209 research outputs found
Guan yu shuang xing - ji duan zhi liang bi xi tong de yan jiu
No full textThree body systems are common in nature, and the stability criterion requires it to be in hi-erarchical configuration. In this configuration, the third body is a massive black hole and is orbited by a stellar-mass black hole binary on a much wider orbit, allowing us to treat the inner and outer as two separate elliptical orbits, characterised by the interaction terms, just like two coupled harmonic oscillators. The interaction terms are usually treated as perturbation. In this work, we used the ADM Hamiltonian formula and derived the interaction term up to 1PN, following the work by previous people. We showed that there are two additional terms that are previously ignored, and that these addition terms allow us to approximate the numerical results better. In addition, we also studied the complicated dynamical evolution of triple system inADM Hamiltonian with leading order (2.5PN) dissipative effect.三星是一種常見的天體系統。穩定的三星系統通常都是由一個緊密的雙星系統，加上一颗相對遥远的第三星組成。這樣的結構使得三星問題可以被看做兩個耦合的雙星系統，而耦合的項一般可以被看作是對於兩個穩定雙星系統的微擾。本篇論文中，我們考慮了1PN 修正的ADM 哈密頓量所帶來的微擾項。與前人的結果不同，我們的計算引入了兩項從未被發現過的微擾項。這兩個微擾項可以使微擾方法的結果更接近於數值計算的結果。此外，我們也研究了這樣的系統在引力輻射的作用下如何演化。Li, Jiale = 關於雙星-極端質量比系統的研究 / 李佳樂.M.Phil. Chinese University of Hong Kong 2020.Includes bibliographical references (leaves 94-98).Abstracts also in Chinese.Title from PDF title page (viewed on November 04, 2022).Li, Jiale = Guan yu shuang xing - ji duan zhi liang bi xi tong de yan jiu / Li Jiale
Mapping knowledge domains for mine heat hazard: a bibliometric analysis of research trends and future needs
No full textAs the shallow mineral resources are nearly depleted, the mining of deep resources has become an urgent problem to be studied. The increase in mine depth can lead to the increase of mine heat hazard, which is a critical concern for mining safety/occupational health and safety. However, there are limited review articles available regarding the prevention of mine heat hazard. To fill in this gap, a bibliometric analysis and knowledge mapping of the field of mine heat hazard prevention are presented in this paper. A total of 314 papers from the Web of Science (WOS) core collection database that published between January 1998 and July 2022 were analyzed using VOSviewer and CiteSpace. China, South Africa, Poland, USA, and Australia are the top five countries in this field. The important journals are Applied Thermal Engineering, Applied Energy, Energies, and International Journal of Mining Science and Technology. In addition, the research focal points and two research fronts were identified and discussed. The knowledge base of mine heat hazard research focuses on mine cooling technology, energy efficiency optimization of cooling systems, thermodynamic theory, and occupational health. There are two research fronts. One is to use the numerical simulation method to study various problems such as simulate the performance of refrigeration systems and thermal comfort in mines. The second is to study the occupational health impact of climate change on miners. Therefore, this paper provides readers and academics with an overview of the intellectual structure and knowledge body that have been developed on the subject of mine heat hazard.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Safety and Security Scienc
An analysis the positional accuracy of a multi-camera ball tracking system
No full textThis paper describes three kinds of pinhole camera models: the camera without lens distortion and calibration, the camera with lens distortion, and the distorted camera calibrated using an inverse lens distortion series of different orders. In this paper, most equations are in matrix form, which makes programming dramatically more comfortable. During the lens distortion process, we used second-order coefficients to simulate the lens distortion. Due to the quantization error when converting image coordinates to pixel coordinates, the actual distortion coefficients are estimated by a least-squares algorithm. The estimated distortion coefficients are applied in the inverse lens distortion series to calibrate the cameras. The position of the ball is triangulated by using a system of eight cameras. The triangulation algorithm will be introduced in Chapter 4. Finally, Chapter 5 compares the performance of the system with and without lens distortion and with various levels of camera calibration to correct for lens distortion.M.S.Includes bibliographical reference
Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework
No full textThis study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). The FS framework sequentially incorporated task-related component analysis (TRCA)-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG epoch, the FS framework first identified its potential SSVEP frequency using the TRCA-based method and then recognized its control state using one of the classifiers trained on the features specifically related to the identified frequency. A frequency-unified (FU) framework that conducted control state detection using a unified classifier trained on features related to all candidate frequencies was proposed to compare with the FS framework. Offline evaluation using data lengths within 1 s found that the FS framework achieved excellent performance and significantly outperformed the FU framework. 14-target FS and FU asynchronous systems were separately constructed by incorporating a simple dynamic stopping strategy and validated using a cue-guided selection task in an online experiment. Using averaged data length of 591.63±5.65 ms, the online FS system significantly outperformed the FU system and achieved an information transfer rate, true positive rate, false positive rate, and balanced accuracy of 124.95±12.35 bits/min, 93.16±4.4%, 5.21±5.85%, and 92.89±4.02%, respectively. The FS system was also of higher reliability by accepting more correctly identified SSVEP trials and rejecting more wrongly identified ones. These results suggest that the FS framework has great potential to enhance the control state detection for high-speed asynchronous SSVEP-BCIs
Mechanical and tribological property of single layer graphene oxide reinforced titanium matrix composite coating
No full textPrivacy-Preserving Federated Unlearning with Certified Client Removal
No full textIn recent years, Federated Unlearning (FU) has gained attention for
addressing the removal of a client's influence from the global model in
Federated Learning (FL) systems, thereby ensuring the ``right to be forgotten"
(RTBF). State-of-the-art methods for unlearning use historical data from FL
clients, such as gradients or locally trained models. However, studies have
revealed significant information leakage in this setting, with the possibility
of reconstructing a user's local data from their uploaded information.
Addressing this, we propose Starfish, a privacy-preserving federated unlearning
scheme using Two-Party Computation (2PC) techniques and shared historical
client data between two non-colluding servers. Starfish builds upon existing FU
methods to ensure privacy in unlearning processes. To enhance the efficiency of
privacy-preserving FU evaluations, we suggest 2PC-friendly alternatives for
certain FU algorithm operations. We also implement strategies to reduce costs
associated with 2PC operations and lessen cumulative approximation errors.
Moreover, we establish a theoretical bound for the difference between the
unlearned global model via Starfish and a global model retrained from scratch
for certified client removal. Our theoretical and experimental analyses
demonstrate that Starfish achieves effective unlearning with reasonable
efficiency, maintaining privacy and security in FL systems
Guaranteeing data privacy in federated unlearning with dynamic user participation
No full textFederated Unlearning (FU) is gaining prominence for its capability to eliminate influences of specific users' data from trained global Federated Learning (FL) models. A straightforward FU method involves removing the unlearned user-specified data and subsequently obtaining a new global FL model from scratch with all remaining user data, a process that unfortunately leads to considerable overhead. To enhance unlearning efficiency, a widely adopted strategy employs clustering, dividing FL users into clusters, with each cluster maintaining its own FL model. The final inference is then determined by aggregating the majority vote from the inferences of these sub-models. This method confines unlearning processes to individual clusters for removing the training data of a particular user, thereby enhancing unlearning efficiency by eliminating the need for participation from all remaining user data. However, current clustering-based FU schemes mainly concentrate on refining clustering to boost unlearning efficiency but without addressing the issue of the potential information leakage from FL users' gradients, a privacy concern that has been extensively studied. Typically, integrating secure aggregation (SecAgg) schemes within each cluster can facilitate a privacy-preserving FU. Nevertheless, crafting a clustering methodology that seamlessly incorporates SecAgg schemes is challenging, particularly in scenarios involving adversarial users and dynamic users. In this connection, we systematically explore the integration of SecAgg protocols within the most widely used federated unlearning scheme, which is based on clustering, to establish a privacy-preserving FU framework, aimed at ensuring privacy while effectively managing dynamic user participation. Comprehensive theoretical assessments and experimental results show that our proposed scheme achieves comparable unlearning effectiveness, alongside offering improved privacy protection and resilience in the face of varying user participation
TTMRN: A topological-geometric two-layer maritime route network modeling for ship intelligent navigation
