Essays in Applied Microeconomics

In Chapter 1, I study how asset test elimination of Medicare Savings Programs affect elderly seniors financial difficulty to access health care. In the United States, most elderly seniors are covered by Medicare. However, the original Medicare could incur non-negligible and uncapped out-of-pocket expenditure to the beneficiaries, which could make health care still unaffordable. Medicare Savings Program (MSP) is a Medicaid program that help eligible Medicare beneficiaries to pay their Medicare share cost. Asset test is often the major hurdle to block income eligible seniors to enroll in MSP. Ten states have eliminated the asset test in MSP. I use difference in difference approach to estimate that removing the asset test in MSP increased elderly seniors' Medicaid coverage rate by 19 percent and reduce their financial difficulty to access health care by 8 percent at extensive margin. Event study result shows that in average it took 3 years for the effect to take off. States should consider removing the asset test or make it less restrictive if doing so will make health care more accessible to elderly seniors and reduce states' administrative cost burden. In Chapter 2, I study how Medicare eligibility at age 65 reduced people's incentive to get Medicaid divorce. To get a divorce, split the joint assets, and allocate most of the assets to the healthy spouse is a strategy to help the sick spouse financially qualify for Medicaid coverage. The exogenous age-based increase in eligibility for Medicare and Medicaid reduces the incentive for people crossing the 65-threshold to implement Medicaid divorce. Using regression discontinuity design, I estimate a 4.1 percent discrete decrease in the prevalence of divorce at the 65-threshold. By examining how the magnitude of the divorce gap is associated with the state-level variation in Medicaid asset test, I argue that the divorce gap at age 65 measures the reduction in Medicaid divorce. In addition, the heterogeneity analysis indicates that the divorce gap is significantly larger for women, which suggests that Medicaid divorce is more prevalent when the sick spouse is the wife. In Chapter 3, I study the relationship between technological change and local labor markets. Between 2000 and 2006, the U.S. economy was expanding and the housing market exhibited prosperity. I examine the heterogeneous effect of the housing boom and the Routine Biased Technological Change (RBTC) on the occupational composition of the U.S. labor market during this period. All 3-digit occupations are classified into eight groups based on their task measures and education requirements. I find that the local housing boom boosted the overall local employment level, while the effect of RBTC was concentrated on low-skill occupations. Among the low-skill occupations intensive in routine tasks, the local housing boom increased the local employment share in office administrative support occupations, but had no significant effect on production occupations. At the same time, the RBTC was shifting the low-skill labor force away from these routine occupations to low-skill local service jobs. Moreover, the production workers were losing jobs even when the economy was good, while the employment share in local service occupations maintained strong even after the housing bubble burst

Theoretical Model For Buoyancy-Induced Heat Transfer Deterioration Under Supercritical Pressure

In an organic Rankine cycle system which utilizes the low-grade heat sources in various industrial processes, operating the heating process at supercritical pressure provides a possibility to increase the system efficiency because more work can be recovered. Under this background, it is necessary to investigate the supercritical heat transfer characteristics of the organic working fluids. In this study, the characteristics of supercritical heat transfer of fluid R245fa, which is commonly applied in the commercial ORC power plant, and R1233zd(E), which is recognized as an alternative working fluid owing to its low-global-warming-potential value, were investigated experimentally under the heating condition in a vertical tube. The test tube has an inner diameter of 4 mm and a length of 1040 mm. The heat flux ranging from 15 to 100 kW/m2 for R245fa and 20 to 80 kW/m2 for R1233zd(E), and mass flux from 400 to 800 kg/m2s for R245fa and 400 to 600 kg/m2sfor R1233zd(E). The experiments were conducted at a pressure of 4.0, 4.5 and 5.0 MPa for R245fa and 3.93 and 4.40 MPa for R1233zd(E). The local heat transfer coefficients were calculated from the measurement parameters. The abrupt deteriorations of heat transfer were found in moderate heat flux and low mass flux at three operating pressure for R245fa, while such a characteristics were not found in the experiment of R1233zd(E). This abrupt deterioration can be attributed to the buoyancy, which was induced by severe variation of density when fluid temperature experienced pseudocritical temperature. When the mass flux is large, there is no abrupt deterioration of heat transfer, and the heat transfer coefficient changes smoothly.For all the experimental conditions, the heat transfer coefficients of R1233zd(E) were higher than that of R245fa

