Dynamic Loss Research Articles

A novel apparent power loss based distributed generator siting and sizing method considering dependent loads

In the present paper, it is first established that even though the standard Active Power Loss Sensitivity Factor (APLSF) based technique for Renewable Energy based Distributed Generation (REDG) system excels with respect to many available technical assessment indicators, its performance falls short in case of line loss reduction and power transfer capacity improvement indicators. On further investigation through Dynamic Fault Tree Analysis technique, it is found that the unsatisfactory performance of APLSF stem from neglecting reactive power loss and line reactance. Therefore, a novel apparent power loss augmented technique coined as Dynamic Loss Evaluation Indicator (DLEI) method is proposed here. The suitability of the proposed method is validated on IEEE 33 and 85 bus distribution systems for both voltage and voltage and frequency dependent load models and it is shown that the aforementioned indicators are improved along with the other indicators like voltage profile and voltage stability to name a few. For further validation, contingency and power quality analysis are also carried out and improved performance is observed in DLEI method when compared against the APLSF based method.

Fast dynamics and low power losses of high-speed solenoid valve based on optimized pre-excitation control algorithm

Aiming at the requirements of high precision and reliability of digital hydraulic systems, high-speed solenoid valve (HSSV) needs to satisfy both fast dynamics and low power losses. In this paper, the dynamic performance and power losses of HSSV are studied by combining the coupling relationship between multi-physical fields, and based on the analysis conclusion, a control algorithm for HSSV is optimized based on pre-excitation control algorithm (PECA) to effectively speed up the switching behavior and reduce power losses. The theoretical and experimental results indicate that increasing the pre-opening voltage (POV) and reducing the pre-closing voltage (PCV) can shorten the switching delay time. However, the copper loss increases with the POV, resulting in a lower mechanical energy conversion efficiency. On the other hand, enlarging the opening voltage (OV) and closing voltage (CV) can speed up the switching process of HSSV, but it causes a significant increase in iron and copper losses. The dynamic performance and power losses of the HSSV are dimensionless mapped and the parameter group (POV = 3.6 V, OV = 14 V, PCV = 2.25 V, CV = −24 V) with optimum and well-balanced behaviors is selected with bubble scatter analysis and variation coefficient. The time required for HSSV to open and close was 1.21 ms and 1.71 ms, respectively, and the final temperature rising is optimized at 40.9 ℃. Therefore, the optimized PECA meets the expected requirements.

Frost resistance and life prediction of recycled brick aggregate concrete with waste polypropylene fiber

Abstract Due to recycled aggregate concrete technology, sustainable resource utilization can be achieved, but the weak frost resistance of this type of concrete affects its application in cold regions. Using waste polypropylene fibers as reinforcing materials can improve the mechanical properties and durability of concrete. This study explores the influence of waste polypropylene fiber on the frost resistance durability and microstructure of recycled brick aggregate (RA) concrete. The results show that with the increase in freeze–thaw cycles, the mass of the concrete first increases and then decreases, while its relative dynamic elastic modulus and compressive strength gradually decrease. After 60 freeze–thaw cycles, the maximum mass loss, maximum relative dynamic elastic modulus loss, and maximum compressive strength loss of the RA concrete are 1.73, 45.1, and 73.7%, respectively. Waste fiber (WF) can improve the frost resistance of concrete, as demonstrated by the obvious reduction in mass loss, relative dynamic elasticity modulus loss, and compressive strength loss, which are 0.11, 33.0, and 64.0%, respectively, after 60 freeze–thaw cycles. The action mechanism of WF on the frost resistance of RA concrete is revealed, and the life prediction model of RA concrete with WF under freeze–thaw conditions is established.

