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A Comparative Study of Model-Based control Schemes for Contouring Accuracy Improvement of 3PRR Planar Parallel-Kinematics Manipulator

A Comparative Study of Model-Based Control Schemes for Conto...

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Robotics, automation and Artificial intelligence (RAAI), ieee International Conference on

作者： Abdur Rosyid Bashar El-Khasawneh Mechanical and Nuclear Engineering Department Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

The highly nonlinear coupled dynamics in a parallel-kinematics manipulator typically cannot be simply treated as disturbance. Model-based control schemes are usually used to address this issue. This paper presents the improvement of contouring accuracy of a planar 3PRR parallel-kinematics manipulator by applying two model-based control schemes namely decentralized PID control with centralized computed torque feedforward and centralized inverse dynamics control with feedforward. The simulation shows that both control schemes provide better contouring accuracy than decentralized PID control scheme. Furthermore, it is shown that the decentralized PID control with centralized computed torque feedforward outperforms the centralized inverse dynamics control with feedforward given an accurate dynamic model of the manipulator.

关键词： Accuracy PI control Torque automation computational modeling Hardware PD control

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WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Low-Light Enhancement 7

WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Lo...

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7th Asian Conference on Artificial intelligence Technology, ACAIT 2023

作者： Wang, Qiangqiang Huang, Peiliang Li, Lei Han, Longfei Anhui University AHU-IAI Ai Joint Laboratory Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Northwestern Polytechnical University School of Automation Xi'an China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory Beijing China Beijing Technology And Business University Beijing China University of Science And Technology of China School of Information Science And Technology Hefei China

ISBN: (纸本)9798350359145

Low-light enhancement task is an essential component of computer low-level visual tasks, which involves processing images captured under dim lighting conditions to make them appear as if they were taken under normal illumination. Currently, deep neural networks have become the mainstream approach for image processing. However, recent works have devoted considerable efforts to designing high-performance models, which often come with high computational complexity and inference time, making real-time processing unfeasible. We observed that some convolutional methods are due to the need for deep layers which results in a large number of parameters. Moreover, enhancing details and removing noise in low-light images remains an open challenge. In order to solve the above problems, we propose a lightweight baseline that combines CNN and sparse grid attention transformer blocks to enable the model to capture a global receptive field at an early stage. Specifically, we propose a High-Frequency Wavelet-aware Block(HFWB) that focuses on processing high-frequency information in the wavelet domain to refine details and suppress noise. With a processing time of only 10.6ms, the performance of our model outperforms that of the current state-of-the-art lightweight models on benchmark low-light datasets. Compared to state-of-the-art models in the LOL dataset, our model achieves a reduction in inference time of over 90% and requires only about 1% of the FLOPS. © 2023 ieee.

关键词： Deep neural networks

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Comparison of different radio communication-based technologies for indoor localization using trilateration

Comparison of different radio communication-based technologi...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Dominik Csík Peter Sarcevic Richard Pesti Ákos Odry Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Department of Mechatronics and Automation University of Szeged Szeged Hungary

With the spread of Internet of Things (IoT) different radio communication-based technologies are gaining more attention, and their application in indoor localization problems where GPS signal is not available, becomes more and more relevant. This paper addresses the trilateration algorithm-based position estimation approach in indoor localization; this approach uses Received Signal Strength Indication (RSSI) value with the Free Space Path Loss (FSPL) model. Comprehensive laboratory measurements were performed with different radio communication-based technologies namely 433 MHz RSSI, 2.4 GHz WiFi RSSI, ultra-wideband (UWB) RSSI, and UWB Time of Flight (TOF). Then, numerical optimization using the Particle Swarm optimization (PSO) algorithm to determine the parameters of the FSPL model for each anchor and each technology. This method enabled the utilization of trilateration to calculate the position of the measurement node. The obtained results show that the 433 MHz RSSI and the UWB TOF outperform the other two technologies. Using UWB TOF technology, the achieved accuracy was 165.97 cm. 433 MHz RSSI technology provided the second-best solution with 166.60 cm. WiFi RSSI provided 227.89 cm accuracy, while the worst case was obtained by UWB RSSI with 252.08 cm. The study experimentally validates that the most appropriate measurements are provided by UWB TOF and 433 MHz RSSI, which enables the implementation of these technologies in sensor fusion algorithms.

