Sparse Bayesian Learning for Gas Path Diagnostics

A gas path diagnostic method based on sparse Bayesian learning is presented. Most gas path diagnostic problems present the case where there are fewer measurements than health parameters. In addition, the measurement readings can be faulty themselves and need to be determined, which further increases the number of unknown variables. The number of unknown variables exceeds the number of measurements in gas path diagnostics, making the estimation problem underdetermined. For gradual deterioration, it is common to apply a weighted-least-square algorithm to estimate the component health parameters at the same time sensor errors are being determined. However, this algorithm may underestimate the real problem and attribute parts of it to other component faults for accidental single fault events. The accidental single fault events impact at most one or two component(s). This translates mathematically into the search for a sparse solution. In this paper, we proposed a new gas path diagnostic method based on sparse Bayesian learning favoring sparse solutions for accidental single fault events. The sparse Bayesian learning algorithm is applied to a heavy-duty gas turbine considering component faults and sensor biases to demonstrate its capability and improved performance in gas path diagnostics.</jats:p

Adaptive gas path diagnostics using strong tracking filter

Kalman filters are very popular in gas path diagnostics. This algorithm estimates the engine state variables to assess engine health conditions and is accurate in tracking gradual deterioration. However, the performance of the Kalman filter deteriorates when an abrupt fault occurs. There could be a long delay with the Kalman filter in diagnosing the abrupt fault. In addition, the Kalman filter may transfer the abrupt fault on to other components. In this article, an adaptive gas path diagnostic method using strong tracking filter is described that can track gradual deterioration and abrupt fault accurately. The strong tracking filter is an adaptive extended Kalman filter, which introduces suboptimal fading factors into the prediction error covariance of the extended Kalman filter algorithm. The suboptimal fading factors automatically increase when an abrupt fault occurs, therefore, more importance is given to the new measurement in state estimation which allows the filter to quickly track abrupt faults. All of the suboptimal fading factors become one when gradual deterioration occurs, and in this situation, the strong tracking filter becomes the common extended Kalman filter to filter the measurement noise. Therefore, the strong tracking filter can track abrupt faults quickly and accurately, filter measurement noise, and obtain noise-free parameter estimation for gradual deterioration. The strong tracking filter is applied to heavy-duty gas turbine gas path diagnostics for a variety of simulated fault cases to demonstrate the capability of the strong tracking filter in accurately tracking gradual deterioration and abrupt fault. </jats:p

Fast 3D Stylized Gaussian Portrait Generation From a Single Image With Style Aligned Sampling Loss

Creating stylized 3D avatars and portraits from just a single image input is an emerging challenge in augmented and virtual reality. While prior work has explored 2D stylization or 3D avatar generation, achieving high-fidelity 3D stylized portraits with text control remains an open problem. In this paper, we present an efficient approach for generating high-quality 3D stylized portraits directly from a single input image. Our core representations are based on 3D Gaussian Splatting for efficient rendering, along with a surface-guided splitting and cloning strategy to reduce noise. To achieve high-fidelity stylized results, we introduce a Stylized Generation Module with a Style-Aligned Sampling Loss that injects the input image&#x2019;s identity information into the diffusion model while stabilizing the stylization process. Furthermore, we incorporate a multi-view diffusion model to enforce 3D consistency by generating multiple viewpoints. Extensive experimentation demonstrates that our approach outperforms existing methods in terms of stylization quality, 3D consistency, and user preference ratings. Our framework enables casual users to easily generate stylized 3D portraits through simple image or text inputs, facilitating engaging experiences in AR/VR applications

Winter Wheat Evapotranspiration Estimation under Drought Stress during Several Growth Stages in Huaibei Plain, China

Estimating crop evapotranspiration under drought stress provides the theoretical foundation for optimizing irrigation schedule and reducing agricultural drought risk. Winter wheat water deficit plot experiments were conducted based on large-scale weighing lysimeters in the Huaibei Plain. The response of evapotranspiration to water deficit during several growth stages was analyzed; four crop coefficients were optimized and then evapotranspiration under drought stress was estimated with the dual crop coefficient method and the genetic algorithm. Drought stress not only reduced evapotranspiration during the current stage, but this influence was also transferred to the following periods. Evapotranspiration could basically return to the normal status after rewatering if the plant did not previously suffer from serious drought stress. Therefore, severe water deficit during continuous wheat growth periods should be avoided, and the deficit intensity during each stage should be controlled. The optimized Kcb ini, Kcb mid, Kcb end and Kc max were 0.32, 1.38, 0.33 and 1.39, and the root mean square error, mean absolute error, and mean relative error of the corresponding estimated evapotranspiration under drought stress were 22.83%, 25.36%, and 11.78% less than those using recommended coefficients, respectively. These optimized crop coefficients provide references for the practical application in the Huaibei Plain, this method for estimating crop evapotranspiration under drought stress can be applied to field circumstances and other regions

Quantitative Response of Soybean Development and Yield to Drought Stress during Different Growth Stages in the Huaibei Plain, China

