Numerical Study of Integrated Micro Ring Resonator and Micro Lens

In this work, we have discovered that a new approach to detect methane. It built a ring resonator structure with methane sensitive-material cryptophane-A. With the concentration of methane changed, the refractive index of cryptophane-A also changed, thus the resonance wavelength shifted. And it also built new two ring resonator for gaining the wavelength shift. That means the ultra-small size of the micro ring resonator structure will detect the concentration of methane by precision spectrometer. What's more, we have discovered that it is possible to utilize tapered nano structure to increase the transmission of phase shifters built with high refractive index materials. A typical grating micro lens is demonstrated to examine the effectiveness of taper-enhancement effect - the focus efficiency is increased from 9% to 28% with properly designed tapered sidewall. Our work will provide a novel method to enhance performance using high refractive index materials in the emerging micro lens field.Master of Science in EngineeringElectrical Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/143215/1/Thesis from Xiaopeng Guo(final vision).pdfDescription of Thesis from Xiaopeng Guo(final vision).pdf : Thesi

Dynamics of mass-spring-belt friction self-excited vibration system

In order to deeply study the non-smooth dynamic mechanism of self-excited vibration, the friction self-excited vibration system model containing the Stribeck friction model is established, which is a nonlinear dynamical mass-spring-belt model. For the established model, the critical instability speed is solved by the first approximate stability criterion of Lyapunov theory, and the stability of limit cycle is determined on the basis of curvature coefficient. Secondly, the bifurcation characteristics and system behaviors under different parameters are analyzed by using numerical simulation method. The results show that the theoretical analysis is feasible. Feed speed, damping coefficient and ratio of dynamic-static friction coefficient are the main factors that affect the system motion state. Thirdly, the Washout filter method is designed to control the bifurcation characteristics. By comparing the pre and post phase diagrams, results show that the amplitude of controlled system is reduced and the topology is improved after introducing the Washout filter. All the researches above prove that adding Washout filter into the system to control the bifurcation phenomenon is a more effective method

A study on the impact of pre-trained model on Just-In-Time defect prediction

Previous researchers conducting Just-In-Time (JIT) defect prediction tasks have primarily focused on the performance of individual pre-trained models, without exploring the relationship between different pre-trained models as backbones. In this study, we build six models: RoBERTaJIT, CodeBERTJIT, BARTJIT, PLBARTJIT, GPT2JIT, and CodeGPTJIT, each with a distinct pre-trained model as its backbone. We systematically explore the differences and connections between these models. Specifically, we investigate the performance of the models when using Commit code and Commit message as inputs, as well as the relationship between training efficiency and model distribution among these six models. Additionally, we conduct an ablation experiment to explore the sensitivity of each model to inputs. Furthermore, we investigate how the models perform in zero-shot and few-shot scenarios. Our findings indicate that each model based on different backbones shows improvements, and when the backbone's pre-training model is similar, the training resources that need to be consumed are much more closer. We also observe that Commit code plays a significant role in defect detection, and different pre-trained models demonstrate better defect detection ability with a balanced dataset under few-shot scenarios. These results provide new insights for optimizing JIT defect prediction tasks using pre-trained models and highlight the factors that require more attention when constructing such models. Additionally, CodeGPTJIT and GPT2JIT achieved better performance than DeepJIT and CC2Vec on the two datasets respectively under 2000 training samples. These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data

Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes

To alleviate the heavy annotation burden for training a reliable crowd counting model and thus make the model more practicable and accurate by being able to benefit from more data, this paper presents a new semi-supervised method based on the mean teacher framework. When there is a scarcity of labeled data available, the model is prone to overfit local patches. Within such contexts, the conventional approach of solely improving the accuracy of local patch predictions through unlabeled data proves inadequate. Consequently, we propose a more nuanced approach: fostering the model's intrinsic 'subitizing' capability. This ability allows the model to accurately estimate the count in regions by leveraging its understanding of the crowd scenes, mirroring the human cognitive process. To achieve this goal, we apply masking on unlabeled data, guiding the model to make predictions for these masked patches based on the holistic cues. Furthermore, to help with feature learning, herein we incorporate a fine-grained density classification task. Our method is general and applicable to most existing crowd counting methods as it doesn't have strict structural or loss constraints. In addition, we observe that the model trained with our framework exhibits a 'subitizing'-like behavior. It accurately predicts low-density regions with only a 'glance', while incorporating local details to predict high-density regions. Our method achieves the state-of-the-art performance, surpassing previous approaches by a large margin on challenging benchmarks such as ShanghaiTech A and UCF-QNRF. The code is available at: https://github.com/cha15yq/MRC-Crowd