Comparative analysis of partitioned stator flux reversal PM machine and magnetically geared machine operating in Stator-PM and Rotor-PM modes

In this paper, the partitioned stator flux reversal permanent magnet (PM) (PS-FRPM) machine and the conventional magnetically geared (MG) machine operating in both stator-PM (SPM) and rotor-PM (RPM) modes are comparatively analyzed in terms of electromagnetic performance to provide design guides for a MG machine regarding: (a) a SPM or RPM type machine and (b) a higher or lower gear ratio machine. It is found that a SPM type machine is recommended, since both PS-FRPM and MG machines operating in SPM modes have a higher phase back-EMF and hence torque than their RPM counterparts, respectively, as a result of a similar phase flux-linkage but a higher electric frequency since the iron piece number is larger than the PM pole-pair number. Moreover, a smaller gear ratio machine is preferred from the perspective of a higher power factor and hence a lower inverter power rating, as the conventional MG machines with higher gear ratios suffer from larger flux-leakage, higher synchronous reactance and hence lower power factors, as well as higher iron losses, than the PS-FRPM machines. However, higher gear ratio machines feature lower cogging torques and torque ripples due to the smaller difference between the PM pole-pair number and iron piece number. Both prototypes of PS-FRPM machine operating in SPM mode and MG machine operating in RPM mode are built and tested to verify the FE predicted results

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model

Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL entails exposing the model to various participants. This poses a risk of unauthorized model distribution or resale by the malicious client, compromising the intellectual property rights of the FL group. To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants. In this paper, we present FedTracker, the first FL model protection framework that provides both ownership verification and traceability. FedTracker adopts a bi-level protection scheme consisting of global watermark mechanism and local fingerprint mechanism. The former authenticates the ownership of the global model, while the latter identifies which client the model is derived from. FedTracker leverages Continual Learning (CL) principles to embedding the watermark in a way that preserves the utility of the FL model on both primitive task and watermark task. FedTracker also devises a novel metric to better discriminate different fingerprints. Experimental results show FedTracker is effective in ownership verification, traceability, and maintains good fidelity and robustness against various watermark removal attacks

Contextual AD Narration with Interleaved Multimodal Sequence

The Audio Description (AD) task aims to generate descriptions of visual elements for visually impaired individuals to help them access long-form video contents, like movie. With video feature, text, character bank and context information as inputs, the generated ADs are able to correspond to the characters by name and provide reasonable, contextual descriptions to help audience understand the storyline of movie. To achieve this goal, we propose to leverage pre-trained foundation models through a simple and unified framework to generate ADs with interleaved multimodal sequence as input, termed as Uni-AD. To enhance the alignment of features across various modalities with finer granularity, we introduce a simple and lightweight module that maps video features into the textual feature space. Moreover, we also propose a character-refinement module to provide more precise information by identifying the main characters who play more significant role in the video context. With these unique designs, we further incorporate contextual information and a contrastive loss into our architecture to generate more smooth and contextual ADs. Experiments on the MAD-eval dataset show that Uni-AD can achieve state-of-the-art performance on AD generation, which demonstrates the effectiveness of our approach. Code will be available at https://github.com/MCG-NJU/Uni-AD

Speed Range Extension of Dual-Stator PM Machine Using Multi-Mode Winding Switching Strategy

In this paper, a novel winding switching (WS) strategy is proposed for the speed range extension of a dual-stator permanent magnet machine (DS-PMM), which can achieve simple and effective dynamic mode conversion over an entire operating region. Two types of WS circuits with an inverter and two switch groups were first designed to enable the winding reconfiguration of the machine, which could operate in three modes. The WS principle was then elucidated by introducing simplified equivalent circuits. Besides, the torque–speed curves of the machine under different operating modes were analyzed, based on the mathematical model. A speed-based WS controller was, subsequently, designed to generate the WS control signal and realize the multi-mode operation according to real-time operating conditions. The feasibility of the proposed WS strategy for extending the speed range of the DS-PMM was, finally, verified by experiments

Clinical Characteristics of Patients with Different N-Terminal Probrain Natriuretic Peptide Levels after Hematopoietic Stem Cell Transplantation

Heart failure (HF) is not uncommon among patients with hematologic malignancies (HM) undergoing hematopoietic stem cell transplantation (HSCT) and is associated with an increased mortality. Among HSCT patients without signs or symptoms of HF, groups with elevated and normal N-terminal probrain natriuretic peptide (NT-proBNP) levels have been poorly characterized in previous literature. Herein, we reviewed consecutive admissions for HM undergoing HSCT (n=301). Based on NT-proBNP levels and clinical signs or symptoms of HF at follow-up (one month after HSCT), patients were grouped into ENPH (elevated NT‐proBNP>125 pg/mL, presence of HF symptoms or signs), ENAH (elevated NT‐proBNP>125 pg/mL, absence of HF symptoms or signs), and NN (normal NT‐proBNP<125 pg/mL). ENPH, ENAH, and NN were observed in 22.9%, 54.5%, and 22.6% of patients, respectively. ENPH patients had a significantly higher baseline NT-proBNP level, followed by the ENAH and NN groups, respectively (P<0.001). Frequencies of HLA partially matched related donors, stem cell source (bone marrow+peripheral blood), and utilization of graft-versus-host disease prophylaxis regimens (ciclosporin+methotrexate+antithymocyte globulin±mycophenolate mofetil) were also the highest in the ENPH group, followed by ENAH and NN groups, respectively (all P<0.05). Uric acid and hemoglobin levels, transplant type, and cyclophosphamide-based conditioning regimens utilized were similar between the ENAH and ENPH groups. We found that ENPH and ENAH are commonly observed in HM hospitalized for HSCT. Serum NT-proBNP levels may allow for earlier identification of HSCT patients at high risk of developing cardiac dysfunction

OAKINK2: A Dataset of Bimanual Hands-Object Manipulation in Complex Task Completion

We present OAKINK2, a dataset of bimanual object manipulation tasks for complex daily activities. In pursuit of constructing the complex tasks into a structured representation, OAKINK2 introduces three level of abstraction to organize the manipulation tasks: Affordance, Primitive Task, and Complex Task. OAKINK2 features on an object-centric perspective for decoding the complex tasks, treating them as a sequence of object affordance fulfillment. The first level, Affordance, outlines the functionalities that objects in the scene can afford, the second level, Primitive Task, describes the minimal interaction units that humans interact with the object to achieve its affordance, and the third level, Complex Task, illustrates how Primitive Tasks are composed and interdependent. OAKINK2 dataset provides multi-view image streams and precise pose annotations for the human body, hands and various interacting objects. This extensive collection supports applications such as interaction reconstruction and motion synthesis. Based on the 3-level abstraction of OAKINK2, we explore a task-oriented framework for Complex Task Completion (CTC). CTC aims to generate a sequence of bimanual manipulation to achieve task objectives. Within the CTC framework, we employ Large Language Models (LLMs) to decompose the complex task objectives into sequences of Primitive Tasks and have developed a Motion Fulfillment Model that generates bimanual hand motion for each Primitive Task. OAKINK2 datasets and models are available at https://oakink.net/v2.Comment: To be appeared in CVPR 2024. 26 page