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12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2020

作者： Mylonas, Costas Karagiannopoulos, Stavros Aristidou, Petros Shchetinin, Dmitry Hug, Gabriela Power Systems Laboratory ETH Zurich Zurich8092 Switzerland Dept. of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus ABB Corporate Research Center Baden5405 Switzerland

ISBN: (纸本)9781839535635

Active Distribution Networks (DNs) are expected to host an increasing number of Distributed Generators (DGs) and other Distributed Energy Resources (DERs), offering new flexible sources and enabling the provision of ancillary services to system operators. Centralized DER controls that use Optimal Power Flow (OPF) methods necessitate tractable and scalable computational tools that can handle large DNs with satisfactory performance. In this paper, we compare an iterative OPF method against.the standard exact AC OPF calculations in terms of the computational effort and solution quality. Furthermore, we highlight the suitability of the selected formulation to offer voltage support (VS) as an ancillary service to the transmission network. The results are demonstrated using a combination of a medium and low voltage grid, and show that tractable OPF formulations can unlock financial business cases for DNs that can actively participate in VS schemes. © 2020 inst.tution of engineering and technology. All rights reserved.

关键词： Acoustic generators

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Performance Analysis for Molecular Communication Under Feedback Channel Using Multipath and Single Path Technique

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SSRN 2022年

作者： Ningthoujam, Sanjit Chingkheinganba, Tekcham Chakraborty, Swarnendu Kumar Elngar, Ahmed A. Chakrabarti, Prasun Chakrabarti, Tulika Praveen, S. Phani Gupta, Amit Margala, Martin School of Computer Science and Engineering VIT University Andhra Pradesh 522237 India BSNL Dimapur797112 India Dept. of Computer Science and Engineering National Institute of Technology Arunachal Pradesh 791112 India Faculty of Computers and Information Techno India NJR Institute of Technology India Department of Chemistry Sir Padampat Singhania University Rajasthan Udaipur313601 India Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh 520007 India Department of ECE Narasaraopeta Engineering College Andhra Pradesh Narasaraopeta522601 India Computer Science School of Computing and Informatics University of Louisiana United States

Nanomachines that can run tasks and molecular communication (MC) for interconnecting nanomachines have been made possible by advances in nanotechnology. Different techniques of Automatic Repeat Request (ARQ) protocols have been devised in MC for acquiring the necessary molecules, although the retransmission of duplicate copies requires special attention. This work presents a diffusion-based MC with a feedback loop to improve MC performance. Based on the received ACK(s) or NACK(s), the Channel state determines if the state is good (G) or bad (B), and then single path or multipath procedures are used to the ‘G' and ‘B' states. The results of the MATLAB simulation illustrate how the systems compare in terms of communication rate, the possibility of complete transmission of the desired molecules, and energy consumption. When the channel state is bad, the multipath approach performs better, but when the channel state is perfect in MC, the single path strategy performs better. © 2022, The Authors. All rights reserved.

关键词： Molecules

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Quality Grading of Beef using Deep Convolutional Neural Network

Quality Grading of Beef using Deep Convolutional Neural Netw...

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HCT Information technology Trends (ITT)

作者： Aarav Sharma Tanupriya Choudhury Tanmay Sarkar Madhusmita Mohanty Nikunj Bansal Sachi Nandan Mohanty Informatics Cluster School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India CSE Dept. University of Petroleum and Energy Studies (UPES) CSE Dept. Graphic Era Hill University Dehradun Uttarakhand India Department of Food Processing Technology West Bengal State Council of Technical Education Govt. of West Bengal Malda Polytechnic Malda India VIT-AP School of Business (VSB) VIT-AP University Amaravati Andhra Pradesh India School of Computer Science & Engineering VIT-AP University Amaravati Andhra Pradesh India

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy.

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Numerical simulation of flow through an axisymmetric two-dimensional plane diffuser based on a new two-fluid turbulence model

Numerical simulation of flow through an axisymmetric two-dim...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Malikov, Zafar Mirzoev, Akmal Madaliev, Murodil Yakhshibayev, Doniyor Usmonov, Alisher Inst. of Mechanics and Seismic Stability of Struct. of the Acad. of Sci. of the Rep. of Uzbekistan Lab. of Fluid and Gas Mechanics Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khorazmiy Dept. of Computer Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khorazmiy Dept. of Algorithmization and Mathematical Modeling Tashkent Uzbekistan

ISBN: (纸本)9781665432580

The paper presents the results of a numerical study of the structure of flow through axisymmetric two-dimensional diffusers. The calculations were performed on the basis of solving the system of nonstationary equations of the two-fluid Malikov turbulence model. As a result, the longitudinal velocity profiles in different cross sections of the channel were investigated, and it was shown how grid changes affect the results of the study. The SIMPLEC algorithm was applied for the difference approximation of the initial equations. The viscous terms were approximated by the central difference and, for the convective terms, the counter-flow A.A. Samarsky scheme was used. The correctness of the results was confirmed for Reynolds number Re=20000 by comparing the numerical results with known experimental data. The comparison showed that the new model is able to predict the separated flows with great accuracy. © 2021 IEEE.