Variational Bayesian Approximations Kalman Filter Based on Threshold Judgment

The estimation of non-Gaussian measurement noise models is a significant challenge across various fields. In practical applications, it often faces challenges due to the large number of parameters and high computational complexity. This paper proposes a threshold-based Kalman filtering approach for online estimation of noise parameters in non-Gaussian measurement noise models. This method uses a certain amount of sample data to infer the variance threshold of observation parameters and employs variational Bayesian estimation to obtain corresponding noise variance estimates, enabling subsequent iterations of the Kalman filtering algorithm. Finally, we evaluate the performance of this algorithm through simulation experiments, demonstrating its accurate and effective estimation of state and noise parameters.Comment: 5 pages, conferenc

Generalized Minimum Error Entropy for Adaptive Filtering

Error entropy is a important nonlinear similarity measure, and it has received increasing attention in many practical applications. The default kernel function of error entropy criterion is Gaussian kernel function, however, which is not always the best choice. In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed. We further derivate the generalized minimum error entropy (GMEE) criterion, and a novel adaptive filtering called GMEE algorithm is derived by utilizing GMEE criterion. The stability, steady-state performance, and computational complexity of the proposed algorithm are investigated. Some simulation indicate that the GMEE algorithm performs well in Gaussian, sub-Gaussian, and super-Gaussian noises environment, respectively. Finally, the GMEE algorithm is applied to acoustic echo cancelation and performs well.Comment: 9 pages, 8 figure

Cubature Kalman filter Based on generalized minimum error entropy with fiducial point

In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the classical cubature Kalman filter (CKF) algorithm. In this letter, a modified generalized minimum error entropy criterion with fiducial point (GMEEFP) is studied to ensure that the error comes together to around zero, and a new CKF algorithm based on the GMEEFP criterion, called GMEEFP-CKF algorithm, is developed. To demonstrate the practicality of the GMEEFP-CKF algorithm, several simulations are performed, and it is demonstrated that the proposed GMEEFP-CKF algorithm outperforms the existing CKF algorithms with impulse noise

An advanced YOLOv3 method for small object detection

Small object detection is a very challenging task in the field of object detection because it is easily affected by large object occlusion and small object itself has relatively little feature information. Aiming at the problem that the YOLOv3 network does not consider the context semantic relationship of small object detection, the detection accuracy of small objects is not high. In this paper, we propose a small object detection network combining multi-level fusion and feature augmentation. First, the feature enhancement module is introduced into the deep layer of the backbone extraction network to enhance the feature information of small objects in the feature map. Second, a multi-level feature fusion module is proposed to better capture the contextual semantic relationship of small objects. In addition, the strategy of combining Soft-NMS and CIOU is used to solve the problem of missed detection of occluded small objects. At last, The ablation experiment of the MS COCO2017 object detection task proves the effectiveness of several modules introduced in this paper for small object detection. The experimental results on the MS COCO2017, VOC2007, and VOC2012 datasets show that the AP of this method is 16.5%, 8.71%, and 9.68% higher than that of YOLOv3, respectively. All experiments show that the method proposed in this paper has better detection performance for small object detection

State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise

Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed Kalman filters for wireless sensor networks in the presence of non-Gaussian impulsive noise. However, these algorithms currently fail to take account of data packet drops. The present work addresses this issue by proposing a distributed maximum correntropy Kalman filter that accounts for data packet drops (i.e., the DMCKF-DPD algorithm). The effectiveness and feasibility of the algorithm are verified by simulations conducted in a wireless sensor network with intermittent observations due to data packet drops under a non-Gaussian noise environment. Moreover, the computational complexity of the DMCKF-DPD algorithm is demonstrated to be moderate compared with that of a conventional distributed Kalman filter, and we provide a sufficient condition to ensure the convergence of the proposed algorithm

Quantized generalized minimum error entropy for kernel recursive least squares adaptive filtering

The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also improves the computational complexity of the KRLS-type algorithms to a certain extent. To reduce the computational load of the KRLS-type algorithms, the quantized GMEE (QGMEE) criterion, in this paper, is combined with the KRLS algorithm, and as a result two kinds of KRLS-type algorithms, called quantized kernel recursive MEE (QKRMEE) and quantized kernel recursive GMEE (QKRGMEE), are designed. As well, the mean error behavior, mean square error behavior, and computational complexity of the proposed algorithms are investigated. In addition, simulation and real experimental data are utilized to verify the feasibility of the proposed algorithms

Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries