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Poster: Conceptual Design for FPGA Based Artifical intelligence Model for HIL Applications

Poster: Conceptual Design for FPGA Based Artifical Intellige...

引用

ieee symposium on Computers and Communications (ISCC)

作者： Farshideh Kordi Christian Barnard Paul Fortier Amine Miled Departement of Electrical and Computer Engineering Université Laval Québec QC Canada

Hardware-in-the-Loop (HIL) simulators play a critical role in the automotive industry by providing extensive testing and validation capabilities for electronic control units (ECUs). One of the main challenges faced by HIL simulators involves the task of constructing a virtual environment that accurately replicate the behavior of the actual system. Artificial intelligence (AI) algorithms can be useful in generating precise virtual environments for HIL simulations of complex systems. Moreover, minimal latency is essential for establishing a reliable virtual environment. FPGA (Field Programmable Gate Array) can effectively reduce latency in HIL simulations by providing high-performance computing resources. This paper aims to address these challenge by introducing a machine learning-driven HIL simulator implemented on FPGA. The proposed architecture employs FPGA technology to enhance the computational speed of a temporal convolutional neural network (TCN).

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DeepReach: A Deep Learning Approach to High-Dimensional Reachability

DeepReach: A Deep Learning Approach to High-Dimensional Reac...

引用

ieee International Conference on Robotics and automation (ICRA)

作者： Bansal, Somil Tomlin, Claire J. Univ Southern Calif ECE Los Angeles CA 90007 USA Univ Calif Berkeley EECS Berkeley CA USA

ISBN: (纸本)9781728190778

Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical control systems. Its advantages include compatibility with general nonlinear system dynamics, formal treatment of bounded disturbances, and the ability to deal with state and input constraints. However, it involves solving a PDE, whose computational and memory complexity scales exponentially with respect to the number of state variables, limiting its direct use to small-scale systems. We propose DeepReach, a method that leverages new developments in sinusoidal networks to develop a neural PDE solver for high-dimensional reachability problems. The computational requirements of DeepReach do not scale directly with the state dimension, but rather with the complexity of the underlying reachable tube. DeepReach achieves comparable results to the state-of-the-art reachability methods, does not require any explicit supervision for the PDE solution, can easily handle external disturbances, adversarial inputs, and system constraints, and also provides a safety controller for the system. We demonstrate DeepReach on a 9D multi-vehicle collision problem, and a 10D narrow passage problem, motivated by autonomous driving applications.

关键词： Deep learning Limiting Handheld computers control systems Safety Complexity theory Nonlinear dynamical systems

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Robot Motion control with Compressive Feedback

Robot Motion Control with Compressive Feedback

引用

ieee International Conference on Robotics and automation (ICRA)

作者： Li, Congjian Wang, Song Wang, Siyu Bi, Sheng Guan, Yisheng Xi, Ning Univ Hong Kong Fac Engn Hong Kong Peoples R China Guangdong Univ Technol Biomimet & Intelligent Robot Lab BIRL Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781728190778

Robot motion control aims to generate control inputs for a robotic system to track a planned trajectory. Feedback provided by sensors plays an essential role in motion control by improving system performance when external disturbances and/or initial errors exist. However, feedback signals, such as images are often of a large size, which imposes a heavy computational burden on the system. In this paper, a new robot motion control scheme is proposed based on compressive feedback to improve feedback rate. The controller is designed in non-vector space using compressive feedback. As an application, visual servoing is formulated under the proposed framework by considering a feedback image as a set, instead of a traditional feature vector. Experiments are conducted to validate the proposed scheme.

关键词： Robot motion Image coding Tracking System performance Surgery Aerospace electronics Robot sensing systems

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The Fault Location Method for Power Communication Networks Based on the Bi-GRU Network

The Fault Location Method for Power Communication Networks B...

