Real-Time Torque Ripple Minimization of Outer Rotor Switched Reluctance Motor

The Switched Reluctance Motor (SRM) is becoming more and more attractive because of its simple structure, robustness and cost-efficiency. It is a good candidate for variable speed applications such as Electric Vehicles (EVs), electric ships, aerospace, wind turbines, etc. However, the SRM inherently suffers from high torque ripple which is the main limitation preventing its use in high-performance applications. To reduce this torque ripple, the turn-on and turn-off angles of the motor phases can be adjusted. In this thesis, the SRM fundamentals are investigated along with the inductance model. For the linear case, the inductance is calculated using the analytical method. The non-linear model is then discussed as an improvement to this method. Control loops are designed based on the system block diagrams which are derived from the small signal model. The turn-on angle is calculated according to the operating conditions, and the turnoff angle is varied within a small range. At each combination of turn-on and turn-off angles, torque ripple, which is defined as the summation of the differences between each instantaneous torque and the average torque, is estimated and compared with other combinations. Based on these results, the best firing angle is selected to achieve the minimum possible torque ripple. The method is verified using simulations in Matlab/Simulink and physical experiments. The control algorithm is implemented on a microcontroller for the experiments and it is able to tune the firing angles in real time at different operating conditions. Spectrum analysis of the torque signal is used to prove the reduction of torque ripple

Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss

Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pre-train the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the-art, delivering the best testing accuracy (98.38%), precision (0.937), and F1-measure (0.924) on the normal scene testing set, together with the best overall accuracy (98.36%) and precision (0.844) in the challenging scene test set, while the training time can be substantially shortened.Comment: 12 pages, 8 figures, under review by journal of IEEE Transactions on Intelligent Transportation System

Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition

Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the overdetected false spans at the span detection stage and the inaccurate and unstable prototypes at the type classification stage remain to be challenging problems. In this paper, we propose a novel Type-Aware Decomposed framework, namely TadNER, to solve these problems. We first present a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names. We then present a type-aware contrastive learning strategy to construct more accurate and stable prototypes by jointly exploiting support samples and type names as references. Extensive experiments on various benchmarks prove that our proposed TadNER framework yields a new state-of-the-art performance. Our code and data will be available at https://github.com/NLPWM-WHU/TadNER.Comment: Accepted to the Findings of EMNLP 2023, camera ready versio

Asphalt Pavement Fatigue Cracking Modeling.

Fatigue is a major structural distress that leads to a reduction in the serviceability of asphalt pavements. In a mechanistic pavement design, asphalt fatigue cracking is considered as one of the three design criteria along with rutting and low-temperature associated cracks. The successful application of this design method to practice thus relies on a reliable crack prediction model. Most existing crack prediction models are based on the results of laboratory beam fatigue tests. Beam tests are not adequate because they can\u27t realistically simulate the propagation of a crack in an asphalt pavement layer. This research studied the asphalt cracking through fatigue tests conducted on asphalt slabs taken from experiment pavements, which can more closely reflect the three dimensional characteristic of a pavement crack than beam fatigue tests. To model the cracking process in asphalt slabs, the stress intensity factor was evaluated for cracked slabs based on three-dimensional FEM models. The fatigue crack, at the underside of a slab, was simulated as a semi-elliptical surface crack. The J-integral approach was used from which the stress intensity factor was calculated. In asphalt slab fatigue tests, the loading cycles for defined crack initiation and propagation stages were recorded for developing crack prediction models. Based on the results of stress and stress intensity factor analyses and asphalt slab fatigue tests, two crack initiation and. propagation relationships were developed for predicting asphalt fatigue life. The crack initiation relationship was based on the critical stress and strain and the crack propagation relationship used the stress intensity factor as an estimator. The fracture parameters, C and n, were also determined based on asphalt slab fatigue tests. The proposed fatigue life prediction relationships were used to estimate the fatigue fives of the pavements at LTRC-ALF experiment site. The existing equations were used to evaluate the stress intensity factor for a fatigue crack in asphalt pavement layer. The predicted results were compared with the observed pavement life. It was found that the predicted pavement life based on the proposed relationship was very close to the observed pavement life