Lightweight and multi-lesion segmentation model for diabetic retinopathy based on the fusion of mixed attention and ghost feature mapping

Diabetic retinopathy is the main cause of blindness, and lesion segmentation is an important basic work for the diagnosis of this disease. The main lesions include soft and hard exudates, microaneurysms, and hemorrhages. However, the segmentation of these four types of lesions is difficult because of their variability in size and contrast, and high intertype similarity. Currently, many network models have problems, such as a large number of parameters and complex calculations, and most segmentation models for diabetic retinopathy focus only on one type of lesion. In this study, a lightweight algorithm based on BiSeNet V2 was proposed for the segmentation of multiple lesions in diabetic retinopathy fundus. First, a hybrid attention module was embedded in the semantic branch of BiSeNet V2 for 8- and 16-fold downsampling, which helped reassign deep feature-map weights and enhanced the ability to extract local key features. Second, a ghost feature-mapping unit was used to optimize the traditional convolution layers and further reduce the computational cost. Third, a new loss function based on the dynamic threshold loss function was applied to supervise the training by adjusting the training weights of the high-loss difficult samples, which enhanced the model's attention to small goals. In experiments on the IDRiD dataset, we conducted an ablation study to verify the effectiveness of each component and compared the proposed model, BiSeNet V2-Pro, with several state-of-the-art models. In comparison with the baseline BiSeNet V2, the segmentation performance of BiSeNet V2-Pro improved by 12.17 %, 11.44 %, and 8.49 % in terms of Sensitivity (SEN), Intersection over Union (IoU), and Dice coefficient (DICE), respectively. Specifically, IoU of MA reaches 0.5716. Compared with other methods, the segmentation speed was significantly improved while ensuring segmentation accuracy, and the number of model parameters was lower. These results demonstrate the superiority of BiSeNet V2-Pro in the multi-lesion segmentation of diabetic retinopathy.

Evaluation of Mechanical Properties, Corrosion Resistance, and Pore Structure of Stepwise PCM Aggregate Concrete

Stepwise phase change material (PCM) aggregate concrete has advantages in controlling temperature and resisting frost heave, but its freeze–thaw resistance performance is still unclear. This paper explored the impact of replacing ordinary coarse aggregate with stepwise aggregate on the freeze–thaw resistance characteristics of concrete. Firstly, the compressive strength, splitting tensile strength, and their relationship were evaluated. Then, the freeze–thaw resistance properties of PCM aggregate concrete were investigated, including macroscopic changes, mass loss, relative dynamic elasticity modulus loss, and compressive strength loss. Subsequently, the pore changes before and after freeze–thaw cycles were tested through non-destructive testing and nuclear magnetic resonance (NMR) testing, and the evolution of pores under freeze–thaw cycles was explored. The results show that adding 100% PCM aggregate reduces the strength of concrete by 32%. However, due to the high porosity in the 100% PCM aggregate concrete, it would have an adverse impact on corrosion resistance. The corrosion resistance of concrete increases firstly and then decreases with the addition of PCM aggregate, which can be attributed to PCM aggregate having a limiting effect on pore development. Overall, a substitution rate of 60% is acceptable for compressive strength and corrosion resistance.

Soft magnetic composite of Ni3Fe/ZnFe2O4 type obtained by mechanical alloying/milling and spark plasma sintering

Soft magnetic composite powder of Ni3Fe/ZnFe2O4 type has been obtained by mechanical milling of nanocrystalline Permalloy (Ni3Fe) particles with ZnFe2O4 particles. The nanocrystalline Ni3Fe particles were synthesised by mechanical alloying of elemental Ni and Fe powder. During the milling time, some of the ZnFe2O4 particles are embedded on the surface, and others are encapsulated in the volume of the Permalloy particles, creating a „raisin-bread” like structure. Also, at the surface of the Ni3Fe particles, a quasi-continuous layer of zinc ferrite is formed. The as formed composite particles have a structure of pseudo core-shell type, with a “raisin-bread” core. Upon mechanical milling, the phases are preserved according to X-ray diffraction (XRD) investigations, although the system magnetisation presents a slight decrease. The densification of the composite powder has been carried out using spark plasma sintering (SPS) technique at a sintering temperature range of 500–700 °C without holding time. The density and the electrical resistivity of the samples increase upon increasing the sintering temperature. Higher magnetisation is obtained for the sample sintered at 700 °C and accompanied by a lower coercive field. Upon sintering, a reaction at interface, between the Ni-based alloy and zinc ferrite occurs, leading to the formation of a new phase. The testing of the toroidal-shaped magnetic composite in the alternative current regime shows that the best results are obtained on the sample sintered at 700 °C. The losses decomposition was performed and showed that the dynamic losses become predominant after 1 kHz. The samples have been investigated by X-ray diffraction, scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDX), vibrating sample magnetometer (VSM) for powder samples and by DC and AC magnetic investigation by hysteresisgraph method and electrical resistivity investigations for the sintered compacts.

