Selected Aspects of Carbon Stock Assessment in Aboveground Biomass

Given the significance of national carbon inventories, the importance of large-scale estimates of carbon stocks is increasing. Accurate biomass estimates are essential for tracking changes in the carbon stock through repeated assessment of carbon stock, widely used for both vegetation and soil, to estimate carbon sequestration. Objectives: The aim of our study was to determine the variability of several aspects of the carbon stock value when the input matrix was (1) expressed either as a vector or as a raster; (2) expressed as in local (1:10,000) or regional (1:100,000) scale data; and (3) rasterized with different pixel sizes of 1, 10, 100, and 1000 m. Method: The look-up table method, where expert carbon content values are attached to the mapped landscape matrix. Results: Different formats of input matrix did not show fundamental differences with exceptions of the biggest raster of size 1000 m for the local level. At the regional level, no differences were notable. Conclusions: The results contribute to the specification of best practices for the evaluation of carbon storage as a mitigation measure, as well as the implementation of national carbon inventories.

A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With LIME Method

Gas turbine is widely used in aviation and energy industries. Gas path fault diagnosis is an important task for gas turbine operation and maintenance. With the development of information technology, especially deep learning methods, data-driven approaches for gas path diagnosis are developing rapidly in recent years. However, the mechanism of most data-driven models are difficult to explain, resulting in lacking of the credibility of the data-driven methods. In this paper, a novel explainable data-driven model for gas path fault diagnosis based on Convolutional Neural Network (CNN) using Local Interpretable Model-agnostic Explanations (LIME) method is proposed. The input matrix of CNN model is established by considering the mechanism information of gas turbine fault modes and their effects. The relationship between the measurement parameters and fault modes are considered to arrange the relative position in the input matrix. The key parameters which contributes to fault recognition can be achieved by LIME method, and the mechanism information is used to verify the fault diagnostic proceeding and improve the measurement sensor matrix arrangement. A double shaft gas turbine model is used to generate healthy and fault data including 12 typical faults to test the model. The accuracy and interpretability between the CNN diagnosis model built with prior mechanism knowledge and built by parameter correlation matrix are compared, whose accuracy are 96.34% and 89.46% respectively. The result indicates that CNN diagnosis model built with prior mechanism knowledge shows better accuracy and interpretability. This method can express the relevance of the failure mode and its high-correlation measurement parameters in the model, which can greatly improve the interpretability and application value.

The recognition of plastic bottle using linear multi hierarchical SVM classifier

Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL.

A Two-Grid Interline Dynamic Voltage Restorer Based on Two Three-Phase Input Matrix Converters

Energy quality problems can cause diverse failures of sensitive equipment. Dynamic voltage restorers (DVRs) are devices that have been proposed to protect sensitive loads from voltage sag effects. However, the compensation capacity of DVRs is limited by the amount of energy stored in the restorer. One way to overcome this limitation is to use the interline DVR (IDVR) structure in which two or more DVRs connected to different feeders share a common DC-link. This paper proposes a new IDVR topology based on two three-phase input matrix converters (TTI-MC) without a capacitor in the dc-link. The TTI-MC integrates the power from two feeders and synthesizes the dc-link voltage. The inverters take the dc-link voltage and generate the appropriate compensation voltages to keep the load voltages stable. Each one of the inverters has its own modulation algorithm which are synchronized with the TTI-MC control. Inverter control is carried out in the reference frame dq and a modified space vector pulse width modulation (MSV-PWM) technique is used. TTI-MC control is performed in the dq reference frame with proportional-integral controllers and the modulation is based on the carrier-based pulse width modulation (MCB-PWM) technique. The proposed Two Input Interline DVR (TI-IDVR) extends its compensation range and has multifunctional capabilities. The efficiency of the proposed TI-IDVR is corroborated by simulations on MATLAB/Simulink.

The Ethics of Tax Evasion: Relevant Demographic Variables within the scope of Higher Education Students and Professors.

