Life Cycle Characteristics of Warm-Season Severe Thunderstorms in Central United States from 2010 to 2014

Weather monitoring systems, such as Doppler radars, collect a high volume of measurements with fine spatial and temporal resolutions that provide opportunities to study many convective weather events. This study examines the spatial and temporal characteristics of severe thunderstorm life cycles in central United States mainly covering Kansas, Oklahoma, and northern Texas during the warm seasons from 2010 to 2014. Thunderstorms are identified using radar reflectivity and cloud-to-ground lightning data and are tracked using a directed graph model that can represent the whole life cycle of a thunderstorm. Thunderstorms were stored in a GIS database with a number of additional thunderstorm attributes. Spatial and temporal characteristics of the thunderstorms were analyzed, including the yearly total number of thunderstorms, their monthly distribution, durations, initiation time, termination time, movement speed and direction, and the spatial distributions of thunderstorm tracks, initiations, and terminations. Results revealed that thunderstorms were most frequent across the eastern part of the study area, especially at the borders between Kansas, Missouri, Oklahoma, and Arkansas. Finally, thunderstorm occurrence is linked to land cover, including a comparison of thunderstorms between urban and surrounding rural areas. Results demonstrated that thunderstorms would favor forests and urban areas. This study demonstrates that advanced GIS representations and analyses for spatiotemporal events provide effective research tools to meteorological studies

Novel particle swarm optimization algorithms with applications to healthcare data analysis

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Optimization problem is a fundamental research topic which has been receiving increasing interest according to its application potential in almost all real-world systems including engineering systems, large-scaled complex networks, healthcare management systems and so on. A large number of heuristic algorithms have been developed with the purpose of effectively solving the optimization problems during the past few decades. Served as a powerful family of heuristic algorithms, the particle swarm optimization (PSO) algorithm has been successfully employed in a variety of practical applications in dealing with optimization problems. The PSO algorithm has exhibited more competitive performance than many popular evolutionary computation approaches because of its easy implementation, fast convergence and comprehensive ability of converging to a satisfactory solution. Nevertheless, there is still much room to improve the PSO algorithm in terms of both the convergence rate and the population diversity. To summarize, there are three challenging problems in developing new variant PSO algorithms with hope to further improve the convergence rate of the PSO algorithm and maintain the population diversity: 1) how to adjust the control parameters of the PSO algorithm; 2) how to achieve the balance between the local search and the global search during the evolution process; and 3) how to guarantee the search ability of the particles and avoid premature convergence. In this thesis, we address the above mentioned challenging problems and aim to design effective variant PSO algorithms with applications in intelligent data analysis. It should be pointed out that all the developed PSO algorithms in this thesis have been evaluated by comparing with some currently popular variant PSO algorithms. • With the aim to improve the convergence rate of the optimizer, an adaptive weighting PSO algorithm is put forward where a sigmoid-function-based weighting strategy is introduced to adjust the acceleration coefficients. With this weighting strategy, the distances from the particle to the global best position and from the particle to its personal best position are both taken into consideration, thereby having the distinguishing feature of enhancing the convergence rate. • As with other evolutionary computation approaches, the modification of parameters is an efficient method for improving the search ability of the algorithm. We present a randomised PSO algorithm where Gaussian white noise with adjustable intensity is utilized to randomly perturb the acceleration coefficients in order to explore and exploit the problem space thoroughly. • To further develop a novel PSO algorithm with promising search ability, we propose a randomly occurring distributedly delayed particle swarm optimization (RODDPSO) algorithm which demonstrates competitive performance in seeking the optimal solution. The randomly occurring distributed time delays not only contribute to a thorough exploration of the search space but also achieve a proper balance between the local exploitation and the global exploration. • To fully investigate the application potential of the developed PSO algorithms, we apply the RODDPSO algorithm to intelligent data analysis (including data clustering and classification problems). We optimize the initial cluster centroids of the K-means clustering algorithm and the hyperparameters of the deep neural network by using the RODDPSO algorithm. The developed PRODDPSO-based algorithms are successfully employed in patients’ triage categorization and patient attendance disposal problems with satisfactory performanc

