Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical Vehicles

Machine learning (ML) applications to time series energy utilization forecasting problems are a challenging assignment due to a variety of factors. Chief among these is the non-homogeneity of the energy utilization datasets and the geographical dispersion of energy consumers. Furthermore, these ML models require vast amounts of training data and communications overhead in order to develop an effective model. In this paper, we propose a communication-efficient time series forecasting model combining the most recent advancements in transformer architectures implemented across a geographically dispersed series of EV charging stations and an efficient variant of federated learning (FL) to enable distributed training. The time series prediction performance and communication overhead cost of our FL are compared against their counterpart models and shown to have parity in performance while consuming significantly lower data rates during training. Additionally, the comparison is made across EV charging as well as other time series datasets to demonstrate the flexibility of our proposed model in generalized time series prediction beyond energy demand. The source code for this work is available at https://github.com/XuJiacong/LoGTST_PSGFComment: 7 pages, 6 figure

Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model

Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a trade-off between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference

A Local/Global Approach to Mesh Parameterization

We present a novel approach to parameterize a mesh with disk topology to the plane in a shape-preserving manner. Our key contribution is a local/global algorithm, which combines a local mapping of each 3D triangle to the plane, using transformations taken from a restricted set, with a global "stitch" operation of all triangles, involving a sparse linear system. The local transformations can be taken from a variety of families, e.g. similarities or rotations, generating different types of parameterizations. In the first case, the parameterization tries to force each 2D triangle to be an as-similar-as-possible version of its 3D counterpart. This is shown to yield results identical to those of the LSCM algorithm. In the second case, the parameterization tries to force each 2D triangle to be an as-rigid-as-possible version of its 3D counterpart. This approach preserves shape as much as possible. It is simple, effective, and fast, due to pre-factoring of the linear system involved in the global phase. Experimental results show that our approach provides almost isometric parameterizations and obtains more shape-preserving results than other state-of-the-art approaches. We present also a more general "hybrid" parameterization model which provides a continuous spectrum of possibilities, controlled by a single parameter. The two cases described above lie at the two ends of the spectrum. We generalize our local/global algorithm to compute these parameterizations. The local phase may also be accelerated by parallelizing the independent computations per triangle.Engineering and Applied Science

Does non-stationarity of extreme precipitation exist in the Poyang Lake Basin of China?

Study region Poyang Lake Basin, China. Study focus This study aimed to investigate whether there are non-stationary characteristics of extreme precipitation in the Poyang Lake Basin (PLB) of China, and the trends of non-stationary characteristics from 1959 to 2019. The spatio-temporal variations of extreme precipitation were analysed from three fundamental aspects: duration, frequency, and intensity, based on the prewhitening Mann-Kendall (PWMK) test. Non-stationary variations and the risk of extreme precipitation were investigated based on the generalized additive models for location, scale, and shape (GAMLSS). New hydrological insights for the region (1) the intensity and frequency of extreme precipitation increased significantly, whereas there was a significant decrease in extreme precipitation duration in the PLB. (2) The duration of extreme precipitation showed significant non-stationary characteristics in the western PLB. At the Nanchang site, 83.3 % of the extreme precipitation intensity indices showed non-stationary characteristics. The RX1day (maximum 1-day precipitation amount) and RX5day (maximum 5-day precipitation amount) increased significantly for different return periods under non-stationary conditions in the northwestern PLB. (3) The risk of extreme precipitation can be captured using the GAMLSS. The stationary method underestimated the extreme precipitation intensity (e.g., RX1day) compared to the GAMLSS for longer return periods in the PLB. More attention should be paid to the increase and fluctuation of the return period of extreme precipitation caused by the mean non-stationarity and variance non-stationarity

Magnetic γ-Fe2O3-Loaded Attapulgite Sorbent for Hg0 Removal in Coal-Fired Flue Gas

A magnetically recoverable composite mercury removal sorbent was produced by introducing magnetic γ-Fe2O3 into attapulgite (ATT) (xFe1ATT) via the co-precipitation method and used to remove Hg0 in the simulated coal-fired power plant flue gas. The as-prepared 0.5Fe1ATT sorbent was characterized by X-ray diffraction, Brunauer–Emmett–Teller, transmission electron microscopy, vibrating sample magnetometer, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy analyses. The results showed that the Hg0 removal performance of the composite of γ-Fe2O3 and ATT was significantly promoted in comparison to pure γ-Fe2O3 and ATT individually. A relatively high magnetization value and good Hg0 removal performance were obtained by the sample of 0.5Fe1ATT. O2 could enhance Hg0 removal activity via the Mars–Maessen mechanism. NO displayed a significant promotion effect on Hg0 removal as a result of the formation of active species, such as NO2 and NO+. SO2 inhibited the removal of Hg0 as a result of its competition adsorption against Hg0 for the active sites and the sulfation of the sorbent. However, the introduction of NO could obviously alleviate the adverse effect of SO2 on the Hg0 removal capability. H2O showed a prohibitive effect on Hg0 removal as a result of its competition with Hg0 for the active sites. The findings of this study are of fundamental importance to the development of efficient and economic magnetic mercury sorbents for Hg0 removal from coal-fired boiler flue gases

Performance analysis and optimization for workflow authorization

Many workflow management systems have been developed to enhance the performance of workflow executions. The authorization policies deployed in the system may restrict the task executions. The common authorization constraints include role constraints, Separation of Duty (SoD), Binding of Duty (BoD) and temporal constraints. This paper presents the methods to check the feasibility of these constraints, and also determines the time durations when the temporal constraints will not impose negative impact on performance. Further, this paper presents an optimal authorization method, which is optimal in the sense that it can minimize a workflow’s delay caused by the temporal constraints. The authorization analysis methods are also extended to analyze the stochastic workflows, in which the tasks’ execution times are not known exactly, but follow certain probability distributions. Simulation experiments have been conducted to verify the effectiveness of the proposed authorization methods. The experimental results show that comparing with the intuitive authorization method, the optimal authorization method can reduce the delay caused by the authorization constraints and consequently reduce the workflows’ response time