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

作者： Qiyu Zhang Honggui Han Fangyu Li Yongping Du 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 Beijing University of Technology Beijing China

Manually designed deep neural networks have successfully forwarded waste recognition tasks in the resource recycling field. However, due to the diversity of waste samples, the feature extraction ability of inherently designed models fails to fully satisfy the requirements of real world applications. In this article, an attention-aware based differentiable architecture search (ADAS) network for waste recognition is proposed, which self-organizes to generate an optimal network structure according to diverse waste data. First, a structured search space with attention-aware modules is designed to enhance the diverse feature representations of waste data. Secondly, an efficient and differentiable structure search is achieved by continuously relaxing the representation of the network architecture and search space. Finally, the optimal architecture search process is evaluated by a bi-level optimization algorithm. Experimental results show that the proposed method achieves more satisfactory classification performances than the manually designed ResNet, DenseNet networks in the TrashNet dataset and the self-built household waste dataset.

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Model Predictive control of a Packed-U-cells Inverter with PV, Boost and Bidirectional Rectifier for Solid-State Transformers

Model Predictive Control of a Packed-U-cells Inverter with P...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Ibrahim Ahmed Lucian Mihet-Popa Engineering Department Østfold University College Fredrikstad Norway

Balancing the power flow in a three-phase system is one of the challenges in distribution networks, which is even more exacerbated with the widespread adoption of renewable energy sources (RES) and the development of smart grids. The transformer is one of the fundamental devices in power distribution that provides a means to link high-voltage, medium-voltage, and low-voltage networks in addition to galvanic isolation. Classical transformers utilize magnetic coupling with a turn ratio between coils to achieve this basic functionality, but they are bulky, expensive, and do not provide additional smart features. The use of a power electronics transformer (PET), also called a solid-state transformer (SST), has been introduced in the literature using a three-phase to single-phase converter to solve this problem. However, the focus was mainly on the inverter and a basic diode rectifier was used without RES integration. This paper bridged this gap by replacing it with a bidirectional converter and investigating its performance using solar power connected to the DC-link of the three-phase to single-phase solid-state transformer. The bidirectional SST was verified using simulations and the results show that the SST is able to maintain the balance between the three-phase system and the integrated renewable sources while providing bidirectional power flow with high efficiency and low Total Harmonic Distortion.

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Intention-Aware Decision-Making for Mixed Intersection Scenarios

Intention-Aware Decision-Making for Mixed Intersection Scena...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Balint Varga Dongxu Yang Sören Hohmann Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe Germany

This paper presents a white-box intention-aware decision-making for the handling of interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street crossing scenario. Moreover, a design framework has been developed, which enables automated parameterization of the decision-making. This decision-making is designed in such a manner that it can understand pedestrians in urban traffic and can react accordingly to their intentions. That way, a human-like response to the actions of the pedestrian is ensured, leading to a higher acceptance of AVs. The core notion of this paper is that the intention prediction of the pedestrian to cross the street and decision-making are divided into two subsystems. On the one hand, the intention detection is a data-driven, black-box model. Thus, it can model the complex behavior of the pedestrians. On the other hand, the decision-making is a white-box model to ensure traceability and to enable a rapid verification and validation of AVs. This white-box decision-making provides human-like behavior and a guaranteed prevention of deadlocks. An additional benefit is that the proposed decision-making requires low computational resources only enabling real world usage. The automated parameterization uses a particle swarm optimization and compares two different models of the pedestrian: The social force model and the Markov decision process model. Consequently, a rapid design of the decision-making is possible and different pedestrian behaviors can be taken into account. The results reinforce the applicability of the proposed intention-aware decision-making.

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Automating Glucose control in Type 1 Diabetes : Plenary Talk 15

Automating Glucose Control in Type 1 Diabetes : Plenary Talk

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15th ieee International symposium on Applied computational intelligence and Informatics, SACI 2021

作者： Bondia, Jorge Instituto Universitario de Automática e Informática Industrial Universitat Politècnica de València València Spain

ISBN: (纸本)9781728195445

Type 1 diabetes (T1D) is a chronic metabolic disease characterised by an autoimmune destruction of the beta cells in the pancreas, responsible of insulin secretion which promotes glucose transport into the cells. The resulting absolute insulin deficiency results in high blood glucose levels (hyperglycaemia). Therefore, people with T1D require exogenous delivery of insulin in order to survive. Sustained elevated glucose levels can lead to long-term complications (retinopathy, nephropathy, heart disease), which place a heavy burden to the healthcare system. Due to the complexity of glucoseinsulin metabolism, achieving good glucose control is often a very difficult task. The development of continuous glucose monitoring (CGM) have opened the door to the development of automatic insulin delivery systems, the so-called artificial pancreas (AP). In the past 10 years there has been an intensive research effort from different players in the artificial pancreas development, which has translated into a first system reaching the market in 2017, although patient intervention for meal control is still needed (this is referred as 'hybrid artificial pancreas'). This has been thanks to important contributions from modelling and control engineering, in combination with technological and pharmacological achievements. © 2021 ieee.

