Test and Finite Element Analysis on Distortional Buckling of Cold-formed Thin-walled Steel Lipped Channel Columns

High-strength cold-formed thin-walled steel sections have been widely used in the recent several years. However, distortional buckli ng or interaction between it and local buckling can occur for high strength cold-formed thin- walled steel members. This paper desc ribes a series of compression tests performed on lipped channel section columns with V-shape intermediate stiffener in the web and flanges fabricated from cold-formed high strength steel of thickness 0.48 and 0.6mm with nominal yield stress 550MPa. The lipped channel sections were tested to failure with both ends of the columns fixed. The test results of 16 specimens show that the local buckling usually appears before distortional buckling of the specimens and it makes the distortional buckling occur in advance. This interaction of local and distortional buckling may have the effect of reducing the stiffness and bearing capacity of the columns. The comparison on ultimate strength and buckling mode between test results and results of finite element analysis considering geometric and material nonlinear show that finite element method (FEM) can simulate the distortional buckling of cold-formed steel channel columns effectively. The calculative results using Direct Strength Method (DSM) of the North American Specification show that this design method couldn’t consider the reverse effect of interaction between local and distortional buckling on ultimate strength. Direct Strength Method (DSM) considering interaction between local and distortional buckling should be developed

Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method

The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. Experimental results on real road networks demonstrate the significant advantages of our method over other counterparts

Noise-resilient approach for deep tomographic imaging

We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantages of our framework may further enable low-photon tomographic imaging.Comment: 2022 CLEO (the Conference on Lasers and Electro-Optics) conference submissio

Reply to comment on `Scrutinizing ππ\pi\pi scattering in light of recent lattice phase shifts'

We reply to the comment [arxiv:2202.08809] by E. van Beveren and G. Rupp on our recent work [arxiv:2202.03124], by further clarifying the reason why bound- and virtual-state poles for the $\sigma$ are necessary in our work in describing the lattice phase shifts at $m_\pi$=391MeV.Comment: 2 pages, 1 figure

Plane kinematic calibration method for industrial robot based on dynamic measurement of double ball bar

Abstract(#br)A new calibration method is proposed to improve the circular plane kinematic accuracy of industrial robot by using dynamic measurement of double ball bar (DBB). The kinematic model of robot is established by the MDH (Modified Denavit-Hartenberg) method. The error mapping relationship between the motion error of end-effector and the kinematic parameter error of each axis is calculated through the Jacobian iterative method. In order to identify the validity of the MDH parameter errors, distance errors and angle errors of each joint axis were simulated by three orders of magnitude respectively. After multiple iterations, the average value of kinematic error modulus of end-effector was reduced to nanometer range. Experiments were conducted on an industrial robot (EPSON C4 A901) in the working space of 180 mm × 490 mm. Due to the measuring radius of DBB, the working space was divided into 30 sub-planes to measure the roundness error before and after compensation. The average roundness error calibrated by the proposed method at multi-planes decreased about 21.4%, from 0.4637 mm to 0.3644 mm, while the standard deviation of roundness error was reduced from 0.0720 mm to 0.0656 mm. In addition, by comparing the results of positioning error measured by the laser interferometer before and after calibration, the range values of motion errors of end-effector were decreasing by 0.1033 mm and 0.0730 mm on the X and Y axes, respectively

Efficient Neural Neighborhood Search for Pickup and Delivery Problems

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. Our implementation for N2S is available online.Comment: Accepted at IJCAI 2022 (short oral

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202

Microwave-assisted rapid and regioselective synthesis of N-(alkoxycarbonylmethyl) nucleobases in water

A facile and eco-friendly approach has been developed for the preparation of N-(ethyoxycarbonylmethyl) nucleobases and N-(iso-propoxycarbonylmethyl) nucleobases, which are important building blocks for Peptide Nucleic Acids (PNA). All the nucleobases are regioselectively alkylated and the desired products are obtained in moderate to high yields under microwave irradiation for 8 min in water as the solvent and in the presence of Et3N as the base