关键词： Turbulence models

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Combining Photometric Features and Relative Position to Detect and Track Target Person 2

Combining Photometric Features and Relative Position to Dete...

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2nd International Conference on computer engineering, Network, and Intelligent Multimedia, CENIM 2019

作者： Dewantara, Bima Sena Bayu Miura, Jun Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia Toyohashi University of Technology Dept. of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9781728129655

Tracking a target person is a vital job in human-robot interaction. The robot must always notice a particular person as the interaction target partner. However, it is sometimes very hard to distinguish the target person because there are many other persons around the target. In this paper, we propose a target person detection and tracking system by combining person's frontal photometric features such as face and clothing color, and coordinate of the person's location in the real world. We apply an illumination invariant face recognition method named OptiFuzz. Hue-Saturation histogram (HS-histogram) is used to obtain the clothing color feature, and a location of the person is acquired from a calibrated single camera view. All these features are then fed into an algorithm of Naive Bayes to discriminate between the target person and others. Our experimental results indicate a successful outcome as it is always possible to detect and track the target person. © 2019 IEEE.

关键词： Human robot interaction

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A Study of Multilayer Omega Networks Operating with Cut-Through Mechanism under Uniform Traffic

A Study of Multilayer Omega Networks Operating with Cut-Thro...

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South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Eleftherios Stergiou John Garofalakis Dimitrios Liarokapis Spiridoula V. Margariti Dept. Informatics & Telecommunications University of Ioannina Arta Greece Dept. of Computer Engineering and Informatics University of Patras and Computer Technology Institute and Press—“DIOPHANTUS” (CTI)) Patras Greece

ISBN: (数字)9781728164458

ISBN: (纸本)9781728164465

There is an enduring interest in developing more efficient multistage interconnection networks (MINs), as they are a key aspect of various switching sectors and the demands for higher data transfer rates. Here, semi-layer Omega type networks operating under the cut-through forwarding policy are selected as a test-bed subject for performance evaluation. This special shuffle-exchange network was modelled, analysed, and simulated. Moreover, this type of fabric is compared with corresponding single-layer Omega networks operating via cut-through or a `store and forward' forwarding technique. The results show, in a quantitative manner, how superior is the under study fabric compared to the corresponding single-layer constructions, in terms of network size, showing the advantages that are provided by both usages: fan-out and cut-through mechanisms. Moreover, the roles of the switch element's size and the reliability metric were investigated. The findings that are derived can be valuable assets for MIN designers, in order to achieve more efficient delivering networks with low overall deployment costs.

关键词： Omega network banyan networks performance evaluation cut-through semi-layer multistage interconnection networks uniform traffic multistage interconnection networks cut-through Performance Evaluation Materials

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Machine learning approach to enhance performance of suspicious activity detection system

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AIP Conference Proceedings 2023年第1期2587卷

作者： Shahanawaj Ahamad B. Bhaskara Rao K. Srikanth V. P. Hara Gopal Pritika Mehra Malik Bader Alazzam College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Dept. of Computer Science and Engineering RGM College of Engineering and Technology Nandyal Kurnool Andhra Pradesh India Dept. of Computer Science and Engineering G Pulla Reddy Engineering College Kurnool Andhra Pradesh India Research Scholar Dept. of Computer Science Guru Nanak Dev University Amritsar Punjab India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

Deep learning algorithms are a subset of the machine learning algorithms, which aim at finding several layers of distributed representations. Recently, many deep learning methods have been suggested to tackle conventional artificial intelligence issues. Machine learning is an important component of artificial intelligence, which is used to build algorithms based on the data trends and historical connections between data. Machine learning is utilized in many areas such as bioinformatics, intrusion detection, Information retrieval, game playing, marketing, virus detection, picture de-convolution and so on. Image processing and deep learning methods are linked to machine learning system to identify the fire, unauthentic vehicle and person in this study. More over gadgets such as AC, LED and other electronic equipment have been controlled in suggested system. The suggested model is offering scalable machine learning based solution to control electrical equipment remotely gives protection against.fire and un authentic person. The purpose of deep learning is to build intelligent trained network that should be competent to track fire and unauthentic items in organization. The aim of study is to consider research connected to machine learning, deep learning and image processing and analyzing the limits of earlier works. Proposed work includes linked machine learning model to collect the signal and forecast and detect on the basis of trained neural network. The presentation of prediction on web page linked to web interface has been created. Simulation guarantees the improvement of performance of model using user specified trained neural network and conducts the comparative analysis of performance. Capturing is made frame by frame in form of picture and captured information are preprocessed to minimize size of graphics then the pattern in frame is identified using deep learning method. Here pre-trained set is utilized to track recurrence of specific event. The combination of

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Deep learning integration to inject intelligence in network routing mechanism

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AIP Conference Proceedings 2023年第1期2587卷

