Search Results (81)

This study presents the optimization of an automated mushroom substrate irrigation system by integrating a Soft Actor-Critic (SAC) reinforcement learning agent with a recursive LSTM prediction model. The system, based on a Siemens S7-1200 PLC, CS650 dielectric sensors, and an Ethernet-based data architecture, provides real-time control of humidity, temperature, and electrical conductivity. Experimental data analysis shows that the SAC agent increases the episodic reward from 20–32 to 90–100 units over 200 episodes, stably maintaining the substrate moisture in the range of 61–65%. The LSTM model achieved a Validation Loss of 0.016–0.022, accurately predicting the hydro-physical parameters. Compared to traditional PID controllers, the SAC-based system reduces humidity deviations by 35–40%, reduces the risk of overwatering and drying out, and increases mycelium colonization. The results confirm that the developed cyber-bioprocess platform increases the stability of the mushroom cultivation process, water use efficiency, and product quality and shows potential for industrial application, which must be validated in larger-scale trials. Full article

The integration of machine vision systems with programmable logic controllers (PLCs) is increasingly crucial for automated quality assurance in Industry 4.0 environments. This paper presents an applied case study of vision–PLC integration, focusing on real-time synchronization, deterministic communication, and practical industrial deployment. The proposed platform combines a Cognex In-Sight 2802C smart camera (Cognex Corporation, Natick, MA, USA) with an Allen-Bradley Compact GuardLogix PLC through Ethernet/IP implicit cyclic exchange. Three representative case studies were investigated: 3D-printed prototypes with controlled defects, automotive electrical connectors inspected using Cognex ViDi supervised learning tools, and fiber optic tubes evaluated via a custom fixture-based heuristic method. Across all scenarios, detection accuracy exceeded 95%, while PLC-level triple verification reduced false classifications by 28% compared to camera-only operation. The work highlights the benefits of PLC-driven inspection, including robustness, real-time performance, and dynamic tolerance adjustment via HMI interfaces. At the same time, several limitations were identified, including sensitivity to lighting variations, limited dataset size, and challenges in scaling to full production environments. These findings demonstrate a replicable integration framework that supports intelligent manufacturing. Future research will focus on hybrid AI–PLC architectures, extended validation on industrial production lines, and predictive maintenance enabled by edge computing. Full article

With the rapid development of the Industrial Internet of Things (IIoT), the application scale of Time-Triggered Ethernet (TTE) technology in the IIoT has been increasingly expanding. To address the issues of rapidly increasing computation time and deteriorating scheduling quality in traditional scheduling algorithms for large-scale TTE applications, this paper proposes a hybrid scheduling algorithm based on critical-link optimization. A large-scale TTE message scheduling model is established based on the characteristics of Time-Triggered (TT) messages, and the constraints of TT scheduling are mathematically abstracted. After identifying the critical link of the network, a time slot balancing scheduling algorithm based on static priority is adopted for the link. The algorithm searches for the optimal scheduling time of current message by time-sliding within the current maximum time gap of TT messages from the center to both sides, maximizing the balance of TT message intervals to reduce the impact on Best-Effort (BE) message transmission performance. An improved genetic algorithm is proposed for the scheduling of the entire network to further enhance the global optimization capability, which takes the scheduling results of the critical link as the genes of initial population. The TT scheduling constraints are converted into the fitness function and the optimized genetic operators are developed for the genetic algorithm. Simulation results showed that the proposed algorithm can significantly reduce computing time and increase the success rate of message scheduling. At the same time, the scheduling results exhibit a better degree of TT message balance and can effectively reduce the transmission delay and jitter of BE messages as message load increases compared with traditional algorithms, making it better meet the scheduling requirements of large-scale TTE application scenarios. Full article

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value ($\mu$) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations. Full article

