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Model-independent measurement of cosmic curvature with the latest H(z) and SNe Ia data: A comprehensive investigation

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Physical Review D 2023年第6期108卷 063522-063522页

作者： Jing-Zhao Qi Ping Meng Jing-Fei Zhang Xin Zhang Key Laboratory of Cosmology and Astrophysics (Liaoning Province) and Department of Physics College of Sciences Northeastern University Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University Shenyang 110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China

In the context of the discrepancies between the early and late universe, we emphasize the importance of independent measurements of the cosmic curvature in the late universe. We present an investigation of the model-independent measurement of the cosmic curvature parameter Ωk in the late universe with the latest Hubble parameter H(z) measurements and type Ia supernovae (SNe Ia) data. For that, we use two reconstruction methods, the Gaussian process (GP) and artificial neural network (ANN) methods, to achieve the distance construction from H(z) data. Our analysis reveals that the GP method provides the most precise constraint on Ωk, with a constraint precision of ξ(Ωk)=0.13, surpassing recent estimations using similar methods. The GP method consistently indicates a preference for a flat universe at the 2σ confidence level. Moreover, we find that the choice of reconstruction method influences the estimation of Ωk. The ANN reconstruction method exhibits higher sensitivity to the addition of BAO H(z) data, resulting in comparable constraint precision to the GP method. A discrepancy exists between the best-fit values obtained by these two reconstruction methods, indicating their dependence on the reconstruction approach. However, we anticipate that with the improvement of sample size and precision of observational H(z) data, the estimation of Ωk using this approach will become more robust and reliable.

关键词： Cosmological parameters

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A Discussion of the Mapping from the Unified Search Space to the Search Space of the Optimized Task in Evolutionary Multitasking optimization

A Discussion of the Mapping from the Unified Search Space to...

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Chinese Automation Congress (CAC)

作者： Shuangshuang Yao Zhiming Dong Xianpeng Wang Qingqing Liu Hangyu Lou Yumeng Zhao College of Information Science and Engineering Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Institute of Industrial & Systems Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665426480

Multitasking evolutionary algorithms enable the communication of information between different tasks and the mining of valuable knowledge by encoding all individuals into a unified search space. The mapping of decision variables of the same dimension for different tasks in the unified search space has the same dimensional index, i.e., the same encoded position. In this paper, we disrupt this mapping relationship so that decision variables of the same dimension for different tasks correspond to different encoded positions in the unified search space, to analyze and discuss the effect of this mapping relationship on the performance of the algorithm. A series of experimental results suggest that the mapping relationship between the decision variables of different tasks in a multitasking evolutionary algorithm has a significant impact on the performance of the algorithm.

关键词： Automation Evolutionary computation Multitasking Encoding Indexes Task analysis optimization

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Research and Application on Cross-source Point Cloud Registration Method Based on Unsupervised Learning

Research and Application on Cross-source Point Cloud Registr...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Xiao J. Sun Wen H. Li Jun W. Huang Dong Y. Chen Tong Jia College of Information Science and Engineering Northeastern University Shenyana China Foshan Graduate School of Innovation Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China

With the development and gradual richness of 3D sensors, cross-source point cloud registration has become one of the emerging research contents in the field of 3D reconstruction in recent years, which can be widely used in the 3D reconstruction of space scenes and the construction of unmanned maps. In view of the above problems, the main content and innovation of this paper are as follows: we propose a point cloud down-sampling algorithm based on adaptive voxel grid filtering and build a point cloud feature extraction model based on deep network. Different from the traditional point cloud registration by searching for correspondences, we propose to build a relationship model between the features of the point cloud and the transformation matrix, and iteratively solve the transformation matrix by improving the LK algorithm. Meanwhile, we propose unsupervised loss function based on bidirectional Euclidian distance. The experimental comparison proves that the 3D reconstruction effect of the cross-source point cloud registration method in this paper is better than that of the traditional point cloud registration.

