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Patel, Chirag
- Monte-Carlo Black-Scholes Implementation using OpenCL Standard
Authors
1 School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 36 (2015), Pagination:Abstract
The OpenCL is a standard parallel language which is based on 'C' language. It offers users to take full advantage and also provide the flexibility of high level language. In this paper, we explore the use of OpenCL language to implement the complex design on FPGAs by describing the design with high level abstraction language. To demonstrate, we consider the most important benchmarks in financial markets known as Monte-Carlo Black-Scholes implementation to estimate the stock price optionKeywords
Embedded Unit, FPGA Implementation, Kernel, Monte-Carlo Simulation, OpenCL Standard.- Manufacturing and Assembly of ITER Cryostat - Welding Challenges and Experiences
Authors
1 ITER-India, Institute For Plasma Research, Gandhinagar, Gujarat, IN
2 ITER Organization, Route de Vinon-sur-Verdon, St Paul Lez Durance Cedex, FR
3 Larsen & Toubro Limited, Heavy Engineering, Hazira Manufacturing Complex, Gujarat, IN
Source
Indian Welding Journal, Vol 54, No 4 (2021), Pagination: 50-56Abstract
The ITER Cryostat-the largest austenitic stainless steel vessel provides ultra-cool environment for the ITER Vacuum vessel and the Superconducting Magnets. It weighs ∼3500 t and measures up to ∼29 meters in diameter and ∼29 meters in height. Material of Construction is dual marked SS 304/304L and thickness varies from 25 mm to 200 mm.
The Design, manufacturing and inspection of the cryostat is as per ASME Section VIII Division 2 with supplementary requirement of ITER. Due to large number of penetrations and transportation limitation at Site calls for the segmentation which results in number of subassemblies. Massive amount of welding deposition is required to join these subassemblies to fabricate the segment.
ITER Specification for Cryostat demands stringent dimensional tolerance requirement (0.3% out of roundness) as compare to ASME. Other challenges are higher thickness weld joints in all position, space constraints, welding accessibility and stringent ITER vacuum requirements. Austenitic Stainless steel is prone to distortion due to low thermal conductivity and high coefficient of thermal expansion. This paper covers improvements done in the traditional welding process SAW, FCAW and to achieve dimension requirements and results are discussed. This paper also covers application NG Hot wire TIG for Site weld joints.
n order to simulate the job conditions, mockup of 40° segment on base section and 60#176; segment for lower cylinder segment was performed for Welding & NDE feasibility, Welding sequence establishment for dimensional achievement. This paper also highlights the Learnings acquired from the mockups and implementation during manufacturing.
Keywords
ITER Cryostat, Dimensional Control, High Thickness, SAW, Hot Wire TIG.References
- Bhardwaj A (2016); Overview and status of ITER Cryostat manufacturing, Fusion Eng. Des.
- ASME, Section VIII Div. 2.
- ITER Vacuum Handbook and Its Appedices.
- Prajapati R (2016); Validation and implementation of sandwich structure bottom plate torib weld joint in the base section of ITER Cryostat, Fusing Eng. Des., pp. 109-111.
- RCC (2007); MR Edition.
- ASME Section IX.
- Plant Disease Idedntification Using Machine Learning and Image Processing
Authors
1 Department of Computer Science and Engineering, Parul University, IN
2 Department of Computer Engineering, Charotar University of Science and Technology, IN
3 Department of Computer Science and Engineering, Indus University, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 4 (2023), Pagination: 3043-3047Abstract
The primary objective of this study is to investigate the detection and diagnosis of plant diseases using Deep Learning and Digital Image Processing. Previous research has primarily focused on single plant disease scenarios using publicly available datasets, often overlooking the image preprocessing phase. In this study, we propose a model that works with 10 different plants and utilizes approximately 50,000 images for training and testing. We classified 36 distinct classes into healthy or infected types based on disease labels. To enhance the accuracy of disease detection, we recommend employing image processing techniques and considering multiple plant scenarios. We utilized a dual-layer Convolutional Neural Network (CNN) for the publicly available dataset and supplemented it with real-time images captured from various farms in Village Rancharda Near Ahmedabad, Gujarat, India (PIN: 38255). Our research introduces several novel elements in the preprocessing steps. We employed HSV segmentation, flood fills segmentation, and a proposed deep learning model for image segmentation. Additionally, we standardized the resolution of all images to ensure uniformity. These preprocessing techniques refine the image data required for accurate classification and enhance the visibility of diseased portions. For image processing, we employed a sliding window mean average deviation technique and stacked the processed images onto the original image, resulting in six-channel images. Our proposed model demonstrates improved performance on the validation data, achieving an accuracy of up to 97.95%. Furthermore, we transformed this model into a TFLite model, which can be easily integrated into client applications without the need for a server. In our case, we implemented the model on an Android platform. These findings indicate the potential of our proposed model to significantly enhance the detection and diagnosis of plant diseases in real-world scenarios.Keywords
Convolutional Neural Network, Image Segmentation, Dual Layered, Sliding Window.References
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- OEC Wheat in Ukraine, Available at: https://oec.world/en/profile/bilateral-product/wheat/reporter/ukr/, Accessed at 2020.
- World Bank Group, “Commodity Markets Outlook: The Impact of the War in Ukraine on Commodity Markets”, Available at https://www.worldbank.org/en/news/press-release/2022/04/26/food-and-energy-price-shocks-from-ukraine-war, Accessed at 2022.
- Plant Disease: Pathogens and Cycles, Available at https://cropwatch.unl.edu/soybean-management/plant-disease, Accessed at 2022.
- Plant Disease Epidemiology: Temporal Aspects, Available at https://www.apsnet.org/edcenter/disimpactmngmnt/topc/EpidemiologyTemporal/Pages/Disease%20Progress.aspx, Accessed at 2021.
- O.C. Maloy, “Plant Disease Management”, The Plant Health Instructor, Vol. 25, pp. 1-13, 2005.
- Ankit Dubey and M. Shanmugasundaram, “Agricultural Plant Disease Detection and Identification”, International Journal of Electrical Engineering and Technology, Vol. 11, No. 3, pp. 354-363, 2020.
- H. Durmuş and M. Kirci, “Disease Detection on the Leaves of the Tomato Plants by using Deep Learning”, Proceedings of International Conference on Agro-Geoinformatics, pp. 1-5, 2016.