Search Results (5,432)

Fifth generation (5G) networks have significantly enhanced connectivity, speed, and reliability, transforming industries with faster and more efficient communication. The Internet of Things (IoT) has introduced unprecedented convenience and automation, revolutionizing sectors such as healthcare, finance, and smart infrastructure. However, both 5G networks and IoT environments are experiencing a high frequency of attacks. Intrusion detection systems (IDSs) built on federated learning (FL) are being proposed to boost data privacy and security. However, these IDSs are related with the inherent drawbacks of FL, namely the existence of non-independently and identically (non-IID) distributed features and the machine learning model complexity. To address these limitations, we present a study that integrates a Mixture of Experts (MoE) into an FL setting in the task of intrusion detection. Specifically, to mitigate the issues of model complexity within the FL setting, we use a sparsely gated MoE layer consisting of a router/gating network and a set of experts. Only a subset of experts is selected via applying noisy top-k gating. To alleviate the issue of non-IID data, we adopt the Label-based Dirichlet Partition method, utilizing Dirichlet sampling with a hyperparameter $\alpha$ to simulate a label-based non-IID data distribution. Four FL strategies are employed. We perform our experiments on the 5G-NIDD and BoT-IoT datasets. Findings show that the proposed approach achieves competitive performance across both datasets under heterogeneous federated settings. Full article

Due to environmental challenges, the global luxury hospitality industry faces increasing pressure to reduce its consumption of natural resources while maintaining service quality. In this paper a conceptual study is conducted to identify three primary problems of the tourism industry and highlight their impact on sustainable water resources and ecosystems: excessive water, electricity and towel/linen consumption in luxury hotels and resorts. This paper proposes a solution that uses a digital smart meter system linked to guest rooms. It is activated upon check-in, and guest participation is optional. It uses tangible or intangible incentives—such as discounts upon departure for future stays or for hotel laundry/meals/beverages—that rationalize consumption without affecting the quality of basic services. This approach may be implemented either independently by a single hotel or collaboratively through strategic alliances among multiple hotels, thus enabling customers to redeem their incentives/credits at any participating property. Guests are grouped into three consumption levels: high-saving guests (high incentives), average-saving guests (average incentives) and third-level guests (low/below-average incentives). Adopting this approach helps luxury hotels/resorts reduce their operational costs and enhance their image by applying green marketing in practice. Moreover, this conceptual paper proposes the provision of badges, including international environmental certifications, to hotels that adopt this responsible approach. This mechanism is a modern model that directly benefits all involved parties: service providers, customers/guests, environmental organizations and the environment. Full article

Accurate electrical power prediction is increasingly critical in industrial smart manufacturing environments, where energy fluctuations and demand variability pose significant operational challenges under the industry 4.0 paradigm. Existing approaches often rely on simulated or secondary data and lack integration with industrial-grade communication protocols, limiting their practical applicability. Incorporating machine learning with real-time data collection is essential for progressing industrial predictive monitoring. This research presents a framework to forecast electrical power usage by utilizing the RS-485 protocol to enhance smart manufacturing processes. The dataset used was obtained from a power meter, recorded over a period of 135 min, resulting in 3100 data. Three learning methods—Random Forest, Extra Trees, and XGBoost—were analyzed, with XGBoost being further refined through PSO for tuning hyperparameters. The models were trained on datasets that included voltage, current, frequency, and power factor, and their effectiveness was evaluated using time-based predictions, standard metrics, and error distributions through cross-validation. The findings illustrate that the PSO-XGBoost consistently surpasses the default XGBoost baseline R² of 0.5746, achieving MAE of 0.14 W, RMSE of 0.21 W, and R² of 0.8355, representing improvements of 41.67% in MAE, 38.24% in RMSE, and 45.40% in R². The RS-485 protocol enables seamless integration with existing industrial infrastructure, supporting anomaly detection and energy optimization aligned with Industry 4.0 interoperability objectives. Full article

