118 research outputs found
Design and Optimise Synchronous Reluctance Machines in Sensorless Control
This thesis researches the design and fabrication of axially laminated, anisotropic synchronous reluctance rotors with a high saliency ratio and low torque ripple for both three- and five-phase machines. One clear novelty of the research is the first method reported that allows skew to be incorporated in the axially laminated anisotropic (ALA) rotor. The designed rotor is then built with the help of the 3D printing technique which significantly reduces the complexity of the prototyping and fabrication process. The thesis then considers the control necessary including sensorless control schemes for the three- and five-phase synchronous reluctance motors with varying levels of skew. The performance and the effectiveness of the sensorless controllers are verified by experiments for all designed rotors under the three- and five-phase excitation.
The three- and five-phase system of the synchronous reluctance motor is first discussed with the stator voltage equations and equivalent circuits. The d-and q-axis inductances are evaluated using finite element analysis. Finite element analysis (FEA) is a common used method in simulating and solving the electrical engineering problems. The FEA shows that for both three- and five-phase motors, the saliency ratio can reach around 10. Further detailed optimization is performed based on the rotor barrier dimensions such as shape, arc length, rib and bridge length, width, and the number of barriers. The final designed rotor in this research is an ALA rotor with 4 poles and 9 layers of magnetic segments. The barrier used is the round-type. The experimental inductances are shown to match the FEA predictions.
The method of fabricating an ALA-type rotor with skew is a significant advance in this research. The FEA analysis for both three- and five-phase motor shows that torque ripple can be significantly reduced with the skew process: for instance, the 5.5° skewed rotor and the 9.5° skewed rotor are predicted to offer the best choice for the three- and five-phase stator designs from a parametric study of skew angles. Three skewed rotors are fabricated (5.5 o, 6 o and 9.5o). The experimental results of the torque ripple are compared. For the three-phase case, the rotors with skew shows a good reduction in torque ripple, the 6° skewed rotor performs better than the 5.5° skewed rotor. For the five-phase case, the 9.5° skewed rotor provides the best torque ripple reduction experimentally.
The sensorless control is achieved for both three- and five-phase systems. According to the speed demand, the high-frequency injection sensorless control is used when the speed is below 500rpm. To further reduce the transient error, two different sliding mode observer methods are used for three and five-phase systems when the speed demand is above 500rpm. For both three- and five-phase synchronous reluctance motors with non-skewed and skewed rotors, the sensorless control can be successfully implemented. The transient and steady-state errors are all controlled in an acceptable range. By suddenly adding full load at rated and zero speed, the effectiveness of the sensorless control is also verified
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection
Out-of-distribution (OOD) detection is the key to deploying models safely in
the open world. For OOD detection, collecting sufficient in-distribution (ID)
labeled data is usually more time-consuming and costly than unlabeled data.
When ID labeled data is limited, the previous OOD detection methods are no
longer superior due to their high dependence on the amount of ID labeled data.
Based on limited ID labeled data and sufficient unlabeled data, we define a new
setting called Weakly-Supervised Out-of-Distribution Detection (WSOOD). To
solve the new problem, we propose an effective method called Topological
Structure Learning (TSL). Firstly, TSL uses a contrastive learning method to
build the initial topological structure space for ID and OOD data. Secondly,
TSL mines effective topological connections in the initial topological space.
Finally, based on limited ID labeled data and mined topological connections,
TSL reconstructs the topological structure in a new topological space to
increase the separability of ID and OOD instances. Extensive studies on several
representative datasets show that TSL remarkably outperforms the
state-of-the-art, verifying the validity and robustness of our method in the
new setting of WSOOD
Role of Protein Charge Density on Hepatitis B Virus Capsid Formation
The role of electrostatic interactions in the viral capsid assembly process was studied by comparing the assembly process of a truncated hepatitis B virus capsid protein Cp149 with its mutant protein D2N/D4N, which has the same conformational structure but four fewer charges per dimer. The capsid protein self-assembly was investigated under a wide range of protein surface charge densities by changing the protein concentration, buffer pH, and solution ionic strength. Lowering the protein charge density favored the capsid formation. However, lowering charge beyond a certain point resulted in capsid aggregation and precipitation. Interestingly, both the wild-type and D2N/D4N mutant displayed identical assembly profiles when their charge densities matched each other. These results indicated that the charge density was optimized by nature to ensure an efficient and effective capsid proliferation under the physiological pH and ionic strength
Zoom Out and Observe: News Environment Perception for Fake News Detection
Fake news detection is crucial for preventing the dissemination of
misinformation on social media. To differentiate fake news from real ones,
existing methods observe the language patterns of the news post and "zoom in"
to verify its content with knowledge sources or check its readers' replies.