关键词： Insulin

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High-Speed Planning in Unknown Environments for Multirotors Considering Drag

High-Speed Planning in Unknown Environments for Multirotors ...

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ieee International Conference on Robotics and automation (ICRA)

作者： Toumieh, Charbel Lambert, Alain Univ Paris Saclay CNRS Lab Rech Informat F-91405 Orsay France

ISBN: (纸本)9781728190778

In this paper, we propose a new planning scheme for high-speed flight in an unknown environment while taking into account drag forces. Drag forces become non-negligible at high speeds and may lead to unfeasible trajectories. The method leverages a new Mixed-Integer Quadratic Program/Model Predictive control formulation that allows to easily account for drag forces. This formulation makes use of a state-of-the-art Safe Corridors generator to guarantee safety. It uses state-ofthe-art mapping algorithms and solvers to achieve a higher computational efficiency than similar state-of-the-art methods. To the best of our knowledge, our method is the first high-speed planner that generates safe trajectories while accounting for drag. The proposed method is tested in simulation and compared to similar state-of-the-art methods for planning in unknown environments in terms of quality and computation time.

关键词： automation Conferences computational modeling Prediction algorithms Generators Planning Trajectory

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Skills Composition Framework for Reconfigurable Cyber-Physical Production Modules

Skills Composition Framework for Reconfigurable Cyber-Physic...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Aleksandr Sidorenko Achim Wagner Martin Ruskowski Innovative Factory System German Research Center for Artificial Intelligence Kaiserslautern Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

While the benefits of reconfigurable manufacturing systems (RMS) are well-known, there are still challenges to their development, including, among others, a modular software architecture that enables rapid reconfiguration without much reprogramming effort. Skill-based engineering improves software modularity and increases the reconfiguration potential of RMS. Nevertheless, a skills' composition framework with a focus on frequent and rapid software changes is still missing. The behavior trees (BTs) framework is a novel approach that enables the intuitive design of modular hierarchical control structures. BTs have been mostly explored from the AI and robotics perspectives, and little work has been done in investigating their potential for composing skills in the manufacturing domain. This paper proposes a framework for skills' composition and execution in skill-based reconfigurable cyber-physical production modules (RCPPMs). It is based on distributed BTs and provides good integration between low-level devices' specific code and AI-based task-oriented frameworks. We have implemented the provided models for the IEC 61499-based distributed automation controllers. This shows the instantiation of the proposed framework with specific industrial technology to enable its evaluation by the automation community.

关键词： Adaptation models Protocols Software architecture computational modeling Production Computer architecture Software Artificial intelligence Robots Manufacturing systems

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Safety-Driven AMR End-to-End Navigation Framework Based on Sparse Sensor Human Behavior Prediction

Safety-Driven AMR End-to-End Navigation Framework Based on S...

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computational intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Fuhua Jia Kai Yang Junlin Xiao Tuo Hu Xiaoying Yang Adam Rushworth Heng Yu Tianxiang Cui Department of Mechanical Materials and Manufacturing University of Nottingham Ningbo China Ningbo China Control System Laboratory University of Nottingham Ningbo China Ningbo China Ningbo Innovation Center Zhejiang University School of Computer Science University of Nottingham Ningbo China Ningbo China

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

Autonomous Mobile Robots (AMRs) have garnered significant attention and widespread application due to their operational efficiency. However, in human-centered environments, such as warehouses, manufacturing facilities, and public spaces, AMRs face the dual challenge of navigating autonomously while perceiving and adapting to human movements to ensure safety. This work proposes a novel safety-driven end-to-end navigation framework for AMRs that processes navigation commands and sparse data collected by onboard 2D LiDAR and IMU sensors to directly control the robot's velocity. The framework comprises two key modules: a human estimation module and a trajectory generation module. The human estimation module employs an LSTM-based architecture to estimate human positions and movement trends from sparse sensor data, generating a dynamic obstacle map. Based on these predictions, a genetic algorithm -based trajectory generation module creates safe-driven dynamic trajectories subject to dynamic constraints. This approach enables accurate forecasting of pedestrian movements, allowing the AMR to effectively avoid dynamic human obstacles, thereby enhancing its safety and practicality in real-world environments. Our contributions are threefold: dynamic human detection and prediction from sparse onboard sensor data, safety-driven dynamic trajectory planning, and extensive real-world experiments demonstrating feasibility and robustness of the proposed framework.

