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control of Steady-State Disturbances in Microgrids using Smell Agent Symbiotic Organism Search

Control of Steady-State Disturbances in Microgrids using Sme...

引用

Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Salisu Mohammed Yusuf A. Sha’Aban Imeh J. Umoh Ahmed T. Salawudeen Muhammed B. Mu’Azu Zaharuddeen Haruna Department of Maintenance Engineering Nigerian National Petroleum Company Kaduna Refining & Petrochemicals Company Limited (KRPC) Kaduna Nigeria Department of Electrical Engineering KSA Faculty of Engineering University of Hafr Al Batin Saudi Arabia Department of Computer Engineering Faculty of Engineering Ahmadu Bello University Zaria Kaduna Nigeria Department of Electrical Engineering Faculty of Engineering University of Jos Jos Nigeria

This paper presents an optimal control of steady-state disturbances in the power flow of microgrids. In microgrid operations, the variation in loads induces instability in line-parameters such as voltage and frequency, resulting in a decrease in power quality and overall system stability. To address these challenges, a Smell Agent Symbiotic Organism Search (SASOS) algorithm, which integrates Smell Agent Optimizer (SAO) and Symbiosis Organism Search (SOS) algorithms, was codified in the design of an optimized controller to improve power quality control. The SASOS, SAO, and SOS algorithms were applied in the Microgrid’s Centralized controller (MCC) for the control of steady-state disturbances. The simulation results showed the viability of the MCC-SASOS technique compared to the MCC-SOS, MCC-SAO, and MCC techniques.

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A Robust Method for Estimating Periods of Block Interleavers

A Robust Method for Estimating Periods of Block Interleavers

引用

computer Science, Electronic Information Engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Shunan Han Yuchen Bing Li Du Yuanzheng Ge Aviation University of Air Force Changchun Jilin China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

For estimating the period of a block interleaver, the robustness of Gaussian elimination-based method is not high. To overcome this limitation, a modified estimation method is proposed. The interleaved sequence is reshaped into a matrix firstly. Then Gaussian elimination is used to estimate the rank of the matrix, and if the number of matrix columns is a multiple of the period of the block interleaver, the matrix is of deficient rank. In the procedure of Gaussian elimination, the threshold of judging dependence of the columns is revised and soft-decision data is used to select reliable bits to decrease bit errors in the matrix. Compared with original method, the robustness of our method improves.

关键词： computer science Estimation Robustness Intelligent control

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Object Size Prediction from Hand Movement Using a Single RGB Sensor 3rd

Object Size Prediction from Hand Movement Using a Single RGB...

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Dagioglou, Maria Soulounias, Nikolaos Giannakopoulos, Theodoros Natl Ctr Sci Res Demokritos Neapoleos 27 & Patriarchou Grigoriou E Aghia Paraskevi 15341 Attica Greece Natl & Kapodistrian Univ Athens Ilissia 15772 Attica Greece

ISBN: (纸本)9783031056437;9783031056420

Human intention prediction is essential, among others, for safe and fluent human-robot collaboration. Prediction of the intention from human movement offers the advantage of a context-free paradigm. Naturally, such robot capabilities require high spatial and temporal resolution. While certain sensing devices, such as the motion capture systems, satisfy these requirements, in many applications visual sensors might be preferable. However, the information regarding human movement from RGB-D cameras is noisy and of lower frequency. In the present work, we study if it is possible to predict human intentions from hand movements observed by a single RGB-D sensor. We collected data from eight participants as they were grasping objects of three different sizes. OpenPose was used to extract the 2D hand and arm joint pixel coordinates that were then processed to extract human movement kinematic information and engineer appropriate features. Traditional machine learning methods were applied to evaluate the usability of our dataset. Overall, the results were significantly above chance level and showed that it is possible to predict the size of the object a human intents to grasp using hand kinematics extracted from a single visual sensor. Finally, it is feasible, under certain conditions, to use this prediction in real-time human-robot collaboration.

