Displacement Measurement Using a Laser Doppler Vibrometer Mounted on an Unmanned Aerial Vehicles

The railroad network in the united states is one of the best in the world, handling around 40 percent of all US freight movement. To maintain the serviceability and cost-effective operation of the railway infrastructure, regular monitoring is essential. Bridges are a critical part of the railway infrastructure and their timely maintenance and repair are important. Measuring transverse bridge displacement under train loading can assist to determine the bridge condition. The traditional methods available for transverse displacement measurement include Linear Variable Differential Transducers (LVDT). However, irregular terrain, remote and inaccessible locations, and the height of railroad bridges make implementation of these sensors for transverse displacement measurement either inadequate, or risky and time-consuming, and sometimes not possible altogether. Alternatively, railroads can monitor transverse bridge displacement using non-contact sensing with instruments such as robotic total station (RTS) and high-speed cameras. In recent years, the use of Laser Doppler Vibrometers (LDV) has started to draw some attention in the field of non-contact transverse bridge displacement measurement. However, in these applications, the instruments are generally placed on a fixed reference close to the bridge. It is not always possible to find this fixed reference point, especially when a bridge is spanning over a large opening, like a water body. In addition, a fixed reference point would require calibration of the measurement for every different bridge individually. Researchers use Unmanned Aerial Systems (UAS) to acquire aerial images for Structural Health Monitoring (SHM). However, this approach requires extensive image post-processing, and in general, complex algorithms development. More importantly, current systems are not capable of measuring dynamic transverse displacements. This MS Thesis presents a novel approach to measure transverse bridge dynamic displacements using non-contact vibrometers mounted on unmanned aerial system. This research proposes algorithms for compensating the measurement errors due to the angular and linear movement vibrometer to obtain accurate transverse bridge displacement measurements. These algorithms are verified in the laboratory using a shake table simulating bridge vibration, and vibrometer movement simulating the motions of a UAS. The results of these tests show that the signal difference between the measured displacements of a moving LDV system and a LVDT are less than 10%. The Root mean squared (RMS) differences are less than 5%. This research also implements and tests the UAV-LDV system in the field. The results of these experiments show that the signal difference between LVDT and the UAS-LDV system is 10%. The RMS difference between the two systems is 8%. The results of this research show that the UAS and LDV can be used together to measure the dynamic transverse bridge displacements and could become an effective tool for campaign monitoring of railroad bridges with application for railroad bridge maintenance and repair prioritization

TONE: A 3-Tiered ONtology for Emotion analysis

Emotions have played an important part in many sectors, including psychology, medicine, mental health, computer science, and so on, and categorizing them has proven extremely useful in separating one emotion from another. Emotions can be classified using the following two methods: (1) The supervised method's efficiency is strongly dependent on the size and domain of the data collected. A categorization established using relevant data from one domain may not work well in another. (2) An unsupervised method that uses either domain expertise or a knowledge base of emotion types already exists. Though this second approach provides a suitable and generic categorization of emotions and is cost-effective, the literature doesn't possess a publicly available knowledge base that can be directly applied to any emotion categorization-related task. This pushes us to create a knowledge base that can be used for emotion classification across domains, and ontology is often used for this purpose. In this study, we provide TONE, an emotion-based ontology that effectively creates an emotional hierarchy based on Dr. Gerrod Parrot's group of emotions. In addition to ontology development, we introduce a semi-automated vocabulary construction process to generate a detailed collection of terms for emotions at each tier of the hierarchy. We also demonstrate automated methods for establishing three sorts of dependencies in order to develop linkages between different emotions. Our human and automatic evaluation results show the ontology's quality. Furthermore, we describe three distinct use cases that demonstrate the applicability of our ontology

