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GestFanz - A Human Machine Interactive Model for Fan automation 3

GestFanz - A Human Machine Interactive Model for Fan Automat...

引用

3rd International Conference on Intelligent Computing, Instrumentation and control Technologies, ICICICT 2022

作者： Reddy, Panyam Rohit Katte, Pranav Soni, Prasoon Agrawal, Garima Kundu, Shreya Santhi, V. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Humans always look for the easiest way possible for doing a particular task. The goal of Human-Computer Interaction is to make the system more efficient and usable for all kinds of people (especially the disabled). The most recent progress that we can see is in improving a normal switchenabled fan to a remote-controlled fan, which is usually a higher-end version that not everybody can afford. But on the contrary, we can see that a fan is the cheapest cooling appliance that any person can afford. So, the requirement of today's world is a cheap viable smart fan that is technologically evolved yet affordable. Even in current researches and studies, although having a motion-gesture controlled fan has been worked upon, yet none of the existing works have actually implemented it in a way that can actually be used by the masses in an economical way. 'GestFanz' focuses on gestures and motion detection techniques giving a product-based outcome. By applying motion detection to camera sensors it concludes if a human is present in the room. Then using hand gestures recognition, fan speed can be changed and turned on/ off. Expressive body actions for interacting with the physical world are better than speaking out or actually doing it manually. © 2022 ieee.

关键词： Android (operating system)

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Congestion control in IP Networks using Machine intelligence

Congestion Control in IP Networks using Machine Intelligence

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Amit Kanungo Prashant Panse Department of Computer Science and Engineering Medi-Caps University Indore India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Congestion control (CC) across the entire network serves as a crucial component of TCP, effectively managing packet loss caused by network congestion resulting from overload. The emergence of numerous network technologies such as SD-WAN, Wi-Fi 6, 5G cellular, network automation, and satellite communications has introduced a multitude of diverse network data transmission scenarios. This expanded landscape poses significant challenges to conventional CC methods, which rely on rule-based approaches and may not guarantee optimal performance across diverse network environments. Consequently, there is a demand for an intelligent CC module that facilitates seamless communication while maximizing resource utilization. Machine learning techniques have demonstrated their significant role in various applications, including communication, networking, and network management. Notably, learning-based TCP CC schemes have garnered considerable attention due to their ability to learn from network interactions and adapt accordingly. This research endeavour aims to develop an experience-driven approach that empowers the congestion module to learn optimal congestion control strategies based on its own historical data, such as runtime statistics. By leveraging reinforcement machine learning algorithms, the proposed approach enables dynamic and intelligent decision-making, ultimately minimizing packet loss and enhancing network performance.

关键词： Machine learning algorithms Runtime Heuristic algorithms computational modeling Packet loss Machine learning Resource management IP networks Satellite communications Machine intelligence

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Deep Reinforcement Learning Framework for Underwater Locomotion of Soft Robot

Deep Reinforcement Learning Framework for Underwater Locomot...

引用

ieee International Conference on Robotics and automation (ICRA)

作者： Li, Guanda Shintake, Jun Hayashibe, Mitsuhiro Tohoku Univ Grad Sch Engn Dept Robot Neurorobot Lab Sendai Miyagi 9808579 Japan Univ Electrocommun Dept Mech & Intelligent Syst Engn 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

ISBN: (纸本)9781728190778

Soft robotics is an emerging technology with excellent application prospects. However, due to the inherent compliance of the materials used to build soft robots, it is extremely complicated to control soft robots accurately. In this paper, we introduce a data-based control framework for solving the soft robot underwater locomotion problem using deep reinforcement learning (DIM). We first built a soft robot that can swim based on the dielectric elastomer actuator (DEA). We then modeled it in a simulation for the purpose of training the neural network and tested the performance of the control framework through real experiments on the robot. The framework includes the following: a simulation method for the soft robot that can be used to collect data for training the neural network, the neural network controller of the swimming robot trained in the simulation environment, and the computer vision method to collect the observation space from the real robot using a camera. We confirmed the effectiveness of the learning method for the soft swimming robot in the simulation environment by allowing the robot to learn how to move from a random initial state to a specific direction. After obtaining the trained neural network through the simulation, we deployed it on the real robot and tested the performance of the control framework. The soft robot successfully achieved the goal of moving in a straight line in disturbed water. The experimental results suggest the potential of using deep reinforcement learning to improve the locomotion ability of mobile soft robots.