关键词： Human action prediction Human intention recognition User studies Datasets Machine learning Robot vision Human-robot interaction Human-robot collaboration

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Design and Development of Intelligent Voice Personal Assistant using Python 3

Design and Development of Intelligent Voice Personal Assista...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Appalaraju, Vadaboyina Rajesh, V. Saikumar, K. Sabitha, P. Kiran, K Ravi Department of Electronics and Computer Engineering Andhra Pradesh Visakhapatnam530049 India Dep of ECE Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram522502 India Department of CSE St. Martin's Engineering College Department of IT Gokaraju Rangaraju Institute of Engineering and Technology India

ISBN: (纸本)9781665438117

In this work, describe a virtual personal assistant for smart phones and other devices that acts as a helping hand to improve various personal responsibilities, allowing people to focus on more essential matters. It completes the assignment based on a variety of different questions provided by the voice assistant. As a result, the design and development of an intelligent voice personal assistant is in high demand to make our lives easier and simpler. The current paper assists us in completing user activities such as searching for information about a query, movie details, writing a note, sending a message to a specific person at a specific time, providing location, playing music, date and time, and opening files. It necessitates the usage of a microphone to receive user input. An attempt is made here to construct an intelligent voice personal assistant using Python, which provides the capacity to operate devices based on voice or speech for extracting information to conduct activities on a desktop or other smart device. © 2021 IEEE.

关键词： Speech recognition

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Detecting Minors According to South African Law Using computer vision Methods 25th

Detecting Minors According to South African Law Using Compu...

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25th international conference on Human-computer Interaction, HCII 2023

作者： Moodley, Tevin Sithungu, Siphesihle University of Johannesburg Kingsway Avenue and University Rd Auckland Park Johannesburg2092 South Africa

ISBN: (纸本)9783031360039

Age estimation is one of the areas of interest in computer vision, which is evident from the increased amount of related research over the last few years. This is largely due to the exceptional levels of classification accuracy demonstrated by Convolutional Neural Networks (CNNs) in computer vision tasks. One of the main challenges faced when training age detection models are accounting for people’s diversity, which raises the importance of using datasets with as much diversity as possible. Another important factor to consider is the reason behind performing age estimation, which can either classify people’s age or detect if someone’s age exceeds (or is below) a specific threshold. This paper presents work done to detect minors according to South African law (which is under 18 years of age). South Africa is a very diverse country. As such, the UTKFace dataset, containing face images with a wide range of ethnicities, was used to train the Inception Resnet V2 to detect minors according to South African Law. The dataset was reduced to only include relevant images with the aim of obtaining an equal distribution of gender and ethnicity to ensure relevance to the South African context. A model accuracy of 99.73% was achieved, demonstrating the model’s ability to distinguish between underage and legal age classes. It was also noted that class imbalance and the reduced number of samples were inhibiting factors to the model’s performance in terms of precision and recall. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

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A Smartnic-Based Secure Aggregation Scheme for Federated Learning 3

A Smartnic-Based Secure Aggregation Scheme for Federated Lea...

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3rd international conference on computer Engineering and Intelligent control, ICCEIC 2022

作者： Zang, Shengyin Fei, Jiawei Ren, Xiaodong Wang, Yan Cao, Zhuang Wu, Jiagui College of Artificial Intelligence Southwest University Chongqing China Defense Innovation Institute Beijing China College of Computer National University of Defense Technology Changsha China School of Physical Science and Technology Southwest University Chongqing China

Federated learning is a widely used distributed machine learning technique where participants collaborate to train neural network models without disclosing private training datasets. Technologies like homomorphic encryption are proposed to avoid private data leakage. However, they are plagued by two problems, including performance degradation caused by excessive computational and communication overhead and insecurity due to the data exposure on the parameter server. Focusing on these problems, we propose an efficient and privacy-preserving federated learning solution that improves performance and security by offloading the aggregation procedure into the hardware data plane, like FPGA-based SmartNICs. Furthermore, we also combine it with differential privacy techniques. Our method has higher security because it is hard to access the data plane. Besides, massive experiments show that, compared to the system employing additively homomorphic encryption, our scheme reduces the communication cost by around 59.5% and offers around 2.5× speedup at the aggregation stage while significantly decreasing the participant’s computational overhead. © 2022 Copyright for this paper by its authors.

