43 research outputs found
Design of the Tsinghua Tabletop Kibble Balance
The Kibble balance is a precision instrument for realizing the mass unit, the
kilogram, in the new international system of units (SI). In recent years, an
important trend for Kibble balance experiments is to go tabletop, in which the
instrument's size is notably reduced while retaining a measurement accuracy of
. In this paper, we report a new design of a tabletop Kibble balance
to be built at Tsinghua University. The Tsinghua Kibble balance aims to deliver
a compact instrument for robust mass calibrations from 10 g to 1 kg with a
targeted measurement accuracy of 50 g or less. Some major features of the
Tsinghua Kibble balance system, including the design of a new magnet, one-mode
measurement scheme, the spring-compensated magnet moving mechanism, and
magnetic shielding considerations, are discussed.Comment: 8 pages, 9 figure
Can Large Language Models Understand Real-World Complex Instructions?
Large language models (LLMs) can understand human instructions, showing their
potential for pragmatic applications beyond traditional NLP tasks. However,
they still struggle with complex instructions, which can be either complex task
descriptions that require multiple tasks and constraints, or complex input that
contains long context, noise, heterogeneous information and multi-turn format.
Due to these features, LLMs often ignore semantic constraints from task
descriptions, generate incorrect formats, violate length or sample count
constraints, and be unfaithful to the input text. Existing benchmarks are
insufficient to assess LLMs' ability to understand complex instructions, as
they are close-ended and simple. To bridge this gap, we propose CELLO, a
benchmark for evaluating LLMs' ability to follow complex instructions
systematically. We design eight features for complex instructions and construct
a comprehensive evaluation dataset from real-world scenarios. We also establish
four criteria and develop corresponding metrics, as current ones are
inadequate, biased or too strict and coarse-grained. We compare the performance
of representative Chinese-oriented and English-oriented models in following
complex instructions through extensive experiments. Resources of CELLO are
publicly available at https://github.com/Abbey4799/CELLO
A Pipe Ultrasonic Guided Wave Signal Generation Network Suitable for Data Enhancement in Deep Learning: US-WGAN
A network ultrasonic Wasserstein generative adversarial network (US-WGAN), which can generate ultrasonic guided wave signals, is proposed herein to solve the problem of insufficient datasets for pipe ultrasonic nondestructive testing based on deep neural networks. This network was trained with pre-enhanced and US-WGAN-enhanced datasets with 3000 epochs; the ultrasound signals generated by the US-WGAN were proved to be of high quality (peak signal-to-noise ratio scores in the range of 30–50 dB) and belong to the same population distribution as the original dataset. To verify the effectiveness of the US-WGAN, a fully connected neural network with seven layers was established, and the performances of the network after data enhancement using the US-WGAN and popular virtual defects were verified for the same network parameters and structures. The results show that adoption of the US-WGAN effectively suppresses the overfitting phenomenon while training the network and increases the dataset size, thereby improving the training and testing accuracies (>97%). Additionally, we noted that a simple, fully connected shallow neural network was sufficient for achieving high-accuracy defect classification using the US-WGAN data enhancement method
Supraharmonics Measurement Based on Colored Noise Suppressed Matrix Pencil Method
Supraharmonics emitted from power electronic devices can cause electromagnetic interference. However, there is currently no unified standard for measuring supraharmonics. This paper presents a supraharmonics measurement method with high-time resolution based on the matrix pencil method. A technique based on the principle of cross-correlation is used to process the measured signal. The original signal is replaced by the processing results, then it is solved by the matrix pencil method. This method can effectively reduce the influence of colored noise to the measurement. After comparing with the results obtained by compressive sensing measurement method, it is proved that the method proposed in this paper has distinct advantages in reducing the interference of colored noise and can effectively improve the accuracy of the measurement of supraharmonics
From μ0 to e: A Survey of Major Impacts for Electrical Measurements in Recent SI Revision
A milestone revision of the International System of Units (SI) was made at the 26th General Conference on Weights and Measures that four of the seven SI base units, i.e., kilogram, ampere, kelvin, and mole, are redefined by fundamental physical constants of nature. The SI base unit founding the electrical measurement activities, i.e., ampere, is defined by fixing the numerical value of the elementary charge to e=1.602176634×10−19C . For electrical measurement, several major adjustments, mostly positive, are involved in this SI revision. In this article, the main impacts of the new SI for electrical measurement activities are surveyed under the new framewor
A Pipe Ultrasonic Guided Wave Signal Generation Network Suitable for Data Enhancement in Deep Learning: US-WGAN
A network ultrasonic Wasserstein generative adversarial network (US-WGAN), which can generate ultrasonic guided wave signals, is proposed herein to solve the problem of insufficient datasets for pipe ultrasonic nondestructive testing based on deep neural networks. This network was trained with pre-enhanced and US-WGAN-enhanced datasets with 3000 epochs; the ultrasound signals generated by the US-WGAN were proved to be of high quality (peak signal-to-noise ratio scores in the range of 30–50 dB) and belong to the same population distribution as the original dataset. To verify the effectiveness of the US-WGAN, a fully connected neural network with seven layers was established, and the performances of the network after data enhancement using the US-WGAN and popular virtual defects were verified for the same network parameters and structures. The results show that adoption of the US-WGAN effectively suppresses the overfitting phenomenon while training the network and increases the dataset size, thereby improving the training and testing accuracies (>97%). Additionally, we noted that a simple, fully connected shallow neural network was sufficient for achieving high-accuracy defect classification using the US-WGAN data enhancement method