Dynamic Response of Piles ─ Case Studies in India

Dynamic behavior of piles is strongly affected by the variation in the soil stiffness with depth and the interaction between the soil and the pile. The dynamic characteristics of the piles can be determined by using simple lumped mass models to complex finite element modeling. There is a need for experimental research, in particular large-scale field tests, to validate existing linear/nonlinear models, which are used to predict the dynamic stiffness and damping of the soil-pile system. The determination of dynamic characteristics of soil-pile system by full-scale lateral dynamic pile load testing is an important aspect in the design of pile foundations subjected to dynamic loads. This paper presents the results of field lateral dynamic load tests conducted at two different petrochemical complex sites in India and the measured dynamic constants of the soil-pile system. A three-dimensional finite element analysis is performed using ABAQUS to predict the nonlinear response of the soil-pile system under dynamic lateral loads. The lateral stiffness estimated from the FE analysis shows good agreement with stiffness measured from field tests. The paper also discusses on the dynamic analysis and design of Primary Air (PA) and Force-Draft (FD) fan foundations of a thermal power plant using finite-element analysis

Primary intra medullary inter locking nailing for open tibia fractures a prospective analysis of functional and radiological outcome

Background: Tibia is the most commonly fractured long bone in the body encountered in today’s practice. The use of non-operative treatment of tibial fractures is associated with a high prevalence of malunion, joint stiffness and poor functional outcome. This study was conducted to analyze the functional and radiological outcome of patients treated for open fractures of tibia with primary intramedullary interlocking nailing within 24 hours of injury.Methods: From October 2013 to May 2015, a prospective and descriptive study was done where 30 consecutive patients with open tibial fractures was treated with primary intramedullary interlocking nailing within 24 hours of injury, at Pondicherry institute of medical sciences, Pondicherry. Functional outcome was assessed using Karlstrom and Olerud scores and radiological outcome was assessed by radiographic union scale of tibial fractures (RUST) scores at 6 weeks, 3rd, 6th, 9th and 12th month post-operatively. Intra-operative and post-operative complications were noted and documented. The results were analyzed using SPSS software.Results: The most common age group who presented was between 18-30 years and the average age was 36.7 years. 36.67% had grade I injury, 40% had grade II injury, meanwhile, grade IIIA injuries constituted 20% and grade IIIB injuries constituted 3.33%. The average functional score was 33.33 which showed overall good result. The average RUST score was found to be 11.33 denoting union.Conclusions:Hence, we observed that intramedullary interlocking nailing is a good procedure in terms of functional and radiological outcome if done within 24 hours of injury.

A Novel Technique to Detect and Track Multiple Objects in Dynamic Video Surveillance Systems

Video surveillance is one of the important state of the art systems to be utilized in order to monitor different areas of modern society surveillance like the general public surveillance system, city traffic monitoring system, and forest monitoring system. Hence, surveillance systems have become especially relevant in the digital era. The needs of the video surveillance systems and its video analytics have become inevitable due to an increase in crimes and unethical behavior. Thus enabling the tracking of individuals object in video surveillance is an essential part of modern society. With the advent of video surveillance, performance measures for such surveillance also need to be improved to keep up with the ever increasing crime rates. So far, many methodologies relating to video surveillance have been introduced ranging from single object detection with a single or multiple cameras to multiple object detection using single or multiple cameras. Despite this, performance benchmarks and metrics need further improvements. While mechanisms exist for single or multiple object detection and prediction on videos or images, none can meet the criteria of detection and tracking of multiple objects in static as well as dynamic environments. Thus, real-world multiple object detection and prediction systems need to be introduced that are both accurate as well as fast and can also be adopted in static and dynamic environments. This paper introduces the Densely Feature selection Convolutional neural Network – Hyper Parameter tuning (DFCNHP) and it is a hybrid protocol with faster prediction time and high accuracy levels. The proposed system has successfully tracked multiple objects from multiple channels and is a combination of dense block, feature selection, background subtraction and Bayesian methods. The results of the experiment conducted demonstrated an accuracy of 98% and 1.11 prediction time and these results have also been compared with existing methods such as Kalman Filtering (KF) and Deep Neural Network (DNN)

Efficient Deduplication and Leakage Detection in Large Scale Image Datasets with a focus on the CrowdAI Mapping Challenge Dataset

Recent advancements in deep learning and computer vision have led to widespread use of deep neural networks to extract building footprints from remote-sensing imagery. The success of such methods relies on the availability of large databases of high-resolution remote sensing images with high-quality annotations. The CrowdAI Mapping Challenge Dataset is one of these datasets that has been used extensively in recent years to train deep neural networks. This dataset consists of $\sim\$280k training images and $\sim\$60k testing images, with polygonal building annotations for all images. However, issues such as low-quality and incorrect annotations, extensive duplication of image samples, and data leakage significantly reduce the utility of deep neural networks trained on the dataset. Therefore, it is an imperative pre-condition to adopt a data validation pipeline that evaluates the quality of the dataset prior to its use. To this end, we propose a drop-in pipeline that employs perceptual hashing techniques for efficient de-duplication of the dataset and identification of instances of data leakage between training and testing splits. In our experiments, we demonstrate that nearly 250k($\sim\$90%) images in the training split were identical. Moreover, our analysis on the validation split demonstrates that roughly 56k of the 60k images also appear in the training split, resulting in a data leakage of 93%. The source code used for the analysis and de-duplication of the CrowdAI Mapping Challenge dataset is publicly available at https://github.com/yeshwanth95/CrowdAI_Hash_and_search .Comment: 9 pages, 2 figure

Interface-aware signal temporal logic

Safety and security are major concerns in the development of Cyber-Physical Systems (CPS). Signal temporal logic (STL) was proposedas a language to specify and monitor the correctness of CPS relativeto formalized requirements. Incorporating STL into a developmentprocess enables designers to automatically monitor and diagnosetraces, compute robustness estimates based on requirements, andperform requirement falsification, leading to productivity gains inverification and validation activities; however, in its current formSTL is agnostic to the input/output classification of signals, andthis negatively impacts the relevance of the analysis results.In this paper we propose to make the interface explicit in theSTL language by introducing input/output signal declarations. Wethen define new measures of input vacuity and output robustnessthat better reflect the nature of the system and the specification in-tent. The resulting framework, which we call interface-aware signaltemporal logic (IA-STL), aids verification and validation activities.We demonstrate the benefits of IA-STL on several CPS analysisactivities: (1) robustness-driven sensitivity analysis, (2) falsificationand (3) fault localization. We describe an implementation of our en-hancement to STL and associated notions of robustness and vacuityin a prototype extension of Breach, a MATLAB®/Simulink®toolboxfor CPS verification and validation. We explore these methodologi-cal improvements and evaluate our results on two examples fromthe automotive domain: a benchmark powertrain control systemand a hydrogen fuel cell system