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Automated Classification of Datapoint Types in Building automation Systems Using Time Series 19th

Automated Classification of Datapoint Types in Building Auto...

引用

19th IFIP WG 5.1 international conference on Product Lifecycle Management (PLM)

作者： Mertens, Noah Wilde, Andreas Fraunhofer IIS EAS Dresden Germany

ISBN: (纸本)9783031251818;9783031251825

To apply energy efficiency monitoring and optimizing systems for heating, ventilation and air conditioning (HVAC) systems at scale metadata of the system components must be extracted automatically and stored in a machine-readable way. The present work deals with the automated classification of datapoints using only historical time series data and methods from the field of artificial intelligence. The dataset used to conduct the research contains multiple time series from a total of 76 buildings and covers the months of January to November of 2018. Based on these data, relevant features of the time series are defined. Using these features, different classification models as well as the influence of seasonal effects are evaluated. Overall, our approach provides very good results and assigns on average about 92% of all datapoints to the correct class. The datapoints of the worst recognized class are still correctly classified to about 88% (validation data) and 90% (test data), respectively. Possible reasons for incorrect classified datapoints are discussed and a promising solution is proposed. The obtained results show that in practice these methods can reduce the effort of creating and validating digital twins and building information modeling (BIM) for retrofits.

关键词： Datapoint HVAC Retrofit Classification Machine learning

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Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

Learning Sampling Distributions Using Local 3D Workspace Dec...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Chamzas, Constantinos Kingston, Zachary Quintero-Pena, Carlos Shrivastava, Anshumali Kavraki, Lydia E. Rice Univ Dept Comp Sci Houston TX 77005 USA

ISBN: (纸本)9781728190778

Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME, two experience-based frameworks for sampling-based planning applicable to complex manipulators in 3D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distribution. SPARK decomposes the environment based on exact geometry while FLAME is more general, and uses an octree-based decomposition obtained from sensor data. We demonstrate the effectiveness of SPARK and FLAME on a real and simulated Fetch robot tasked with challenging pick-and-place manipulation problems. Our approaches can be trained incrementally and significantly improve performance with only a handful of examples, generalizing better over diverse tasks and environments as compared to prior approaches.

关键词： Geometry Three-dimensional displays automation conferences Fires Robot sensing systems Manipulators

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Adaptive Dictionary Generation Model based Texture Recognition

Adaptive Dictionary Generation Model based Texture Recogniti...

引用

international conference on Advanced robotics and Mechatronics (ICARM)

作者： Pengwen Xiong Yuerou Wang Weiping Zhang Changcheng Wu School of Advanced Manufacturing Nanchang University Nanchang China Jiangxi Key Laboratory of Intelligent Robot Nanchang China Department of Ultrasound First Affiliated Hospital Jiangxi Medical College Nanchang University Nanchang China School of Automation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

While traditional dictionary learning models under one-shot learning always have the problem of low accuracy of texture recognition, in this paper we propose a novel adaptive multilayer dictionary learning (AMDL) algorithm. An adaptive dictionary generation model is creatively proposed and designed, based on which each sample is able to learn the idiosyncratic dictionary, thus reducing the interference between different material samples. A method of learning label groups for each test sample is proposed to ensure that the test sample learns the correct material category. Support vector guided dictionary learning with locality constraints algorithm is proposed to achieve accurate texture classification. The results of validation experiments on the LMT-108 dataset show that the AMDL algorithm proposed in this paper obtains a better performance in one-shot texture recognition with a recognition rate of 96.71% compared with other similar dictionary learning algorithms.

关键词： Adaptation models Dictionaries Accuracy Mechatronics One shot learning Interference Nonhomogeneous media Vectors Classification algorithms Robots

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Learning from Demonstration for Real-Time User Goal Prediction and Shared Assistive Control

Learning from Demonstration for Real-Time User Goal Predicti...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Qiao, Calvin Z. Sakr, Maram Muelling, Katharina Admoni, Henny Univ British Columbia Dept Mech Engn Vancouver BC Canada Carnegie Mellon Univ Robot Inst Pittsburgh PA USA

