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Design and development of a pico hydro turbine for small scale power generation system

AIP Conference Proceedings 2023年第1期2530卷

作者： Firas Basim Ismail Thabit Sultan Mohammed Duane Yeoh Du-Wei Mohammed Najah Mahdi Power Generation Unit Institute of Power Engineering (IPE) Universiti Tenaga Nasional Kajang 43000 Malaysia Computer Technical Engineering Dept. Al-Qalam University College Kirkuk Iraq Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Kajang 43000 Malaysia

The prospective for run-of-river energy harvesting is potentially fruitful and beneficial to rural communities. In this work, the constant flow is harvested by converting kinetic energy into electricity. The necessary parts are the turbine, canal, connecting shaft, and a generator. Critical parts are made from stainless steel whereby all of them had a safety factor of more than 2.0. The total cost for this project would be recouped in 2.1 months with the current national tariffs and operating round the clock. This paper describes the design as well as development of Pico hydropower, which offers a cost-effective, reliable and efficient alternative power source, and handy at remote locations. Static and flow analyses are validated and related to the findings presented in this paper. A proof of concept in the shape of a 30% scale model was constructed out of 3D printed PLA, which differs from the proposed full-scale model of various grades of metal. The results of the scale model testing are in line with other researchers and within the behavior of these components. For a flow rate of (0.1704 m3/s, the calculated power output of the system is (2005.95 watts). On-site hydropower generation is a viable concept that can further be adopted by rural communities, especially with the proposed cost reductions compared to other competing systems.

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Unsupervised image classification through time-multiplexed photonic multi-layer spiking convolutional neural network

arXiv

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arXiv 2020年

作者： Skontranis, Menelaos Sarantoglou, George Deligiannidis, Stavros Bogris, Adonis Mesaritakis, Charis University of the Aegean Dept. of Information and Communication Systems Engineering Palama 2 Samos83200 Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece

We present results of a deep photonic spiking convolutional neural network, based on two-section VCSELs, targeting image classification. Training is based on unsupervised spike-timing dependent plasticity, whereas neuron time-multiplexing and ultra-fast response are exploited towards a a reduction of the physical neuron count by 90%. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Clustering big data for novel health care system

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AIP Conference Proceedings 2023年第1期2587卷

作者： R. Saravanakumar S. P. Maniraj Anindita Das Barshan Santanu Das Hasibul Hasan Malik Bader Alazzam Dept. of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India Dept. of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamilnadu Chennai India Dept. of Medicine Dhaka Medical College Hospital Dhaka Bangladesh Dept. of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Dept. of Applied Nutrition and Food Technology Islamic University Bangladesh Faculty of Computer Science and Informatics Amman Arab University Jordan

In research, the relevance of data science in health care is being explored. It has been proposed that the health-care sector benefit from a Nutrition Prescription System. In the proposed model, the Map Reduce, Enhanced K-Mean clustering technique was used. Actual datasets are represented by rectangular rectangles, which contain the health care centre dataset and frequency dataset generated by the map reduction method. Operations in original data flow are represented by the elliptical boxes. Patients will be able to use this technique to access specific and specialized health care institution. As a consequence, consumers will get better services that are tailored to their specific requirements. Patients would have secure access to planned system because of backup facilities. Because diagnosis has no side effects, the concept of nutrition prescription system is better than that of medicine prescription system. In suggested dietary prescription system, patient selects symptoms. The patient is provided information on his nutritional deficits, requirements and food supply. In addition, the patient is provided additional nutritional data stored in an unstructured dataset. The outcome was derived from the data set. In milliseconds, the report also shows the time spent on traditional and contemporary duties.

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Analyzing Restricted Boltzmann Machine Neural Network for Building Recommender Systems 2

Analyzing Restricted Boltzmann Machine Neural Network for Bu...

