المؤلفون: Moncho-Jordá, Arturo^1,2 (AUTHOR) moncho@ugr.es, Groh, Sebastien³ (AUTHOR), Dzubiella, Joachim^3,4 (AUTHOR)

المصدر: Journal of Chemical Physics. 1/14/2024, Vol. 160 Issue 2, p1-12. 12p.

مصطلحات موضوعية: *MACROMOLECULES, *LOCALIZATION (Mathematics), *LIQUIDS, *DENSITY functional theory, *MEAN field theory, *DEGREES of freedom

مستخلص: We explore theoretically the effects of external potentials on the spatial distribution of particle properties in a liquid of explicitly responsive macromolecules. In particular, we focus on the bistable particle size as a coarse-grained internal degree of freedom (DoF, or "property"), σ, that moves in a bimodal energy landscape, in order to model the response of a state-switching (big-to-small) macromolecular liquid to external stimuli. We employ a mean-field density functional theory (DFT) that provides the full inhomogeneous equilibrium distributions of a one-component model system of responsive colloids (RCs) interacting with a Gaussian pair potential. For systems confined between two parallel hard walls, we observe and rationalize a significant localization of the big particle state close to the walls, with pressures described by an exact RC wall theorem. Application of more complex external potentials, such as linear (gravitational), osmotic, and Hamaker potentials, promotes even stronger particle size segregation, in which macromolecules of different size are localized in different spatial regions. Importantly, we demonstrate how the degree of responsiveness of the particle size and its coupling to the external potential tune the position-dependent size distribution. The DFT predictions are corroborated by Brownian dynamics simulations. Our study highlights the fact that particle responsiveness can be used to localize liquid properties and therefore helps to control the property- and position-dependent function of macromolecules, e.g., in biomedical applications. [ABSTRACT FROM AUTHOR]

المؤلفون: Yan, Jing¹ (AUTHOR) yanjing@mail.xjtu.edu.cn, Wang, Yanxin¹ (AUTHOR), Zhou, Yang¹ (AUTHOR), Wang, Jianhua¹ (AUTHOR), Geng, Yingsan¹ (AUTHOR)

المصدر: IET Generation, Transmission & Distribution (Wiley-Blackwell). May2024, Vol. 18 Issue 9, p1785-1794. 10p.

مصطلحات موضوعية: DIGITAL twins, LOCALIZATION (Mathematics), PARTIAL discharges

مستخلص: Due to the requirement for highly precise synchronous sampling and the substantial reliance on time difference calculations, the current partial discharge (PD) localization based on the time difference of arrival is only applicable in certain situations. As digital twin technology has advanced, it is possible to employ virtual models to support gas‐insulated switchgear (GIS) PD localization. To do this, we propose a meta‐learning (ML) network with the aid of digital twin for actual GIS PD localization. Firstly, a GIS digital twin model was established to acquire an auxiliary simulated sample library. Then, a temporal convolutional network is established to extract the discriminable features, effectively obtain the time dependence between features, and improve the accuracy of localization. Next, ML is adopted to quickly learn meta‐knowledge that can be applied across tasks, and the model's sensitivity to task changes is improved. Finally, the model is fine‐tuned through a limited number of samples from the target task, and high precise PD localization is achieved. The experimental results demonstrate that the ML has an average localization error of only 9.25 cm and a probability density rose to 93% within 20 cm, which is clearly superior to previous methods. [ABSTRACT FROM AUTHOR]

: Copyright of IET Generation, Transmission & Distribution (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Yu, Ming¹ (AUTHOR), Liu, Jiali¹ (AUTHOR), Liu, Yi¹ (AUTHOR) liuyi@hebut.edu.cn, Yan, Gang¹ (AUTHOR)

المصدر: Journal of Intelligent & Fuzzy Systems. 2024, Vol. 46 Issue 2, p4543-4556. 14p.

