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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是421-430 订阅

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An Overview of Optimized Inventory Management Models in Technology Companies: Historical Developments, Practical Applications, and AI-driven Approaches 7

An Overview of Optimized Inventory Management Models in Tech...

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7th International conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Arnawtee, Mohammed Mahde Mahmood Zaiter, Murooj Fadhil Zaiter Institute of radio electronics and information technologies Ural Federal University Dept. Big Data Analysis and Methods of Analysis Ekaterinburg Russia

ISBN: (纸本)9798331529314

this article provides a comprehensive overview of improved inventory management models in technology companies and how AI methodologies such as machine learning, predictive analytics, and optimization algorithms are revolutionizing inventory management by enabling real-time decision making, accurate demand forecasting, and automated inventory replenishment by integrating historical context with state-of-the-art AI techniques. this article provides a thorough understanding of how technology companies can leverage advanced inventory management models to maintain a competitive edge and challenges associated with implementing AI- driven inventory systems. the findings offer valuable insights for both industry practitioners and academics, contributing to the development of more resilient and adaptive inventory management strategies within the rapidly evolving technology sector. © 2024 ieee.

关键词： Inventory control

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Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals 7

Compact Deep Neural Networks for Computationally Efficient G...

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7th ieee International conference on Biomedical Robotics and Biomechatronics (BIOROB)

作者： Hartwell, Adam Kadirkamanathan, Visakan Anderson, Sean R. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S13JD S Yorkshire England

ISBN: (纸本)9781538681831

Machine learning classifiers using surface electromyography are important for human-machine interfacing and device control. Conventional classifiers such as support vector machines (SVMs) use manually extracted features based on e.g. wavelets. these features tend to be fixed and non-person specific, which is a key limitation due to high person-to-person variability of myography signals. Deep neural networks, by contrast, can automatically extract person specific features - an important advantage. However, deep neural networks typically have the drawback of large numbers of parameters, requiring large training data sets and powerful hardware not suited to embedded systems. this paper solves these problems by introducing a compact deep neural network architecture that is much smaller than existing counterparts. the performance of the compact deep net is benchmarked against an SVM and compared to other contemporary architectures across 10 human subjects, comparing Myo and Delsys Trigno electrode sets. the accuracy of the compact deep net was found to be 84.2 +/- 0.06% versus 70.5 +/- 0.07% for the SVM on the Myo, and 80.3 +/- 0.07% versus 67.8 +/- 0.09% for the Delsys system, demonstrating the superior effectiveness of the proposed compact network, which had just 5,889 parameters - orders of magnitude less than some contemporary alternatives in this domain while maintaining better performance.

关键词： Electrodes Neural networks Feature extraction Support vector machines Graphics processing units Convolution Embedded systems

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Integrated data-driven and Physics-driven Multi-Module Magnetic Equilibrium Calculation and Analysis Tool 7

Integrated Data-Driven and Physics-Driven Multi-Module Magne...

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7th International conference on Advanced Algorithms and control Engineering, ICAACE 2024

作者： Bai, Shuhang Wan, Chenguang Hu, Yemin Yu, Zhi Anhui University Hefei China Institute Of Plasma Physics Hefei Institutes Of Physical Science Chinese Academy Of Sciences Hefei China

ISBN: (纸本)9798350361445

the modelling and analysis of complex physical systems can be approached through data-driven and physics-driven methods. To leverage their different advantages, it is necessary to couple multiple computational models that use different methods. However, integrating these models requires additional work due to their different computational requirements and characteristics. An integrated analytical and computational tool has been developed for the tokamak plasma magnetic equilibrium problem in magnetic confinement fusion. It integrates data-driven and physics-driven models. Two execution modes have been designed and developed to optimize their synergy and improve the overall utility, taking into account the differences in timescales and computational efficiency. the tool has undergone full testing and is now operational. © 2024 ieee.

关键词： Computational efficiency

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Context-Aware learning from Demonstration: Using Camera data to Support the Synergistic control of a Multi-Joint Prosthetic Arm 7

Context-Aware Learning from Demonstration: Using Camera Data...