However, these methods neglect the information in the external news environment
where a fake news post is created and disseminated. The news environment
represents recent mainstream media opinion and public attention, which is an
important inspiration of fake news fabrication because fake news is often
designed to ride the wave of popular events and catch public attention with
unexpected novel content for greater exposure and spread. To capture the
environmental signals of news posts, we "zoom out" to observe the news
environment and propose the News Environment Perception Framework (NEP). For
each post, we construct its macro and micro news environment from recent
mainstream news. Then we design a popularity-oriented and a novelty-oriented
module to perceive useful signals and further assist final prediction.
Experiments on our newly built datasets show that the NEP can efficiently
improve the performance of basic fake news detectors.Comment: ACL 2022 Main Conference (Long Paper
Near-Optimal Distributed Band-Joins through Recursive Partitioning
We consider running-time optimization for band-joins in a distributed system,
e.g., the cloud. To balance load across worker machines, input has to be
partitioned, which causes duplication. We explore how to resolve this tension
between maximum load per worker and input duplication for band-joins between
two relations. Previous work suffered from high optimization cost or considered
partitionings that were too restricted (resulting in suboptimal join
performance). Our main insight is that recursive partitioning of the
join-attribute space with the appropriate split scoring measure can achieve
both low optimization cost and low join cost. It is the first approach that is
not only effective for one-dimensional band-joins but also for joins on
multiple attributes. Experiments indicate that our method is able to find
partitionings that are within 10% of the lower bound for both maximum load per
worker and input duplication for a broad range of settings, significantly
improving over previous work
VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
The ability to perceive how objects change over time is a crucial ingredient
in human intelligence. However, current benchmarks cannot faithfully reflect
the temporal understanding abilities of video-language models (VidLMs) due to
the existence of static visual shortcuts. To remedy this issue, we present
VITATECS, a diagnostic VIdeo-Text dAtaset for the evaluation of TEmporal
Concept underStanding. Specifically, we first introduce a fine-grained taxonomy
of temporal concepts in natural language in order to diagnose the capability of
VidLMs to comprehend different temporal aspects. Furthermore, to disentangle
the correlation between static and temporal information, we generate
counterfactual video descriptions that differ from the original one only in the
specified temporal aspect. We employ a semi-automatic data collection framework
using large language models and human-in-the-loop annotation to obtain
high-quality counterfactual descriptions efficiently. Evaluation of
representative video-language understanding models confirms their deficiency in
temporal understanding, revealing the need for greater emphasis on the temporal
elements in video-language research.Comment: 23 pages, 6 figures, 18 tables, data is available at
https://github.com/lscpku/VITATEC
Joint Intrinsic and Extrinsic LiDAR-Camera Calibration in Targetless Environments Using Plane-Constrained Bundle Adjustment
This paper introduces a novel targetless method for joint intrinsic and
extrinsic calibration of LiDAR-camera systems using plane-constrained bundle
adjustment (BA). Our method leverages LiDAR point cloud measurements from
planes in the scene, alongside visual points derived from those planes. The
core novelty of our method lies in the integration of visual BA with the
registration between visual points and LiDAR point cloud planes, which is
formulated as a unified optimization problem. This formulation achieves
concurrent intrinsic and extrinsic calibration, while also imparting depth
constraints to the visual points to enhance the accuracy of intrinsic
calibration. Experiments are conducted on both public data sequences and
self-collected dataset. The results showcase that our approach not only
surpasses other state-of-the-art (SOTA) methods but also maintains remarkable
calibration accuracy even within challenging environments. For the benefits of
the robotics community, we have open sourced our codes
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