关键词： Navigation Market research Robustness Real-time systems Trajectory Sensors Safety Mobile robots Long short term memory Genetic algorithms

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Analysis of Important Phrases in Bidding Documents Using Table Encoder Joint Extraction

Analysis of Important Phrases in Bidding Documents Using Tab...

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Proceedings of the ieee/IAFE computational intelligence for Financial Engineering (CIFEr)

作者： Tomoki Ito Shun Nakagawa MITSUI & CO. LTD.

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

Bidders often take a long time to understand bidding documents because it often requires specialized knowledge to understand these documents and these documents often have a large number of pages. Even though the market size of the tender business is large, a method for solving the above problem has not been established. To solve this issue, this paper aims to develop a function that extracts important words and phrases from bidding documents and visualizes the results in a form that is easy for users to understand. Here, in order to achieve our aim, we have to solve the following two types of challenges, namely, “Challenge for Bidding Domain Dataset” and “Challenge for Extraction considering Table Format.” To solve the first challnge, we develop a domain-spacific dataset for sequence labeling in bidding documents. To solve the second one, we propose Table Encoder Joint Extraction, which can extract important words and phrases considering Table format inforamtion in PDF. Experimental evaluation demonstrates that our approach is promising. In addition, as an application, we develop Important Bidding Phrase Analyzer (IBPA), which can extracts important words with specific labels using Table Encoder Joint Extraction and then highlights the important words in a form that type of word-level labels can be seen in a user-friendly manner on web application.

关键词： Economics Visualization Bidirectional control Portable document format Encoding Labeling Data mining computational intelligence Business

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An Efficient Planning and control Framework for Quadrotors Based on Heuristic Search and Data-Driven Model Predictive control

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ieee Transactions on Industrial Electronics 2025年

作者： Wang, Sifei Wang, Yaonan Miao, Zhiqiang Wang, Xiangke He, Wei Wang, Hesheng Hunan University College of Electrical and Information Engineering Changsha 410082 China National Engineering Research Center for Robot Visual Perception and Control Changsha 410082 China National University of Defense Technology College of Intelligence Science and Technology Changsha 410073 China Beijing Information Science and Technology University School of Automation Institute of Artificial Intelligence Beijing 100083 China University of Science and Technology School of Intelligence Science and Technology Beijing 100083 China Shanghai Jiao Tong University Department of Automation Shanghai 200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai 200240 China Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin 150001 China

This article presents a framework for quadrotors that integrate planning and control, which employs a heuristic depth-first search (HDFS) with data-driven model predictive control (MPC). The proposed framework intends to enable multiquadrotor systems to traverse cluttered environments containing obstacles and disturbances. The heuristic function enables quadrotors to identify a secure route in a timely manner. The heuristic function setup employed by HDFS results in a reduction in the number of rasters accessed, particularly in scenarios that are not labyrinthine. This consequently leads to an enhancement in computational speed for multiquadrotor systems when compared to the A* algorithm. Furthermore, data-driven MPC enhances the nominal model of the quadrotor by modeling the disturbance while tracking the desired states. Consequently, data-driven MPC is capable of attaining superior tracking precision in a disturbance environment when compared to MPC without data-driven. The transformation from the path to the desired states of the quadrotor is achieved through the utilization of B-splines and differential flatness. Simulations and experiments substantiate the efficacy of the proposed approach. © 1982-2012 ieee.

关键词： Collision avoidance data-driven depth-first search model predictive control multiquadrotor systems

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Practical and Accurate Generation of Energy-Optimal Trajectories for a Planar Quadrotor

Practical and Accurate Generation of Energy-Optimal Trajecto...

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ieee International Conference on Robotics and automation (ICRA)

作者： Morbidi, Fabio Pisarski, Dominik Univ Picardie Jules Verne MIS Lab Amiens France Polish Acad Sci IPPT PAN Inst Fundamental Technol Res Warsaw Poland

ISBN: (纸本)9781728190778

Motivated by the limited flight time of battery-powered multi-rotor UAVs, in this paper we address the problem of generating energy-optimal trajectories for a planar quadrotor. More specifically, by considering an accurate electrical model for the brushless DC motors and rest-to-rest maneuvers between two predefined boundary states, we explicitly compute the minimum-energy curves by adopting a free and a fixed end-time optimal control formulation. The numerical solution of these optimal control problems hinges upon a simple yet effective indirect projected gradient method. Simulation experiments illustrate the theory in a variety of realistic flight scenarios.

关键词： Gradient methods automation Brushless DC motors computational modeling Conferences Optimal control Fasteners

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