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7th ieee International conference on Biomedical Robotics and Biomechatronics (BIOROB)

作者： Vasan, Gautham Pilarski, Patrick M. Univ Alberta Dept Comp Sci Edmonton AB T6G 2E1 Canada Univ Alberta Dept Med Edmonton AB T6G 2E1 Canada

ISBN: (纸本)9781538681831

Muscle synergies in humans are context-dependent-they are based on the integration of vision, sensorimotor information and proprioception. In particular, visual information plays a significant role in the execution of goal-directed grasping movements. Based on a desired motor task, a limb is directed to the correct spatial location and the posture of the hand reflects the size, shape and orientation of the grasped object. Such contextual synergies are largely absent from modern prosthetic robots. In this work, we therefore introduce a new algorithmic contribution to support the context-aware, synergistic control of multiple degrees-of-freedom of an upper-limb prosthesis. In our previous work, we showcased an actor- critic reinforcement learning method that allowed someone with an amputation to use their non-amputated arm to teach their prosthetic arm how to move through a range of coordinated motions and grasp patterns. We here extend this approach to include visual information that could potentially help achieve context-dependent movement. To study the integration of visual context into coordinated grasping, we recorded computer vision information, myoelectic signals, inertial measurements, and positional information during a subject's training a robotic arm. Our approach was evaluated via prediction learning, wherein our algorithm was tasked with accurately distinguishing between three different muscle synergies involving similar myoelectric signals based on visual context from a robot-mounted camera. these preliminary results suggest that even simple visual data can help a learning system disentangle synergies that would be indistinguishable based solely on motor and myoelectric signals recorded from the human user and their robotic arm. We therefore suggest that integrating learned, vision-contingent predictions about movement synergies into a prosthetic control system could potentially allow systems to better adapt to diverse situations of daily-life prosthesis

关键词： Prosthetics Muscles Electromyography Grasping Robots Training Wrist

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A CNN-ELM Compound Fault Diagnosis Method Based on Joint Distribution Modification 7

A CNN-ELM Compound Fault Diagnosis Method Based on Joint Dis...

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7th International conference on Information, Cybernetics, and Computational Social systems, ICCSS 2020

作者： Dong, Jiechao Yan, Liping Xia, Yuanqing Zhang, Jinhui Wang, Xianghua Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems Shandong266590 China

ISBN: (纸本)9781728162461

In the industrial field, compound faults often occur on rolling bearings and it's difficult to diagnose them correctly. To solve this problem, this article proposes a CNN-ELM compound fault diagnosis method based on joint distribution modification. Firstly, considering the complementarity and coupling of data from multiple sensors, a data input trick of multi-sensor data connected in parallel is designed. Secondly, due to the discrepancy of distribution between the compound fault data features and the single fault data features, the marginal distribution matrix and the posterior distribution matrix are used to modify the CNN-ELM network, so that the network can extract more reliable data features for fault diagnosis. Finally, referring to the categories and criteria of bearing damage proposed by Paderborn University, the label code is defined. the corresponding data set is used to verify the proposed algorithm. Experimental results show that the algorithm can accurately obtain detailed fault information such as fault location, fault type, and fault severity. © 2020 ieee.

关键词： Roller bearings

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Knowledge at first glance: A model for a data visualization recommender system suited for non-expert users 7

Knowledge at first glance: A model for a data visualization ...

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7th International conference on data Science, Technology and Applications, data 2018

作者： Kubernátová, Petra Friedjungová, Magda Van Duijn, Max Leiden Institute of Advanced Computer Science Leiden University Netherlands Faculty of Information Technology Czech Technical University in Prague Czech Republic

ISBN: (纸本)9789897583186

In today's age, there are huge amounts of data being generated every second of every day. through data visualization, humans can explore, analyse and present it. Choosing a suitable visualization for data is a difficult task, especially for non-experts. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such as low accessibility and indecisiveness. the aim of this study is to create a model for a data visualization recommender system for non-experts that resolves these issues. Based on existing work and a survey among data scientists, requirements for a new model were identified and implemented. the result is a question-based model that uses a decision tree and a data visualization classification hierarchy in order to recommend a visualization. Furthermore, it incorporates both task-driven and data characteristics-driven perspectives, whereas existing solutions seem to either convolute these or focus on one of the two exclusively. Based on testing against existing solutions, it is shown that the new model reaches similar results while being simpler, clearer, more versatile, extendable and transparent. the presented model can be applied in the development of new data visualization software or as part of a learning tool. Copyright © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

关键词： data visualization

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Autonomous data-driven Water Management Using IoT and Machine learning

Autonomous Data-Driven Water Management Using IoT and Machin...