关键词： Learning systems

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Design and Implementation of Superstick for Blind People using Internet of Things 3

Design and Implementation of Superstick for Blind People usi...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Singh, Archit Mallikarjuna, Basetty Murtuza, Mohammad Tiwari, Vaibhav Galgotias University Greater Noida Department of Computer Engineering India Galgotias University Greater Noida School of Computing Science and Engineering India

ISBN: (纸本)9781665438117

Technologies are rising very rapidly, allowing people to make their lives safer and simpler. The smart stick is a tool for helping people who are sightless to recognise their path. Blind people suffer from a loss of ability to perform their day-to-day activities, from going down the street to visiting friends or relatives, or any other everyday things. Therefore by making a stick that will allow the person to walk safely without fear of hitting anyone on the way or other solid objects, the solution to this major problem is designed. The stick was designed using tools from Solid Jobs. Using Proteus tools to design and model electrical circuits, the electric circuit was simulated. We used ultrasonic sensors to do this. In the stick, one sensor was mounted and the other two were located on both sides, left and right. Vibrating motor and buzzer alarms were used to sense the motion from almost every side to warn the person if some obstruction is identified near him and led to tell the person who is coming towards him. © 2021 IEEE.

关键词： Internet of things

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Enhancing Smart Grid Security with Quantum Cryptography: A BB84-based Framework

Enhancing Smart Grid Security with Quantum Cryptography: A B...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Shail Garg Yerukola Gayatri A. Surya Kausthub Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The study investigates how quantum cryptography can be employed to improve security in smart grid communication networks. Attempting to use the BB84 QKD protocol, it allows secure creation and management of those cryptographic keys that cannot be broken with the help of quantum and traditional hacking techniques. It depends on symmetric encryption mechanisms like AES to provide high data confidentiality during transmission. Regarding the key management, the system provides a key management server that distributes keys to the grid nodes and performs key updating to ensure control with the managing authority. It also presents reliability under practical conditions through performance analogs such as Quantum Bit Error Rates (QBER), and system latency. This approach shows that quantum cryptography can provide a highly secure and efficient communication network for the next-generation smart grid.

关键词： Protocols computer network reliability Bit error rate Smart grids Encryption Quantum key distribution Reliability Communication networks Quantum cryptography Standards

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Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and computer vision Systems

Big Data and Deep Learning in Smart Cities: A Comprehensive ...

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Computing and Machine Intelligence (ICMI), international conference on

作者： Victor Adewopo Nelly Elsayed Zag Elsayed Murat Ozer Constantinos L. Zekios Ahmed Abdelgawad Magdy Bayoumi School of IT University of Cincinnati Cincinnati USA Dept. of Elec. & Comp. Eng. Florida International University Florida United States School of Eng. & Tech. Central Michigan University Michigan USA Dept. of Elec. & Comp. Eng. University of Louisiana Lafayette Louisiana

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In the dynamic urban landscape, where interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This dataset is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

关键词： computer vision Visualization Smart cities Tracking Video sequences Transportation Transforms

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Intent-Based Network Management in 6G Core Networks 13

Intent-Based Network Management in 6G Core Networks

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13th international conference on Information and Communication Technology Convergence, ICTC 2022

作者： Lingga, Patrick Kim, Jeonghyeon Joshua Jeong, Jaehoon Paul Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Supporting network intelligence is one of the main goals of 6G core networks. Network intelligence can provide a self-driving network that optimizes and adjusts itself with the least amount of human interaction. Intent-Based Networking (IBN) is an approach that can be combined with 6G core networks to provide automation. The IBN concept provides a closed-loop control architecture that is able to adapt to the status of the network by monitoring data from network functions. Network Data Analytics Function (NWDAF) is proposed by the 3rd Generation Partnership Project (3GPP) to collect monitoring data from multiple network functions. This paper proposes an architecture that combines the NWDAF with the IBN concept to merge it with Artificial Intelligence (AI) and Machine Learning (ML) to analyze the monitoring data. The proposed scheme creates a system that will be able to provide automatic verification and optimization for the 6G core networks. © 2022 IEEE.

关键词： Data Analytics

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