Thrust distribution in Higgs decays up to the fifth logarithmic order

In this work, we extend the resummation for the thrust distribution in Higgs decays up to the fifth logarithmic order. We show that one needs the accurate values of the three-loop soft functions for reliable predictions in the back-to-back region. This is especially true in the gluon channel, where the soft function exhibits poor perturbative convergence.Comment: 31 pages, 6 figures, 3 table

Learning to Rank in Generative Retrieval

Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generation models and represents a new paradigm distinct from traditional learning-to-rank methods. However, despite its rapid development, current generative retrieval methods are still limited. They typically rely on a heuristic function to transform predicted identifiers into a passage rank list, which creates a gap between the learning objective of generative retrieval and the desired passage ranking target. Moreover, the inherent exposure bias problem of text generation also persists in generative retrieval. To address these issues, we propose a novel framework, called LTRGR, that combines generative retrieval with the classical learning-to-rank paradigm. Our approach involves training an autoregressive model using a passage rank loss, which directly optimizes the autoregressive model toward the optimal passage ranking. This framework only requires an additional training step to enhance current generative retrieval systems and does not add any burden to the inference stage. We conducted experiments on three public datasets, and our results demonstrate that LTRGR achieves state-of-the-art performance among generative retrieval methods, indicating its effectiveness and robustness

Analysis of the impact on the gravity field determination from the data with the ununiform noise distribution using block-diagonal least squares method

AbstractThe block-diagonal least squares method, which theoretically has specific requirements for the observation data and the spatial distribution of its precision, plays an important role in ultra-high degree gravity field determination. On the basis of block-diagonal least squares method, three data processing strategies are employed to determine the gravity field models using three kinds of simulated global grid data with different noise spatial distribution in this paper. The numerical results show that when we employed the weight matrix corresponding to the noise of the observation data, the model computed by the least squares using the full normal matrix has much higher precision than the one estimated only using the block part of the normal matrix. The model computed by the block-diagonal least squares method without the weight matrix has slightly lower precision than the model computed using the rigorous least squares with the weight matrix. The result offers valuable reference to the using of block-diagonal least squares method in ultra-high gravity model determination

Next-to-leading order corrections for gg→ZHgg \to ZH with top quark mass dependence

In this Letter, we present for the first time a calculation of the complete next-to-leading order corrections to the $gg \to ZH$ process. We use the method of small mass expansion to tackle the most challenging two-loop virtual amplitude, in which the top quark mass dependence is retained throughout the calculations. We show that our method provides reliable numeric results in all kinematic regions, and present phenomenological predictions for the total and differential cross sections at the Large Hadron Collider and its future upgrades. Our results are necessary ingredients towards reducing the theoretical uncertainties of the $pp \to ZH$ cross sections down to the percent-level, and provide important theoretical inputs for future precision experimental collider programs

Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture, Classification & Monitoring Scheme, and Site Selection Algorithm

With social progress and the development of modern medical technology, the amount of medical waste generated is increasing dramatically. The problem of medical waste recycling and treatment has gradually drawn concerns from the whole society. The sudden outbreak of the COVID-19 epidemic further brought new challenges. To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the description of architectural design and the first two modules, especially the module on site selection. Specifically, regarding the medical waste classification & monitoring module, three main entities, i.e., relevant government departments, hospitals, and logistics companies, are identified, which are involved in the five management functions of this module. Detailed data flow diagrams are provided to illustrate the information flow and the responsibilities of each entity. Regarding the site selection module, a multi-objective optimization model is developed, and considering different types of waste collection sites (i.e., prioritized large collection sites and common collection sites), a hierarchical solution method is developed employing linear programming and K-means clustering algorithms sequentially. The proposed site selection method is verified with a case study and compared with the baseline, it can immensely reduce the daily operational costs and working time. Limited by length, detailed descriptions of the whole system and the remaining route optimization module can be found at https://shorturl.at/cdY59.Comment: 8 pages, 6 figures, submitted to and under review by the IEEE Intelligent Vehicles Symposium (IV 2023