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arXiv 2023年

作者： Yang, Yang Lang, Jin Wu, Jian Zhang, Yanyan Zhao, Xiang Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education 110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Northeastern University Shenyang110819 China Institute of Industrial & Systems Engineering Northeastern University Shenyang110819 China

The increasing installation rate of wind power poses great challenges to the global power system. In order to ensure the reliable operation of the power system, it is necessary to accurately forecast the wind speed and power of the wind turbines. At present, deep learning is progressively applied to the wind speed prediction. Nevertheless, the recent deep learning methods still reflect the embarrassment for practical applications due to model interpretability and hardware limitation. To this end, a novel deep knowledge-based learning method is proposed in this paper. The proposed method hybridizes pre-training method and auto-encoder structure to improve data representation and modeling of the deep knowledge-based learning framework. In order to form knowledge and corresponding absorbers, the original data is preprocessed by an optimization model based on correlation to construct multi-layer networks (knowledge) which are absorbed by sequence to sequence (Seq2Seq) models. Specifically, new cognition and memory units (CMU) are designed to reinforce traditional deep learning framework. Finally, the effectiveness of the proposed method is verified by three wind prediction cases from a wind farm in Liaoning, China. Experimental results show that the proposed method increases the stability and training efficiency compared to the traditional LSTM method and LSTM/GRU-based Seq2Seq method for applications of wind speed forecasting. Copyright © 2023, The Authors. All rights reserved.

关键词： Wind power

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Metavit-Trans: A Framework for Mixed-Type Defect Detection of Wafers with Vision Transformer Combined with Meta-Learning and Transfer Learning

Metavit-Trans: A Framework for Mixed-Type Defect Detection o...

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China Semiconductor Technology International Conference (CSTIC)

作者： Junfeng Zhao Lixin Tang National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Shenyang China

The defect detection of semiconductor wafer patterns is essential in the process of chip manufacturing. With the improvement of the IC process and design level, the types of wafer surface defects become more complex. In addition, the sample size of wafer map defect data is usually small, and the class is unbalanced, which poses higher challenges to the existing methods. To effectively identify mixed-type defects, a mixed-type defect detection framework with Vision Transformer (ViT) combined with meta-learning and transfer learning (MetaViT-Trans) is proposed. The results show that the MetaViT-Trans framework has a good defect feature extraction ability for imbalanced small-scale wafer map defect data, and has a good multi-scale information learning ability, which can effectively identify a variety of mixed-type defects.

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Lithography Hotspot Detection Based on Transfer Learning With High Resolution Networks

Lithography Hotspot Detection Based on Transfer Learning Wit...

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China Semiconductor Technology International Conference (CSTIC)

作者： Hongzhe Wang Lixin Tang National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China The Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang China

As integrated circuit (IC) technology continues to advance, lithography hotspot detection is of importance in physical verification flow and can affect the turn-around time and the yield of IC manufacturing. In this paper, a deep learning-based lithography hotspot detection method is proposed to overcome the problem of unbalanced positive and negative samples in detection. The proposed method uses the High-Resolution Network (HR-Net18) and pre-trained model from ImageNet to improve transferability. ICCAD 2012 benchmark suits is used for model train and test. The experimental results show that the proposed method performs well in terms of the values of AUC and precision.

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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 2 × 1016 – 5 × 1017 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. Our results indicate that although Insight-HXMT does not have an advantage in the first two scenarios, when considering the power-law model, its exceptional sensitivity in the hard X-ray regime and sufficiently high upper energy limit significantly strengthen the constraints on PBHs with masses greater than 1017 g compared to previous limits. Furthermore, the exclusion limit for PBHs as dark matter has reached 4 × 1017 g, which is comparable to the current threshold. Copyright © 2024, The Authors. All rights reserved.

关键词： Dark Matter

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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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Revisiting Holographic Dark Energy from the Perspective of Multi-Messenger Gravitational Wave Astronomy: Future Joint Observations with Short Gamma-Ray Bursts

arXiv

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arXiv 2024年

作者： Han, Tao Li, Ze Zhang, Jing-Fei 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

The advent of third-generation (3G) gravitational-wave (GW) detectors opens new opportunities for multi-messenger observations of binary neutron star merger events, holding significant potential for probing the history of cosmic expansion. In this paper, we investigate the holographic dark energy (HDE) model by using the future GW standard siren data observed from the 3G GW detectors and the short γ-ray burst THESEUS-like detector joint observations. We find that GW data alone can achieve a relatively precise estimation of the Hubble constant, with precision of 0.2–0.6%, but its ability to constrain other cosmological parameters remains limited. Nonetheless, since the GW data can break parameter degeneracies generated by the mainstream EM observations, CMB + BAO + SN (CBS), GW standard sirens play a crucial role in enhancing the accuracy of parameter estimation. With the addition of GW data to CBS, the constraints on cosmological parameters H0, c and Ωm can be improved by 63–88%, 27–44% and 55–70%. In summary, observations of GW standard sirens from 3G GW detectors could be pivotal in probing the fundamental nature of dark energy. Copyright © 2024, The Authors. All rights reserved.