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2nd International Conference on engineering Technology and its Applications, IICETA 2019

作者： Jabbar, Ismael Abdul Sattar Alhamdani, Rafah Shihab Abdullah, Mohammed Najm Mustansiriyah University College of Science Baghdad Iraq Iraqi Commission for Computers Informatics Baghdad Iraq University of Technology Computer Engineering Dept. Baghdad Iraq

ISBN: (纸本)9781728141053

Deep learning becomes a useful technology in many fields for solving many problems with high ratio of user satisfaction as well playing a big role as alternative smart techniques for handling big data problems. While recommender system one of the hottest fields that deep learning achieves huge success in case of predication the user needs based on their preferences. Restricted Boltzmann machine (RBM neural network) used for building recommender system with various types of the recommender. This paper analyzes the factors (Batch size, and number of epochs) that affect RBM to build a such recommender while shows the errors and time consumption as analysis factors on a dataset with 1 million movie rating. © 2019 IEEE.

关键词： Recommender systems

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A study on effects of GST on farmers and agricultural sector: An empirical analysis in India

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AIP Conference Proceedings 2023年第1期2587卷

作者： Arun Gautam Gitanjali Shrivastava A. Pankajam Rohit Bansal Ankur Gupta Malik Bader Alazzam Faculty of Management JECRC University Jaipur Rajasthan India Dept. of Law Symbiosis Law School Pune India Dept. of Business Administration Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore Tamil Nadu India Dept. of Management Studies Vaish College of Engineering Rohtak Haryana India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

After years of discussion, debate and arguments GST finally got implemented in India. GST is a tax collected on manufacture, sales and consumption of goods and services at a National level. Implementation of GST impacted every sector of the Indian Economy, especially the agricultural sector and the people whose livelihood are dependent of the sector. Agricultural sector holds approximately 16% of the total GDP of India and around 60% of population is employed by the Agricultural sector. Agri inputs are most important for agriculture and their supply on time is very important for increasing crop productivity. These includes products like pesticides, fertilizers etc. The change in tax regime has resulted in changes in price structure of products used in agriculture, inputs, imports and exports and manufacturer and dealer margin. It has relieved farmers to some extent but also put them into mild pressure which we will see.

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Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning

arXiv

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arXiv 2022年

作者： Huo, Cuiying He, Dongxiao Li, Yawen Jin, Di Dang, Jianwu Zhang, Weixiong Pedrycz, Witold Wu, Lingfei College of Intelligence and Computing Tianjin University Tianjin300350 China School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China Dept of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Anytime.AI ArmonkNY10504 United States

Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming. Self-supervised contrastive learning has been proposed to address the problem of requiring annotated data by mining intrinsic properties in the given data. However, the existing contrastive learning methods are not suitable for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e.g., meta-path) in graph data while ignoring noises in node attributes and graph topologies. We develop the first novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidances of node attributes and graph topologies and integrates and enhances them by a reciprocally contrastive mechanism to better model heterogeneous graphs. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately. We further use both attribute similarity and topological correlation to construct high-quality contrastive samples. Extensive experiments on four large real-world heterogeneous graphs demonstrate the superiority and robustness of HGCL over several state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Optimal Encoding and Decoding Algorithms for the RAID-6 Liberation Codes

Optimal Encoding and Decoding Algorithms for the RAID-6 Libe...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Zhijie Huang Hong Jiang Zhirong Shen Hao Che Nong Xiao Ning Li Dept. of Computer Science and Engineering University of Texas at Arlington Arlington USA School of Informatics Xiamen University Xiamen China School of Data and Computer Science Sun Yat-Sen University Guangzhou China

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

RAID-6 is gradually replacing RAID-5 as the dominant form of disk arrays due to its capability of tolerating concurrent failures of any two disks, as well as the case of encountering an uncorrectable read error during recovery. Implementing a RAID-6 system relies on some erasure coding schemes, and so far the most representative solutions are EVENODD codes [1], RDP codes [2] and Liberation codes [3], none of which has emerged as a clear "all-around" winner. In this paper, we are interested in revealing the undiscovered potential of the Liberation codes, since these codes have the following attractive features: (a) they have the best update performance, (b) they have better scalability, and (c) they are open-sourced and publicly available, as well as the following drawbacks: fair encoding performance and, more importantly, relatively poor decoding performance. Specificly, we present novel optimal encoding and decoding algorithms for the Liberation codes by introducing an alternative, geometric presentation of these codes. The proposed algorithms completely eliminate redundant computations during the encoding and decoding procedures by extracting and reusing common expressions between the two types of parity constraints, and do not involve any matrix operations on which the original algorithms are based. Our experiment results show that compared with the original solution, the proposed encoding and decoding algorithms reduce the number of XOR's by up to 16 percent and 15 ~20 percent respectively, and the encoding and decoding throughputs are increased by 22.3 percent and at most 155 percent respectively. Moreover, the encoding complexity reaches the theoretical lower bound, while the decoding complexity is also very close to the theoretical lower bound.

