123 research outputs found
A Multi-objective Particle Swarm Optimization Algorithm Based on Reverse Learning
In order to solve the contradiction between population diversity and convergence in particle swarm optimization algorithm, in this paper, a particle swarm optimization algorithm with reverse learning is proposed. On this basis, the values of learning factor and constraint factor parameters are modified, and the linear decreasing weight strategy was adopted. By modifying the learning factor and the constraint factor, the algorithm improves the particle optimization ability. It balances the global search and local search of the particle, and the convergence speed is improved by using the inertia weight. When it is detected that the algorithm falls into the local optimal region, the position information of these poor particles is used to guide some particles to reverse learning at a faster flight speed, and the particles are quickly pulled out of the local optimal region. The reverse learning process can not only improve the diversity of particle population, but also ensure the global detection ability of the algorithm. Experimental results show that, compared with the basic MOPSO algorithm, this algorithm has fast convergence speed and high solution accuracy in function optimization
Research and Implementation of Instrument System on a Light-Duty Electric Aircraft Simulator
Considering the demands of the instrument system on a light-duty electric aircraft simulator, a semi-physical simulation instrument system, which is based on virtual reality and microcontroller technologies, is designed and implemented. Meanwhile, some key technologies are discussed and a general development method is put forward in this paper. After being completed, this simulated instrument system is connected with the flight-computing system to test its performances. The results show that it has real effect, stable operation and real time response. In practice, the instrument system not only meets the demands of the light-duty electric aircraft simulator, but also can be seen as a certain reference to develop the instrument system of other aircraft simulators
A study on the diagnosis of compound faults in rolling bearings based on ITD-SVD
Considering the difficulty in the diagnosis of compound faults in rolling bearings, the paper combines Intrinsic Time-scale Decomposition (ITD) and Singular Value Decomposition (SVD) for extracting the characteristics of compound faults from rolling bearings. Rotational components obtained from ITD decomposition are denoised according to Singular Value Decomposition algorithm; signal is reconstructed by denoised rotational components; at last, characteristics of compound faults of rolling bearings are extracted by Hilbert spectrum envelope of reconstructed signal. In validation, the paper has made a comparative study on the proposed ITD-SVD method and conventional one based on ITD algorithm and PCA method, and the result shows that ITD-SVD method works better on noise control and thereby provides more precise extraction of characteristic frequency of compound faults from rolling bearings of aero-engine
CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification
In pathology image analysis, obtaining and maintaining high-quality annotated
samples is an extremely labor-intensive task. To overcome this challenge,
mixing-based methods have emerged as effective alternatives to traditional
preprocessing data augmentation techniques. Nonetheless, these methods fail to
fully consider the unique features of pathology images, such as local
specificity, global distribution, and inner/outer-sample instance
relationships. To better comprehend these characteristics and create valuable
pseudo samples, we propose the CellMix framework, which employs a novel
distribution-oriented in-place shuffle approach. By dividing images into
patches based on the granularity of pathology instances and shuffling them
within the same batch, the absolute relationships between instances can be
effectively preserved when generating new samples. Moreover, we develop a
curriculum learning-inspired, loss-driven strategy to handle perturbations and
distribution-related noise during training, enabling the model to adaptively
fit the augmented data. Our experiments in pathology image classification tasks
demonstrate state-of-the-art (SOTA) performance on 7 distinct datasets. This
innovative instance relationship-centered method has the potential to inform
general data augmentation approaches for pathology image classification. The
associated codes are available at https://github.com/sagizty/CellMix
CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
Pathological image analysis is a crucial field in computer-aided diagnosis,
where deep learning is widely applied. Transfer learning using pre-trained
models initialized on natural images has effectively improved the downstream
pathological performance. However, the lack of sophisticated domain-specific
pathological initialization hinders their potential. Self-supervised learning
(SSL) enables pre-training without sample-level labels, which has great
potential to overcome the challenge of expensive annotations. Thus, studies
focusing on pathological SSL pre-training call for a comprehensive and
standardized dataset, similar to the ImageNet in computer vision. This paper
presents the comprehensive pathological image analysis (CPIA) dataset, a
large-scale SSL pre-training dataset combining 103 open-source datasets with
extensive standardization. The CPIA dataset contains 21,427,877 standardized
images, covering over 48 organs/tissues and about 100 kinds of diseases, which
includes two main data types: whole slide images (WSIs) and characteristic
regions of interest (ROIs). A four-scale WSI standardization process is
proposed based on the uniform resolution in microns per pixel (MPP), while the
ROIs are divided into three scales artificially. This multi-scale dataset is
built with the diagnosis habits under the supervision of experienced senior
pathologists. The CPIA dataset facilitates a comprehensive pathological
understanding and enables pattern discovery explorations. Additionally, to
launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL
pre-training and downstream evaluation are specially conducted. The CPIA
dataset along with baselines is available at
https://github.com/zhanglab2021/CPIA_Dataset
Rapid and Unconditional Parametric Reset Protocol for Tunable Superconducting Qubits
Qubit initialization is a critical task in quantum computation and
communication. Extensive efforts have been made to achieve this with high
speed, efficiency and scalability. However, previous approaches have either
been measurement-based and required fast feedback, suffered from crosstalk or
required sophisticated calibration. Here, we report a fast and high-fidelity
reset scheme, avoiding the issues above without any additional chip
architecture. By modulating the flux through a transmon qubit, we realize a
swap between the qubit and its readout resonator that suppresses the excited
state population to 0.08% 0.08% within 34 ns (284 ns if photon depletion
of the resonator is required). Furthermore, our approach (i) can achieve
effective second excited state depletion, (ii) has negligible effects on
neighbouring qubits, and (iii) offers a way to entangle the qubit with an
itinerant single photon, useful in quantum communication applications.Comment: 38 pages, 15 figure
Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease
Coronary heart disease (CHD) is top risk factor for health in modern society, causing high mortality rate each year. However, there is no reliable way for early diagnosis and prevention of CHD so far. So study the mechanism of CHD and development of novel biomarkers is urgently needed. In this study, metabolomics and metagenomics technology are applied to discover new biomarkers from plasma and urine of 59 CHD patients and 43 healthy controls and trace their origin. We identify GlcNAc-6-P which has good diagnostic capability and can be used as potential biomarkers for CHD, together with mannitol and 15 plasma cholines. These identified metabolites show significant correlations with clinical biochemical indexes. Meanwhile, GlcNAc-6-P and mannitol are potential metabolites originated from intestinal microbiota. Association analysis on species and function levels between intestinal microbes and metabolites suggest a close correlation between Clostridium sp. HGF2 and GlcNAc-6-P, Clostridium sp. HGF2, Streptococcus sp. M143, Streptococcus sp. M334 and mannitol. These suggest the metabolic abnormality is significant and gut microbiota dysbiosis happens in CHD patients
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