引用

ieee Information Technology, Networking, Electronic and automation control Conference

作者： Lin Liu Guilong Wu Xiaofang Feng Zhimin Yang Power Dispatching & Control Center of China Southern Power Grid Guangzhou China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

In recent years, the topology of power communication networks has become increasingly complex, resulting in diverse and intricate alarm data. Traditional correlation algorithms struggle with fault localization and identification, proving inefficient and inadequate for the timely processing of the large volume of network disturbance alarms in power communication networks. The continuous development of artificial intelligence offers new approaches and robust methods for addressing fault localization issues in power communication networks. This paper proposes a neural network model based on a Bidirectional Gated Recurrent Unit (Bi-GRU) with an attention mechanism. The model captures long-term dependencies in time series data using the Bi-GRU and employs the attention mechanism to highlight critical time points. This enables direct identification of root alarms, which are the sources of faults in the communication network, thus improving the accuracy and efficiency of fault localization. Experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of fault localization accuracy and response time through learning and training on the alarm correlation dataset in power communication networks.

关键词： Location awareness Training Accuracy Correlation Attention mechanisms computational modeling Neural networks Topology Communication networks Long short term memory

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Evaluation of Voice Recognition Platforms and Methods for Edge AI Devices

Evaluation of Voice Recognition Platforms and Methods for Ed...

引用

International Artificial intelligence and Data Processing symposium (IDAP)

作者： Yunus Koç Ahmet Ayberk Tarçın Doğukan Köse Inf. and Comm. Eng. Dept Istanbul Technical University Istanbul Turkey R&D Department Beko Global Istanbul Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

AI-driven voice recognition technologies have an important application area for remote control of devices ranging from smart home appliances, automotive systems to consumer electronics and industrial automation. Thus, many devices are being designed to have AI based voice processing capabilities. However, AI models need to be implemented in edge devices for some applications that have no access for cloud computing. So, it is very critical to optimize the resource usage as well as improving the model due to hardware limitations. On this issue, this paper presents implementation and comparative analysis of AI based voice recognition methods for edge devices via different MCU platforms that we named as Platform 1, Platform 2, and Platform 3. In this context, phoneme-based and word-based voice recognition models with related platforms were evaluated to observe recognition performance and CPU/memory usage. According to the results, Platform 1 with phoneme-based model showed best accuracy performance while Platform 3 with phoneme-based model provided the lowest memory usage in our experimental scenarios.

关键词： Performance evaluation Accuracy computational modeling Speech recognition Edge AI Smart homes Data models Remote control Context modeling Signal to noise ratio

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A Hierarchical Filter Transfer Learning Algorithm for Heterogeneous Domains

A Hierarchical Filter Transfer Learning Algorithm for Hetero...

引用

ieee International Conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Mengmeng Li Xiaolong Wu Honggui Han Ziyun Fang Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Artificial Intelligence Institute and Beijing Laboratory for Urban Mass Transit Beijing University of Technology Beijing China

A main goal of heterogeneous transfer learning algorithms is to solve the domain adaptation problem of different feature spaces. However, some existing heterogeneous transfer learning methods usually only extract common features from the source domain and target domain, ignoring specific features, which may damage the performance of transfer learning. Therefore, a hierarchical filter transfer learning algorithm (HFTLA) for heterogeneous domains in is proposed. First, a nonlinear mapping is constructed to learn the potential relationship between the features of different domains. Then, the feature space can be aligned by learning common features and specific features, which can ensure the integrity of the features. Second, a hierarchical filter framework is developed to play different roles in different stages of transfer learning. In the pretransfer phase, a knowledge filter based on genetic principle is designed to increase the diversity of knowledge with different genetic operators. In the post-transfer phase, a guided filter is established to achieve a coupling balance between source knowledge and target information. Finally, experimental results on heterogeneous domains illustrate the effectiveness of HFTLA.

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ASVLite: a high-performance simulator for autonomous surface vehicles

ASVLite: a high-performance simulator for autonomous surface...

引用

ieee International Conference on Robotics and automation (ICRA)

作者： Thomas, Toby Bossens, David M. Tarapore, Danesh Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

ISBN: (纸本)9781728190778

The energy of ocean waves is the key distinguishing factor of marine environments compared to other aquatic environments such as lakes and rivers. Waves significantly affect the dynamics of marine vehicles;hence it is imperative to consider the dynamics of vehicles in waves when developing efficient control strategies for autonomous surface vehicles (ASVs). However, most marine simulators available open-source either exclude dynamics of vehicles in waves or use methods with high computational overhead. This paper presents ASVLite, a computationally efficient ASV simulator that uses frequency domain analysis for wave force computation. ASVLite is suitable for applications requiring low computational overhead and high run-time performance. Our tests on a Raspberry Pi 2 and a mid-range desktop computer show that the simulator has a high run-time performance to efficiently simulate irregular waves with a component wave count of up to 260 and large-scale swarms of up to 500 ASVs.