To quantitatively access the effects of drought stress during different growth stages of soybean on development process and yield, a pot-culture experiment was conducted in China&rsquo;s Huaibei Plain with different irrigation treatments over two seasons (2015 and 2016). Two drought stress levels (mild and severe) were applied at four growth stages for the experiment (S: seedling stage; B: branching stage; FPS: flowering and pod-setting stage; and PF: pod-filling stage). The effects of drought stress at different stages on growth and yield were evaluated and compared. Results of this two-year study showed that all growth and yield parameters were significantly affected by the water deficit during the sensitive FPS. Compared to the full irrigation treatment, severe drought stress during FPS caused a 22% loss of final plant height, 61% loss of the leaf area per plant (LAP), and 67% loss of final aboveground dry matter (ADM). Yield components also declined dramatically with water deficits during FPS and PF. Significant seed yield losses of 73&ndash;82% per plant were observed in the plants exposed to drought stress during FPS, and were also associated with the highest nonviable pod percentage of 13%. The greatest losses in 100-seed weight (42&ndash;48%) were observed under drought stress during PF. A rising trend in response to increasing soil water deficit (SWD) was observed for LAP, yield, and ADM losses. The slope (k) values of these fitting curves varied at different treatments, the highest value of k (7.37 and 8.47 in two years, respectively) was also observed in the sensitive FPS

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

To provide a scientific reference for formulating an effective soybean irrigation schedule in the Huaibei Plain, potted water deficit experiments with nine alternative irrigation schemes during the 2015 and 2016 seasons were conducted. An irrigation scheme decision-making index system was established from the aspects of crop water consumption, crop growth process and crop water use efficiency. Moreover, a grey entropy weight method and a grey relation&ndash;projection pursuit model were proposed to calculate the weight of each decision-making index. Then, nine alternative schemes were sorted according to the comprehensive grey relation degree of each scheme in the two seasons. The results showed that, when using the entropy weight method or projection pursuit model to determine index weight, it was more direct and effective to obtain the corresponding entropy value or projection eigenvalue according to the sequence of the actual study object. The decision-making results from the perspective of actual soybean growth responses at each stage for various irrigation schemes were mostly consistent in 2015 and 2016. Specifically, for an integrated target of lower water consumption and stable biomass yields, the scheme with moderate-deficit irrigation at the soybean branching stage or seedling stage and adequate irrigation at the flowering-podding and seed filling stages is relatively optimal

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

To provide a scientific reference for formulating an effective soybean irrigation schedule in the Huaibei Plain, potted water deficit experiments with nine alternative irrigation schemes during the 2015 and 2016 seasons were conducted. An irrigation scheme decision-making index system was established from the aspects of crop water consumption, crop growth process and crop water use efficiency. Moreover, a grey entropy weight method and a grey relation–projection pursuit model were proposed to calculate the weight of each decision-making index. Then, nine alternative schemes were sorted according to the comprehensive grey relation degree of each scheme in the two seasons. The results showed that, when using the entropy weight method or projection pursuit model to determine index weight, it was more direct and effective to obtain the corresponding entropy value or projection eigenvalue according to the sequence of the actual study object. The decision-making results from the perspective of actual soybean growth responses at each stage for various irrigation schemes were mostly consistent in 2015 and 2016. Specifically, for an integrated target of lower water consumption and stable biomass yields, the scheme with moderate-deficit irrigation at the soybean branching stage or seedling stage and adequate irrigation at the flowering-podding and seed filling stages is relatively optimal.</jats:p

Quantitative Diagnosis of Water Resources Carrying Capacity Obstacle Factors Based on Connection Number and TOPSIS in Huaibei Plain

To further quantitatively assess the water resources carrying capacity (WRCC) system and analyze and identify the regional water resources carrying state and the physical mechanism of the state change, WRCC and obstacle factor diagnosis were carried out. In this paper, we proposed the mobility matrix to determine the connection number components, considered the dynamic attributes of the difference degree coefficient, and calculated it using the semi-partial subtraction set pair potential and triangular fuzzy number, so as to construct the quantitative diagnosis method of regional WRCC obstacle factors based on the connection number and TOPSIS. The results applied to six cities in the Huaibei Plain showed that the WRCC fluctuated around grade 2 and was in a poor state, which was mainly due to the insufficient support force; the water resources carrying state of the six cities gradually improved from 2011 to 2018, but the state became worse in 2019, which was related to the low precipitation in that year, the reduction in water resources, and the high degree of water resource utilization. The WRCC of Fuyang and Huainan was worse than that of the other four cities; over the 9 years, the average grades of Fuyang and Huainan were 2.26 and 2.43, while those of Huainan, Bozhou, Suzhou, and Bengbu were 2.19, 2.12, 2.05, and 2.05, respectively. The key obstacles limiting the improvement in the WRCC of the Huaibei Plain were per capita water resources, annual water production modulus, per capita water supply, vegetation coverage ratio, utilization ratio of water resources, water consumption per 104 yuan value-added by industry, and population density. In time, the key obstacle factors in neighboring years generally tended to have similarity, and conversely appeared as a difference; in space, neighboring regions showed similarity and conversely presented as a difference. The results of this study can offer technical support and a decision-making basis for water resources management in the Huaibei Plain. The method constructed in this paper is extremely interpretive, easy to calculate, highly sensitive, and reliable in application results, which opens up a new perspective for the rational determination of the connection number and the difference degree coefficient and provides a new intelligent way to determine the state of a complex set pair system and its causal mechanism analysis and diagnosis of obstacle factors