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Impact of the Energy Crisis on Demand for Plug-in Hybrid Vehicles

Impact of the Energy Crisis on Demand for Plug-in Hybrid Veh...

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International symposium on computational intelligence and Informatics

作者： Ágnes Szeghegyi Patrik Viktor Keleti Károly Faculty of Business and Management Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

Our research is about plug-in hybrid vehicle choice patterns for the Volkswagen Group from 2016 to the present day for urban use. In the theoretical part, we describe the characteristics and operational aspects of hybrid vehicles, and then present the beginnings of the automotive industry in Hungary. In the primary research, a questionnaire survey was carried out for the period before the energy crisis and then for the period of the energy crisis. The sample size was 2516 in both cases. In our research, we provided the choice decision criteria and their set of values. Then, using the DoctuS knowledge-based framework, we determined the most important attributes for the decision and their degree of informativeness using an inductive inference procedure. Subsequently, analysis of variance was carried out for energy use specific data, which were used to draw conclusions and inferences.

关键词： Industries Computer science Mechatronics automation Knowledge based systems Informatics Robots

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A novel WKNN algorithm for fingerprinting-based fusion of different radio communication technologies for indoor positioning

A novel WKNN algorithm for fingerprinting-based fusion of di...

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International symposium on computational intelligence and Informatics

作者： Dominik Csík Ákos Odry Richard Pesti Peter Sarcevic Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Department of Mechatronics and Automation University of Szeged Szeged Hungary

Indoor Positioning Systems (IPSs), which commonly rely on Wireless Sensor Networks (WSN), are gaining attention in various fields due to the emergence of Industry 4.0. In this paper, a novel Weighted K-Nearest Neighbor (WKNN) -based localization algorithm is proposed for the fusion of different wireless parameters. An experiment was set up for evaluation and measurements were performed in a laboratory. The essence of the novel method is that different wireless communication technologies, namely, 2.4 GHz WiFi Received Signal Strength Indicator (RSSI), 433 MHz CC1101 RSSI, Ultra-Wideband (UWB) RSSI, and UWB Time of Arrival (TOA) were combined for localization purposes. The proposed algorithm has two steps. In the first step the technologies are used separately to calculate the searched position. In the second step the average of the estimated coordinates is taken in all possible combination of the technologies. The original WKNN method considers the data together provided by each technology. The proposed WKNN algorithm provided better results in 5 cases than the original WKNN method. The improvement ranged from 0.7% to 15%. The largest improvement was achieved by using WiFi and CC1101 RSSI together, when the average error was reduced from 1.86 m to 1.58 m. The results show that it is worthwhile to adaptively select the appropriate fusion of technologies, as this can significantly reduce the localization error.

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The implementation of Thread Network for a Smart Factory

The implementation of Thread Network for a Smart Factory

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ieee symposium Series on computational intelligence (SSCI)

作者： Camille Mavic Tupas Castro Anurag Sharma Dhivya Sampath Kumar Khalid Abidi Noori Kim Newcastle University Singapore Institue of Technology

ISBN: (纸本)9781665487696

In an excessively busy factory, a shortage of manpower is more likely to happen due to the overload of orders which may lead to immoderate overtime for workers. This is a customary hindrance to a factory's higher production aims where the initial practice yielded a long lead time for packing of servers that are ready for delivery. The packing of servers includes the manual search of a specific server in multiple trolley racks, which may take an average of 7 hours for an order. Readily available information such as the Internet-of-Things(IoT) can be fully utilized to implement automation in such companies. The proposed solution is to implement the Pack Predictive Scheduling (PPS) System for a Smart Factory and track these travelling servers by attaching a router to it that has indicative LED lighting connected to it for easier detection. It also encompasses the study of standardized networking protocols and the necessary steps taken to achieve the desired interoperability.

关键词： Performance evaluation Mesh networks automation Instruction sets Prototypes Production facilities Routing protocols

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An Improved KNNG Driven Graph Transfer Diagnosis Method Via Edge Predictor 5

An Improved KNNG Driven Graph Transfer Diagnosis Method Via ...