关键词： Frequency-domain analysis Force Dynamics Ocean waves Lakes computational efficiency Rivers

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Structure and Randomness : Plenary Talk

Structure and Randomness : Plenary Talk

引用

International symposium on computational intelligence and Informatics

作者： Jozsef Solymosi Óbuda University

ISBN: (纸本)9798350398830

Probabilistic methods are very important and useful tools in many domains, from theoretical mathematics to applied engineering. In this — mostly mathematical — talk I will list several examples where randomness and quasirandomness helps to work with mathematical and physical structures. For example the key of data compression is taking smartly selected random samples from the data. Random variables are very useful, but extracting random bits is an “expensive” process. Purely random systems are relatively easy to work with. For example if a large graph is random, where the edges are selected independently at random with probability 1/2, then we know (almost) exactly the size of the largest complete and empty subgraph. On the other hand, constructing graphs with similarly small complete and empty subgraphs deterministically is a very difficult problem. Most systems we are working with are not random, but not completely deterministic either. To illustrate a general method let us suppose that a function y=f(x) is given. In order to work with it or to understand its behaviour, we would like to write it as f(x)=g(x) + h(x)+ r(x) where g(x) is very simple (like a step function), h(x) is a random function and r(x) is the error term which is hopefully small in the range we are working in. The talk is intended for a general audience, no advanced mathematical background is expected

关键词： Computer science Mechatronics automation Data compression Probabilistic logic Mathematics Random variables

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Graph Neural Network Based Deep Reinforcement Learning for Volt - Var control in Distribution Grids

Graph Neural Network Based Deep Reinforcement Learning for V...

引用

ieee International symposium on Power Electronics for Distributed Generation Systems (PEDG)

作者： Aoxiang MA Jun CAO Pedro RODRIGUEZ CORTES LIST Esch-Belval Luxembourg

ISBN: (数字)9798350361001

ISBN: (纸本)9798350361018

Voltage reactive power control (VVC) plays a vital role in future-oriented smart grids, which not only ensures the stable operation of the power system but also optimizes energy distribution and consumption. However, with the widespread access to distributed renewable power generation resources, the structure and operation mode of the power grid, especially the distribution network, are becoming more and more complex. The traditional VV C method is difficult to cope with this new situation due to its inherent limitations. challenge. These limitations include, but are not limited to, the static nature of the control strategy, lack of adaptability to dynamic changes in the grid, and computational inefficiency when dealing with large-scale networks. With the widespread use of artificial intelligence in smart grids, some deep-reinforcement learning algorithms have been used to solve this problem. However, because the algorithm is not good enough for controlling complex environments, in order to overcome these challenges, this paper proposes innovative methods based on deep reinforcement learning (DRL) and graph neural networks (GNN). GNN is used to extract graphical representations from topological maps of power systems and is trained with data from the VVC environment. Improve the efficiency and reliability of VV C by improving algorithms and optimization strategies.

关键词： Reactive power Technological innovation Accuracy computational modeling Heuristic algorithms Power system stability Deep reinforcement learning

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International Standards for Cybersecurity in Smart Devices for the Power Sector

International Standards for Cybersecurity in Smart Devices f...

引用

computational intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： P Kaliappan S. Sudha D. Shankar Metering and Utility Automation Division Central Power Research Institute Bengaluru India

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

The power sector is one of the most essential infrastructures globally. Digital transformation is underway in the power sector, making it more vulnerable to cyberattacks. The power sector is experiencing an increase in cyber incidents day by day. To protect power sector infrastructure against cyber attacks, technology, procedures, and people must work together; only then can prevention be achieved. The power sector needs to adhere to standards, regulations, and guidelines available for the effective implementation of a cyber security framework. The power sector has gained more attention and focus in the last five years. Numerous standards, including the NIST Cybersecurity Framework, IEC 62443, ieee 1686, IEC 62351, and IEC 62056, tackle cybersecurity concerns within control systems. Every standard covers and focuses on different domains and parts of the overall system. This paper presents applicable international standards for cyber security in the power sector.

关键词： Prevention and mitigation NIST Regulation Smart meters Computer crime IEC Standards Smart devices Standards Cyberattack Guidelines

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