No full textThe construction of maritime route networks holds significant importance for autonomous navigation of vessels. In this study, a two-layer maritime route network modeling method based on huge amounts of ship trajectory data was proposed. Firstly, we introduce a novel method for extracting nodes of the marine route network, which identifies feature points in ship trajectories through clustering. Secondly, we use a spatial computing method to transform ship trajectory data into a sequence of waypoint regions and establish a node connection matrix to realize the nodes' connection of the topological layer route network. And routes are extracted between waypoint regions to characterize the connection relationship of the geometric layer network. Finally, by connecting nodes of the topological layer with the support of the connection matrix and waypoint regions of the geometric layer with the route, the two-layer maritime route network that combines topological and geometric layers is constructed. The proposed method was applied to the waters of Vancouver, successfully constructing a topological-geometric two-layer maritime route network. Overall, the proposed method is beneficial for improving the safety and efficiency of autonomous navigation of ships, and has a positive impact on the development of smart shipping industry.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Safety and Security Scienc
Guaranteeing Data Privacy in Federated Unlearning with Dynamic User Participation
No full textFederated Unlearning (FU) is gaining prominence for its capability to eliminate influences of Federated Learning (FL) users\u27 data from trained global FL models. A straightforward FU method involves removing the unlearned users and subsequently retraining a new global FL model from scratch with all remaining users, a process that leads to considerable overhead. To enhance unlearning efficiency, a widely adopted strategy employs clustering, dividing FL users into clusters, with each cluster maintaining its own FL model. The final inference is then determined by aggregating the majority vote from the inferences of these sub-models. This method confines unlearning processes to individual clusters for removing a user, thereby enhancing unlearning efficiency by eliminating the need for participation from all remaining users. However, current clustering-based FU schemes mainly concentrate on refining clustering to boost unlearning efficiency but overlook the potential information leakage from FL users\u27 gradients, a privacy concern that has been extensively studied. Typically, integrating secure aggregation (SecAgg) schemes within each cluster can facilitate a privacy-preserving FU. Nevertheless, crafting a clustering methodology that seamlessly incorporates SecAgg schemes is challenging, particularly in scenarios involving adversarial users and dynamic users. In this connection, we systematically explore the integration of SecAgg protocols within the most widely used federated unlearning scheme, which is based on clustering, to establish a privacy-preserving FU framework, aimed at ensuring privacy while effectively managing dynamic user participation. Comprehensive theoretical assessments and experimental results show that our proposed scheme achieves comparable unlearning effectiveness, alongside offering improved privacy protection and resilience in the face of varying user participation.Accepted by IEEE Transactions on Dependable and Secure Computin
Cluster-and-Connect: An Algorithmic Approach to Generating Synthetic Electric Power Network Graphs
No full textabstract: Understanding the graphical structure of the electric power system is important
in assessing reliability, robustness, and the risk of failure of operations of this criti-
cal infrastructure network. Statistical graph models of complex networks yield much
insight into the underlying processes that are supported by the network. Such gen-
erative graph models are also capable of generating synthetic graphs representative
of the real network. This is particularly important since the smaller number of tradi-
tionally available test systems, such as the IEEE systems, have been largely deemed
to be insucient for supporting large-scale simulation studies and commercial-grade
algorithm development. Thus, there is a need for statistical generative models of
electric power network that capture both topological and electrical properties of the
network and are scalable.
Generating synthetic network graphs that capture key topological and electrical
characteristics of real-world electric power systems is important in aiding widespread
and accurate analysis of these systems. Classical statistical models of graphs, such as
small-world networks or Erd}os-Renyi graphs, are unable to generate synthetic graphs
that accurately represent the topology of real electric power networks { networks
characterized by highly dense local connectivity and clustering and sparse long-haul
links.
This thesis presents a parametrized model that captures the above-mentioned
unique topological properties of electric power networks. Specically, a new Cluster-
and-Connect model is introduced to generate synthetic graphs using these parameters.
Using a uniform set of metrics proposed in the literature, the accuracy of the proposed
model is evaluated by comparing the synthetic models generated for specic real
electric network graphs. In addition to topological properties, the electrical properties
are captured via line impedances that have been shown to be modeled reliably by well-studied heavy tailed distributions. The details of the research, results obtained and
conclusions drawn are presented in this document.Dissertation/ThesisMasters Thesis Electrical Engineering 201
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