This paper presents the design and implementation of a mobile robotic platform modelled as a layered Cyber–Physical System (CPS). Inspired by architectures commonly used in industrial Distributed Control Systems (DCSs) and large-scale scientific infrastructures, the proposed system incorporates modular hardware, distributed embedded control, and multi-level coordination. The robotic platform, named MapBot, is structured according to a five-layer CPS model encompassing component, control, coordination, supervisory, and management layers. This structure facilitates modular development, system scalability, and integration of advanced features such as a digital twin. The platform is implemented using embedded computing elements, diverse sensors, and communication protocols including Ethernet and I2C. The system operates within the ROS2 framework, supporting flexible task distribution across processing nodes. As a use case, two localization techniques—Adaptive Monte Carlo Localization (AMCL) and pose graph SLAM—are deployed and evaluated, highlighting the performance trade-offs in map quality, update frequency, and computational load. The results demonstrate that CPS-based design principles offer clear advantages for robotic platforms in terms of modularity, maintainability, and real-time integration. The proposed approach can be generalised for other robotic or mechatronic systems requiring structured, layered control and embedded intelligence. Full article

Modern networked industrial applications often require low-latency communication. Some applications evolve over time, however, are tied to yet existing infrastructures, like power grids spanning across large areas. For instance, medium voltage direct current (MVDC) grids are evolving to a promising alternative to traditional medium voltage alternating current (MVAC) grids due to their efficiency and suitability for novel use cases like electric mobility. MVDC grids, however, require an active control and fault handling strategy. Some strategies demand for a continuous state exchange of the converter substations via a low-latency communication channel with less than 1 millisecond. While some communication approaches for MVDC grids are described in the literature, none of them is inherently designed to be secure. In this paper, we present a protocol for ultra-low-latency secure state exchange (PULLSE) based on conventional non-deterministic Ethernet and AES-GCM. We chose Ethernet in order to not limit the approaches usability in terms of hardware requirements or communication patterns. PULLSE is designed to prevent traffic eavesdropping, replay, and manipulation attacks. Full article

As the automotive industry continues to evolve rapidly, there is a growing demand for high-throughput reliable communication systems within vehicles. This paper presents the implementation and verification of a fault-tolerant Ethernet-based communication protocol tailored for automotive applications operating at 1 Gbps and above. The proposed system introduces a multi-port Network Interface Controller (NIC) architecture that supports real-time communication and robust fault handling. To ensure adaptability across various in-vehicle network (IVN) scenarios, the system allows for configurable packet sizes and transmission rates and supports diverse data formats. The architecture integrates cyclic redundancy check (CRC)-based error detection, real-time recovery mechanisms, and file-driven data injection techniques. Functional validation is performed using Verilog HDL simulations, demonstrating deterministic timing behavior, modular scalability, and resilience under fault injection. This paper presents a fault-tolerant Network Interface Controller (NIC), architecture incorporating CRC-based error detection, real-time recovery logic, and file-driven data injection. The system is verified through Verilog HDL simulation, demonstrating correct timing behavior, modular scalability, and robustness against injected transmission faults. Compared to conventional dual-port NICs, the proposed quad-port architecture demonstrates superior scalability and error tolerance under injected fault conditions. Experimental results confirm that the proposed NIC architecture achieves stable multi-port communication under embedded automotive environments. This study further introduces a novel quad-port NIC with an integrated fault injection algorithm and evaluates its performance in terms of error tolerance. Full article

The evolution of the internet towards supporting time-critical applications, such as industrial cyber-physical systems (CPSs) and autonomous systems, has created an urgent demand for networks capable of providing deterministic, low-latency communication. Autonomous vehicles represent a particularly challenging use case within this domain, requiring both reliability and determinism for massive data streams—a requirement that traditional Ethernet technologies cannot satisfy. This paper addresses this critical gap by proposing a comprehensive scheduling framework based on Time-Aware Shaping (TAS) within the Time-Sensitive Networking (TSN) standard. The framework features two key contributions: (1) a novel baseline scheduling algorithm that incorporates a sub-flow division mechanism to enhance schedulability for high-bandwidth streams, computing Gate Control Lists (GCLs) via an iterative SMT-based method; (2) a separate heuristic-based computation acceleration algorithm to enable fast, scalable GCL generation for large-scale networks. Through extensive simulations, the proposed baseline algorithm demonstrates a reduction in end-to-end latency of up to 59% compared to standard methods, with jitter controlled at the nanosecond level. The acceleration algorithm is shown to compute schedules for 200 data streams in approximately one second. The framework’s effectiveness is further validated on a real-world TSN hardware testbed, confirming its capability to achieve deterministic transmission with low latency and jitter in a physical environment. This work provides a practical and scalable solution for deploying deterministic communication in complex autonomous and cyber-physical systems. Full article