Fluid Dynamic Loss Model With Wide Applicability for Aeroengine Transmission Gears

Abstract In high-speed gear systems for aeroengines, it is important to reduce the fluid dynamic loss, which accounts for the majority of power loss, to improve fuel efficiency. For reasonable loss-reduction and the standardization of low-power-loss designs, a fluid dynamic loss model with wide applicability is needed. However, there are few reports of loss models considering the gear shroud effect on the oil dynamic loss. This study developed a loss model based on fluid dynamic loss phenomena. Specifically, fluid dynamic loss models were developed for the “air side-flow loss,” “air pumping loss,” “oil-jet acceleration loss,” and “oil re-acceleration loss” in the gear meshing part, and “air vortex loss” and “oil churning loss” in the gear peripheral part, with consideration to the shrouding effect. Moreover, an experimental method and a numerical method for loss classification are proposed. The fluid dynamic loss models were validated by experiments or experimentally validated numerical simulations. To demonstrate the effectiveness of the loss model for low-power-loss design, the influence of the design parameters was investigated, and the typical parameters were optimized.

Applicability of Numerical Simulation to the Classification of Fluid Dynamic Loss in Aeroengine Transmission Gears

Abstract In high-speed gear systems for aeroengines, reducing the fluid dynamic loss, which accounts for the majority of power loss, can significantly improve fuel economy. However, few practical numerical examples are available regarding high-speed gas–liquid two-phase flows involving gear meshing and gear shrouds (gear enclosures, which are effective for loss reduction). Therefore, in this study, the porosity method for object boundaries including the gear meshing, the volume of fluid method, and the surface compression method for the gas–liquid interface was used as fast and numerically stable calculation methods. In addition, a gap was provided at the contact surface of the gear tooth surface to improve the calculation stability, and the oil properties were set considering the difference between the flow resistance in a two-phase flow and that in a single-phase flow (due to the separation of oil particles) to improve the calculation accuracy. To validate the numerical simulation method, a two-axis helical gearbox with a maximum peripheral speed of 100 m/s with specifications equivalent to aeroengine gears was used, and the air flow, oil flow, and fluid dynamic losses were validated. Once the practical accuracy was confirmed, the numerical simulation was used to understand the relationship between air and oil flows, torque, and the effect of the shroud. Consequently, the fluid dynamic loss could be classified phenomenologically.

Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty

Abstract. The Antarctic Ice Sheet represents the largest source of uncertainty in future sea level rise projections, with a contribution to sea level by 2100 ranging from −5 to 43 cm of sea level equivalent under high carbon emission scenarios estimated by the recent Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). ISMIP6 highlighted the different behaviors of the East and West Antarctic ice sheets, as well as the possible role of increased surface mass balance in offsetting the dynamic ice loss in response to changing oceanic conditions in ice shelf cavities. However, the detailed contribution of individual glaciers, as well as the partitioning of uncertainty associated with this ensemble, have not yet been investigated. Here, we analyze the ISMIP6 results for high carbon emission scenarios, focusing on key glaciers around the Antarctic Ice Sheet, and we quantify their projected dynamic mass loss, defined here as mass loss through increased ice discharge into the ocean in response to changing oceanic conditions. We highlight glaciers contributing the most to sea level rise, as well as their vulnerability to changes in oceanic conditions. We then investigate the different sources of uncertainty and their relative role in projections, for the entire continent and for key individual glaciers. We show that, in addition to Thwaites and Pine Island glaciers in West Antarctica, Totten and Moscow University glaciers in East Antarctica present comparable future dynamic mass loss and high sensitivity to ice shelf basal melt. The overall uncertainty in additional dynamic mass loss in response to changing oceanic conditions, compared to a scenario with constant oceanic conditions, is dominated by the choice of ice sheet model, accounting for 52 % of the total uncertainty of the Antarctic dynamic mass loss in 2100. Its relative role for the most dynamic glaciers varies between 14 % for MacAyeal and Whillans ice streams and 56 % for Pine Island Glacier at the end of the century. The uncertainty associated with the choice of climate model increases over time and reaches 13 % of the uncertainty by 2100 for the Antarctic Ice Sheet but varies between 4 % for Thwaites Glacier and 53 % for Whillans Ice Stream. The uncertainty associated with the ice–climate interaction, which captures different treatments of oceanic forcings such as the choice of melt parameterization, its calibration, and simulated ice shelf geometries, accounts for 22 % of the uncertainty at the ice sheet scale but reaches 36 % and 39 % for Institute Ice Stream and Thwaites Glacier, respectively, by 2100. Overall, this study helps inform future research by highlighting the sectors of the ice sheet most vulnerable to oceanic warming over the 21st century and by quantifying the main sources of uncertainty.

