378 research outputs found
FIG. 3 in Epibionts and parasites of Macrobrachium rosenbergii and Metapenaeus dobsoni from Gosthani estuary
No full textFIG. 3. Trophozoite of Nematopsis indicus attached to the intestinal mucosa of M. dobsoni, Ö250.Published as part of Jayasree, L., Janakiram, P. & Madhavi, R., 2001, Epibionts and parasites of Macrobrachium rosenbergii and Metapenaeus dobsoni from Gosthani estuary, pp. 157-167 in Journal of Natural History 35 (2) on page 161, DOI: 10.1080/00222930150215297, http://zenodo.org/record/527600
Building Block Approach’ for Structural Analysis of Thermoplastic Composite Components for Automotive Applications
No full textAdvanced thermoplastic prepreg composite materials stand out with regard to their ability to allow complex designs with high specific strength and stiffness. This makes them an excellent choice for lightweight automotive components to reduce mass and increase fuel efficiency, while maintaining the functionality of traditional thermosetting prepreg (and mechanical characteristics) and with a production cycle time and recyclability suited to mass production manufacturing. Currently, the aerospace and automotive sectors struggle to carry out accurate Finite Elements (FE) component analyses and in some cases are unable to validate the obtained results. In this study, structural Finite Elements Analysis (FEA) has been done on a thermoplastic fiber reinforced component designed and manufactured through an integrated injection molding process, which consists in thermoforming the prepreg laminate and overmolding the other parts. This process is usually referred to as hybrid molding, and has the provision to reinforce the zones subjected to additional stresses with thermoformed themoplastic prepreg as required and overmolded with a shortfiber thermoplastic resin in single process. This paper aims to establish an accurate predictive model on a rational basis and an innovative methodology for the structural analysis of thermoplastic composite components by comparison with the experimental tests results
Novel multi-zone self-heated composites tool for out-of-autoclave aerospace components manufacturing
Full text link© 2020 N. Jayasree et al. In this paper, a multi-zone self-heating composite tool is developed to manufacture out-of-autoclave complex and high-quality business jet lower wing stiffened composite panel. Autoclave manufacturing is regarded as a benchmark for manufacturing aerospace-grade composite parts. However, high accruing operational costs limit production rates thereby not being practical for smaller-scale companies. Therefore, significant work towards developing out-of-autoclave manufacturing is underway. In this study, a production line tool is developed with embedded heating fabric that controls temperature at the desired zones, replacing the need for autoclave cure. It investigates and identifies the optimal design parameters of the self-heating setup namely the placement of the heating fabric, zones, thermal management system, temperature distribution, heating rate and thermal performance using a thermal FEA model. The associated thermal characterisation of the tooling material and the part are measured for accurate simulation results. The design developed in this study will be used as production guideline for the actual tool.European Union’s Horizon 2020 research and innovation program, COMBUSS project [19], Clean Sky 2 Joint Undertaking grant agreement No 821297
Process analysis for structural optimisation of thermoplastic composite component using the building block approach
No full textThe paper aim is to establish and optimise the prediction model of a thermoplastic fibre reinforced component designed and manufactured through an integrated injection moulding process (Hybrid Moulding). This is done by the Finite Element Analysis (FEA) and then the process simulations, considering the composite material as an elastic anisotropic woven fabric to study the deformations undergone during the manufacturing process. The proposed methodology for creating the predictive model is fairly accurate, and it is a novel method which can be easily integrated and adapted into a components initial design phase. This optimisation technique can replace the expensive and traditional trial and error procedures during the design and prototyping phase, and it significantly decreases the time to build the final component. The final scope of the research is to simplify the product development phase of general lightweight automotive thermoplastic components by creating an innovative methodology for predictive modelling
Benford’s Law and Stock Market—The Implications for Investors: The Evidence from India Nifty Fifty
No full text Benford’s law which is also known as first digit law states that data follow a certain frequency. This law was applied to accounting by Nigrini (2012, Benford’s Law: Applications for forensic accounting, auditing, and fraud detection [Vol. 586], John Wiley & Sons) and later on, an exhaustive study was carried out by Amiram, Bozanic, and Rouen (2015, Review of Accounting Studies, 20(4), 1540–1593) to explore the applicability of the law to detect accounting frauds which was proven to be working. The literature has substantial evidence on relationship between accounting numbers and stock returns. The application of Benford’s law to stock trade and returns was explored and it was found that stock trade that included volume, number of trades, and turnover confirmed the distribution but stock returns did not conform the distribution (Jayasree, 2017, Jindal Journal of Business Research, 6(2), 172–186). In this context, the present study attempts to understand its implications to investors by examining the three-day moving average of stock prices and volatility volume by using Chainkin money flow during announcement and post-announcement period of observation. The study also examines whether stocks conforming the distribution and stocks not conforming the distribution are significantly different in buying and selling. </jats:p