The accurate prediction of autonomous vessel CO₂ emissions is critical for achieving IMO 2050 carbon neutrality and optimizing low-carbon maritime operations. Traditional models face limitations in real-time multi-source data analysis and dynamic cross-variable dependency modeling, hindering data-driven decision-making for sustainable autonomous shipping. This study proposes a Multi-scale Channel-aligned Transformer (MCAT) model, integrated with a 5G–satellite–IoT communication architecture, to address these challenges. The MCAT model employs multi-scale token reconstruction and a dual-level attention mechanism, effectively capturing spatiotemporal dependencies in heterogeneous data streams (AIS, sensors, weather) while suppressing high-frequency noise. To enable seamless data collaboration, a hybrid transmission framework combining satellite (Inmarsat/Iridium), 5G URLLC slicing, and industrial Ethernet is designed, achieving ultra-low latency (10 ms) and nanosecond-level synchronization via IEEE 1588v2. Validated on a 22-dimensional real autonomous vessel dataset, MCAT reduces prediction errors by 12.5% MAE and 24% MSE compared to state-of-the-art methods, demonstrating superior robustness under noisy scenarios. Furthermore, the proposed architecture supports smart autonomous shipping solutions by providing demonstrably interpretable emission insights through its dual-level attention mechanism (visualized via attention maps) for route optimization, fuel efficiency enhancement, and compliance with CII regulations. This research bridges AI-driven predictive analytics with green autonomous shipping technologies, offering a scalable framework for digitalized and sustainable maritime operations. Full article

The rising cost of energy and the urgent need for sustainability have driven industries to adopt smarter solutions for monitoring and optimizing resource consumption. In this study, we present an Industrial Internet of Things (IIoT)-based approach for real-time energy and air consumption monitoring in manufacturing, focusing on a legacy Turret Punch Press (TPP) at Mitsubishi Electric Air Conditioning Systems Europe Ltd. (M-ACE). Due to its age and lack of modern monitoring capabilities, the machine was suspected to be inefficient, requiring a retrofitting strategy for improved transparency and optimization. To address these challenges, a structured IIoT-enabled monitoring system was deployed, integrating KEYENCE MP-F series sensors, an energy monitoring module, and Ethernet communication via Modbus TCP/IP. A comprehensive dashboarding system was developed for real-time visualization and analysis of energy consumption trends, identifying inefficiencies and optimizing machine usage. The data-driven approach revealed significant energy savings of up to 56% and uncovered hidden inefficiencies, including a persistent air leak. By implementing a smart shut-off valve triggered by real-time power consumption data, unnecessary air leakage was eliminated, reducing compressed air waste and overall energy costs. The results demonstrate the effectiveness of IIoT-based retrofitting for industrial energy efficiency, showcasing a scalable framework that can be applied across various machines and production environments. This study highlights the importance of data-driven decision-making in smart manufacturing, contributing to both cost reduction and sustainability goals in industrial settings. Full article

Quantum information has emerged as a frontier in scientific research and is transitioning to real-world technologies and applications. In this work, we explore the integration of quantum secure direct communication (QSDC) with time-sensitive networking (TSN) for the first time, proposing a novel framework to address the security and latency challenges of Ethernet-based networks. Because our QSDC-TSN protocol inherits all the advantages from QSDC, it will enhance the security of the classical communications both in the traditional TSN- and QKD-based TSN by the quantum principle and reduce the communication latency by transmitting information directly via quantum channels without using keys. By analyzing the integration of QSDC and TSN in terms of time synchronization, flow control, security mechanisms, and network management, we show how QSDC enhances the real-time performance and security of TSN. These advantages enable our QSDC-TSN to keep the balance between and meet the requirements of both high security and real-time performance in industrial control, in a digital twin of green power and green hydrogen systems in distributed energy networks, etc., showing its potential applications in future quantum-classical-hybrid systems. Full article