Adaptive tracking control of a unicycle subject to uncertainties and perturbations

An adaptive trajectory tracking control scheme is proposed for the command of a unicycle robot which is subject to dynamic uncertainties (inertia, mass, viscous parameter, etc), unmodeled dynamics, unknown disturbances, slipping effects, and loss of effectiveness (LOE). The latter are adaptively estimated and compensated in control laws. Moreover, projection algorithms are added to bound estimators, preventing them from drifting. The control objective is developed in two steps. First, a kinematic control law is designed to compute the necessary velocities (linear and angular velocities) that allow the robot to reach the desired trajectory. Afterward, sliding mode based adaptive control torques are developed in order to track velocities computed in the first step. The proposed control strategy guarantees the stability of the closed-loop system by ensuring the asymptotic convergence to both desired trajectory and necessary velocities. Finally, simulation results illustrate the applicability and effectiveness of the proposed control scheme.

A Review on the Effect of Electrical Steel Manufacturing Processes on the Performance of Electric Machines

The manufacturing processing of Electrical Steel Laminations (ESLs) for electric machines comprises cutting, stacking, and housing techniques which can result in plastic deformation and residual stress in the soft magnetic material. These manufacturing processes result in decreasing the magnetic quality and a local increase in both the static and dynamic hysteresis losses near the cut edges and consequently a reduction in the performance of the designed motor. The iron losses resulting from cutting can vary by a factor of two or more depending on geometrical, material, and processing parameters as well as the magnetic field strength. It is advantageous to consider these manufacturing effects in the design stage to reduce the probability of underperforming mass production. In this paper, the manufacturing processes of ESLs and their modelling methods are comprehensively surveyed. The gaps in scientific understanding and the research need for the expansion of accurate modelling of the cutting and joining of ESLs are subsequently discussed.

Mixed-type defect pattern recognition in noisy labeled wafer bin maps

In semiconductor manufacturing, classification of defect patterns in wafer bin maps (WBMs) helps engineers detect process failures and identify their causes. In recent studies on WBMs, convolutional neural networks (CNNs) have demonstrated effective classification performance based on their high expressive power. However, previous studies have implicitly assumed that the labels of WBMs used for training CNNs are correct, even though labels are often incorrect. When trained on mislabeled data, CNNs with standard cross-entropy loss can easily overfit mislabeled samples, leading to poor generalization for testing data. To overcome this issue, we propose a novel training algorithm called sample bootstrapping. Sample bootstrapping identifies which samples have clean or noisy labels by using a two-component beta mixture model, and measures the uncertainty of each identified label. Then, samples with low uncertainty of their estimated labels are selected to build mini-batches via weighted random sampling. Finally, CNNs are trained on the selected mini-batches with dynamic bootstrapping loss. In this manner, we can correct only the samples that are highly likely to have noisy labels and prevent the risk of false correction of correctly labeled samples. Experiments on simulated and real datasets demonstrate the effectiveness of the proposed method.

Dynamic Loss For Robust Learning.

