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Accelerated Computer Vision Inference with AI on the Edge

Accelerated Computer Vision Inference with AI on the Edge

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International conference on Communication systems and Network Technologies (CSNT)

作者： Varnit Mittal Bharat Bhushan CSE Dept. HMRITM (GGSIPU) New Delhi India

ISBN: (数字)9781728149769

ISBN: (纸本)9781728149776

Computer vision is not just about breaking down images or videos into constituent pixels, but also about making sense of those pixels and comprehending what they represent. Researchers have developed some brilliant neural networks and algorithms for modern computer vision. Tremendous developments have been observed in deep learning as computational power is getting cheaper. But data-driven deep learning and cloud computing based systems face some serious limitations at edge devices in real-world scenarios. Since we cannot bring edge devices to the data-centers, so we bring AI to the edge devices with AI on the Edge. OpenVINO toolkit is a powerful tool that facilitates deployment of high-performance computer vision applications to the edge devices. It converts existing applications into hardwarefriendly and inference-optimized deployable runtime packages that operate seamlessly at the edge. the goals of this paper are to describe an in-depth survey of problems faced in existing computer vision applications and to present AI on the Edge along with OpenVINO toolkit as the solution to those problems. We redefine the workflow for deploying computer vision systems and provide an efficient approach for development and deployment of edge applications. Furthermore, we summarize the possible works and applications of AI on the Edge in future in regard to security and privacy.

关键词： Artificial Intelligence Deep learning Neural Networks Computer Vision AI on the Edge OpenVINO images Artificial Intelligence computer vision Artificial Insemination Edge Device Edge of a figure or solid Neural network coboundary Machine learning deployment Depth measurement Computer Vision systems

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Reduced Sensor Based Vector control of Grid- Interactive DFIG with Wind Power Levelling Capability

Reduced Sensor Based Vector Control of Grid- Interactive DFI...

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ieee Power India conference

作者： Souvik Das Sambasivaiah Puchalapalli Bhim Singh Department of Electrical Engineering Indian Institute of Technology New Delhi India

ISBN: (数字)9781728166643

ISBN: (纸本)9781728166650

Improving controller reliability and tackling wind variability are important aspects, essential in modern wind power conversion systems. this paper addresses both facets, and presents a reduced sensor control strategy for a variable speed wind turbine driven doubly fed induction generator (DFIG) with wind power levelling capability. the reduction in sensor count aids in system simplicity. Another imperative feature of the reduced sensor control scheme is its adaptability, meaning, it can effectively be used in lieu of conventional DFIG vector control algorithm during abrupt failure of any stator or rotor current sensors. AC network is directly connected to stator terminals of DFIG, while its connection to the rotor, is through a system of back-to-back connected voltage source inverters. A battery energy storage connected directly at the DC link, is used to address the challenge of power levelling during wind uncertainty. the frequency distribution of hourly wind speed data of an actual wind farm, is utilized for designing the controller and to avoid an oversized battery design. this reduced sensor power levelling scheme is extensively tested on a prototype developed in the laboratory and test results exhibit its effectiveness in filtering wind power variations.

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A Fuzzy data-driven Paradigmatic Predictor 9

A Fuzzy Data-Driven Paradigmatic Predictor

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9th IFAC/IFIP/IFORS/IISE/INFORMS conference on Manufacturing Modelling, Management and control (IFAC MIM)

作者： Amirjavid, Farzad Nemati, Hamidreza Barak, Sasan Univ Toronto Edward S Rogers Dept Elect & Comp Engn Toronto ON Canada Univ Lancaster Dept Engn Lancaster England Univ Lancaster Sch Management Dept Management Sci Lancaster England

data-driven prediction of future events is to provide decision-makers Predictive Information (PI) to decrease human-error. they usually desire possession of a predictor which works independently from the humanized configurations and works efficiently and accurately. the accurate data-driven prediction of the systems' behavior is the primary focus of this paper. We define the future state of a system is a set of uncertain values, which can be modeled by fuzzy numbers. the future machine state is not very dissimilar to the current status, and the next event is a sort of behavior repetition. the PI also justifies the system being in a trend to achieve a goal, and it counts the unplanned contextual reactions of the system. In this paper, we come up with a fuzzy data-driven predictor application to foretell the system behavior. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Fuzzy logic temporal data analytics adaptive learning systems theory

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A SELF-ADAPTED SWARM ARCHITECTURE TO HANDLE BIG data FOR "FACTORIES OF thE FUTURE" 9

A SELF-ADAPTED SWARM ARCHITECTURE TO HANDLE BIG DATA FOR "FA...