Slave devices in Ethernet-based fieldbus networks often require extensive reprogramming of applications and replacement of protocol stacks and Ethernet drivers whenever the fieldbus protocol needs to be changed. To address this challenge, we develop a common application programming interface (API) and stack interfaces that enable seamless protocol switching among EtherCAT, PROFINET, and EtherNet/IP without requiring protocol-specific code modifications. The real-time data exchange between the API and each protocol stack is realized in the stack interface by using the synchronization mechanism provided by FreeRTOS. The developed common API and stack interfaces facilitate the development of slave device applications that are universally compatible with multiple protocols, EtherCAT, PROFINET, and EtherNet/IP. Moreover, once a required protocol is selected in the integrated development environment (IDE) software before building the slave device firmware, the corresponding protocol stack and Ethernet drivers are automatically specified and the need to replace protocol stacks or Ethernet drivers is even eliminated when switching protocols. To validate the developed common API and stack interfaces, they were implemented on a slave device using TI’s TMDS243EVM board, and a fieldbus network was built by connecting the slave device to a master device executed by Beckhoff’s TwinCAT on a Windows PC. Experimental results confirmed the API’s functionality, reliability, and practical applicability in streamlining protocol management for Ethernet-based fieldbus networks. Full article

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

The food industry increasingly depends on technological assets to improve the efficiency and accuracy of fruit processing and quality control. This article enhances the application of computer vision with collaborative robotics to create a non-destructive system. The system can automate the detection and handling of fruits, particularly tomatoes, reducing the reliance on manual labor and minimizing damage during processing. This system was developed with a Raspberry Pi 5 to capture images of the fruit using a PiCamera module 3. After detecting the object, a command is sent to a Universal Robotics UR3e robotic arm via Ethernet cable, using Python code that integrates company functions and functions developed specifically for this application. Four object detection models were developed using the TensorFlow Object Detection API, converted to TensorFlow Lite, to detect two types of fruit (tomatoes) using deep learning techniques. Each fruit had two versions of the models. The models obtained 67.54% mAP for four classes and 64.66% mAP for two classes, A rectangular work area was created for the robotic arm and computer vision to work together. After 640 manipulation tests, a reliable area of 262 × 250 mm was determined for operating the system. In fruit sorting facilities, this system can be employed to automatically classify fruits based on size, ripeness, and quality. This ensures consistent product standards and reduces waste by sorting fruits according to pre-defined criteria. The system’s ability to detect multiple fruit types with high accuracy enables it to integrate into existing workflows, thereby increasing productivity and profitability for food processing companies. Additionally, the non-destructive nature of this technology allows for the inspection of fruits without causing any damage, ensuring that only the highest-quality produce is selected for further processing. This application can enhance the speed and precision of quality control processes, leading to improved product quality and customer satisfaction. Full article

Programmable Logic Controllers (PLCs) are indispensable for current and future industrial development, especially in smart factories, smart home technology, automated production lines, and machinery manufacturing. This study presents the trends in PLC software and hardware development through a technology roadmap and offers relevant suggestions to help industries achieve sustainable development, enhance market competitiveness, and provide references for research. Through expert interviews and fuzzy Delphi analysis, this study points out that future PLC development needs to focus on editing interfaces, syntax, Central Processing Units, Memory Units, and Communication Modules. Specific recommendations include visualizing regional/global label settings and connection settings, adding Python, JAVA, LabVIEW, and Scratch syntax, improving instruction execution speed, expanding program and expansion capacities, and adopting dual-channel Ethernet and connections to external networks and wireless networks. Fuzzy hierarchical analysis shows that Communication Modules are the most important component, followed by Central Processing Units and syntax expansion, and, finally, program and expansion capacity enhancements. These suggestions aim to promote product innovation and social environment demand evaluation, enhance product competitiveness, and achieve sustainable development goals. Full article