Label noise and class imbalance are common challenges encountered in real-world datasets. Existing approaches for robust learning often focus on addressing either label noise or class imbalance individually, resulting in suboptimal performance when both biases are present. To bridge this gap, this work introduces a novel meta-learning-based dynamic loss that adapts the objective functions during the training process to effectively learn a classifier from long-tailed noisy data. Specifically, our dynamic loss consists of two components: a label corrector and a margin generator. The label corrector is responsible for correcting noisy labels, while the margin generator generates per-class classification margins by capturing the underlying data distribution and the learning state of the classifier. In addition, we employ a hierarchical sampling strategy that enriches a small amount of unbiased metadata with diverse and challenging samples. This enables the joint optimization of the two components in the dynamic loss through meta-learning, allowing the classifier to effectively adapt to clean and balanced test data. Extensive experiments conducted on multiple real-world and synthetic datasets with various types of data biases, including CIFAR-10/100, Animal-10N, ImageNet-LT, and Webvision, demonstrate that our method achieves state-of-the-art accuracy. The code for our approach will soon be made publicly available.

Novel EEG tools to predict early cognitive impairments in Parkinson’s disease

AbstractBackgroundParkinson’s disease (PD) is a neurodegenerative disorder mainly characterised by motor symptoms but often also associated with dementia, increasing in likelihood with duration of disease. Electroencephalography (EEG) can aid in the early diagnosis of those at risk of developing dementia. Therefore, we analysed EEG recordings across conscious states from PD patients with and without cognitive symptoms (Latreille et al in 2016, https://doi.org/10.1093/brain/aww018).MethodBaseline data (REM sleep, NREM sleep, WAKE) from 38 PD patients (15 with and 23 without mild cognitive impairment) and 27 healthy subjects provided power spectra (qEEG), relative band power, as well as dominant frequency and its standard deviation to investigate markers for cognitive impairment. At follow‐up (an average 4.5 years after baseline), 11 patients developed dementia (PDD). Connectivity measures for left and right occipital and central electrodes were obtained as described in Crouch et al, 2018, https://doi.org/10.1038/s41598‐018‐19707‐1.ResultqEGG analyses confirmed previous reports of enhanced theta power during REM, NREM and WAKE for patients who developed PDD. A reduction in alpha power during WAKE was observed for MCI and PDD groups when compared to controls. Alpha rhythms generally decrease with age, alongside enhanced theta power, and this outcome may contribute to the global cognitive status of patients. A reduced and less variable dominant frequency was evident for MCI and PDD patients during WAKE, indicative of reduced flexibility of the network. Finally, rPDC detected disease‐specific changes, such as enhanced cross‐hemispheric connectivity in PDD in the alpha band during NREM sleep that indicate a lack of dynamism in the network. Also noted was the diminished interhemispheric communication in delta power between both central and occipital channels in PDD during WAKE.ConclusionTogether, we here confirm that changes of theta and alpha power are optimal discriminatory biomarkers for PD with cognitive decline (with MCI and at risk of dementia). Also, spectral and rPDC analyses indicate that dementia in PD is preceded by a loss of dynamism in EEG activity during MCI stages, both at the electrode and network level.

Dynamic torque coordination control of dual-motor all-wheel drive axles to suppress the longitudinal jerk of the vehicle

The switching of multiple working modes between power sources or axles through an economic torque distribution strategy can cause longitudinal jerk problems in electric vehicles equipped with multiple power sources and multi-axle drive systems. To address this issue, an online real-time dynamic torque distribution strategy was proposed. This strategy can suppress the longitudinal jerks in real-time and minimize the total power loss of the vehicle. In this study, a vehicle dynamics model, which considers a nonlinear system, is developed to reflect the transient process of the torque dynamic response of a vehicle transmission system. The main factors influencing the longitudinal jerk of the vehicle were analyzed. To minimize the total power loss of the vehicle, a dynamic power loss model of the electric drive system is established, and the model accuracy is experimentally verified. Through optimal control theory, this study proposes an online real-time dynamic torque distribution strategy that achieves the lowest total power loss of a vehicle while suppressing the longitudinal jerk. Under WLTC conditions, the longitudinal jerk is reduced by 79.46 % during mode switching, resulting in improved driving comfort, and the effectiveness of this strategy is verified via the experimental bench of the dual-motor four-wheel-drive electric drive system.