作者： Vivek Veeraiah Mohammed Rahmath Karunendra Verma Umesh Pratap Singh Ankur Gupta Malik Bader Alazzam Dept. of R & D Computer Science Adichunchanagiri University Mandya Karnataka India Dept. of Computer Science Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India Dept. of Computer Science and Engineering Shri Ramswaroop Memorial Group of Professional Colleges Lucknow India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Mubis Jordan

Deep learning is a type of machine learning that imitates the way humans gain certain types of knowledge. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. Intelligent Routing (or Skills-based Routing), is a technology contact centers use to gather customer inquiries through voice, digital, or social channels, and then applies rules to route it to the agent best fit to resolve the issue. Research aims to inject intelligence in network routing mechanism using deep learning. Research work is working on routing mechanism to reduce the problems of previous researches. Proposed work increases the speed of routing, and less time consumption. Proposed work is based on deep learning technology for intelligent routing mechanism. The weight of a node route is determined in the first phase by taking into account these parameters. Rewards and penalties are assigned to each node based on its route once it has successfully sent data. Six scenarios and ten associated nodes were used in the route selection simulation. Nodes 1, 2, 3, 4, and 5 form a network in this architecture. This opportunistic network has nodes 6, 7, 8, 9, and 10. Black and blue nodes in the opportunistic network are shown in simulations, respectively. Using the node route table, a node’s connectivity is discovered. The system has become sophisticated enough to make decisions thanks to the usage of neural fuzzy logic. The optimization approaches choose the shortest route to pick a network for a real-world application while taking into account the best solution. The sensing mechanism, as well as animal and crop scouting mechanisms have all been examined in this study. The information would be sent in the form of analogue signals via the various sensor nodes that are linked to the Neuro fuzzy controller. The proposed work would provide flexible and scalable solution to implement opportunistic routing in precision

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Expert-driven Rule-based Refinement of Semantic Segmentation Maps for Autonomous Vehicles

Expert-driven Rule-based Refinement of Semantic Segmentation...

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IEEE Symposium on Intelligent Vehicle

作者： Eric L. Manibardo Ibai Laña Javier Del Ser Alexander Carballo Kazuya Takeda Tecnalia Basque Research and Technology Alliance (BRTA) Derio Spain University of the Basque Country (UPV/EHU) Bilbao Spain Dept. of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Institute of Innovation for Future Society Nagoya University Nagoya Japan Tier IV Inc Nagoya University Open Innovation Center Nagoya Japan Graduate School of Informatics Nagoya University Nagoya Japan

Semantic segmentation aims at assigning labels to every pixel of a given image. In the context of autonomous vehicles, semantic segmentation models should be trained with data collected from the traffic network through which vehicles are expected to circulate. Road regulation, weather conditions, and other context features may differ between regions, making local semantic segmentation datasets extremely valuable. However, the high ground truth annotation costs represent a hindrance to the development of such models. The upsurge of powerful feature learning architectures leaves room for semantic segmentation models trained on an unsupervised fashion. This observation vertebrates the purpose of this work: to produce coarse segmentation maps for scene understanding without the need of annotated data. We depart from an unsupervised model that yields low-quality results. The proposed methodology establishes a set of guidelines for the enhancement of segmentation maps. Obtained results expose an improvement of the segmentation quality thanks to the application of our devised guidelines, paving the way for the automatic generation of semantic segmentation datasets.

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CT Image Harmonization for Enhancing Radiomics Studies

CT Image Harmonization for Enhancing Radiomics Studies

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Md Selim Jie Zhang Baowei Fei Guo-Qiang Zhang Jin Chen Dept. of Computer Science University of Kentucky Lexington KY Dept. of Radiology University of Kentucky Lexington KY Dept. of Bioengineering University of Texas at Dallas Dallas TX Dept. of Neurology University of Texas Health Science Center at Houston Houston TX Inst. for Biomedical Informatics University of Kentucky Lexington KY

ISBN: (纸本)9781665429825

While remarkable advances have been made in Computed Tomography (CT), most of the existing efforts focus on imaging enhancement while reducing radiation dose. How to normalize CT images acquired using non-standard protocols is vital for decision-making in cross-center large-scale radiomics studies but remains the boundary to explore. We develop a novel GAN-based image standardization algorithm called RadiomicGAN to mitigate the discrepancy caused by using non-standard acquisition protocols. In RadiomicGAN, a pre-trained U-Net has been adopted as part of the generator to learn radiomic feature distributions efficiently, and a novel training approach, called Window Training, has been developed to smoothly transform the pre-trained model to the medical imaging domain. In the experiments, we compared RadiomicGAN with four state-of-the-art CT image standardization approaches on both patient and phantom CT images acquired using three different reconstruction kernels. We objectively evaluated model performance based on more than 1,000 radiomic features. The results show that RadiomicGAN clearly outperforms the compared models. The source code, manual, and sample data are available at https://*** selim-iitdu/radiomicGAN.

关键词： Training Protocols Computed tomography Transfer learning Standardization Transforms Feature extraction

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