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ABC: An Auction-Based Blockchain Consensus-Incentive Mechanism

ABC: An Auction-Based Blockchain Consensus-Incentive Mechani...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhengpeng Ai Yuan Liu Xingwei Wang Software College Northeastern University Shenyang China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry (NEU) Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

The rapid development of blockchain technology and its various applications have attracted huge attention in the last five years. The consensus mechanism and incentive mechanism are the backbone of a blockchain network. The consensus mechanism plays a crucial role in sustaining the network security, integrity, and efficiency. The incentive mechanism motivates the distributed nodes to “mine” so as to participate the consensus mechanism. The existing mechanisms bear the fairness and justice issues. In this paper, from the perspective of mechanism design, we propose a consensus-incentive mechanism through applying continuous double auction theory, which is abbreviated as ABC mechanism. Our mechanism consists of four stages, including initiation stage, auction stage, completion stage, and confirmation stage. The auction model in use is the continuous double auction to ensure the transactions are stored in a real-time manner. Through extensive experimental evaluations, our mechanism is proven to improve the fairness and justice of the blockchain network.

关键词： Conferences Blockchain Real-time systems Security Communication networks

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Single-Stage Grasping Algorithm for the Manipulator Based on Rotational Gaussian Encoding

Single-Stage Grasping Algorithm for the Manipulator Based on...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Zhenghao Liu Yizhe Liu Guowei Yang Dongyue Chen Tong Jia College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University China

As we all know, there is an unsolved problem for robots to accurately and quickly grasp unknown objects in an unstructured environment. In order to describe the pose information of objects more accurately, a rotational Gaussian encoding method is proposed in this paper. Different from the traditional 2-D Gaussian encoding to represent ground-truth, this method introduces rotation information based on 2-D Gaussian. Grasping is made more accurate and robust by treating the grasp pose as a rotating bounding box in the image plane. In addition, a balanced loss function is introduced in this paper. This loss function not only solves the problem of positive and negative sample imbalance in the traditional cross-entropy loss but also reduces the penalty of points around the Gaussian center location. In this paper, a pixel-level single-stage grasping algorithm is designed in an end-to-end manner without the intermediate process of using anchor points, which saves time and improves the accuracy of grasping simultaneously. Then, the proposed model in this paper is evaluated on two standard datasets, Cornell dataset and Jacquard dataset, achieving 96.8% and 94.7% accuracy, respectively. Finally, we use a 6 DoF UR5e robotic arm for real-world grasping experiments. The success rate of single-object grasping is 94.7% and the time to generate the grasp frame is 8 ms. Experiments demonstrat that the algorithm is equally effective in real-world environments and has a faster detection time.

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EFC-CenterNet: A Lightweight Network for Prohibited Object Detection

EFC-CenterNet: A Lightweight Network for Prohibited Object D...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Cheng Han Yinyimo Wu Tong Jia Dongyue Chen College of Information Science and Engineering Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China Foshan Graduate School of Innovation Northeastern University

Security inspection is an indispensable aspect of contemporary life and it plays a crucial role in ensuring personal safety at all times. In this regard, the accurate and prompt detection of prohibited objects is imperative. To further improve the detection accuracy of CenterNet on prohibited objects images while maintaining its high-speed detection capabilities, we make the following improvements. Firstly, to tackle the challenges related to complex and overlapping x-ray images, the Efficient Channel Attention module (ECA-Net) has been introduced to augment the feature extraction ability of the CenterNet for prohibited objects. Secondly, the Feature Pyramid Network (FPN) has been employed to amplify the feature acquisition capability of small prohibited objects. Finally, the Complete-IoU (CIoU) loss has been implemented to achieve faster convergence by minimizing the distance between the predicted bounding box and the corre-sponding ground truth while ensuring scale invariance of the two bounding boxes. The experimental results demonstrate that the EFC-CenterNet can effectively balance both accuracy and speed in real-time detection of contraband items, achieving an impressive 84.27% mAP and 52.48 FPS.