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5th International Conference on Intelligent control, Measurement and Signal Processing, ICMSP 2023

作者： Yang, Chaoying Hu, Youmin Yang, Shuangye Liu, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Drilling Equipment Co. Ltd. CNPC National Engineering Research Center for Oil&gas Baoji China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350336030

By establishing relationships between cross-domain samples, graph data-driven transfer diagnosis methods have achieved a great success. However, there are still some limitations: 1) Cross-domain relationships cannot be correctly established due to data distribution discrepancy, thereby degenerating graph quality;2) Calculation of distance matrix consumes much computational cost. To overcome above limitations, an improved KNNG driven graph transfer diagnosis method via edge predictor is proposed. Firstly, few source-domain samples are used to construct initial K-nearest neighbor graph (IKNNG) for edge predictor training. Then, a pre-trained edge predictor, learning the graph construction strategy and restoring the graph structure of the input graph, directly outputs distance matrix without calculation of distance matrix. All the source-domain and target-domain samples are input to the pre-trained edge predictor, obtaining corresponding distance matrix. Based on the generated distance matrix, source-domain and target-domain samples are converted into source-domain and target-domain graphs, respectively. Both two kinds of graphs are used to train the Chebyshev graph convolutional network, achieving transfer diagnosis tasks. Comparative experimental results show that the proposed method achieves a competitive result. © 2023 ieee.

关键词： Convolution

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Modelling and implementation of Single Line Diagram data in IEC 61850 environment 19

Modelling and implementation of Single Line Diagram data in ...

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19th ieee World symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Potovei, Danut Adrian Bulac, Constantin Tristiu, Ion Camachi, Balduino Anton, Nicolae Univ Politehn Bucuresti Fac Power Engn Bucharest Romania Univ Politehn Bucuresti Fac Automat Control & Comp Sci Bucharest Romania

ISBN: (纸本)9781728180533

The aim of this paper is to improve the automation processes of electrical power substations based on IEC 61850 standard, with the focus on data management related to data structure, path, and communication model. When modelling substations items a simple and clear approach is necessary to effectively match binary or analog data from the switchyard with the IEC 61850 data object model. The study is made by simply transforming data from a single line diagram and its functionalities into object model by explaining its hierarchy structure and how to use it to create an operational process interface. This possible through the three key components of the IEC 61850 standard: Client-Server based on TCP/IP MMS (Manufacturing Messaging Specification) which perform the monitoring and control functions, GOOSE (Generic Object-Oriented Substation Event) protocol used in applications between IEDs (Intelligent Electronic Device) like interlocking signals and trip messages and Sampled Values (SV) protocol used for fast transmission of analogue values over the network

关键词： SLD IED IEC 61850 data modelling

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A Method for Detecting the Reducers' Grease Failure Based on Vibration Signal Analysis

A Method for Detecting the Reducers' Grease Failure Based on...

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2023 CAA symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Ditong Zhang, Hong Zheng, Ying Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan China Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350337754

Real time detection of the lubricating grease's effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task. © 2023 ieee.

关键词： Support vector machines

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Application of a Neural Network to Feed Bag Tag Detection Systems

Application of a Neural Network to Feed Bag Tag Detection Sy...

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ieee International Conference on Automatica (ICA-ACCA)

作者： Luis Loaiza Wellington Villota Vicente Peñaranda Michelle Cardenas Nino Vega Electronics and Automation Salesian Polytechnic University Guayaquil Ecuador Institute IITP Catholic University Guayaquil Ecuador Mechatronic Salesian Polytechnic University Guayaquil Ecuador

ISBN: (数字)9798350378115

ISBN: (纸本)9798350378122

This paper describes the implementation of an advanced artificial intelligence-based system for the detection and labeling of feed sacks, using the Jetson Nano development board and the YOLOv8 neural network model. This system employs computer vision techniques to accurately identify the sacks and their side labels. Ensuring the presence of a label on each sack is crucial for product monitoring and quality control of the labeling process. The methodology involves a rigorous model training process, using data captured in real production conditions, and system configuration to optimize detection performance. The results obtained demonstrate the system’s robustness and efficiency, with an accuracy rate of 98.84% in controlled conditions and 96.42% in a real production environment. This underscores the system’s practicality and its potential to significantly enhance logistics and quality control processes in the shrimp feed industry. The technology’s implementation can lead to improved product traceability and operational efficiency throughout the supply chain, potentially reducing costs, minimizing errors, and increasing end-customer satisfaction.

关键词： Industries Visualization Accuracy Costs computational modeling Neural networks Process control Quality control Labeling Feeds

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