关键词： Dark energy

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Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows

arXiv

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arXiv 2024年

作者： Xiong, Chun-Yu Sun, Tian-Yang Zhang, Jing-Fei 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

Gravitational wave (GW) detection is of paramount importance in fundamental physics and GW astronomy, yet it presents formidable challenges. One significant challenge is the removal of noise transient artifacts known as "glitches", which greatly impact the search and identification of GWs. Recent research has achieved remarkable results in data denoising, often using effective modeling methods to remove glitches. However, for glitches from uncertain or unknown sources, current methods cannot completely eliminate them from the GW signal. In this work, we leverage the inherent robustness of machine learning to obtain reliable posterior parameter distributions directly from GW data contaminated by glitches. Our network model provides reasonable and rapid parameter inference even in the presence of glitches, without needing to remove them. We also investigate various factors affecting the rationality of parameter inference in our normalizing flow network, including glitch and GW parameters. The results demonstrate that the normalizing flow can reasonably infer the source parameters of GWs even with unknown contamination. We find that the nature of the glitch itself is the only factor that can affect the rationality of the inferred results. With improvements to our model, we anticipate accelerating the localization of electromagnetic counterparts and providing priors for more accurate deglitching, thereby speeding up subsequent data processing procedures. Copyright © 2024, The Authors. All rights reserved.

关键词： data handling

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Finite-time H∞ Control for Switched Systems:An Event-triggered Bumpless Transfer Approach

Finite-time H∞ Control for Switched Systems:An Event-trigge...

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第35届中国控制与决策会议

作者： Jinghan Li Guangxu He Qiang Zeng Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of Education College of Information Science and Engineering Northeastern University

In this paper,the event-triggered finite-time H∞bumpless transfer control problem of switched systems is *** event-triggered bumpless transfer control method with local constraints is proposed to reduce the bump generated by switching event-triggered controller.A sufficient condition is given to ensure the solvability of the switched systems with eventtriggered finite-time H bumpless transfer *** positive lower bound of event-triggered integral is obtained,which implies that Zeno behavior is *** using the multiple Lyapunov functions method,a state-dependent switching law,an eventtriggered mechanism and a bumpless transfer controller are ***,a simulation example is given to verify the effectiveness of the proposed method.

关键词： switched systems bumpless transfer event-triggered finite-time H_∞control

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Output Bumpless Transfer Control for Switched Linear Systems Under the State-Dependent Switching

Output Bumpless Transfer Control for Switched Linear Systems...

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第42届中国控制会议

作者： Jinghan Li Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of Education College of Information Science and Engineering Northeastern University

In this paper,we discuss the output bumpless transfer control for a class of switched linear systems with statedependent ***,the concept of output bumpless transfer performance is given,which ensures the stability of the closed-loop system as well as the output bumpless transfer at the switching *** using multiple Lyapunov function method and imposing the proposed output bumps limitation,a state-dependent switching,and the controller are ***,an example is given to verify the effectiveness of the proposed method.

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Geometry-aided Underwater 3D Mapping Using Side-scan Sonar

Geometry-aided Underwater 3D Mapping Using Side-scan Sonar

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yiqiao Yang Chenglin Pang Chengdong Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, the interest in underwater exploration with Autonomous Underwater Vehicles (AUVs) equipped with side-scan sonars (SSS) has grown considerably. However, state-of-the-art SSS Simultaneous Localization and Mapping (SLAM) systems encounter challenges in data association across large viewpoint changes. Additionally, these systems assume that the seabed is a flat surface, leading to significant mapping error in uneven underwater terrains. To address these challenges, we propose a framework that leverages the side-scan sonar geometry to facilitate data association and improve mapping accuracy. The framework begins with a preprocessing module that extracts feature points and provides initial estimates of the elevation angles of the landmarks. Then, a non-consecutive data association module applies epipolar line search to establish correspondences between the current and historical frames. Finally, the mapping module uses side-scan sonar bundle adjustment to recover the positions of the landmarks. The proposed method is evaluated using an underwater terraced fields dataset. Our method achieves over 90% matching rate and reduces the average mapping error from 3.799 to 0.134.

关键词： Geometry Bundle adjustment Autonomous underwater vehicles Simultaneous localization and mapping Accuracy Geometric modeling Feature extraction Side-scan sonar optimization Intelligent robots

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