The rapid integration of artificial intelligence (AI) into higher education is reshaping how learners develop academic, linguistic, and research competencies. This mixed-methods study examines how second-year EFL computer science students employ prompt engineering techniques across four task domains—research summarization, academic video note-taking, style transformation, and concept mapping—within a smart learning environment. Sixty-nine students completed a structured survey requiring AI-assisted draft generation followed by student-led revision. Quantitative analyses included descriptive statistics, chi-square tests, Cramer’s V, t-tests, ANOVA, Kruskal–Wallis tests, and three text-similarity measures (cosine, Jaccard, and Levenshtein). Qualitative evidence was drawn from students’ revised outputs and reflective responses. Results indicate that students consistently preserved semantic meaning while significantly rephrasing AI-generated text, demonstrating moderate conceptual alignment but substantial lexical and structural transformation. Frequent AI users said they were better at searching and revising, but the type of prompt didn’t have much of an effect on how deep the revision was or how well they learned. Iterative prompting and revision emerged as central drivers of metacognitive growth, academic language development, and sustainable learning behaviors. Across tasks, students viewed AI prompts as effective scaffolds for organizing information and synthesizing multimodal input, though reliance varied by learner. The findings underscore that sustainable AI use in EFL technical education depends not on AI output alone, but on structured prompting, iterative human revision, and critical engagement—practices that cultivate autonomy, digital literacy, and long-term academic resilience. Full article

Generative AI (GenAI) is increasingly applied in urban planning for text production, visualization, analytics, stakeholder communication, and participatory engagement. Large language models (LLMs) enable the creation of synthetic participants to support the early-stage design, analysis, and testing of participatory tools. This article demonstrates an innovative use of GenAI through synthetic inhabitants and experts in an immersive digital urban planning environment. DigitalTurku serves as a proof-of-concept for an immersive planning tool within an urban digital twin. The case relies on synthetic personas—residents and expert stakeholders—to evaluate how a GenAI-assisted urban platform may shape participation experiences and trust in local urban planning. The findings indicate that synthetic experts expressed a reduced bureaucratic distance, enhanced transparency, and more meaningful participation. However, assessments of tools and digital environment usability varied according to digital skills and demographic characteristics embedded in the personas. The use of synthetic personas helps identify opportunities and challenges in immersive urban planning environments and supports the design of digital tools in smart cities to strengthen human residents’ spatial understanding and experiential engagement in planning processes. The creation of synthetic data and participants is convenient with LLMs. Despite these tools’ limitations, they can play a valuable role in piloting participatory planning processes to support and complement human-based participation. Full article

Excessive tool wear can compromise machining precision and increase costs, rendering accurate tool remaining useful life (RUL) prediction imperative in intelligent manufacturing. Traditional methods exhibit intrinsic limitations in cross-modal modeling accuracy and capturing temporal dependencies, failing to meet practical requirements. To transcend these bottlenecks, this study proposes a robust tool RUL prediction framework that combines a multi-channel CNN and a Cross-Modal Transformer. The CNN performs convolution operations to extract local features from wear signals, while the Transformer adaptively synchronizes heterogeneous features (cutting force, vibration, and acoustic emission) to capture long-term degradation trends. Empirical evaluations conducted on the PHM2010 dataset demonstrate the model’s robustness and generalization capability: under the random shuffle–split protocol, the proposed method achieves an R² of up to 0.99, with the RMSE and MAE reaching 2.51 and 1.98, respectively. To further evaluate the framework’s extrapolation ability under domain shifts, a cross-cutter validation protocol was implemented. Under this condition, the experimental results yield an R² of 0.961, an RMSE of 6.92, and an MAE of 6.09. Additionally, the correlation between modality-specific attention weights and their corresponding physical interpretations is systematically investigated. These results confirm the model’s potential for cross-cutter life cycle management in smart manufacturing, providing stable and physically consistent wear estimation and remaining useful life prediction in noise-intensive environments. Full article

Simulator-based digital twins are widely used in robotics education and industrial development to accelerate prototyping and enable safe experimentation. However, they often hide implementation details that are essential for understanding, diagnosing, and correcting system failures. This paper introduces a technology-independent model-based design framework that provides students with full visibility of the computational mechanisms underlying robotic controllers while remaining feasible within a 150-h undergraduate course. The approach relies on representing controller behavior using networks of Extended Finite State Machines (EFSMs) and their stacked extension (EFS2M), which unify all abstraction levels of the control architecture—from low-level reactive behaviors to high-level deliberation—under a single formal model. A structured programming template ensures traceable, optimization-free software synthesis, facilitating debugging and enabling self-diagnosis of design flaws. The framework includes real-time synchronized simulation, transparent switching between virtual and physical robots, and a smart data logger that captures meaningful events for model updating and error detection. Integrated into the Intelligent Robots course, the system supports topics such as kinematics, control, perception, and simultaneous localization and mapping (SLAM) while avoiding dependency on specific middleware such as Robot Operating System (ROS) 2. Over three academic years, students reported positive hands-on experiences, strong adaptability to diverse modeling approaches, and consistently high survey ratings reflecting the course’s overall quality. The proposed environment thus offers an effective methodology for teaching end-to-end robot controller design through transparent, simulation-driven digital twins. Full article