715. Dynamic gain and loss of carbapenem-resistant Enterobacterales by VHA patients during 2016-2022: loss of CRE exceeds gain of CRE

Abstract Background Carbapenem-resistant Enterobacterales (CRE) are an important public health threat. The long-term prognosis and duration that patients harbor CRE is not well described. Methods We conducted a retrospective study of patients who had a culture that grew CRE within the VHA from 2016-2022. For these subjects, all microbiology data was extracted from the VHA database. CRE was defined using the VHA 2019 definition: Escherichia coli, Klebsiella, Enterobacter, or Citrobacter spp. & resistant to imipenem, meropenem, or doripenem. The first CRE isolate was considered the index CRE culture, and its collection date was set as time 0 (Figure 1). Cultures within 30 days of the index CRE culture were not included in the analysis. Cultures were assigned one to the following time periods: 6 months, 1 year, 2 years, 3 years, 4 years, and after 4 years. Time periods when culture(s) grew CRE were labelled ‘R’, time periods when cultures grew bacteria that were not CRE were labelled ‘S’, time periods when no cultures were collected were labelled ‘N’, time periods during and after patient’s death were labelled ‘D’.Figure 1:Identification of eligibility Results We identified 8,161,898 isolates during the study period, of which 681,179 had eligible Enterobacterales. There were 4,461 CRE isolates from 2,609 unique patients. The Sankey diagram (Figure 2) shows the dynamics after the index CRE culture. At 6 months after index CRE culture, 15% of subjects still had a CRE+ culture. At 1 year and 2 years, 8% subjects still had CRE. Importantly, during each time period, study subjects steadily changed from R (grew CRE during time period) to S (grew non-CRE during time period). The rate of change from R to S remained steady throughout all time periods (range: 24% - 36%) (Table 2). The rate of re-gaining CRE (ie. status change from S to R, N to R) was low (range: 0% to 6%). The 4-year survival after the index CRE culture was 45%.Figure 2:Sankey diagram shows dynamic CRE status changes over time R status (red nodes): CRE grew during the prior time period. N status (grey nodes): no microbiology cultures collected during prior time period. S status (blue nodes): non-CRE grew during the prior time period; D status (black nodes): patient died. Red ribbons: indicates conversion from N status to R status, or S status to R status; blue ribbons: indicates conversion from R status to S status, or N status to S status. Nodes are vertical bars at each time period (6 months, 1 year, 2 years, 3 years, 4 years, and >4 years after index CRE+ culture. Links are ribbons between nodes.Table 1:Risk for CRE+ culture after index CRE culture (time 0)Table 2:CRE+ culture gain & loss dynamics over time N: no bacterial cultures; S: cultures without CRE; R: cultures with CRE. Percentages are the number of subjects that changed status divided by total at prior state. For example N to R: 42 (4%) means 42 subjects changed from N to R status during the time period. 4% represented the 42 subjects that changed status were out of 1,011 that started the time period in N status (42/1011 = 4%). Conclusion After an index CRE culture, subjects steadily changed from R status (grew CRE during time period) to S status (grew non-CRE during time period) at the rate of 30% to 36% per year. After subjects converted to S status (grew non-CRE during time period), the rate of reversion to R status (grew CRE during time period) was 2% to 6% per year. Disclosures Andrew Chou, MD, MSc, Entasis Therapeutics: Stocks/Bonds Barbara Trautner, MD, PhD, Genentech: Grant/Research Support|Peptilogics: Grant/Research Support