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2023 ieee CHILEAN conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon 2023

作者： Zaidi, Syed Waqas Hussain Naqvi, Syeda Tayyaba Ali Mehbodniya, Abolfazl Webber, Julian L. Department of Avionics Engineering Islamabad Pakistan University of the Punjab College of Information Technology Department of Software Engineering Lahore Pakistan Department of Electronics and Communication Engineering Doha Area 7th Ring Road Kuwait

ISBN: (纸本)9798350369533

the Internet of things (IoT) based applications rapidly engulf human-driven systems. the inherent benefits, like process automation, have motivated researchers to propose novel IoT applications in multifaceted domains, contributing towards the economy of effort and conservation of precious resources. Existing literature emphasizes innovative water monitoring systems, but more attention is needed to efficiently use data from water sensors. the paper initially proposes a novel IoT-enabled water management system for data collection and analysis over an extended period. Based on the data collected after system deployment, we leverage the power of statistical techniques and Machine learning to identify valuable trends from actual water consumption records, such as isolating the source of water wastage, forecasting future water demands, detecting anomalies in data and evaluating the impact of different environmental variables on water consumption. this paper also proposes a subsystem to control the usage of water. © 2023 ieee.

关键词： Internet of things

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Usage of Fully Convolutional Network with Clustering for Traffic Light Detection 7

Usage of Fully Convolutional Network with Clustering for Tra...

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7th Mediterranean conference on Embedded Computing (MECO)

作者： Yudin, Dmitry Slavioglo, Dmitry Belgorod State Technol Univ Dept Tech Cybernet Belgorod Russia

ISBN: (纸本)9781538656839

In this paper we consider a traffic light detector constructed on the basis of a fully convolutional neural network for segmenting traffic lights on image and subsequent clustering, which allows us to obtain bounding boxes for traffic lights. the proposed approach is compared with one of the most effective object detectors - Single Shot Multibox detector (SSD). We implemented algorithms for objects detection on an embedded system based on the NVidia Jetson TX2 platform. Traffic light detection recall for the proposed approach is better than SSD and higher than 0.9 on both the testing and training samples from relatively small data set (500 training images and 107 testing images). Time of traffic lights detection on one frame is about 50 ms. the results of the traffic light detection prove the possibility of applying the approach based on fully convolutional neural network with clustering for embedded autonomous vehicle control systems and driver assistance systems.

关键词： image recognition detection traffic light deep learning fully convolutional network clustering single shot multibox detector embedded system

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A Statistical learning Approach for Cable-driven Robot in Rehabilitation Training 7

A Statistical Learning Approach for Cable-driven Robot in Re...

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7th ieee International conference on Advanced Robotics and Mechatronics

作者： Zheng, Jianlin Xie, Chenglin Song, Rong Xu, Ting Sun Yat Sen Univ Guangdong Prov Engn & Technol Ctr Adv & Portable Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Biomed Engn Key Lab Sensing Technol & Biomed Instrument Guang Guangzhou Guangdong Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

Cable-driven rehabilitation robots can be used to recover the upper-limb motor function of hemiplegia patients after stroke. In this study, a statistical approach was proposed to enable a 3-DOF upper-limb cable-driven rehabilitation robot (UCDRR) to predict the human intention by learning human skills from demonstrations. Specifically, in the learning phase, Gaussian mixture model (GMM) was exploited to statistically analyze the human-robot interaction features (i.e., Electromyography (EMG) and interaction force) based on the recorded demonstrator-robot interaction data. then, during prediction, the robot estimated the interaction force from the measured EMG by using Gaussian mixture regression (GMR). the experimental results of the interaction force prediction in simulation demonstrated that the proposed approach could statistically extract the variable information from the multiple demonstrations successfully and estimate subject-robot interaction forces with high accuracy. this study will help the UCDRR to provide assistance as needed to hemiplegia patients during rehabilitation training, thereby promoting the active participation.

关键词： Training Mechatronics Force Statistical learning Robot control Human-robot interaction Stroke (medical condition)

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A new deep learning-based food recognition system for mobile terminal

A new deep learning-based food recognition system for mobile...

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data driven control and learning systems (ddcls)

作者： Wenze Chen Ruizhuo Song School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing

With the improvement of people's health awareness, people pay more attention to their health. In recent years, the intelligent health management system based on food recognition technology has become popular, which can help users maintain healthy eating habits. However, applying the current deep learning method in mobile phones and other terminal devices is difficult, mainly because the terminal devices have the low computing power and the network needs to perform many calculations during operation. In this paper, we have adopted the methods of parameter reconstruction and calculation graph fusion to reduce the network computing load so that it can run in real-time in terminal devices, and the detection speed on Snapdragon 778G SOC exceeds 7 FPS. Besides, experiments on the VIPER-FoodNet (VFN) dataset show that our model has a high mean average precision (mAP) of 9.17% compared with the current advanced model.

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