关键词： Encoding Decoding Arrays Complexity theory Measurement Standards Reed-Solomon codes

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Elastic Scale-Free Networks Model Based on the Mediaton-Driven Attachment Rule

Elastic Scale-Free Networks Model Based on the Mediaton-Driv...

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IEEE International Conference on Data Stream Mining & Processing (DSMP)

作者： Vadim Shergin Larysa Chala Serhii Udovenko Mariya Pogurskaya Artificial Intelligence dept. Kharkiv National University of Radio Electronics Kharkiv Ukraine Informatics and Computer Engineering dept Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132150

The problem of modeling scale-free networks is considered. Mediation-driven attachment rule with nonconstant number of added links is proposed. Applying the copy-factor as the control parameter instead of traditionally used number of incoming links makes it possible to generate networks model, which combines the key benefits of mediation- driven attachment, such as indirect using the nodes degree, and elasticity, such as different relative growth rates for links and nodes. The properties of proposed networks model were studied analytically and confirmed by simulation results.

关键词： Elasticity Artificial intelligence Biological system modeling Computational modeling Data models Social network services Numerical models

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Hardware Acceleration of Monte-Carlo Sampling for Energy Efficient Robust Robot Manipulation

arXiv

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arXiv 2020年

作者： Liu, Yanqi Calderoni, Giuseppe Bahar, R. Iris Dept.of Computer Science Brown University ProvidenceRI United States Dept.of Automation and Informatics Politecnico di Torino Torino Italy School of Engineering Dept. of Computer Science Brown University ProvidenceRI United States

Algorithms based on Monte-Carlo sampling have been widely adapted in robotics and other areas of engineering due to their performance robustness. However, these sampling-based approaches have high computational requirements, making them unsuitable for real-time applications with tight energy constraints. In this paper, we investigate 6 degree-of-freedom (6DoF) pose estimation for robot manipulation using this method, which uses rendering combined with sequential Monte-Carlo sampling. While potentially very accurate, the significant computational complexity of the algorithm makes it less attractive for mobile robots, where runtime and energy consumption are tightly constrained. To address these challenges, we develop a novel hardware implementation of Monte-Carlo sampling on an FPGA with lower computational complexity and memory usage, while achieving high parallelism and modularization. Our results show 12X–21X improvements in energy efficiency over low-power and high-end GPU implementations, respectively. Moreover, we achieve real time performance without compromising accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Robotics

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Image Classification of Cyanobacteria Microcystis Aeruginosa in raw Water Samples in Curitiba's Region

Image Classification of Cyanobacteria Microcystis Aeruginosa...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Geisla de Albuquerque Melo Antonio Carlos Sobieranski Thelma Alvim Veiga Ludwig Lucas Ferrari de Oliveira Informatics Dept. Federal University of Parana Curitiba Brazil Computer Engineering Dept. Federal University of Santa Catarina Ararangua Brazil Federal University of Parana Curitiba Brazil

ISBN: (数字)9781728194295

ISBN: (纸本)9781728194301

Surveys studies carried out in different countries demonstrated that about 75% of lake water samples contain toxic cyanobacteria [4, 5]. The toxin that is must recurrent in Brazilian water bodies are the microcystin [6]. Microcystins are hepatotoxins produced by some species of cyanobacteria, and act by inflicting damage to cells from the liver and other organs [7]. Cases around the world have been reported over time, including health injuries by contact direct or not with contaminated water, as well livestock and humans deaths [4]. Due to its sanitary importance, the water that the population consumes needs regular monitoring in order to avoid contact and possible adverse effects. The objective of this work is to propose a computational approach for the identification of Microcystis aeruginosa from images, in raw water reservoirs, automatically, using artificial intelligence techniques. The identification was developed using convolutional neural networks (CNN), where two distinct model were compared. The obtained results demonstrated the effectiveness of the proposed approach to solve the problem of recognizing cyanobacterias.

关键词： Training Microscopy Water pollution Convolutional neural networks Reservoirs Organisms

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