关键词： Training Learning systems Aquatic robots computational modeling Neural networks Robot vision systems Reinforcement learning

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Time Synchronization Extension for the IO-Link Industrial Communication Protocol

Time Synchronization Extension for the IO-Link Industrial Co...

引用

ieee International symposium on Precision Clock Synchronization for Measurement, control and Communication, ISPCS

作者： András Wiesner Péter Kaszás Franz-Otto Witte Tamás Kovácsházy Department of Artificial Intelligence and Systems Engineering Budapest University of Technology and Economics Budapest Hungary TEConcept GmbH Freiburg Germany

ISBN: (数字)9798350366112

ISBN: (纸本)9798350366129

Modern industrial automation systems are real-time, distributed systems utilizing industry and manufacturer-specific, competing communication technologies. These networks are built using Industrial Ethernet Networking solutions today, such as PROFINET, EtherCAT, EtherNet/IP, CC-Link, etc. Hopefully, the industry will move towards the ieee Time Sensitive Networking (TSN) standard in the near future. However, Ethernet-based technologies are not viable under a certain price point, nor they will be in the coming decades. Therefore, low-cost sensor and actuator networking solutions are used to aggregate sensors and actuators on the Industrial Ethernet Network. One of these is IO-Link. IO-Link has no concept of synchronized system time, and therefore, IO-Link devices cannot participate in real-time distributed systems based on a time-driven operation. The paper introduces a time synchronization extension for IO-Link to allow IO-Link devices to acquire global time and operate based on that with sub-microsecond precision. The extension is fully additive, i.e., backward compatible with standard IO-Link protocol and functions, and requires minimal hardware modification to current IO-Link devices. A proof of concept implementation is presented in the paper showing how an actual IO-Link master can be augmented for time synchronization, and how relevant hardware features of the IO-Link devices, such as timer bit numbers, influence the achievable precision.

关键词： Performance evaluation Industries Actuators Protocols Prototypes Ethernet Hardware Real-time systems Synchronization Standards

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Myocarditis Diagnosis: A Method using Mutual Learning-Based ABC and Reinforcement Learning

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

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International symposium on computational intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

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An Artificial Immune System for Adaptive Test Selection

An Artificial Immune System for Adaptive Test Selection

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Rosenbauer, Lukas Stein, Anthony Hahner, Jorg BSH Home Appliances Gewerbepk B35 Regensburg Germany Univ Hohenheim Garbenstr 9 Stuttgart Germany Univ Augsburg Eichleitnerstr 30 Augsburg Germany

ISBN: (纸本)9781728125473

The verification of a new product is a major challenge for companies. With the rise of test automation, companies can rely on vast numbers of test cases. There are situations where it is not feasible to run all available tests due to time constraints. Hence a test suite of critical tests must be compiled. The corresponding test suite is usually chosen according to a quality metric. We combine the newest insights about germinal centre artificial immune systems (GCAIS) to produce test suites with high requirement coverage and low execution time. Tests and requirements might change over the lifetime of a project and hence the test suite must be adapated. We exploit results about the usage of memory to reduce the runtime of the test suite's recomputation.

关键词： Testing Weighted Minimum Set Cover computational intelligence Artificial Immune System

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Parallel hospital: ACP-based hospital smart operating system 1

Parallel hospital: ACP-based hospital smart operating system

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1st ieee International Conference on Digital Twins and Parallel intelligence, DTPI 2021

作者： Wang, Yongjun Wang, Fei-Yue Wang, Xiao Guan, Zhongjun Ouyang, Liwei Wang, Jiangong Yan, Lan Zheng, Wenbo Zhang, Wenwen Beijing Tiantan Hospital Capital Medical University Beijing Beijing100070 China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China School of Software Engineering Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9781665433372

This article aims to propose a parallel hospital based on parallel medicine. The parallel hospital takes the parallel idea of virtual and real interaction as its core concept and uses parallel idea's artificial scenarios, computational experiments, and parallel execution (ACP) theory as the guiding theory to construct a new generation of intelligent hospital management system with virtual and real interaction. We conducted a case study of the parallel hospital practice case of Tiantan Hospital-Tiantan Smart Brain, so as to confirm the feasibility and scientificity of the proposed parallel hospital. © 2021 ieee.

