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42 pages, 16651 KiB  
Article
Internet of Things-Cloud Control of a Robotic Cell Based on Inverse Kinematics, Hardware-in-the-Loop, Digital Twin, and Industry 4.0/5.0
by Dan Ionescu, Adrian Filipescu, Georgian Simion and Adriana Filipescu
Sensors 2025, 25(6), 1821; https://doi.org/10.3390/s25061821 - 14 Mar 2025
Cited by 1 | Viewed by 1200
Abstract
The main task of the research involves creating a Digital Twin (DT) application serving as a framework for Virtual Commissioning (VC) with Supervisory Control and Data Acquisition (SCADA) and Cloud storage solutions. An Internet of Things (IoT) integrated automation system with Virtual Private [...] Read more.
The main task of the research involves creating a Digital Twin (DT) application serving as a framework for Virtual Commissioning (VC) with Supervisory Control and Data Acquisition (SCADA) and Cloud storage solutions. An Internet of Things (IoT) integrated automation system with Virtual Private Network (VPN) remote control for assembly and disassembly robotic cell (A/DRC) equipped with a six-Degree of Freedom (6-DOF) ABB 120 industrial robotic manipulator (IRM) is presented in this paper. A three-dimensional (3D) virtual model is developed using Siemens NX Mechatronics Concept Designer (MCD), while the Programmable Logic Controller (PLC) is programmed in the Siemens Totally Integrated Automation (TIA) Portal. A Hardware-in-the-Loop (HIL) simulation strategy is primarily used. This concept is implemented and executed as part of a VC approach, where the designed PLC programs are integrated and tested against the physical controller. Closed loop control and RM inverse kinematics model are validated and tested in PLC, following HIL strategy by integrating Industry 4.0/5.0 concepts. A SCADA application is also deployed, serving as a DT operator panel for process monitoring and simulation. Cloud data collection, analysis, supervising, and synchronizing DT tasks are also integrated and explored. Additionally, it provides communication interfaces via PROFINET IO to SCADA and Human Machine Interface (HMI), and through Open Platform Communication—Unified Architecture (OPC-UA) for Siemens NX-MCD with DT virtual model. Virtual A/DRC simulations are performed using the Synchronized Timed Petri Nets (STPN) model for control strategy validation based on task planning integration and synchronization with other IoT devices. The objective is to obtain a clear and understandable representation layout of the A/DRC and to validate the DT model by comparing process dynamics and robot motion kinematics between physical and virtual replicas. Thus, following the results of the current research work, integrating digital technologies in manufacturing, like VC, IoT, and Cloud, is useful for validating and optimizing manufacturing processes, error detection, and reducing the risks before the actual physical system is built or deployed. Full article
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31 pages, 17989 KiB  
Article
IoT-Cloud, VPN, and Digital Twin-Based Remote Monitoring and Control of a Multifunctional Robotic Cell in the Context of AI, Industry, and Education 4.0 and 5.0
by Adrian Filipescu, Georgian Simion, Dan Ionescu and Adriana Filipescu
Sensors 2024, 24(23), 7451; https://doi.org/10.3390/s24237451 - 22 Nov 2024
Cited by 3 | Viewed by 2649
Abstract
The monitoring and control of an assembly/disassembly/replacement (A/D/R) multifunctional robotic cell (MRC) with the ABB 120 Industrial Robotic Manipulator (IRM), based on IoT (Internet of Things)-cloud, VPN (Virtual Private Network), and digital twin (DT) technology, are presented in this paper. The approach integrates [...] Read more.