The purpose of our study is to determine the depth of various arguments that have emerged to justify tax evasion as an ethical procedure considering several demographic variables. Data collection was done using a questionnaire addressed to professors and students of higher management and non-management courses. This instrument was based on the 18 statements reflecting the three views of tax evasion ethics used by McGee and Benk [1]. Using a 5-point Likert scale, it is intended to evaluate whether the arguments contained in the statements have an effect on the perception of tax evasion as an ethical procedure and whether the previous effect varies according to age, sex, bachelor degree and income level. A universe of 406,980 individuals was determined using official information (sample: 384 individuals). Principal Component Analysis was used, as well as the Kaiser-Meyer-Olkin Statistics in order to measure the adequacy of the input matrix. After the extraction of the components three variables were identified: “Always Ethical”, “Waste, Corruption and Injustice” and “Discrimination and Oppressive Regimes” (Cronbach's Alpha results: 0.887, 0.85 and 0.862). “Discrimination and Oppressive Regimes” is the one that has values ​​closest to “totally agree” that tax evasion is ethical. In general, older men with higher incomes tend to disagree about the ethics of tax evasion. The originality of the study is reflected in the controversial relationship between Ethics and Evasion and the source of the data collected. Interacting with professors and students allows the business and academic components to be combined.

Multi-Variable Adaptive Control Method for Turbofan Engine With Dynamic and Input Uncertainties

The dynamic characteristics of the turbofan engine vary greatly in the full flight envelope, which makes the problem of dynamic uncertainty and input uncertainty very prominent. This brings different degrees of performance impact to the engine control system and even makes it lose stability. This paper proposes an adaptive variable parameter control method for dealing with multivariable dynamic uncertainty and input uncertainty. In this paper, the dynamic uncertainty and input uncertainty are mathematically converted into standard matched uncertainty, which can be handled more conveniently. Firstly, in the state space model, for the case where the number of state variables is less than or equal to the number of input variables and the input matrix satisfies the full-rank condition of the row, the existence of the right pseudo-inverse matrix of the input matrix can be guaranteed. So the dynamic uncertainty can be separated from the system matrix, and the input uncertainty can be separated from the input matrix. Thus these uncertainties are equivalently transformed into parametric matched uncertainty. Then the matched uncertainty model with two vectors of bounded basis functions is established. Secondly, the Lyapunov quadratic function is constructed by the closed-loop tracking error vector and the adaptively adjustable control parameter estimation errors, and the Lyapunov stability constraint is considered. Then, under the premise of considering the dynamic characteristics of the actuator, an adaptive control algorithm for multivariable matched uncertainty model of turbofan engine is derived. Finally, ground and high altitude simulations are carried out on the dual-loop control system based on the nonlinear dynamic model of the turbofan engine. The results show that the control system has robust stability and anti-interference performance for dynamic uncertainty and input uncertainty of turbofan engine in the full flight envelope. The fan speed control loop basically achieves no static error tracking. The dynamic error of the core speed control loop is less than 0.6% and the steady state error is less than 0.05%. By introducing stronger parameter change rate information to the controller, its performance can be further improved, and the transient state control is more stable.

Quantum-inspired algorithms in practice

We study the practical performance of quantum-inspired algorithms for recommendation systems and linear systems of equations. These algorithms were shown to have an exponential asymptotic speedup compared to previously known classical methods for problems involving low-rank matrices, but with complexity bounds that exhibit a hefty polynomial overhead compared to quantum algorithms. This raised the question of whether these methods were actually useful in practice. We conduct a theoretical analysis aimed at identifying their computational bottlenecks, then implement and benchmark the algorithms on a variety of problems, including applications to portfolio optimization and movie recommendations. On the one hand, our analysis reveals that the performance of these algorithms is better than the theoretical complexity bounds would suggest. On the other hand, their performance as seen in our implementation degrades noticeably as the rank and condition number of the input matrix are increased. Overall, our results indicate that quantum-inspired algorithms can perform well in practice provided that stringent conditions are met: low rank, low condition number, and very large dimension of the input matrix. By contrast, practical datasets are often sparse and high-rank, precisely the type that can be handled by quantum algorithms.