ISBN: (纸本)9781728190778

In shared autonomy, the user input is blended with the assistive motion to accomplish a task where the user goal is typically unknown to the robot. Transparency between the human and robot is essential for effective collaboration. Prior works have provided methods for the robot to infer the user goal;however, they are usually dependent on the distance between the robot and object, which may not be directly associated with the real-time user control intention and thus cause low control feelings. Here, we propose a real-time goal prediction method driven by assistive motion generated by learning from demonstration (LfD) allowing more reactive assistive behaviors. This LfD-generated assistive motion is blended with the user input based on goal predictions to achieve targeted tasks. The LfD policy was learned offline and used with different users. To evaluate our proposed method, we compared it with a state-of-the-art Partially Observable Markov Decision Process (POMDP) based method using a distance cost, and a direct control method (i.e., joystick). A pilot study (N = 6) was conducted to control a 6-DoF Kinova Mico robotic arm to carry out three tasks: (1) reaching-and-grasping, (2) pouring, and (3) object-returning with the three control methods. We used both objective and subjective measures in the comparative study. Results show that our method has the shortest task completion time, the lowest amount of joystick control inputs among all three control methods, as well as a significantly lower angular difference between the user input and assistive motion compared to the POMDP-based method. Besides, it obtains the highest subjective score in the user preference and perceived speed ratings, and the second-highest in the control feeling and the robot did what I wanted ratings.

关键词： Costs conferences Process control Prediction methods Markov processes Manipulators Real-time systems

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Kinematic Simulation of Serpentine Soft Robotic Arm

Kinematic Simulation of Serpentine Soft Robotic Arm

引用

IEEE international conference on Mechatronics and automation

作者： Yongshuang Ma Qiang Fu Peng Chen School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

In order to meet the demands of complex spatial environments, a novel design of a linear-driven continuous-type robotic arm was proposed. Based on geometric analysis, a simple modeling approach for the linear-driven continuous-type robotic arm was proposed, analyzing the kinematic mapping relationships among joint units, actuation units, and operational units. Additionally, a decoupled kinematic algorithm for flexible joints was provided. The motion model of the robotic arm was established using an improved Denavit-Hartenberg (D-H) parameter method, and a motion simulation model of the robotic arm was constructed using Matlab. The snake-like soft robotic arm could accomplish the expected motion trajectory. Therefore, in this paper, we modeled and decoupled the soft body robotic arm by using the improved D-H parametric method. This method can simplify the modeling process compared with the traditional D-H parametric method and is more suitable for flexible structure robots. The kinematic solution and control process of the snake-shaped flexible robotic arm is more stable and reliable, which lays the foundation for further investigation of the motion control and practical application of the robotic arm.

关键词： Analytical models Process control Kinematics Arms Soft robotics Manipulators Mathematical models

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Experimental and numerical study of rigid polyurethane foams for mechanical characteristics using finite element analysis 3

Experimental and numerical study of rigid polyurethane foams...

引用

3rd international conference on Intelligent robotics, Mechatronics and automation Systems, IRMAS 2023

作者： Jeyanthi, S. Nivedhitha, D.M. Jeyamani, Elammaran Lazar, Prince Jeya Lal Krishnaswamy, Prabhu School of Mechanical Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu 600127 India Department of Mechanical Engineering Swinburne University of Technology Sarawak Malaysia Department of Mechanical Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai600124 India Department of Automotive Engineering Vellore Institute of Technology Tamil Nadu Vellore India

Over the past three decades, the global market has attracted polyurethane (PU) foams. It has been estimated that three-quarters of global consumption of polyurethane products are mainly foams. Based on hardness and density, foams can be classified into flexible and rigid. Features like flexibility, durability, stiffness, lightweight, less cost, and low density make foams more suitable for a wide range of automotive, industrial and agricultural industries. In this aspect, rigid foams are largely used as base materials for insulating purposes, seals, gaskets, tires, bedding, and seating of trucks. Generally, these PU foams are synthesized by mixing two chemicals: polyol and isocyanates. But unfortunately, the utilisation of Petro-based polyols makes PU foam restricted due to the rapid depletion of fossil fuels. Hence, this study attempts to replace Petro-based polyols with castor oil-based polyols. Other mechanical properties, such as compression strength, were tested to evaluate its ductile and flow behaviour. Finally, the developed Kelvin foam models were used for Finite Elemental Analysis (FEM) using ANSYS software to validate experimental results. Based on the results shows that both experimental and numerical analysis of castor oil PU foams resulted in greater compressive strength when compared to Petro-based PU foams. © Published under licence by IOP Publishing Ltd.