引用

9th IFAC/IFIP/IFORS/IISE/INFORMS conference on Manufacturing Modelling, Management and control (IFAC MIM)

作者： Guerreiro, Guilherme Costa, Ruben Figueiras, Paulo Graca, Diogo Jardim-Goncalves, Ricardo Univ Nova Lisboa FCT Dep Engn Eletrotecn CTSUNINOVA P-2829516 Caparica Portugal Volkswagen Autoeuropa Quinta Marquesa P-2954024 Quinta Do Anjo Portugal

Currently, the manufacturing sector is facing a technological evolution with the so-called Industry 4.0. this poses a paradigm shift, enabling companies to be more competitive by taking advantage of innovative technologies(cloud computing, cyber-physical systems, big data analytics and deep learning), pursuing near-zero fault, near real-time reactivity to any problem, better traceability, more predictability in manufacturing, while working to achieve cheaper product customization. the challenges arise when the dimensionality of the data generated by manufacturing processes grows, affecting the performance of algorithms, decreasing it quickly as the dimension of the search space increases. Handling large datasets with a good performance in a limited time should be the main concern in Big data analytics. this paper focuses on a logistic process of car manufacturing, where batteries are unloaded from trucks to warehouse, and then to the point of fit, where they are assembled into the car. It presents a complete data-driven architecture, using a swarm approach for distributed data processing among all data stages, where processing nodes with different tasks and technologies can work cooperatively to complete a job. the work presented in this paper is funded by the EU project BOOST4.0, focusing on a smart manufacturing scenario for the automotive sector. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Swarm Intelligence Industry4.0 Big data Manufacturing data Processing Cyber-Physical systems

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Fault Diagnosis of High-speed Train Bogie Based on Spectrogram and Multi-channel Voting 7

Fault Diagnosis of High-speed Train Bogie Based on Spectrogr...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Su, Liyuan Ma, Lei Qin, Na Huang, Deqing Kemp, Andrew Southwest Jiaotong Univ Inst Syst Sci & Technol Sch Elect Engn Chengdu 611756 Sichuan Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

ISBN: (纸本)9781538626184

Fault diagnosis of high-speed train bogie is of great importance in ensuring the safety of train operation. the multi-channel vibration signals measured at different positions on the bogies characterize the dynamics of the vehicle and contain key information describing the performance of the bogie components. However, due to the complexity and uncertainty of the signals, it is hard to extract stable features that represent the characteristics of the signals. Besides, manual selection of reliable channels is indispensable in existing works. this paper presents an ensemble of methods for fault type recognition of high-speed train bogie based on spectrogram images and voting method. First, vibration signals of bogies are transformed to spectrogram images that are then taken as the input of Random Forests (RFs). In the next, four voting methods including Plurality Voting (PV), Classification Entropy (CE), Winner Takes All (WTA), as well as a novel method we proposed using neural network (NN) is applied for combining all the channels' classification results to give a final decision on fault type. the proposed method not only avoid complicated feature extraction procedures by using a simple transform, but also make the best of multiple channels by automatic combination. Experiments conducted on the dataset based on SIMPACK simulations have verified the efficacy of the presented method in classifying key component(s) failures, with accuracy near 100%. Further, a more complex fault state in which the components of bogies only lose their effectiveness partially, instead of fully, has been tested and analyzed, where near 90% of accuracy is achieved. these results demonstrate the high robustness of the new method.

关键词： High-speed train Bogie Fault Severities Classification Spectrogram Random forest Voting Multi-channel

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data-driven Optimization of Energy Efficiency and Comfort in an Apartment 9

Data-driven Optimization of Energy Efficiency and Comfort in...

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9th International conference on Intelligent systems (IS)

作者： Avendano, Diego Nieves Ruyssinck, Joeri Vandekerckhove, Steven Van Hoecke, Sofie Deschrijver, Dirk Univ Ghent IMEC IDLab Ghent Belgium Renson Ventilat Waregem Belgium

ISBN: (纸本)9781538670972

An important challenge in home automation is the energy efficient optimization of the indoor environment. this relies on the solution of a multi-objective optimization problem where energy efficiency and comfort parameters are maximized simultaneously. this paper presents three data-driven control algorithms based on machine learning techniques, which offer an alternative to traditional control methods. the results demonstrate that some data-driven methods can achieve similar results than rule-based systems. Moreover, they require no prior expert knowledge and have better scalability than standard approaches.

关键词： Model predictive control Deep reinforcement learning Multi-objective optimization Home automation Energy efficiency

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A data-driven Method for Current Sensor Fault Diagnosis in Single-Phase PWM Rectifier

A Data-Driven Method for Current Sensor Fault Diagnosis in S...