The monitoring and control of an assembly/disassembly/replacement (A/D/R) multifunctional robotic cell (MRC) with the ABB 120 Industrial Robotic Manipulator (IRM), based on IoT (Internet of Things)-cloud, VPN (Virtual Private Network), and digital twin (DT) technology, are presented in this paper. The approach integrates modern principles of smart manufacturing as outlined in Industry/Education 4.0 (automation, data exchange, smart systems, machine learning, and predictive maintenance) and Industry/Education 5.0 (human–robot collaboration, customization, robustness, and sustainability). Artificial intelligence (AI), based on machine learning (ML), enhances system flexibility, productivity, and user-centered collaboration. Several IoT edge devices are engaged, connected to local networks, LAN-Profinet, and LAN-Ethernet and to the Internet via WAN-Ethernet and OPC-UA, for remote and local processing and data acquisition. The system is connected to the Internet via Wireless Area Network (WAN) and allows remote control via the cloud and VPN. IoT dashboards, as human–machine interfaces (HMIs), SCADA (Supervisory Control and Data Acquisition), and OPC-UA (Open Platform Communication-Unified Architecture), facilitate remote monitoring and control of the MRC, as well as the planning and management of A/D/R tasks. The assignment, planning, and execution of A/D/R tasks were carried out using an augmented reality (AR) tool. Synchronized timed Petri nets (STPN) were used as a digital twin akin to a virtual reality (VR) representation of A/D/R MRC operations. This integration of advanced technology into a laboratory mechatronic system, where the devices are organized in a decentralized, multilevel architecture, creates a smart, flexible, and scalable environment that caters to both industrial applications and educational frameworks. Full article
(This article belongs to the Special Issue Intelligent Robotics Sensing Control System)
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19 pages, 16612 KiB  
Article
ST-PN: A Spatial Transformed Prototypical Network for Few-Shot SAR Image Classification
by Jinlei Cai, Yueting Zhang, Jiayi Guo, Xin Zhao, Junwei Lv and Yuxin Hu
Remote Sens. 2022, 14(9), 2019; https://doi.org/10.3390/rs14092019 - 22 Apr 2022
Cited by 19 | Viewed by 2932
Abstract
Few-shot learning has achieved great success in computer vision. However, when applied to Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR), it tends to demonstrate a bad performance due to the ignorance of the differences between SAR images and optical ones. What is more, [...] Read more.
Few-shot learning has achieved great success in computer vision. However, when applied to Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR), it tends to demonstrate a bad performance due to the ignorance of the differences between SAR images and optical ones. What is more, the same transformation on both images may cause different results, even some unexpected noise. In this paper, we propose an improved Prototypical Network (PN) based on Spatial Transformation, also known as ST-PN. Cascaded after the last convolutional layer, a spatial transformer module implements a feature-wise alignment rather than a pixel-wise one, so more semantic information can be exploited. In addition, there is always a huge divergence even for the same target when it comes to pixel-wise alignment. Moreover, it reduces computational cost with fewer parameters of the deeper layer. Here, a rotation transformation is used to reduce the discrepancies caused by different observation angles of the same class. Thefinal comparison of four extra losses indicates that a single cross-entropy loss is good enough to calculate the loss of distances. Our work achieves state-of-the-art performance on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Full article
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29 pages, 23243 KiB  
Article
PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images
by Jialei Zhan, Yaowen Hu, Weiwei Cai, Guoxiong Zhou and Liujun Li
Symmetry 2021, 13(12), 2260; https://doi.org/10.3390/sym13122260 - 27 Nov 2021
Cited by 43 | Viewed by 4672
Abstract
The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we [...] Read more.
The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods. Full article
(This article belongs to the Special Issue Symmetry in Computer Vision and Its Applications)
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15 pages, 1919 KiB  
Article
Identification and Validation of QTLs for Yield and Yield Components under Long-Term Salt Stress Using IR64 CSSLs in the Genetic Background of Koshihikari and Their Backcross Progenies
by Nguyen Sao Mai, Dao Duy Hanh, Mai Nakashima, Kotaro Kumamoto, Nguyen Thi Thu Thuy, Tohru Kobata, Kuniyuki Saitoh and Yoshihiko Hirai
Agriculture 2021, 11(8), 777; https://doi.org/10.3390/agriculture11080777 - 15 Aug 2021
Cited by 2 | Viewed by 3165
Abstract
Unraveling the complex genetic bases and mechanisms underlying salt tolerance is of great importance for developing salt-tolerant varieties. In this study, we evaluated 42 chromosome segment substitution lines (CSSLs) carrying chromosome segments from IR64 on the genetic background of Koshihikari under salt stress. [...] Read more.
Unraveling the complex genetic bases and mechanisms underlying salt tolerance is of great importance for developing salt-tolerant varieties. In this study, we evaluated 42 chromosome segment substitution lines (CSSLs) carrying chromosome segments from IR64 on the genetic background of Koshihikari under salt stress. Two CSSLs, SL2007 and SL2038, produced higher plant dry weight and grain yield than did Koshihikari under the stress condition. These CSSLs also showed lower Na+ and Cl accumulation in the leaf and whole plant at the full heading stage, which might be related to the higher grain yield and yield components. To understand the genetic control of its grain yield and yield components, a SL2007/Koshihikari F2 population was generated for quantitative trait locus (QTL) analysis. Six QTLs for grain yield and yield-related traits were detected on chromosome 2. Using near-isogenic lines (NILs) from a SL2007/Koshihikari F5 population, qSTGY2.2 was delimited to a 2.5 Mb region and novel qSTPN2 was delimited to a 0.6 Mb region. We also detected a novel QTL, qSTGF2, for grain filling, which was considered an important contributor to grain yield under salt stress in this CSSL. Our results provide insights into mechanisms conferring grain yield under salinity stress and new genetic resources for cloning and breeding. Full article
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