مصطلحات موضوعية: LOCALIZATION (Mathematics), NOISE

مستخلص: Most existing RGB-D salient object detection (SOD) methods extract features of both modalities in parallel or adopt depth features as supplementary information for unidirectional interaction from depth modality to RGB modality in the encoder stage. These methods ignore the influence of low-quality depth maps, and there is still room for improvement in effectively fusing RGB features and depth features. To address the above problems, this paper proposes a Feature Interaction Network (FINet), which performs bi-directional interaction through feature interaction module (FIM) in the encoder stage. The feature interaction module is divided into two parts: depth enhancement module (DEM) filters the noise in the depth features through the attention mechanism; and cross enhancement module (CEM) effectively interacts RGB features and depth features. In addition, this paper proposes a two-stage cross-modal fusion strategy: high-level fusion adopts the semantic information of high level for coarse localization of salient regions, and low-level fusion makes full use of the detailed information of low level through boundary fusion, and then we progressively refine high-level and low-level cross-modal features to obtain the final saliency prediction map. Extensive experiments show that the proposed model achieves better performance than eight state-of-the-art models on five standard datasets. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Lin, Song¹ (AUTHOR), He, Zhiyong^1,2 (AUTHOR) hezhiyong@suda.edu.cn, Sun, Lining¹ (AUTHOR)

المصدر: Journal of Intelligent Manufacturing. Feb2024, Vol. 35 Issue 2, p703-726. 24p.

مصطلحات موضوعية: LOCALIZATION (Mathematics), CONVOLUTIONAL neural networks, CLASSIFICATION

مستخلص: A surface micro-defect is characterized by a small size and a susceptibility to noise. Micro-defect detection and classification is very challenging. This paper proposes a Micro-defect classification system based on attention enhancement (MDCS) for solving the detection and classification of micro-defects. We combine defect detection with defect classification in MDSC. Micro-defects classification can be better realized based on the auxiliary task of defect detection. In this system, the aim of attention formation in bionic vision is to guide the system to focus on the target by zooming in and out on micro-defects. To avoid noise interference, an attention module based on trilinear feature confluence has been incorporated. Last but not least, the enhancement process based on the attention map improves the classification ability of micro-defects. As part of comparative experiment, we analyzed data including 19,200 fabric images and 4,800 bamboo images. In the micro-defect classification experiment based on MDCS(ResNet-50), the accuracy of fabric data and bamboo data is 88.2% and 89.4% respectively. Compared with ResNet-50, the classification accuracy (64.8%, 67.7%) is improved by 23.4% and 21.7% respectively. In the object detection experiment of micro-defects based on MDCS (ResNet-50), the accuracy of fabric data and bamboo data is 65.1% mAPs and 63.3% mAPs respectively. Compared with HRDNet, the detection accuracy (59.6% mAPs, 52.2% mAPs) is improved by 5.5% mAPs and 11.1% mAPs respectively. Experimental results demonstrate that the proposed system can counteract the interference caused by noise in small object detection, localize micro-defects accurately, and improve micro-defect classification accuracy significantly. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Jia, Tianyi^1,2,3 (AUTHOR) jiatianyi@xidian.edu.cn, Gao, Chang^1,2 (AUTHOR), Shen, Xiaohong³ (AUTHOR), Liu, Hongwei¹ (AUTHOR)

المصدر: Telecommunication Systems. Mar2024, Vol. 85 Issue 3, p415-424. 10p.