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Constraining primordial black hole abundance with Insight-HXMT

arXiv

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arXiv 2024年

作者： Yang, Chen Zhang, Xin Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University Shenyang110819 China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China

Primordial black holes (PBHs) are a major candidate for dark matter and they have been extensively constrained across most mass ranges. However, PBHs in the mass range of 1017 – 1021 g remain a viable explanation for all dark matter. In this work, we use observational data from the Hard X-ray Modulation Telescope (Insight-HXMT) to refine constraints on PBHs within the mass range of 1016 – 1018 g. Our analysis explores three scenarios: directly using observational data, incorporating the astrophysical background model (ABM), and employing the power-law spectrum with an exponential cutoff. Additionally, we also account for photon flux from positron annihilation due to Hawking radiation in our calculations. Our findings indicate that directly using observational data significantly tightens constraints on PBHs with masses above 1017 g, owing to Insight-HXMT’s exceptional sensitivity to hard X-rays. Incorporating the ABM further enhances the constraints by approximately 0.8 orders of magnitude. The exclusion limit for PBHs as dark matter extends to 8 × 1017 g, nearly doubling the current threshold. Moreover, extrapolating the energy limit with a power-law model improves constraints by approximately one order of magnitude compared to the ABM scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Dark Matter

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PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX

PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Mingyuan Li Bowen Ma Hao Wang Yingbo Li Dongyue Chen Tong Jia College of Information Science and Engineering Northeastern-University Shenyang China Foshan Graduate School of Innovation Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University China

X-ray prohibited items detection is an effective and crucial measure in various security inspection scenarios. However, the overlapping phenomenon in X-ray images exacerbates the foreground-background class imbalance, and the imaging principle of X-ray images results in missing texture features. To address these challenges, we propose an end-to-end X-ray Prohibited Items Detector (PID-YOLOX) based on YOLOX-Tiny, which offers fast detection speed and high accuracy. Specifically, we introduce the Generalized Label Assignment (GLA) scheme to tackle the foreground-background class imbalance problem, and the Multi-Cardinality Attention (MCA) mechanism to alleviate the issue of missing texture features. Our experimental results show that PID-YOLOX achieves 54.9% average precision (AP) on the PIXray dataset, surpassing YOLOX-Tiny by 2.2% AP. Furthermore, extensive experiments demonstrate that PID-YOLOX is superior to the state-of-the-art methods, indicating its potential applications in the prohibited items detection field.

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Prospects for cosmological research with the FAST array: 21-cm intensity mapping survey observation strategies

arXiv

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arXiv 2024年

作者： Pan, Jun-Da Wu, Peng-Ju Du, Guo-Hong Li, Yichao Zhang, Xin Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University Shenyang110819 China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China

Precise cosmological measurements are essential for understanding the evolution of the universe and the nature of dark energy. The Five-hundred-meter Aperture Spherical Telescope (FAST), the most sensitive single-dish radio telescope, has the potential to provide the precise cosmological measurements through neutral hydrogen 21 cm intensity mapping sky survey. This paper primarily explores the potential of technological upgrades for FAST in cosmology. The most crucial upgrade begins with equipping FAST with a wide-band receiver (0 0) = 0.09 and σ(wa) = 0.33. Copyright © 2024, The Authors. All rights reserved.

关键词： Dark energy

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Binocular active stereo matching based on multi-scale random forest

Binocular active stereo matching based on multi-scale random...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Jiatong Liu Mingqi Pu Ghutian Wang Dongyue Chen Tang Jia College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University. Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China

With the rapid development of 3D vision technology, the existing passive binocular cameras can no longer meet the practical needs of depth perception. Therefore, this paper proposes a binocular active stereo matching method based on multi-scale random forest. Firstly, a binocular active vision system consists of a near-infrared random speckle projector and a binocular camera is constructed, and the system was calibrated using Zhang's calibration method. Secondly, gamma image enhancement and image difference method are used for processing to reduce the impact of ambient light on the measurement results. Next, extract the points of interest and use the multi-scale random forest algorithm to match the window where the points of interest are located to generate a sparse structured light anchor disparity map globally. Finally, using the Census transforms as the matching cost, iterate continuously, refine the disparity and get a dense disparity map. The experimental results show that this system can achieve good depth perception accuracy and robustness under complex indoor lighting conditions.

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