Search Results (2,108)

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to be important components of the IoT system (WSN-IoT) to create smart applications and automate processes. As the number of connected IoT devices increases, privacy and security issues become more complicated due to their external working environments and limited resources. Hence, solutions need to be updated to ensure that data and user privacy are protected from threats and attacks. To support the safety and reliability of such systems, in this paper, security issues in the WSN-IoT are addressed and classified as identifying security challenges and requirements for different kinds of attacks in either WSNs or IoT systems. In addition, security solutions corresponding to different types of attacks are provided, analyzed, and evaluated. We provide different comparisons and classifications based on specific goals and applications that hopefully can suggest suitable solutions for specific purposes in practical. We also suggest some research directions to support new security mechanisms. Full article

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate. Full article

With the increasing scale of industrial devices and the growing complexity of multi-source heterogeneous sensor data, traditional methods struggle to address challenges in fault detection, data security, and trustworthiness. Ensuring tamper-proof data storage and improving prediction accuracy for imbalanced anomaly detection for potential deployment in the Industrial Internet of Things (IIoT) remain critical issues. This study proposes a blockchain-powered Long Short-Term Memory Network (LSTM)–Attention hybrid model: an LSTM-based Encoder–Attention–Decoder (LEAD) for industrial device anomaly detection. The model utilizes an encoder–attention–decoder architecture for processing multivariate time series data generated by industrial sensors and smart contracts for automated on-chain data verification and tampering alerts. Experiments on real-world datasets demonstrate that the LEAD achieves an $F_{0.1}$ score of 0.96, outperforming baseline models (Recurrent Neural Network (RNN): 0.90; LSTM: 0.94; and Bi-directional LSTM (Bi-LSTM, 0.94)). We simulate the system using a private FISCO-BCOS network with a multi-node setup to demonstrate contract execution, anomaly data upload, and tamper alert triggering. The blockchain system successfully detects unauthorized access and data tampering, offering a scalable solution for device monitoring. Full article

The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. This research aims to study the ML and DL systematic applications in optimizing the wastewater treatment processes from an industrial plant, such as the modeling of complex physical–chemical processes, real-time monitoring and prediction of critical wastewater quality indicators, chemical reactants consumption reduction, minimization of plant energy consumption, plant effluent quality prediction, development of data-driven type models as support in the decision-making process, etc. To perform a detailed analysis, 87 articles were included from an initial set of 324, using criteria such as wastewater combined with ML, DL, and artificial intelligence (AI), for articles from 2010 or newer. From the initial set of 324 scientific articles, 300 were identified using Litmaps, obtained from five important scientific databases, all focusing on addressing the specific problem proposed for investigation. Thus, this paper identifies gaps in the current research, discusses ML and DL algorithms in the context of optimizing wastewater treatment processes, and identifies future directions for optimizing these processes through data-driven methods. As opposed to traditional models, IA models (ML, DL, hybrid and ensemble models, digital twin, IoT, etc.) demonstrated significant advantages in wastewater quality indicator prediction and forecasting, in energy consumption forecasting, in temporal pattern recognition, and in optimal interpretability for normative compliance. Integrating advanced ML and DL technologies into the various processes involved in wastewater treatment improves the plant systems’ predictive capabilities and ensures a higher level of compliance with environmental standards. Full article

The evolution of Industry 4.0 and the emerging paradigm of Industry 5.0 have introduced disruptive technologies that are reshaping modern manufacturing environments. Among these, Digital Twin (DT) and Industrial Metaverse (IM) technologies are increasingly recognized for their potential to enhance Occupational Health and Safety (OHS). However, a comprehensive understanding of how these technologies integrate to support OHS in manufacturing remains limited. This study systematically explores the transformative role of DT and IM in creating immersive, intelligent, and human-centric safety ecosystems. Following the PRISMA guidelines, a Systematic Literature Review (SLR) of 75 peer-reviewed studies from the SCOPUS and Web of Science databases was conducted. The review identifies key enabling technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Internet of Things (IoT), Artificial Intelligence (AI), Cyber-Physical Systems (CPS), and Collaborative Robots (COBOTS), and highlights their applications in real-time monitoring, immersive safety training, and predictive hazard mitigation. A conceptual framework is proposed, illustrating a synergistic digital ecosystem that integrates predictive analytics, real-time monitoring, and immersive training to enhance the OHS. The findings highlight both the transformative benefits and the key adoption challenges of these technologies, including technical complexities, data security, privacy, ethical concerns, and organizational resistance. This study provides a foundational framework for future research and practical implementation in Industry 5.0. Full article

