Nondestructive testing of fiber array with multiple missing fibers

National audienceOur goal is to detect defects in composite materialscomposed by multilayer planar plates with a periodicset of circular cylindrical fibers embedded in each layer. As astarter, the work presented is electromagnetic (EM) modelingand imaging of missing fibers within a fiber array standingin air. The multiple scattering method is utilized to analyzethe electromagnetic behavior, and the corresponding imagingmodel is established directly from Lippman-Schwinger integralformulation. Standard MUltiple SIgnal Classification (MUSIC)and the proposed joint sparsity which borrows the idea ofsparse theory are applied to retrieve the locations of missingfibers. Numerical results are provided to confirm availabilityand accuracy of EM modeling and defect imaging

Investigations into Building Block Structure and Method of Preparation on the Properties of Nanomaterials

This dissertation research is primarily focused on the preparation of polymer-based nanostructures as potential diagnostic agents and therapeutics delivery vehicles. Various polymers, nanoparticles and conjugation techniques were developed to meet the specific requirements of each application. Shell crosslinked nanoparticles: SCKs) are characterized by their structural integrity and available functionality to attach multiple agents on the shell, such as receptor-recognizing or receptor-specific ligands, .imaging agents, Cell transduction components, etc. In this work, SCKs derived from amphiphilic poly(acrylic acid)-block-polystyrene: PAA-b-PS) have been studied as potential diagnostic and therapeutic agent delivery vehicles: Chapters 2 and 4). SCK nanoparticles bearing a cyclic KCRGDC peptide which specifically binds to avb3 integrin receptor were developed as potential delivery system for treatment of acute vascular injuries. Methods were developed to afford clean nanoparticles with significant binding abilities. Nanoscale contrast agents for magnetic resonance imaging were also developed based on SCKs derived from PAA-b-PS and a Gadolinium-DOTA complex to achieve high relaxivity contrast agents. Our results showed that SCKs may serve well as potential diagnostic and therapeutic agent delivery vehicles Meanwhile, these SCKs were also studied as the template for mineralization of silver nanoparticles, along with a nuleating peptide, AG-P35, as a co-template: Chapter 5). Various morphologies of silver nanoparticles were obtained and it\u27s found that the morphology was highly dependent on polymer and peptide concentrations and incubation time. Micelles from a novel hyperbranched fluoropolymer with small sizes able to pass the blood brain barrier were synthesized: Chapter 3). After conjugation with F3 peptide which targets to nucleolin in most tumor cells, and loaded with doxorubicin as the drug to kill the tumor cells, both in vitro and in vivo studies were performed. It was found that F3-peptide conjugated nanoparticle not only specifically bind to the tumor-associated angiogenic endothelial cells, doxorubicin carried by these nanoparticles also caused apoptotic effects on the targeted tumor cells

Rotor Speed and Stator Resistance Identification Scheme for Sensorless Induction Motor Drives

This paper proposes a rotor speed identification method for sensorless induction motor drives based on a model reference adaptive system (MRAS). In this scheme, the error between estimated stator current and real stator current is regarded as the system error to estimate the rotor speed. Adaptive full-order flux observers for estimating the rotor speed are developed using Lyapunov’s stability theory. The stator resistance identification algorithm is developed with rotor speed estimating method in a systematic manner. Because of the stator resistance varies with inner temperature of the motor, the influence of motor speed estimation due to stator resistance identification error is analyzed. The error compensation method for stator resistance estimation is also proposed. Simulation and experimental results show the good performance for the proposed scheme in speed and robustness for sensorless induction motor drives. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.186

Obliquity pacing of the western Pacific Intertropical Convergence Zone over the past 282,000 years