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ieee International conference on Power and Energy systems (ICPES)

作者： Yang Xia Bin Gou Yan Xu School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

In practical drive systems, the occurrence of sensor faults may lead to control signal deviation and system performance degradation. thus, this paper designs a data-based approach for sensor fault diagnosis strategy. this data-driven method's principle is derived from the sensor signal prediction in single-phase PWM rectifier. the faults are detected and diagnosed by the normalized residual between predicted and actual signals. As the most important part, the signal predictor is designed by combining nonlinear autoregressive exogenous (NARX) learning model and a randomized learning technique, Extreme learning Machine (ELM). To validate the effectiveness and accuracy, several hardware-in-loop tests are carried out to demonstrate the feasibility and reliability of the proposed data-driven method.

关键词： autoregressive processes fault diagnosis feedforward neural nets power engineering computing PWM rectifiers sensors

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data-driven Economic control of Battery Energy Storage System Considering Battery Degradation

Data-driven Economic Control of Battery Energy Storage Syste...

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ieee International conference on Power and Energy systems (ICPES)

作者： Ziming Yan Yan Xu Yu Wang Xue Feng School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Institute of Technology Singapore

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

Battery energy storage system (BESS) provides a potential solution to mitigate real-time power imbalance by participating in frequency control. However, the battery aging resulted from intensive charge-discharge cycles will inevitably lead to batter degradation, which could potentially incur high control cost. It is therefore important to design an optimal control method for battery energy storage systems (BESS) which can achieve power balance and consider tradeoff of battery aging simultaneously. To achieve this purpose, this paper employs a data-driven approach based on deep reinforcement learning to design an economically optimal controller for BESS. An actor-critic model is proposed for optimizing the BESS controller performance. A cost model based on battery cycle aging cost, unscheduled interchange price and generation cost is employed to estimate the operational cost of BESS in power systems. the effectiveness of the proposed optimal BESS control method is verified in a three-area power system.

关键词： Battery energy storage system battery cycle aging frequency control deep reinforcement learning battery energy storage system Frequency control control Methods Batteries power systems data driven Battery Life learning (artificial intelligence)

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Interpretable Machine learning: Convolutional Neural Networks with RBF Fuzzy Logic Classification Rules 9

Interpretable Machine Learning: Convolutional Neural Network...

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9th International conference on Intelligent systems (IS)

作者： Xi, Zhen Panoutsos, George Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)9781538670972

A convolutional neural network (CNN) learning structure is proposed, with added interpretability-oriented layers, in the form of Fuzzy Logic-based rules. this is achieved by creating a classification layer based on a Neural-Fuzzy classifier, and integrating it into the overall learning mechanism within the deep learning structure. Using this new structure, one could extract linguistic Fuzzy Logic-based rules from the deep learning structure directly, which enhances the interpretability of the overall system. the classification layer is realised via a Radial Basis Function (RBF) Neural-Network, that is a direct equivalent of a class of Fuzzy Logic-based systems. In this work, the development of the RBF neural-fuzzy system and its integration into the deep-learning CNN is presented. the proposed hybrid CNN RBF-NF structure can from a fundamental building block, towards building more complex deep-learning structures with Fuzzy Logic-based interpretability. Using simulation results on a benchmark data-driven modelling and classification problem (labelled handwriting digits, MNIST 70000 samples) we show that the proposed learning structure maintains a good level of forecasting/prediction accuracy (> 96% on unseen data) compared to state-of-the-art CNN deep learning structures, while providing linguistic interpretability to the classification layer.

关键词： Fuzzy Logic Deep learning Convolutional Neural Networks

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Proceedings - 9th ACM/ieee International conference on Cyber-Physical systems, ICCPS 2018

Proceedings - 9th ACM/IEEE International Conference on Cyber...

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9th ACM/ieee International conference on Cyber-Physical systems, ICCPS 2018

ISBN: (纸本)9781538653012

the proceedings contain 42 papers. the topics discussed include: SAT-based synthesis of spoofing attacks in cyber-physical control systems;guaranteed physical security with restart-based design for cyber-physical systems;cyber-physical system checkpointing and recovery;cloaking the clock: emulating clock skew in controller area networks;CityResolver: a decision support system for conflict resolution in smart cities;dynamic integration of heterogeneous transportation modes under disruptive events;impact driven sensor placement for leak detection in community water networks;SDCWorks: a formal framework for software defined control of smart manufacturing systems;towards a framework for realizable safety critical control through active set invariance;co-regulation of computational and physical effectors in a quadrotor unmanned aircraft system;OpenUAV: a UAV testbed for the CPs and robotics community;learning and control using Gaussian processes;distributed optimal control synthesis for multi-robot systems under global temporal tasks;fly-by-logic: control of multi-drone fleets with temporal logic objectives;context-aware detection in medical cyber-physical systems;a data-driven approach to artificial pancreas verification and synthesis;platform for model-based design and testing for deep brain stimulation;and autoware on board: enabling autonomous vehicles with embedded systems.

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