Many-particle charged-particle plasma simulations using spatial meshes for the electromagnetic field solutions, particle-in-cell (PIC) merged with Monte Carlo collision (MCC) calculations, are coming into wide use for application to partially ionized gases. The author emphasizes the development of PIC computer experiments since the 1950s starting with one-dimensional (1-D) charged-sheet models, the addition of the mesh, and fast direct Poisson equation solvers for 2-D and 3-D. Details are provided for adding the collisions between the charged particles and neutral atoms. The result is many-particle simulations with many of the features met in low-temperature collision plasmas; for example, with applications to plasma-assisted materials processing, but also related to warmer plasmas at the edges of magnetized fusion plasmas. >

Particle-in-Cell Charged Particle Simulations, plus Monte Carlo Collisions with Neutral Atoms, PIC-MCC

Neighborhood rough set based heterogeneous feature subset selection

http://www.yuhuaqian.net/Cms_Data/Contents/SXU_YHQ/Folders/JournalPapers/~contents/KD22X6U6AUY584JT/Positive%20approximation-an%20accelerator%20for%20attribute%20reduction%20in%20rough%20set%20theory.pdf

Positive approximation: An accelerator for attribute reduction in rough set theory

A review of deep learning for renewable energy forecasting

The increasing global demand for energy and the potential environmental impact of increased energy consumption require greener, safer, and more cost-efficient energy storage technologies. Lithium-ion batteries (LIBs) have been successful in meeting much of today’s energy storage demand; however, lithium (Li) is a costly metal, is unevenly distributed around the world, and poses serious safety and environmental concerns. Alternate battery technologies should thus be developed. Zinc-ion batteries (ZIBs) have recently attracted attention due to their safety, environmental friendliness, and lower cost, compared to LIBs. They use aqueous electrolytes, which give them an advantage over multivalent ion batteries (e.g., Mg2+, Ca2+, Al3+) that require more complex electrolytes. However, as with every new technology, many fundamental and practical challenges must be overcome for ZIBs to become commercial products. In this manuscript, we present a timely review and offer perspectives on recent developments and future directions in ZIBs research. The review is divided into five parts: (i) cathode material development, including an understanding of their reaction mechanism; (ii) electrolyte development and characterization; (iii) zinc anode, current collector, and separator design; (iv) applications; and (v) outlook and perspective.

Zinc-ion batteries: Materials, mechanisms, and applications

Rough set theory has proven to be an efficient tool for modeling and reasoning with uncertainty information. By introducing probability into fuzzy approximation space, a theory about fuzzy probabilistic approximation spaces is proposed in this paper, which combines three types of uncertainty: probability, fuzziness, and roughness into a rough set model. We introduce Shannon's entropy to measure information quantity implied in a Pawlak's approximation space, and then present a novel representation of Shannon's entropy with a relation matrix. Based on the modified formulas, some generalizations of the entropy are proposed to calculate the information in a fuzzy approximation space and a fuzzy probabilistic approximation space, respectively. As a result, uniform representations of approximation spaces and their information measures are formed with this work

Fuzzy probabilistic approximation spaces and their information measures

Overview of wind power intermittency: Impacts, measurements, and mitigation solutions

Research progress on strut-equipped supersonic combustors for scramjet application

One-day-ahead probabilistic wind speed forecast based on optimized numerical weather prediction data

Relations and relation matrices are important concepts in set theory and intelligent computation. Some general uncertainty measures for fuzzy relations are proposed by generalizing Shannon's information entropy. Then, the proposed measures are used to calculate the diversity quantity of multiple classifier systems and the granularity of granulated problem spaces, respectively. As a diversity measure, it is shown that the fusion system whose classifiers are of little similarity produces a great uncertainty quantity, which means that much complementary information is achieved with a diverse multiple classifier system. In granular computing, a ''coarse-fine'' order is introduced for a family of problem spaces with the proposed granularity measures. The problem space that is finely granulated will get a great uncertainty quantity compared with the coarse problem space. Based on the observation, we employ the proposed measure to evaluate the significance of numerical attributes for classification. Each numerical attribute generates a fuzzy similarity relation over the sample space. We compute the condition entropy of a numerical attribute or a set of numerical attribute relative to the decision, where the greater the condition entropy is, the less important the attribute subset is. A forward greedy search algorithm for numerical feature selection is constructed with the proposed measure. Experimental results show that the proposed method presents an efficient and effective solution for numerical feature analysis.

Daren Yu

Papers

Fuzzy probabilistic approximation spaces and their information measures

Overview of wind power intermittency: Impacts, measurements, and mitigation solutions

Research progress on strut-equipped supersonic combustors for scramjet application

One-day-ahead probabilistic wind speed forecast based on optimized numerical weather prediction data

Short communication: Uncertainty measures for fuzzy relations and their applications