مصطلحات موضوعية: *TIME management, DOPPLER effect, LOCALIZATION (Mathematics), MOTION detectors

مستخلص: This paper considers active localization of a stationary target using time delay or together with Doppler shift measurements obtained by moving sensors. Non-negligible sensor motion effect occurs in the localization system when the dynamic monostatic sensors send and receive signals during its motion. The proposed new accurate models for time delay and Doppler shift are able to make up the sensor motion effect. Closed-form solutions when ignoring the sensor motion of the proposed models are derived to give an approximate position estimate. The solutions are further refined by the proposed models and Taylor series linearization to remove the effect caused by sensor motion. The simulations show that the refined solutions can increase the localization performance efficiently and the accuracy can reach the Cramér-Rao lower bound (CRLB). [ABSTRACT FROM AUTHOR]

: Copyright of Telecommunication Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Zhou, Jie¹ (AUTHOR), Mi, Binbin¹ (AUTHOR) mibinbin1991@126.com, Xia, Jianghai¹ (AUTHOR), Zhang, Hao² (AUTHOR), Liu, Ya¹ (AUTHOR), Chen, Xinhua¹ (AUTHOR), Guan, Bo¹ (AUTHOR), Hong, Yu¹ (AUTHOR), Ma, Yulong¹ (AUTHOR)

المصدر: Geophysical Journal International. Jul2024, Vol. 238 Issue 1, p513-536. 24p.

مصطلحات موضوعية: *DEEP learning, *LOCALIZATION (Mathematics), *MICROSEISMS, *NOISE, *SPARSE matrices, *CARBON dioxide, *INFORMATION resources

مستخلص: Ambient noise source localization is of great significance for estimating seismic noise source distribution, understanding source mechanisms and imaging subsurface structures. The commonly used methods for source localization, such as the matched field processing and the full-waveform inversion, are time-consuming and not applicable for time-lapse monitoring of the noise source distribution. We propose an efficient alternative of using deep learning for noise source localization. In the neural network, the input data are noise cross-correlation functions and the output are matrices containing the information of noise source distribution. It is assumed that the subsurface structure is a horizontally layered earth model and the model parameters are known. A wavefield superposition method is used to efficiently simulate ambient noise data with quantities of local noise sources labelled as training data sets. We use a weighted binary cross-entropy loss function to address the prediction inaccuracy caused by a sparse label matrix during training. The proposed deep learning framework is validated by synthetic tests and two field data examples. The successful applications to locate an anthropogenic noise source and a carbon dioxide degassing area demonstrate the accuracy and efficiency of the proposed deep learning method for noise source localization, which has great potential for monitoring the changes of the noise source distribution in a survey area. [ABSTRACT FROM AUTHOR]

المؤلفون: Jiang, Xiaoxu¹ (AUTHOR) jxx_315@163.com, Yang, David K.² (AUTHOR) dayang02@uw.edu, Tian, Zhenyu¹ (AUTHOR) zero_tin@163.com, Liu, Gang³ (AUTHOR) liu_gang@tsinghua.edu.cn, Lu, Mingquan³ (AUTHOR)

المصدر: Sensors (14248220). Jun2024, Vol. 24 Issue 12, p3927. 19p.

مصطلحات موضوعية: *LIDAR, *LOCALIZATION (Mathematics), *SPACE-based radar, *ROBOTICS

مستخلص: Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments. [ABSTRACT FROM AUTHOR]

المؤلفون: Liu, Wei-jian¹ (AUTHOR), Xiao, Yang¹ (AUTHOR) yangxiao157999@163.com, Wang, Hao-nan¹ (AUTHOR), Hou, Meng-jie¹ (AUTHOR), Tian, Guang-hui¹ (AUTHOR), Dong, Sen-sen² (AUTHOR)

المصدر: Applied Geophysics: Bulletin of Chinese Geophysical Society. Jun2024, Vol. 21 Issue 2, p331-342. 12p.