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions. Full article

The digital wave, represented by new technologies such as big data, IoT, and artificial intelligence, is sweeping the globe, driving all industries toward digitalization and intelligent transformation. Digital twins are becoming an indispensable opportunity for new infrastructure initiatives. As geotechnical engineering constitutes a critical component of new infrastructure, its corresponding digital transformation is essential to align with these initiatives. However, due to the difficulty of modeling, the demand for computing resources, interdisciplinary integration, and other issues, current digital twin applications in geotechnical engineering remain in their nascent stage. This paper delineates the developmental status of geotechnical digital twin technology in China, and it focuses on the advantages and disadvantages of digital twins in five application fields, identifying key challenges, including intelligent sensing and interconnectivity of multi-source heterogeneous physical entities, integrated sharing of 3D geological models and structural models, unified platforms for lifecycle information management, standardization of digital twin data protocols, and theoretical frameworks for digital twin modeling. Furthermore, this study systematically expounds future research priorities across four dimensions: intelligent sensing and interoperability technologies for geotechnical engineering; knowledge graph development and model-based systems engineering; integrated digital twin entity technologies combining 3D geological bodies with engineering structures; and precision enhancement, temporal extension, and spatial expansion of geotechnical digital twins. This paper systematically reviews the application status of digital twin technology in geotechnical engineering for the first time, reveals the common technical challenges in cross-domain implementation, and proposes a theoretical framework for digital twin accuracy improvement and spatiotemporal expansion for geotechnical engineering characteristics, which fills the knowledge gap in the adaptability of existing research in professional fields. These insights aim to provide references for advancing digitalization, intelligent transformation, and sustainable development of geotechnical engineering. Full article

This study addresses the problem of agricultural waste utilization and nutrition for older adults by developing a food product based on a circular design approach. Pineapple core was used to produce a clean-label dietary powder without chemical or enzymatic treatment, relying on repeated rinsing and hot-air drying. The development process followed a structured analysis of physical, chemical, and sensory properties. The powder contained 83.46 g/100 g dietary fiber, 0° Brix sugar, pH 4.72, low water activity (aw < 0.45), and no detectable heavy metals or microbial contamination. Sensory evaluation by expert panelists confirmed that the product was acceptable in appearance, aroma, and texture, particularly for older adults. These results demonstrate the feasibility and safety of valorizing agri-waste into functional ingredients. The process was guided by the Transformative Circular Product Blueprint, which integrates clean-label processing, IoT-enabled solar drying, and decentralized production. This model supports traceability, low energy use, and adaptation at the community scale. This study contributes to sustainable food innovation and aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation and Infrastructure), and 12 (Responsible Consumption and Production). Full article

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

As industrial companies transition from the Industry 4.0 stage to the more human-centric and resilient Industry 5.0 paradigm, there is a growing need for structured assessment tools to prioritize modern technologies. This paper presents an integrated multi-criteria decision analysis (MCDA) approach to support the strategic assessment of technologies from three complementary perspectives: economic, organizational, and technological. The proposed model encompasses six key transformation areas and 22 technologies representing both the Industry 4.0 and 5.0 paradigms. A hybrid approach combining the DEMATEL (Decision-Making Trial and Evaluation Laboratory) and PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation) methods is used to identify cause–effect relationships between the transformation areas and to construct technology rankings in each of the assessed perspectives. The results indicate that technologies such as the Internet of Things (IoT), cybersecurity, and supporting IT systems play a central role in the transition process. Among the Industry 5.0 technologies, hyper-personalized manufacturing, smart grids and new materials stand out. Moreover, the economic perspective emerges as the dominant assessment dimension for most technologies. The proposed analytical framework offers both theoretical input and practical decision-making support for companies planning their transformation towards Industry 5.0, enabling a stronger alignment between implemented technologies and long-term strategic goals. Full article

The article examines the application of collaborative robots (cobots) as a modern engineering tool for the transformation of the food industry following the principles of Industry 5.0. A conceptual engineering model has been developed that integrates collaborative robots with IoT systems, digital twins, and predictive analytics to increase the flexibility, safety, and sustainability of production processes. The proposed model is validated through a practical case study focused on a yogurt packaging line in the dairy sector, where cobot systems demonstrate a significant improvement in operational efficiency and process safety. A step-by-step strategic roadmap is presented to guide industrial enterprises through the various stages of implementation, from the initial assessment to the full-scale integration of solutions. Additionally, a comparative analysis has been performed between traditional automated systems and the integrated approach with collaborative robots, which highlights the technological, economic, and human-oriented advantages of the latter. The results of the study confirm that collaborative robotics offers an effective and applicable path for transforming the food and beverage industry towards a sustainable, adaptive, and human-centered manufacturing ecosystem characteristic of Industry 5.0. Full article

The scope of this paper is the development of a cost-effective wireless torque measurement system for vehicle drivetrain shafts. The prototype integrates strain gauges, an HX711 conditioner, a Wemos D1 Mini ESP8266, and a rechargeable battery directly on the rotating shaft, forming a self-contained sensor node. Calibration against a certified dynamometric wrench confirmed an operating span of ±5–50 N·m. Within this range, the device achieved a mean absolute error of 0.559 N·m. It also maintained precision better than ±2.5 N·m at 95% confidence, while real-time data were transmitted via Wi-Fi. The total component cost is below EUR 30 based on current prices. The novelty of this proof-of-concept implementation demonstrates that reliable, IoT-enabled torque sensing can be realized with low-cost, readily available parts. The paper details assembly, calibration, and deployment procedures, providing a transparent pathway for replication. By aligning with Industry 4.0 requirements for smart, connected equipment, the proposed torque measurement system offers an affordable solution for process monitoring and predictive maintenance in automotive and industrial settings. Full article