Target Tracking with Binary Sensor Networks

Binary Sensor Networks are widely used in target tracking and target parameter estimation. It is more computationally and financially efficient than surveillance camera systems. According to the sensing area, binary sensors are divided into disk shaped sensors and line segmented sensors. Different mathematical methods of target trajectory estimation and characterization are applied. In this thesis, we present a mathematical model of target tracking including parameter estimation (size, intrusion velocity, trajectory, etc.) with line segmented sensor networks. Software simulation and hardware experiments are built based on the model. And we further analyze how the quantization noise affects the results

Genetic Algorithms in Stochastic Optimization and Applications in Power Electronics

Genetic Algorithms (GAs) are widely used in multiple fields, ranging from mathematics, physics, to engineering fields, computational science, bioinformatics, manufacturing, economics, etc. The stochastic optimization problems are important in power electronics and control systems, and most designs require choosing optimum parameters to ensure maximum control effect or minimum noise impact; however, they are difficult to solve using the exhaustive searching method, especially when the search domain conveys a large area or is infinite. Instead, GAs can be applied to solve those problems. And efficient computing budget allocation technique for allocating the samples in GAs is necessary because the real-life problems with noise are often difficult to evaluate and require significant computation effort. A single objective GA is proposed in which computing budget allocation techniques are integrated directly into the selection operator rather than being used during fitness evaluation. This allows fitness evaluations to be allocated towards specific individuals for whom the algorithm requires more information, and this selection-integrated method is shown to be more accurate for the same computing budget than the existing evaluation-integrated methods on several test problems. A combination of studies is performed on a multi-objective GA that compares integration of different computing budget allocation methods into either the evaluation or the environmental selection steps. These comparisons are performed on stochastic problems derived from benchmark multi-objective optimization problems and consider varying levels of noise. The algorithms are compared regarding both proximity to and coverage of the true Pareto-optimal front, and sufficient studies are performed to allow statistically significant conclusions to be drawn. Finally, the multi-objective GA with selection integrated sampling technique is applied to solve a multi-objective stochastic optimization problem in a grid connected photovoltaic inverter system with noise injected from both the solar power input and the utility grid

Provable Training for Graph Contrastive Learning

Graph Contrastive Learning (GCL) has emerged as a popular training approach for learning node embeddings from augmented graphs without labels. Despite the key principle that maximizing the similarity between positive node pairs while minimizing it between negative node pairs is well established, some fundamental problems are still unclear. Considering the complex graph structure, are some nodes consistently well-trained and following this principle even with different graph augmentations? Or are there some nodes more likely to be untrained across graph augmentations and violate the principle? How to distinguish these nodes and further guide the training of GCL? To answer these questions, we first present experimental evidence showing that the training of GCL is indeed imbalanced across all nodes. To address this problem, we propose the metric "node compactness", which is the lower bound of how a node follows the GCL principle related to the range of augmentations. We further derive the form of node compactness theoretically through bound propagation, which can be integrated into binary cross-entropy as a regularization. To this end, we propose the PrOvable Training (POT) for GCL, which regularizes the training of GCL to encode node embeddings that follows the GCL principle better. Through extensive experiments on various benchmarks, POT consistently improves the existing GCL approaches, serving as a friendly plugin

Adjustment and Optimization of the Cropping Systems Under Water Constraint

The water constraint on agricultural production receives growing concern with the increasingly sharp contradiction between demand and supply of water resources. How to mitigate and adapt to potential water constraint is one of the key issues for ensuring food security and achieving sustainable agriculture in the context of climate change. It has been suggested that adjustment and optimization of cropping systems could be an effective measure to improve water management and ensure food security. However, a knowledge gap still exists in how to quantify potential water constraint and how to select appropriate cropping systems. Here, we proposed a concept of water constraint risk and developed an approach for the evaluation of the water constraint risks for agricultural production by performing a case study in Daxing District, Beijing, China. The results show that, over the whole growth period, the order of the water constraint risks of crops from high to low was wheat, rice, broomcorn, foxtail millet, summer soybean, summer peanut, spring corn, and summer corn, and the order of the water constraint risks of the cropping systems from high to low was winter wheat-summer grain crops, rice, broomcorn, foxtail millet, and spring corn. Our results are consistent with the actual evolving process of cropping system. This indicates that our proposed method is practicable to adjust and optimize the cropping systems to mitigate and adapt to potential water risks. This study provides an insight into the adjustment and optimization of cropping systems under resource constraints

Factors influencing self-healing mechanisms of cementitious materials: A review

The increasing awareness of climate change and global warming has pushed industries to be more conscious of their environmental impact, especially in the construction industry with the main contributor being concrete. Concrete is a material that is in very high demand in the construction industry for structural applications. However, it’s a material with a major concern with the challenges of microcracking. New technology has seen the development of self-healing material, using novel techniques to bring cementitious materials back to its original state. This paper reviews and evaluates the novel techniques adopted by the researchers in the field to achieve a self-healing material, with the main focus being on the factors influencing the mechanisms of autogenous healing and bacteria-based healing. Various parameters including bacteria type, pH, temperature, nutrient, urea, and Ca2+ concentration, bacteria concentration and application, pre-cracking, healing condition, cement type, and crack width are all important for healing efficiency, although the use of water to facilitate both autogenous and ureolytic bacteria healing mechanism is paramount for the triggering of healing processes. This study thoroughly presents various factors and their correlation to the healing mechanisms of autogenous healing and ureolytic bacteria healing. Further studies are identified to better understand the exact mechanism taking place and which healing process contributed to how much of the healing, and this review could serve as an informative platform for these pursues

Testing for Low-Speed Skid Resistance of Road Pavements

10.1080/14680629.2018.1552619Road Materials and Pavement Design21051312-132