Spoof Face Recognition in Video Using KSVM
No full textAbstractWith the advancement of video and its transmission technologies, video has found its use extensively in surveillance, security, especially in detecting forgery. Unlike still images, video provide large amount of intra-personal variations, making face recognition a significant concept in the field of biometric security. Automatic face recognition is a widely used concept in implementing security, which is also prone to various spoof attacks. Spoof attacks accounts to reproducing a person's face using printed photos or by replaying a video. A large number of face recognition and spoof detection algorithms have been developed for still images, but those concerning videos are less in number. Face recognition from videos and related spoof detections are less explored. This paper deals with countering such spoof attacks in facial recognition using videos, where KSVM (K-Means and SVM) is used to identify the recognized images to be real or spoof. KSVM, a combined concept of K-Means and SVM outperforms simple SVM
Automated Liver Tumor Detection Using Markov Random Field Segmentation
No full textAbstractLiver cancers are one of the most popular cancers occurring now a days. The majority of liver carcinomas are dueto alcohol related cirrhosis and hepatitis. Also there are metastatic liver cancer, in which cancer originated from other organs extends to liver. Early detection of liver cancer helps to improve life expectancy. We also need to know the tumor status during treatment stages. Manual segmentation and detection is time consuming. Here we propose an automated computer aided diagnosis of liver tumors from CT images. Initially liver is segmented using MRF embedded level set method. It provides robustness to noise and fast segmentation. The shape ambiguities of the segmented liver is found out by shape analysis methods which uses training set for correction. From the corrected liver segmentation, hepatic tumors are detected by graph cut method and feature extraction is done to classify them using SVM classifier
Recognizing Surgically Altered Face Images and 3D Facial Expression Recognition
No full textAbstractAltering Facial appearances using surgical procedures are common now days. But it raised challenges for face recognition algorithms. Plastic surgery introduces non linear variations. Because of these variations it is difficult to be modeled by the existing face recognition system. Here presents a multi objective evolutionary granular algorithm. It operates on several granules extracted from a face images at multiple level of granularity. This granular information is unified in an evolutionary manner using multi objective genetic approach. Then identify the facial expression from the face images. For that 3D facial shapes are considering here. A novel automatic feature selection method is proposed based on maximizing the average relative entropy of marginalized class-conditional feature distributions and apply it to a complete pool of candidate features composed of normalized Euclidian distances between 83 facial feature points in the 3D space. A regularized multi-class AdaBoost classification algorithm is used here to get the highest average recognition rate
Optimised composite crash structure development with focus of life cycle analysis for a fuel cell electric vehicle
No full text555/716 ©2022 Jayasree et al. doi:10.5075/epfl-298799_978-2-9701614-0-0 published under CC BY-NC 4.0 licenseThis conference paper is part of a six volume book of the proceedings edited by: Prof. Anastasios P. Vassilopoulos, CCLab/EPFL, Prof. Véronique Michaud, LPAC/EPFL. All six volumes will have the same title and each will have a single subtitle: Vol 1: Materials Vol 2: Manufacturing Vol 3: Characterization Vol 4: Modeling and Prediction Vol 5: Applications and Structures Vol 6: Life Cycle Assessment.Copyright © 2022 Jayasree et al.. Low-speed accidents see a year-on-year increase. To improve crash performance in these accidents, a crash box is attached between the vehicle bumper structure and the side rail. The determination of the crash box material and geometry is critical to absorb the impact energy to result in safer vehicles and minimised repair costs. As the automotive industry transitions to more sustainable platforms, it is seeking to use lightweight materials including in the crash structure. This study develops an innovative crash box with optimal impact energy-absorption capabilities for a fuel cell electric vehicle. The concept is based on topology optimisation considering the composite structure and crash energy dissipation. In further work, the results from the life cycle analysis are utilised, and a comparative study between carbon fibre reinforced polymers and biocomposites in crash structures is performed. The latter includes an extensive characterisation campaign under static and dynamic conditions.
Keywords: Composites; crash box;The PROTECT project has received funding from Innovate UK under reference number 68148
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