COMPUTER OPTIMIZATION OF SWITCHING FREQUENCY OF TRANSISTOR PULSE-WIDTH CONVERTERS

The aim of the work is to develop algorithm for determining the optimal switching frequency of a transistor pulse-width converter (PWM) to minimize the total electrical losses in a DC drive. Electrical losses in the armature winding and in PWM transistors are divided into two components: static from the direct current component and dynamic. The latter include losses in the armature winding from the harmonic components of the current and losses in transistors from transient switching currents. Since the dynamic losses in transistors increase with increasing frequency, and in the armature winding from current harmonics they decrease, there is an optimal switching frequency value at which the total dynamic losses in the PWM drive will be minimal. This aim is achieved by solving the problem of determining the analytical dependence of dynamic electrical losses in the armature winding on the PWM switching frequency and computer simulation of a transistor DC drive. As a result of the research, an analytical expression was obtained for calculating the relative dynamic electrical losses in the armature winding with polyharmonic power supply. An algorithm is proposed for determining the optimal switching frequency of the PWM: 1) on computer models of the DC motor and PWM, the dependences of dynamic electrical losses on the switching frequency are determined; 2) build a graph of the dependence of the total dynamic electrical losses, on the which determine the point of minimum losses, which corresponds to the optimal value of the switching frequency. The novelty of the work lies in the fact that the theory of electrical losses in the armature windings with polyharmonic power has been further developed.

The deep soil water dynamics and its environmental controls after long-term revegetation

The increase in the extreme drought trend, reduction in soil erosion, and acceleration of deep soil desiccation are renewing interests in soil water dynamics and its environmental controls after long-term (>30 years) grass and shrub revegetation on the Chinese Loess Plateau (CLP). However, inconsistent findings have been reported on soil water dynamics in these typical land uses due to the multi-dependent environmental controls. The objectives of this study are to identify the spatial variability of soil water dynamics and its controls in a small watershed. For this purpose, spatiotemporal soil water storage in 1 m interval to a depth of 5 m was decomposed into a temporal mean (i.e., time-stable pattern) and temporal anomaly (Atn), which varies over time and is related to soil water dynamics. Atn was then decomposed into a space-invariant temporal anomaly (i.e., spatial mean of Atn) and a space-variant temporal anomaly (Rtn), which reflects the spatial variability of soil water dynamics. The Rtn was further decomposed using the empirical orthogonal function (EOF) to identify its major controls. Results showed that soil water dynamics (gain and loss) decreased from grassland to abandoned cropland followed by shrubland after long-term revegetation. The hot spots in deep soil water dynamics (2–5 m) were identified in layered soils with an overlying sand layer and underlying silt layer, and grassland with the coexistence of tree-shrub-grass and high deep soil organic carbon (SOC) content. 50–70% of the total variations of Rtn were explained by the first two spatial patterns (EOFs). Silt and SOC content controlled EOF1 of Rtn at 0–2 and 2–5 m, respectively. Plant canopy and soil profile properties (e.g., layered soils with an overlying sand layer and underlying silt layer) controlled EOF2 of Rtn at 0–1 and 2–5 m, respectively. The EOF analysis suggests that silt and SOC dominated soil water storage capacity, plant canopy regulated water flux, and variations of hydraulic properties in soil profiles may jointly drive spatial variability of soil water dynamics.

Stressing dynamic loss models

Stress testing, and in particular, reverse stress testing, is a prominent exercise in risk management practice. Reverse stress testing, in contrast to (forward) stress testing, aims to find an alternative but plausible model such that under that alternative model, specific adverse stresses (i.e. constraints) are satisfied. Here, we propose a reverse stress testing framework for dynamic models. Specifically, we consider a compound Poisson process over a finite time horizon and stresses composed of expected values of functions applied to the process at the terminal time. We then define the stressed model as the probability measure under which the process satisfies the constraints and which minimizes the Kullback-Leibler divergence to the reference compound Poisson model.We solve this optimization problem, prove existence and uniqueness of the stressed probability measure, and provide a characterization of the Radon-Nikodym derivative from the reference model to the stressed model. We find that under the stressed measure, the intensity and the severity distribution of the process depend on time and state, and hence the stressed model is not a compound Poisson process. We illustrate the dynamic stress testing by considering stresses on VaR and both VaR and CVaR jointly and provide illustrations of how the stochastic process is altered under these stresses. We generalize the framework to multivariate compound Poisson processes and stresses at times other than the terminal time. We illustrate the applicability of our framework by considering “what if” scenarios, where we answer the question: What is the severity of a stress on a portfolio component at an earlier time such that the aggregate portfolio exceeds a risk threshold at the terminal time? Furthermore, for general constraints, we propose an algorithm to simulate sample paths under the stressed measure, thus allowing to compare the effects of stresses on the dynamics of the process.

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.