关键词： international trade

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Multi-level Deep Learning Kalman Filter

Multi-level Deep Learning Kalman Filter

引用

international conference on Advanced robotics and Mechatronics (ICARM)

作者： Shi Yan Yan Liang Binglu Wang School of Automation Northwestern Polytechnical University PR China

The well-known Kalman filter and its adaptive variants belong to model-based optimization, and their optimality depends on reliable prior information such as system models, which is sometimes hard to obtain. To reasonably introduce prior domain knowledge on the basis of offline data learning, a multi-level deep learning Kalman filter is designed in this paper with dynamic model parameter learning for evolution trend prediction, process noise covariance learning to obtain the optimal gain, and compensation term learning to correct the errors after the filtering update. The gated recurrent unit is used to construct offline learning modules, which endow the multi-level filter with nonlinear model fitting and memory iterative learning capabilities. The proposed algorithm is validated in maneuvering target tracking tasks, showcasing significant enhancements.

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Steering Through Waters: Exploring Turning Control Techniques for Soft Underwater Snake Robots

Steering Through Waters: Exploring Turning Control Technique...

引用

IEEE international conference on robotics and Biomimetics

作者： Hang Shi Wenlong Cui Yali Meng Shuting Wang Yangmin Xie Department of Automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China

Taking inspiration from the two turning mechanisms exhibited by biological snakes, this paper presents two turning methods for soft underwater snake robots: head steering and unbalanced gait steering. The structural design, driving principles, and turning motion model of the proposed snake are introduced. Through experimental validations under the same conditions, the results demonstrate the turning feasibility of two steering methods in underwater snake robots, revealing superior turning capabilities of head steering compared to unbalanced gait steering utilized by conventional ground-based snake robots. This paper pioneers head-turning investigation in underwater snake robots and offers insights for future applications involving the agile maneuvering of underwater robots in aquatic environments.

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Formation Planning with Multi-Agent Trajectory Prediction

Formation Planning with Multi-Agent Trajectory Prediction

引用

Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Yijie Wang Gang Wang Ziyu Zhou Jian Sun KAUIS and School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Unmanned aerial vehicle (UAV) formation plays a pivotal role in diverse civil and military applications. In civil use, it can revolutionize tasks like precision agriculture, mapping vast landscapes, and enhancing emergency response. In the military, coordinated UAV formations are essential for strategic surveillance and tactical operations. Currently, much of the UAV formation research has been restricted to obstacle-free scenarios. However, the evolving landscape of modern missions demands UAVs to proficiently handle complex terrains. To overcome the hurdles of formation flight in such complex settings, scholars have proposed an array of methods. Control-based ones emphasize maintaining UAV stability and coordination, while trajectory optimization-based methods seek optimal flight paths. Our novel formation planning method combines multi-agent trajectory prediction and optimization. By integrating prediction outcomes into the optimization objective function, it achieves the dual goals of obstacle avoidance and formation integrity. Compared to prior techniques, our method stands out. It bolsters formation maintenance, ensuring UAVs stay in sync even amidst disruptions. Moreover, it streamlines the optimization process, saving computational time and resources. With this inventive approach, UAV formation flights in complex, obstacle-ridden environments will be more dependable and efficient. It paves the way for enhanced mission success rates and broader application possibilities, opening new vistas for UAV technology utilization.

关键词： Precision agriculture Trajectory planning Service robots Surveillance Robot kinematics Autonomous aerial vehicles Trajectory Planning Synchronization Optimization

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Anomaly Detection for Aircraft Based on Multivariate LSTM and Predictive Residual Test

Anomaly Detection for Aircraft Based on Multivariate LSTM an...

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automation, robotics and Computer Engineering (ICARCE), international conference on

作者： Chaoqi Zhang Wu Wei Gongcheng Zhou Gang Xiang Ruishi Lin Langfu Cui Dongpeng Li Yu Peng Guizhen Yu School of Transportation Science and Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Aerospace Automatic Control Institute Beijing China Beihang University Beijing China

With the increasing complexity and high reliability requirements of aircraft, researching more accurate anomaly detection methods and real-time monitoring of aircraft status is of great significance for ensuring equipment safety. This paper proposes a method based on multivariate LSTM and residual testing for anomaly detection in high-dimensional complex data. Firstly, by utilizing maximum mutual information to analyze the correlation of multidimensional data, a multivariate LSTM model is established based on high correlation parameters to predict the parameters. Then, combined with the idea of statistical hypothesis testing, test the statistical distribution of residuals on the training data of the prediction model using Kolmogorov-Smirnov method, and the dynamic threshold for anomaly detection is obtained through significance testing. Finally, real-time calculation of residuals between online monitoring data and predicted values is calculated, and anomaly detection is achieved by comparing the residual with threshold. The method proposed in this paper exhibits excellent anomaly detection performance on real flight data of aircraft.

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