关键词： Hospitals

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Data Ingestion and Harmonisation for the Maritime Domain

Data Ingestion and Harmonisation for the Maritime Domain

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ieee International Conference on Engineering, Technology and Innovation

作者： Bruno Rêga Paulo Figueiras André Grilo Amin Khodamoradi Luis Lourenço Carlos Agostinho Ricardo Jardim-Gonçalves UNINOVA – Centre of Technology and Systems (CTS) Caparica Portugal

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

The Maritime Industry is a massive business, connecting the entire world, as the main means of trading of essential goods. Nevertheless, there are challenges with the ever-increasing maritime traffic complexity, safety, performance, energy efficiency and automation. These challenges are driving the industry to embrace a digital transformation of the sector, with the application of state-of-the-art Artificial intelligence, Big Data and High-Performance Computing technologies. With the extremely large amount of data generated by shipping, it is possible to apply these technologies to model the ships and their behaviours, create digital twins of the ships, as well as to model the traffic patterns in the sea, make optimal route predictions, etc. However, due to the vast number of actors in the Maritime Industry, the large amounts of data generated by the different actors is wildly varied, heterogeneous and complex. To use this data to train Machine Learning models and Artificial intelligence technologies, there is a need for all the data coming from the different actors in the industry to be homogenised into a single unified format. To accomplish this, the authors propose the creation of the VesselAI Data Ingestion and Harmonisation Services, a tool that enables ingestion and harmonisation of generic maritime datasets. This tool provides the ability to map a raw dataset of choice to a harmonised schema with the application of Natural Language Processing algorithms, with no need to use scripts or develop code.

关键词： Industries Technological innovation computational modeling Machine learning Predictive models Traffic control Data models Natural language processing Safety Marine vehicles

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Decoupling control of Traction-and-guidance in Bilateral Air-core Linear Synchronous Motor 13

Decoupling Control of Traction-and-guidance in Bilateral Air...

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13th International symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Jia, Zhen Li, Jie Peng, Leng Chen, Qiang College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781728172101

In this paper, the electromagnetic force model of the motor is established based on the mutual inductance gradient of the coil. By analyzing the relationship between the electromagnetic force, electromagnetic gap and d-q axis current, a decoupling control method of traction-and-guidance in bilateral air-core linear synchronous motor is *** method realizes the independence of motor lateral movement and traction movement. Its effectiveness is verified by simulation, which provides a method and idea for the guidance control of the motor under ultra-high speed operation and other industrial applications. © 2021 ieee

关键词： Synchronous motors

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A More Efficient Deep-learning Processing Unit Architecture with Runtime Configurable Parallelism

A More Efficient Deep-learning Processing Unit Architecture ...

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2021 China automation Congress, CAC 2021

作者： Wang, Xiaofeng Liu, Guoming Ge, Yifan Li, Yue Zhao, Xiongbo Li, Chaoran Beijing Aerospace Automatic Control Institute National Aerospace Intelligence Control Technology Laboratory Beijing China

ISBN: (纸本)9781665426473

Typically, the loop dimension varies greatly between the different layers of a Convolutional Neural Network (CNN). However, the loop dimension's parallelism degree of most Deep-learning Processing Units (DPUs) is not runtime configurable. It results in low theoretical performance utilization for most high parallelism DPUs. To solve the problem, we propose a new DPU architecture, named Dataflow Driven Multicore Architecture (DDMA). It consists of multiple function cores, and the direction of data flow between them can be configured at runtime by a routing module. This design allows DDMA to improve parallelism degree while maintaining computational efficiency. To verify the property of DDMA, we designed a Basic-DPU (B-DPU) based on it and obtained the Extended-DPU (E-DPU) by improving the parallelism degree of B-DPU. The experimental results show that the peak performance of B-DPU reaches 512 GOPS, and the computational efficiency reaches 91.9%, which is 1.69 times that of the peer FPGA implementation under the same test algorithm. The peak performance of E-DPU reaches 1024 GOPS, and the actual performance is 1.96 times that of B-DPU. Meanwhile, Us computational efficiency is 90.15%, almost the same as B-DPU. © 2021 ieee

关键词： computational efficiency

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