OntoDSumm : Ontology based Tweet Summarization for Disaster Events

The huge popularity of social media platforms like Twitter attracts a large fraction of users to share real-time information and short situational messages during disasters. A summary of these tweets is required by the government organizations, agencies, and volunteers for efficient and quick disaster response. However, the huge influx of tweets makes it difficult to manually get a precise overview of ongoing events. To handle this challenge, several tweet summarization approaches have been proposed. In most of the existing literature, tweet summarization is broken into a two-step process where in the first step, it categorizes tweets, and in the second step, it chooses representative tweets from each category. There are both supervised as well as unsupervised approaches found in literature to solve the problem of first step. Supervised approaches requires huge amount of labelled data which incurs cost as well as time. On the other hand, unsupervised approaches could not clusters tweet properly due to the overlapping keywords, vocabulary size, lack of understanding of semantic meaning etc. While, for the second step of summarization, existing approaches applied different ranking methods where those ranking methods are very generic which fail to compute proper importance of a tweet respect to a disaster. Both the problems can be handled far better with proper domain knowledge. In this paper, we exploited already existing domain knowledge by the means of ontology in both the steps and proposed a novel disaster summarization method OntoDSumm. We evaluate this proposed method with 4 state-of-the-art methods using 10 disaster datasets. Evaluation results reveal that OntoDSumm outperforms existing methods by approximately 2-66% in terms of ROUGE-1 F1 score

PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT

Disaster summarization approaches provide an overview of the important information posted during disaster events on social media platforms, such as, Twitter. However, the type of information posted significantly varies across disasters depending on several factors like the location, type, severity, etc. Verification of the effectiveness of disaster summarization approaches still suffer due to the lack of availability of good spectrum of datasets along with the ground-truth summary. Existing approaches for ground-truth summary generation (ground-truth for extractive summarization) relies on the wisdom and intuition of the annotators. Annotators are provided with a complete set of input tweets from which a subset of tweets is selected by the annotators for the summary. This process requires immense human effort and significant time. Additionally, this intuition-based selection of the tweets might lead to a high variance in summaries generated across annotators. Therefore, to handle these challenges, we propose a hybrid (semi-automated) approach (PORTRAIT) where we partly automate the ground-truth summary generation procedure. This approach reduces the effort and time of the annotators while ensuring the quality of the created ground-truth summary. We validate the effectiveness of PORTRAIT on 5 disaster events through quantitative and qualitative comparisons of ground-truth summaries generated by existing intuitive approaches, a semi-automated approach, and PORTRAIT. We prepare and release the ground-truth summaries for 5 disaster events which consist of both natural and man-made disaster events belonging to 4 different countries. Finally, we provide a study about the performance of various state-of-the-art summarization approaches on the ground-truth summaries generated by PORTRAIT using ROUGE-N F1-scores

TECHNIQUES USED FOR BIOCHEMICAL INVESTIGATION IN RELATION TO FORENSIC ANALYSIS

The aim of this review was to apply the knowledge & technology of science for the definition & enforcement of such laws. The forensic analysis is investigation the crime and examines material evidence. In forensic analysis various biochemical investigation techniques are used to examine the crimes like Hair analysis, Polygraphic test, serology test and finger print analysis. Several instruments are used in forensic analysis like IR, Chromatography, UV and Mass spectrophotometer. The characterization results showed that Forensic pharmacists engage in work relating to litigation, the regulatory process, or the criminal justice system

Comparing ASCAT and CYGNSS Winds near Tropical Convection

Gradient Features identified in ASCAT (Advanced Scatterometer) data correspond well to observed CYGNSS (Cyclone Global Navigation Satellite System) wind shifts: Comparing ASCAT and CYGNSS winds near tropical convection. Gradient wind magnitude in ASCAT observations has been recently shown to be a useful proxy for the presence of tropical convection cold pools. To help confirm this in the vicinity of precipitation we perform a comparison with the L-band CYGNSS wind dataset. Integrated Multi-satellite Retrievals for GPM (Global Precipitation Measurement)) IMERG

DESIGN, DEVELOPMENT AND EVALUATION OF LIPID BASED TOPICAL FORMULATIONS OF SILVER SULFADIAZINE FOR TREATMENT OF BURNS AND WOUNDS

Abstract: The aim of this research was to develop a novel lipid based film forming gel based on polymer and to investigate its potential as slow-release wound healing vehicle. The lipid based is composed of water soluble gel with model drug (Silver Sulfadiazine) and an egg oil, which acted as a remove scars. The morphology, rheology, mechanical properties, in-vitro drug release profiles were investigated. A smooth film layers was produced. The characterization results showed that film has superior mechanical and rheological properties than the ointment and cream. The lipid based gel treating low suppurating wounds and suitable for slow release application on wound surfaces. The lipid based gel also provided a significant higher healing rate in-vivo, with well-formed epidermis with faster granulation tissue formation when compared to the controls. In conclusions, a novel polymer-based lipid film gel was developed and results suggested that they can be exploited as slow-release wound dressings. Key Words: Wound healings, slow release, silver sulfadiazine and film ge