The Intertropical Convergence Zone (ITCZ) encompasses the heaviest rain belt on the Earth. Few direct long-term records, especially in the Pacific, limit our understanding of long-term natural variability for predicting future ITCZ migration. Here we present a tropical precipitation record from the Southern Hemisphere covering the past 282,000 years, inferred from a marine sedimentary sequence collected off the eastern coast of Papua New Guinea. Unlike the precession paradigm expressed in its East Asian counterpart, our record shows that the western Pacific ITCZ migration was influenced by combined precession and obliquity changes. The obliquity forcing could be primarily delivered by a cross-hemispherical thermal/pressure contrast, resulting from the asymmetric continental configuration between Asia and Australia in a coupled East Asian-Australian circulation system. Our finding suggests that the obliquity forcing may play a more important role in global hydroclimate cycles than previously thought

FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels

Federated learning with noisy labels (F-LNL) aims at seeking an optimal server model via collaborative distributed learning by aggregating multiple client models trained with local noisy or clean samples. On the basis of a federated learning framework, recent advances primarily adopt label noise filtering to separate clean samples from noisy ones on each client, thereby mitigating the negative impact of label noise. However, these prior methods do not learn noise filters by exploiting knowledge across all clients, leading to sub-optimal and inferior noise filtering performance and thus damaging training stability. In this paper, we present FedDiv to tackle the challenges of F-LNL. Specifically, we propose a global noise filter called Federated Noise Filter for effectively identifying samples with noisy labels on every client, thereby raising stability during local training sessions. Without sacrificing data privacy, this is achieved by modeling the global distribution of label noise across all clients. Then, in an effort to make the global model achieve higher performance, we introduce a Predictive Consistency based Sampler to identify more credible local data for local model training, thus preventing noise memorization and further boosting the training stability. Extensive experiments on CIFAR-10, CIFAR-100, and Clothing1M demonstrate that \texttt{FedDiv} achieves superior performance over state-of-the-art F-LNL methods under different label noise settings for both IID and non-IID data partitions. Source code is publicly available at https://github.com/lijichang/FLNL-FedDiv.Comment: To appear in AAAI-2024; correct formats to meet standards of the AAAI manuscrip

Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup

Mixup is a popular data-dependent augmentation technique for deep neural networks, which contains two sub-tasks, mixup generation, and classification. The community typically confines mixup to supervised learning (SL) and the objective of the generation sub-task is fixed to selected sample pair instead of considering the whole data manifold. To overcome such limitations, we systematically study the mixup generation objective and propose Scenario-Agnostic Mixup for both SL and Self-supervised Learning (SSL) scenarios, named SAMix. Specifically, we hypothesize and verify the objective function of mixup generation as optimizing local smoothness between two mixed classes subject to global discrimination from other classes. Therefore, we propose $\eta$-balanced mixup loss for complementary learning of the two sub-objectives. Meanwhile, we parameterize the generation sub-task as a learnable sub-network, Mixer, with mixing attention which avoids trivial solutions and improves transferable abilities. To eliminate the computational cost of online training, we introduce a pre-trained version, SAMix$^\mathcal{P}$, that achieves efficient performance in various tasks. Extensive experiments on SL and SSL benchmarks demonstrate that SAMix consistently outperforms leading methods.Comment: Preprint under review. 9 pages main body, 8 pages appendix, 4 pages referenc

Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences

To mitigate the computational complexity in the self-attention mechanism on long sequences, linear attention utilizes computation tricks to achieve linear complexity, while state space models (SSMs) popularize a favorable practice of using non-data-dependent memory pattern, i.e., emphasize the near and neglect the distant, to processing sequences. Recent studies have shown the priorities by combining them as one. However, the efficiency of linear attention remains only at the theoretical level in a causal setting, and SSMs require various designed constraints to operate effectively on specific data. Therefore, in order to unveil the true power of the hybrid design, the following two issues need to be addressed: (1) hardware-efficient implementation for linear attention and (2) stabilization of SSMs. To achieve this, we leverage the thought of tiling and hierarchy to propose CHELA (short-long Convolutions with Hardware-Efficient Linear Attention), which replaces SSMs with short-long convolutions and implements linear attention in a divide-and-conquer manner. This approach enjoys global abstraction and data-dependent selection from stable SSM and linear attention while maintaining real linear complexity. Our comprehensive experiments on the Long Range Arena benchmark and language modeling tasks demonstrate the effectiveness of the proposed method.Comment: ICML 2024 camera read