Identification, Representation, and Analysis of Convective Storms

Large amount of time series of spatial snapshot data have been collected or generated for the monitoring and modeling of environmental systems. Those data provide an opportunity to study the movement and dynamics of natural phenomena. While the snapshot organization is conceptually simple and straightforward, it does not directly capture or represent the dynamic characteristics of the phenomena. This study presents computational methods to identify dynamic events from time series of spatial snapshots. Events are represented as directed spatiotemporal graphs to characterize their initiation, development, movement, and cessation. Graph-based algorithms are then used to analyze the dynamics of the events. The method is demonstrated using the time series radar reflectivity images during one of the deadliest storm outbreaks that impacted 15 states of southeastern U.S. between April 23 and 29, 2011. As shown in this case study, convective storm events identified using our methods are consistent with previous studies and our analysis indicates that the left split/merger occurs more than right split/merger in those convective storm events, which confirms theory, numerical simulations, and other observed case studies. This study also examines the spatial and temporal characteristics of thunderstorm life cycles in central United States mainly covering Kansas, Oklahoma, and northern Texas during the warm seasons from 2010 to 2014. Radar reflectivity and cloud-to-ground lightning data were used to identify thunderstorms. The thunderstorms were stored in a GIS database with a number of additional thunderstorm attributes. The spatial and temporal characteristics of thunderstorm occurrence, duration, initiation time, termination time, movement speed, and direction were analyzed. Results revealed that thunderstorms were most frequent in the eastern part of the study area, especially at the borders among Kansas, Missouri, Oklahoma, and Arkansas. We also linked thunderstorm features to land cover types and compared thunderstorm characteristics between urban and surrounding rural areas. Our results indicated that thunderstorms favor forests and urban areas. This research demonstrates that advanced GIS representations and analyses for spatiotemporal events provide insights in thunderstorm climatology study

Graph-based representation and analysis for storm events

This presentation was given as part of the GIS Day@KU symposium on November 18, 2015. For more information about GIS Day@KU activities, please see http://www.gis.ku.edu/gisday/2015/.Platinum Sponsors: KU Department of Geography and Atmospheric Science; KU School of Business. Gold Sponsors: Bartlett & West; Kansas Biological Survey; KU Environmental Studies Program; KU Institute for Policy & Social Research; KU Libraries. Silver Sponsors: State of Kansas Data Access and Support Center (DASC). Bronze Sponsors: KU Center for Remote Sensing of Ice Sheets (CReSIS); TREKK Design Group, LLC; Wilson & Company, Engineers and Architects

Being Aware of Localization Accuracy By Generating Predicted-IoU-Guided Quality Scores

Localization Quality Estimation (LQE) helps to improve detection performance as it benefits post processing through jointly considering classification score and localization accuracy. In this perspective, for further leveraging the close relationship between localization accuracy and IoU (Intersection-Over-Union), and for depressing those inconsistent predictions, we designed an elegant LQE branch to acquire localization quality score guided by predicted IoU. Distinctly, for alleviating the inconsistency of classification score and localization quality during training and inference, under which some predictions with low classification scores but high LQE scores will impair the performance, instead of separately and independently setting, we embedded LQE branch into classification branch, producing a joint classification-localization-quality representation. Then a novel one stage detector termed CLQ is proposed. Extensive experiments show that CLQ achieves state-of-the-arts' performance at an accuracy of 47.8 AP and a speed of 11.5 fps with ResNeXt-101 as backbone on COCO test-dev. Finally, we extend CLQ to ATSS, producing a reliable 1.2 AP gain, showing our model's strong adaptability and scalability. Codes are released at https://github.com/PanffeeReal/CLQ

Analytical modeling of Lamb wave propagation in composite laminate bonded with piezoelectric actuator based on Mindlin plate theory

Dynamic analysis of plate structures based on the Mindlin plate theory has become one of the usual modeling methods for the structural health monitoring (SHM) of composite structures in recent years. Compared to the classical plate theory (CPT) based on Kirchhoff hypothesis, the Mindlin plate theory considers the influence of transverse shear deformation and moment of inertia on displacements. Thus it is more suitable for dynamic analysis of composite laminate with low transverse shear stiffness and large transverse shear deformation. Combining the adhesive layer coupling model of the piezoelectric actuator with the Mindlin plate theory, the dispersion curve of Lamb wave in any direction and mechanical parameters of any point in the composite are obtained, and thus after the substitution of boundary condition, the modeling of piezoelectric wafer excited Lamb wave propagating in composite laminate is realized. The validation experiment is performed on a carbon fiber composite laminate. It proves that the analytical modeling effectively reflects the propagation characteristics of Lamb wave in composite laminate and promotes the engineering application of SHM

Measurements of fluence profiles in femtosecond laser filaments in air

We introduce a technique to measure fluence distributions in femtosecond laser beams with peak intensity of up to several hundred terawatts per square centimeter. Our ap- proach is based on the dependence of single-shot laser abla- tion threshold for gold on the angle of incidence of the laser beam on the gold sample. We apply this technique to the profiling of fluence distributions in femtosecond laser fila- ments at a wavelength of 800 nm in air. The peak intensity is found to be clamped at a level that depends on the ex- ternal beam focusing. The limiting value of the peak inten- sity attainable in long-range 800 nm air filaments, under very loose focusing conditions (f -number above ∼500), is about 55 TW∕cm2