Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

On the spectral radius of weighted trees with fixed diameter and weight set

AbstractThe spectrum of weighted graphs are often used to solve the problems in the design of networks and electronic circuits. We first give some perturbational results on the spectral radius of weighted graphs when some weights of edges are modified, then we derive the weighted tree with the largest spectral radius in the set of all weighted trees with fixed diameter and weight set. Furthermore, an open problem of spectral radius on weighted paths is solved [H.Z. Yang, G.Z. Hu, Y. Hong, Bounds of spectral radii of weighted tree, Tsinghua Science and Technology 8 (2003) 517–520]

Ultimate bearing capacity of circular shallow foundations in frozen clay

This paper presents a study on the ultimate bearing capacity of circular shallow foundation in frozen clay. The bearing capacity were determined by model test, numerical simulation and analytical solution. In numerical simulation, the temperature field considering the phase transition was transformed into a temperature load and applied to a three-dimensional solid model. The generalized Kelvin model was used to describe the creep of frozen clay, and step loading was used. Based on the tests results that frozen soil fails because of local shear, we proposed an analytical model to estimate the ultimate bearing capacity of circular shallow foundation with local shear failure mechanisms. Based on the limit equilibrium theory, it was assumed that the fracture plane of the model only develops to the boundary between the transition zone and the passive zone. The results from present study and some other method are presented and compared, which has shown and verified the feasibility of our method. And the analytical solution is in good consistent with the results of the model test and numerical simulation

Bis[(E)-4-bromo-2-(methoxy­imino­meth­yl)phenolato-κ2 N,O 1]copper(II)

In the title centrosymmetric mononuclear copper(II) complex, [Cu(C8H7BrNO2)2], the CuII atom, lying on an inversion centre, is four-coordinated in a trans-CuN2O2 square-planar geometry by two phenolate O atoms and two oxime N atoms from two symmetry-related N,O-bidentate oxime-type ligands. Inter­molecular C—H⋯O hydrogen bonds link neighbouring mol­ecules into a one-dimensional supra­molecular structure with an R 2 2(14) ring motif. This structure is further stabilized by π–π stacking inter­actions between adjacent benzene rings [centroid–centroid distance = 3.862 (1) Å]

RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model

Inspired by the recent success of large language models (LLMs) like ChatGPT, researchers start to explore the adoption of LLMs for agile hardware design, such as generating design RTL based on natural-language instructions. However, in existing works, their target designs are all relatively simple and in a small scale, and proposed by the authors themselves, making a fair comparison among different LLM solutions challenging. In addition, many prior works only focus on the design correctness, without evaluating the design qualities of generated design RTL. In this work, we propose an open-source benchmark named RTLLM, for generating design RTL with natural language instructions. To systematically evaluate the auto-generated design RTL, we summarized three progressive goals, named syntax goal, functionality goal, and design quality goal. This benchmark can automatically provide a quantitative evaluation of any given LLM-based solution. Furthermore, we propose an easy-to-use yet surprisingly effective prompt engineering technique named self-planning, which proves to significantly boost the performance of GPT-3.5 in our proposed benchmark