مصطلحات موضوعية: *ACOUSTIC emission testing, *LOCALIZATION (Mathematics), *THEORY of wave motion, *ANISOTROPY

مستخلص: The impact of vibration wave propagation and various anisotropic media can cause divergence problems in the traditional linear positioning solution process in practical engineering applications. To address this problem, a mathematical algorithm combined with the microseismic positioning principles is developed to formulate a microseismic nonlinear optimal positioning method. The developed algorithm introduces a downhill factor to enhance its stability and convergence for achieving global convergence. Moreover, a multidirectional iterative concept is proposed to improve the precision and scientific validity of the positioning results. This approach effectively minimizes errors in each direction of the localization results. The stability of the algorithm and the accuracy of source results were verified by comparing a rectangular coal block acoustic emission source test with a traditional linear localization algorithm. Its application in engineering fields has also demonstrated its efficacy in reducing the influence associated with the deployment of a microseismic station network. [ABSTRACT FROM AUTHOR]

المؤلفون: Yue, Shengzhe¹ (AUTHOR) 3120215111@bit.edu.cn, Wang, Zhengjie¹ (AUTHOR) wangzhengjie@bit.edu.cn, Zhang, Xiaoning¹ (AUTHOR)

المصدر: Sensors (14248220). May2024, Vol. 24 Issue 10, p3063. 19p.

مصطلحات موضوعية: *MACHINE learning, *LOCALIZATION (Mathematics), *INPAINTING

مستخلص: To address the challenges of reduced localization accuracy and incomplete map construction demonstrated using classical semantic simultaneous localization and mapping (SLAM) algorithms in dynamic environments, this study introduces a dynamic scene SLAM technique that builds upon direct sparse odometry (DSO) and incorporates instance segmentation and video completion algorithms. While prioritizing the algorithm's real-time performance, we leverage the rapid matching capabilities of Direct Sparse Odometry (DSO) to link identical dynamic objects in consecutive frames. This association is achieved through merging semantic and geometric data, thereby enhancing the matching accuracy during image tracking through the inclusion of semantic probability. Furthermore, we incorporate a loop closure module based on video inpainting algorithms into our mapping thread. This allows our algorithm to rely on the completed static background for loop closure detection, further enhancing the localization accuracy of our algorithm. The efficacy of this approach is validated using the TUM and KITTI public datasets and the unmanned platform experiment. Experimental results show that, in various dynamic scenes, our method achieves an improvement exceeding 85% in terms of localization accuracy compared with the DSO system. [ABSTRACT FROM AUTHOR]

المؤلفون: Hu, Yonggang¹ (AUTHOR) yongganghu@outlook.com, Mao, Tianpeng¹ (AUTHOR), Wei, Hewen¹ (AUTHOR), Niu, Siliang¹ (AUTHOR), Wang, Wei¹ (AUTHOR), Zhu, Xuchen¹ (AUTHOR)

المصدر: Journal of the Acoustical Society of America. May2024, Vol. 155 Issue 5, p2919-2933. 15p.

مصطلحات موضوعية: *DIRECTION of arrival estimation, *LOCALIZATION (Mathematics), *SPHERICAL harmonics, *ACOUSTIC models, *AZIMUTH

مستخلص: Traditionally, direction-of-arrival (DOA) estimations under near- and far-field scenarios are treated as independent tasks based on the corresponding acoustic model, hence necessitating a proper soundfield detector as an upstream processing tool, whereas there may not be a distinct boundary between different soundfield types, especially the mixed-field scenarios where both near- and far-field sources coexist simultaneously. To handle this issue, this article investigates a multisource DOA estimator that equally localizes multiple near-, far-, and mixed-field sources, not requiring any specialized adjustments. We (i) define a signal-invariant multichannel feature denoted generalized relative harmonic coefficients in the spherical harmonics domain; (ii) derive the analytical expression of this feature and summarize its unique properties, exhibiting consistence for both near- and far-field sources; (iii) estimate source elevation and azimuth using the magnitude and phase parts of this feature, respectively; (iv) detect single-source dominated periods from the mixed measurements based on an investigated distance measure; and (v) count the number of sources and localize their DOAs by clustering the single-source dominated estimates. Extensive experimental results, in both simulated and real-life environments, finally confirm the effectiveness of the proposed algorithm under diverse acoustic scenarios, and a superiority over baseline approaches in localizing mixed-field sources. [ABSTRACT FROM AUTHOR]