Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem

Evolutionary computing (EC) is widely used in dealing with combinatorial optimization problems (COP). Traditional EC methods can only solve a single task in a single run, while real-life scenarios often need to solve multiple COPs simultaneously. In recent years, evolutionary multitasking optimization (EMTO) has become an emerging topic in the EC community. And many methods have been designed to deal with multiple COPs concurrently through exchanging knowledge. However, many-task optimization, cross-domain knowledge transfer, and negative transfer are still significant challenges in this field. A new evolutionary multitasking algorithm based on adaptive seed transfer (MTEA-AST) is developed for multitasking COPs in this work. First, a dimension unification strategy is proposed to unify the dimensions of different tasks. And then, an adaptive task selection strategy is designed to capture the similarity between the target task and other online optimization tasks. The calculated similarity is exploited to select suitable source tasks for the target one and determine the transfer strength. Next, a task transfer strategy is established to select seeds from source tasks and correct unsuitable knowledge in seeds to suppress negative transfer. Finally, the experimental results indicate that MTEA-AST can adaptively transfer knowledge in both same-domain and cross-domain many-task environments. And the proposed method shows competitive performance compared to other state-of-the-art EMTOs in experiments consisting of four COPs

Modeling of Uncontrolled Fluid Flow in Wellbore and its Prevention

Uncontrolled fluid flow in the wellbore is one of the most critical safety concerns for the oil and gas industry. The major focus of this dissertation is on blowout events given the most severe consequences associated with such incidents. The past tragedies reflect a strong need for not only understanding the mechanisms of blowout to accurately estimate the consequence, but also the approaches to managing and controlling the risks, uncertainties, and hazards associated with blowout events. A fully integrated analytical model that couples the reservoir and wellbore has been proposed to investigate the fluid behaviors during the blowout events. This model could be used to simulate any potential blowout events for gas, oil, or oil/gas wells at onshore or offshore facilities. The reservoir, wellbore, and their interactions are coupled together to demonstrate a full picture of the potential well blowout incidents. The results reveal that understanding the importance of heat transfer and multi-phase flow behaviors is essential to accurately estimate the consequence of well blowouts. Well-established computational algorithms are developed to effectively estimate the blowout rate and total discharge amount during blowout incidents. The statistical analysis identifies the independent variables responsible for the maximum discharge; both reservoir permeability and the connected reservoir volume are the key variables. The results of the blowout modeling could serve as the input for both consequence-based and risk-based approaches to assess the risk associated with the blowout events. The application of such approaches is demonstrated by the case study. The consequence-based approach is easier to be implemented and provide guidance to the operators based on the realistic worst-case scenario. It would be useful for drilling site location selection and preparation of emergency response plan. On the other hand, the risk-based approach enables the operators to have a comprehensive understanding of the particular well that they are working on, so that the risk associated with the blowout events can be effectively managed and controlled. The risk reduction plan based on the blowout risk assessment is also discussed in this dissertation

Dopamine Surface Modification of Trititanate Nanotubes: Proposed In‐Situ Structure Models

Two models for self‐assembled dopamine on the surface of trititanate nanotubes are proposed: individual monomer units linked by π–π stacking of the aromatic regions and mono‐attached units interacting through hydrogen bonds. This was investigated with solid state NMR spectroscopy studies and powder X‐ray diffraction.Double bind: Two models for self‐assembled dopamine on the surface of trititanate nanotubes are proposed: individual trimer units linked by π–π stacking of the aromatic regions and mono‐attached units interacting through hydrogen bonds. This was investigated by solid state NMR spectroscopy studies and powder X‐ray diffraction.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/1/chem201600075.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/2/chem201600075_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/3/chem201600075-sup-0001-misc_information.pd

Safety-driven Interactive Planning for Neural Network-based Lane Changing

Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure the safety of systems with neural network based components, especially in dense and highly interactive traffic environments. In this work, we propose a safety-driven interactive planning framework for neural network-based lane changing. To prevent over conservative planning, we identify the driving behavior of surrounding vehicles and assess their aggressiveness, and then adapt the planned trajectory for the ego vehicle accordingly in an interactive manner. The ego vehicle can proceed to change lanes if a safe evasion trajectory exists even in the predicted worst case; otherwise, it can stay around the current lateral position or return back to the original lane. We quantitatively demonstrate the effectiveness of our planner design and its advantage over baseline methods through extensive simulations with diverse and comprehensive experimental settings, as well as in real-world scenarios collected by an autonomous vehicle company

Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling

Trajectory generation and trajectory prediction are two critical tasks for autonomous vehicles, which generate various trajectories during development and predict the trajectories of surrounding vehicles during operation, respectively. However, despite significant advances in improving their performance, it remains a challenging problem to ensure that the generated/predicted trajectories are realistic, explainable, and physically feasible. Existing model-based methods provide explainable results, but are constrained by predefined model structures, limiting their capabilities to address complex scenarios. Conversely, existing deep learning-based methods have shown great promise in learning various traffic scenarios and improving overall performance, but they often act as opaque black boxes and lack explainability. In this work, we integrate kinematic knowledge with neural stochastic differential equations (SDE) and develop a variational autoencoder based on a novel latent kinematics-aware SDE (LK-SDE) to generate vehicle motions. Our approach combines the advantages of both model-based and deep learning-based techniques. Experimental results demonstrate that our method significantly outperforms baseline approaches in producing realistic, physically-feasible, and precisely-controllable vehicle trajectories, benefiting both generation and prediction tasks.Comment: 7 pages, conference paper in motion generatio

TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

Trajectory generation and prediction are two interwoven tasks that play important roles in planner evaluation and decision making for intelligent vehicles. Most existing methods focus on one of the two and are optimized to directly output the final generated/predicted trajectories, which only contain limited information for critical scenario augmentation and safe planning. In this work, we propose a novel behavior-aware Trajectory Autoencoder (TAE) that explicitly models drivers' behavior such as aggressiveness and intention in the latent space, using semi-supervised adversarial autoencoder and domain knowledge in transportation. Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making. Experimental results demonstrate that our method achieves promising performance on both trajectory generation and prediction.Comment: an updated version, change figures and references. 8 pages, robotics conference, about trajectory augmentation and prediction for intelligent vehicle system

Cylindrical roller bearing fault diagnosis based on VMD-SVD and Adaboost classifier method

Fault diagnosis for cylindrical roller bearing is of great significance for industry. In order to excavate the features of the vibration signal adequately, and to construct an effective classifier for complex vibration signals, this paper proposed a new fault diagnosis method based on Variational Mode Decomposition (VMD), Singular Value Decomposition (SVD) and Adaboost classifier. Firstly, the VMD was applied to decompose the sampled vibration signal in time-frequency domain. Subsequently, the features were extracted by using SVD. Finally, the constructed Adaboost classifier were employed to fault detection and diagnosis, which were trained by using the extracted features. Experimental data measured in a rotating machinery fault diagnosis experiment platform was used to verify the proposed method. The results demonstrate that the proposed method was effective to detect and diagnose the outer ring fault and rolling element fault in cylindrical roller bearing

Tree-ring-based precipitation reconstruction in the source region of Weihe River, northwest China since AD 1810

A tree-ring width chronology of Picea purpurea Mast from Mt. Shouyang in the source region of Weihe River (SWR), northwest China, was developed in this study. Correlation analysis showed that the precipitation from previous August to current July was the limiting climate factor of tree growth. Using a reliable and stable linear regression model, which explained 42.6% of the variance of the actual precipitation during the calibration period from 1958 to 2014, a 205-year long precipitation series was reconstructed for the SWR. The dry years in the reconstruction were well supported by historical documents, and famous historical droughts were also recorded in the dry periods of a low-frequency scale of the reconstructed precipitation. As demonstrated by the spatial correlation patterns, the reconstructed series compared well with other hydroclimate records for northwest China, indicating that it could represent large-scale hydroclimate changes. The 2-8-year interannual cycles and the interdecadal quasiperiods of 15.9 years and 18.6 years revealed that the precipitation in this region was probably affected by the El Nino-Southern Oscillation and North Atlantic Oscillation. The dry/wet years corresponded well with the El Nino/La Nina events and the SWR commonly experienced droughts during the low periods of North Atlantic Oscillation

Safety-Assured Speculative Planning with Adaptive Prediction

Recently significant progress has been made in vehicle prediction and planning algorithms for autonomous driving. However, it remains quite challenging for an autonomous vehicle to plan its trajectory in complex scenarios when it is difficult to accurately predict its surrounding vehicles' behaviors and trajectories. In this work, to maximize performance while ensuring safety, we propose a novel speculative planning framework based on a prediction-planning interface that quantifies both the behavior-level and trajectory-level uncertainties of surrounding vehicles. Our framework leverages recent prediction algorithms that can provide one or more possible behaviors and trajectories of the surrounding vehicles with probability estimation. It adapts those predictions based on the latest system states and traffic environment, and conducts planning to maximize the expected reward of the ego vehicle by considering the probabilistic predictions of all scenarios and ensure system safety by ruling out actions that may be unsafe in worst case. We demonstrate the effectiveness of our approach in improving system performance and ensuring system safety over other baseline methods, via extensive simulations in SUMO on a challenging multi-lane highway lane-changing case study