Learning Robustness with Bounded Failure: An Iterative MPC Approach

We propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task. The system is subject to an additive disturbance, and the goal is to learn to satisfy state and input constraints robustly. Using disturbance measurements after each iteration, we construct Confidence Support sets, which contain the true support of the disturbance distribution with a given probability. As more data is collected, the Confidence Supports converge to the true support of the disturbance. This enables design of an MPC controller that avoids conservative estimate of the disturbance support, while simultaneously bounding the probability of constraint violation. The efficacy of the proposed approach is then demonstrated with a detailed numerical example.Comment: Added GitHub link to all source code

Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control

Stereo matching algorithms work with multiple images of a scene, taken from two viewpoints, to generate depth information. Authors usually use a single matching function to generate similarity between corresponding regions in the images. In the present research, the authors have considered a combination of multiple data costs for disparity generation. Disparity maps generated from stereo images tend to have noisy sections. The presented research work is related to a methodology to refine such disparity maps such that they can be further processed to detect obstacle regions.  A novel entropy based selective refinement (ESR) technique is proposed to refine the initial disparity map. The information from both the left disparity and right disparity maps are used for this refinement technique. For every disparity map, block wise entropy is calculated. The average entropy values of the corresponding positions in the disparity maps are compared. If the variation between these entropy values exceeds a threshold, then the corresponding disparity value is replaced with the mean disparity of the block with lower entropy. The results of this refinement are compared with similar methods and was observed to be better. Furthermore, in this research work, the v-disparity values are used to highlight the road surface in the disparity map. The regions belonging to the sky are removed through HSV based segmentation. The remaining regions which are our ROIs, are refined through a u-disparity area-based technique.  Based on this, the closest obstacles are detected through the use of k-means segmentation.  The segmented regions are further refined through a u-disparity image information-based technique and used as masks to highlight obstacle regions in the disparity maps. This information is used in conjunction with a kalman filter based path planning algorithm to guide a mobile robot from a source location to a destination location while also avoiding any obstacle detected in its path. A stereo camera setup was built and the performance of the algorithm on local real-life images, captured through the cameras, was observed. The evaluation of the proposed methodologies was carried out using real life out door images obtained from KITTI dataset and images with radiometric variations from Middlebury stereo dataset

Influence of Sisal Fiber on Resilient Modulus of Hot Mix Asphalt with Addition of Marble Dust.

A material's resilient modulus is extremely a gauge of its modulus of elasticity (E). While the modulus of springiness is pressure isolated by stress for a step by step associated load, the resilient modulus is stress isolated by strain for immediately applied loads – like those practiced by pavements. This can be resolved in the laboratory by leading test according to system indicated AASHTO T30-99 (2003) and the resilient modulus estimated in the indirect tensile mode as indicated byASTMD 4123. Bituminous concrete comprises of a blend of totals aggregates evaluated from most extreme size, normally under25 mm, through fine filler that is littler than0.075 mm. Adequate bitumen is added to the blend with the goal that the compacted blend is successfully impermeable and will have satisfactory dissipative and elastic properties. Mineral filler characteristics and their effect on the permanent deformation characteristics (rutting) of bituminous mixes vary with the type and amount of filler added to the mix. About 3.8 million tons of sisal fibres are cultivated every year throughout the world. Roughly 3500 metric ton of marble powder produced every day amid the preparing of marble squares. The present paper portrays the outcomes from a progression of resilient modulus tests that were led in a lab situation utilizing a repeated load triaxial test setup. The impacts of optimum marble filler and sisal fibres and different binding and deviatoric stress of levels on the resilient modulus (MR) response of treated modified blend were contemplated. MR estimations of conventional and modified are 2416 and 2777 MPa, which enhancements the improvements with marble filler and sisal fibres .rutting and fatigue resistances investigated with immersion wheel tracking device, indirect tensile strength, and repeated load test. The examination demonstrates that 3.30mm rutting depth and 28.1 msa fatigue life for the modified blend

Deep Learning Methodologies for Diagnosis of Respiratory Disorders from Chest X-ray Images: A Comparative Study

Chest radiography needs timely diseases diagnosis and reporting of potential findings in the images, as it is an important diagnostic imaging test in medical practice. A crucial step in radiology workflow is the fast, automated, and reliable detection of diseases created on chest radiography. To overcome this issue, an artificial intelligence-based algorithm such as deep learning (DL) are promising methods for automatic and fast diagnosis due to their excellent performance analysis of a wide range of medical images and visual information. This paper surveys the DL methods for lung disease detection from chest X-ray images. The common five attributes surveyed in the articles are data augmentation, transfer learning, types of DL algorithms, types of lung diseases and features used for detection of abnormalities, and types of lung diseases. The presented methods may prove extremely useful for people to ideate their research contributions in this area

Primary health care and hospital management during COVID-19: Systematic review & meta analysis

Introduction: The COVID-19 outbreak has brought to light the issues and risks that frontline healthcare professionals face (HCW). The goal of this study was to describe the clinical outcomes and risk variables in HCW infected with SARS-CoV-2 and thus evaluate the primary health care and hospital management. Methods: A total of 328 articles were found after searching three databases. Only 97 full-text articles were screened because 225 articles did not match the inclusion criteria. Finally, 30 articles were included in the systematic review and 28 were used in the meta-analysis following further revision. Results: Twenty-eight studies with a total of 119,883 patients were found. The patients' average age was 38.37 years (95 percent CI 36.72–40.03), and males made up 21.4 percent of the HCW population (95 percent CI 12.4–34.2). COVID-19 positivity was found in 51.7 percent of HCW (95 percent confidence interval: 34.7–68.2). In seven investigations, the overall prevalence of comorbidities was 18.4 percent (95 percent confidence interval 15.5–21.7). Fever and cough were the most common symptoms, with 27.5 percent (95 percent CI 17.6–40.3) and 26.1 percent (95 percent CI 18.1–36) respectively