacts on tundra vegetation productivity, we applied a photosynthetic light use efficiency model to simulated climate change treatments of tundra vegetation. We examined changes in the Normalized Difference Vegetation Index (NDVI) and photosynthetic light use efficiency (ε) in experimental warming and moisture treatments designed to simulate climate change in northern Alaska. Plots were warmed either passively, using Open Top Chambers, or actively using electric heaters in the soil. In one set of plots water table depth was actively altered, while other plots were established in locations that were naturally wet or dry. Over two growing seasons, plot-level carbon flux and spectral reflectance measurements were collected, and the results were used to derive a light use efficiency model that could explore the effects of moisture and temperature treatments using remote sensing.Warming increased values of canopy greenness (NDVI) relative to control plots, this effect being more pronounced in wet plots than in dry plots. Light use efficiency (LUE), the relationship between absorbed photosynthetically active radiation (PAR) and gross ecosystem production (GEP), was consistent across warming treatments, growing season, subsequent years, and sites. However, LUE was affected by vegetation type, which varied with moisture; plots in naturally dry locations showed reduced light use efficiency relative to moist plots. Additionally moss exhibited reduced LUE relative to vascular plants. Understory moss production, not accounted for by the usual definition of the fraction of absorbed PAR (f_APAR), was found to be a significant part of total GEP, particularly in areas with low vascular plant cover. These results support the use of light use efficiency models driven by spectral reflectance for estimating GEP in tundra vegetation, provided effects of vegetation functional type (e.g. mosses versus vascular plants) and microtopography are considered.     

Parametric polynomial spectral factorization using the sum of roots and its application to a control design problem

This paper presents an algebraic approach to polynomial spectral factorization, an important mathematical tool in signal processing and control. The approach exploits an intriguing relationship between the theory of Gröbner bases and polynomial spectral factorization which can be observed through the sum of roots, and allows us to perform polynomial spectral factorization in the presence of real parameters. It is discussed that parametric polynomial spectral factorization enables us to express quantities such as the optimal cost in terms of parameters and the sum of roots. Furthermore an optimization method over parameters is suggested that makes use of the results from parametric polynomial spectral factorization and also employs two quantifier elimination techniques. This proposed approach is demonstrated in a numerical example of a particular control problem.     

An intuitive design pattern for sequentially estimating parameters of a 2 factorial experiment with active confounding avoidance and least treatment combinations

2^k Full factorial designs may be prohibitively expensive when the number of factors k is large. The most popular technique developed to reduce the number of treatment combinations is the fractional factorial design; confounding in estimating the model parameters naturally results in various resolution and aberration levels. While very useful, these resolution levels may not satisfy experimenters’ requirements for estimatibility and cost reduction. For example, while Resolution V ensures a common requirement that no two-factor interactions are confounded, it also imposes an often undesired restriction that a main effect cannot be confounded with a three-factor interaction, which may very well be non-existent or negligible. We propose a new concept of “active confounding avoidance” whose goal is to identify, for any given set of parameters, a set of treatment combinations such that estimates of the parameters are not confounded with one another. We show that the “least-treatment-combinations” methods developed for identifying a minimal set of m treatment combinations for a 2^k model with only m non-zero parameters achieve this goal. We then propose a simple design pattern that achieves active confounding avoidance for parameters spanning from all main effects up to all i-factor interactions, for all i = 1, 2, … , k, all with the least number of treatment combinations. The pattern also specifies how treatment combinations should be sequenced for experimentation according to a parameter sequence of decreasing magnitude possibly specified by the experimenter based on prior knowledge. The former sequence is optimal in that the experimentation can stop whenever the current model is deemed adequate and experiments already conducted could be considered necessary.     

Output feedback design for saturated linear plants using deadzone loops

In this paper, we present an LMI-based synthesis approach on output feedback design for input saturated linear systems by using deadzone loops. Algorithms are developed for minimizing the upper bound on the regional L₂ gain for exogenous inputs with L₂ norm bounded by a given value, and for minimizing this upper bound with a guaranteed reachable set or domain of attraction. The proposed synthesis approach will always lead to regionally stabilizing controllers if the plant is exponentially unstable, to semi-global results if the plant is non-exponentially unstable, and to global results if the plant is already exponentially stable, where the only requirement on the linear plant is detectability and stabilizability. The effectiveness of the proposed techniques is illustrated with one example.     

A new equation of state and Fortran 77 program to calculate vapor–liquid phase equilibria of CH4–H2O system at low temperatures

A new equation of state (EOS) and the corresponding computer program package VLEWM are developed to calculate vapor–liquid phase equilibria and volumetric properties of CH₄–H₂O system at low temperatures. The EOS can predict vapor–liquid equilibria and volumetric properties of CH₄–H₂O system accurately at temperatures 273–383 K, and at pressures 0–1000 bar. The program package VLEWM is written in FORTRAN 77. It provides two main functions: (1) to calculate the composition in vapor phase and liquid phase of CH₄–H₂O system at equilibrium and (2) to judge the phase and to calculate molar volume of CH₄–H₂O mixture.     

Exponential stability and L 2-gain analysis for a class of faulty systems

This article is concerned with the exponential stability and L ₂-gain problems for a class of systems with failures. Possible failures of sensors, actuators and/or controllers are considered simultaneously. Inspired by a switching control scheme, we develop an idea that is turning the class of faulty systems into a switched system composed of stable and unstable subsystems. Based on an average dwell‐time method, an activation time ratio scheme between stable and unstable subsystems is derived. It is shown that, under the proposed scheme, the faulty systems ensure exponential stability as well as a desire weighted L ₂-gain performance level. A numerical example and a practical simulation example on the flight control system are given to illustrate the practicability of the proposed method.     

Convergence and convergence rate of the balanced realization truncations for infinite-dimensional discrete-time systems

In this paper, we study approximation via balanced realizations for a large class of infinite-dimensional discrete-time linear systems. We give properties of the truncated system and prove that the approximation is L₂-convergent. In the case of nuclear systems, we prove convergence in nuclear norm and give an estimation of the L_∞-convergence rate.     

Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner

Plant structure and chlorophyll content strongly affect rates of photosynthesis. Rapid, objective, and repeatable methods are needed to measure these vegetative parameters to advance our understanding and modeling of plant ecophysiological processes. Terrestrial laser scanners (TLS) can be used to measure structural and potentially chemical properties of objects by quantifying the x,y,z coordinates and intensity of laser light, respectively, returned from an object's surface. The objective of this study was to determine the potential usefulness of TLS with a green (532 nm) laser to simultaneously measure the spatial distribution of chlorophyll a and b content (Chl_ab), leaf area (LA), and leaf angle (LAN). The TLS measurements were obtained from saplings of two tree species (Quercus macrocarpa and Acer saccharum) and from an angle-adjustable cardboard surface. The green laser return intensity value was strongly correlated with wet-chemically determined Chl_ab (r² = 0.77). Strong agreement was shown between measured and TLS-derived LA (r² = 0.95, intercept = − 1.43, slope = 0.97). The TLS derived LANs of both species followed a plagiophile LAN distribution, and the measured angles of the cardboard surface allowed us to quantify that these LAN values were strongly correlated with TLS derived angles (r² = 1.0, intercept and slope = 0.98). Our results show that terrestrial laser scanners are feasible for simultaneous measurement of LA, LAN, and Chl_ab in simple canopies of small broadleaved plants. Further research is needed in more complex and larger canopies.     

Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis

In this work, a discrete time series model of a supply chain system is derived using material balances and information flow. Transfer functions for each unit in the supply chain are obtained by z-transform. The entire chain can be modeled by combining these transfer functions into a close loop transfer function for the network. The model proves to be very useful in revealing the dynamics characteristic of the system. The system can be viewed as a linear discrete system with lead time and operating constraints. The stability of the system can be analyzed using the characteristic equation. Controllers are designed using frequency analysis. The bullwhip effect, i.e. magnification of amplitudes of demand perturbations from the tail to upstream levels of the supply chain, is a very important phenomenon for supply chain systems. We proved that intuitive operation of a supply chain system with demand forecasting will cause bullwhip. Moreover, lead time alone would not cause bullwhip. It does so only when accompanied by demand forecasting. Furthermore, we show that by implementing a proportional intergral or a cascade inventory position control and properly synthesizing the controller parameters, we can effectively suppress the bullwhip effect. Moreover, the cascade control structure is superior in meeting customer demand due to its better tracking of long term trends of customer demand.