The pursuit of energy efficiency and sustainability in the built environment has placed façade systems at the forefront of innovation in architectural design. This study proposes an integrated framework that combines generative design techniques with artificial intelligence (AI) to optimize composite façade configurations for next-generation smart buildings. Using parametric modeling, a wide design space of façade geometries and material compositions was generated, capturing trade-offs between thermal performance, daylight, structural strength, and aesthetic variability. Artificial intelligence algorithms, particularly machine learning models, are trained on simulation-derived performance datasets to rapidly predict key indicators such as energy consumption, thermal transmittance (U-value) and solar heat gain coefficients. The proposed approach achieved a predictive accuracy of 99.85%, enabling efficient exploration of optimal solutions across high-dimensional design alternatives. A multi-objective optimization strategy was further implemented to balance energy efficiency with structural and aesthetic constraints, producing façade configurations that outperform conventional designs. The findings demonstrate that integrating generative design with AI-based prediction not only accelerates the façade design process but also provides actionable pathways toward net-zero energy buildings. This research highlights the transformative potential of AI-driven generative workflows in advancing sustainable architecture and delivering intelligent, adaptive and performance-oriented façades for future urban environments. Full article

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation. Full article

This study examines intelligent judgment formation in blockchain-based public digital wallet systems within smart city environments. Drawing on an integrated framework that combines cognitive evaluation, social influence, and trust–risk appraisal, this research conceptualizes intelligent decision-making as a socially embedded and contextually enacted evaluative process rather than a fixed cognitive attribute. A structural equation modeling approach is employed to analyze the interrelationships among perceived usefulness, perceived ease of use, subjective norms, social electronic word of mouth, trust–risk appraisal, attitude, and behavioral intention. The findings indicate that socially distributed information signals play a dominant role in shaping evaluative integration and decision readiness, while cognitive and institutional appraisals operate primarily through mediated pathways. The results suggest that intelligent action in public digital service ecosystems emerges from the coordinated interaction of usability perception, institutional confidence, and socially calibrated information flows. These findings contribute to theoretical extensions of technology acceptance models in public governance contexts and offer implications for the design of socially responsive digital service infrastructures. Full article

The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this paper, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with a passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. When the system detects an unfamiliar individual, it sends out alert notifications and emails to the control room with the captured picture using IoT. A web interface has also been set up to control the robot from a distance through Wi-Fi connection. The proposed face recognition method is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrate the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results indicate that the algorithm can identify human faces with an accuracy of 98.63%. Full article

This paper presents initial developments towards a high-frequency condition monitoring framework designed for Autonomous Mobile Robots (AMRs) in Smart Factory environments. The proposed approach focuses on data acquisition and edge-level processing at the ultrasound range specifically (>20 kHz), using Micro-Electro-Mechanical System (MEMS) sensors. The system integrates real-time data acquisition, embedded fixed-point frequency-domain processing via a 1024-point FFT, and the integration of Industrial Internet-of-Things (IIoT) infrastructure based on the TIG (Telegraf, InfluxDB, and Grafana) stack, for data aggregation and remote visualization. To ensure timing precision at a sampling rate of 160 kHz, a software-based calibration routine is implemented to compensate for microcontroller overhead. Furthermore, the architecture’s alignment with IEEE 1451 principles is discussed to support interoperable and scalable sensor integration. Experimental results validate the reliable acquisition and processing of ultrasonic signals up to 80 kHz using controlled acoustic sources. This work provides a foundational infrastructure for condition-based monitoring, enabling future development of automated anomaly detection for mechanical components, such as bearings, which exhibit early-stage fault signatures in the ultrasonic spectrum. Full article

Todetect upward and downward parameter changes in high-quality processes (HQPs), time between events (TBE) charts have traditionally been used. However, in practice, when the process parameter is unknown and short production runs (SPRs) occur in a smart manufacturing environment, the TBE chart’s characteristics need to be studied carefully. To circumvent this issue, a Shewhart TBE chart with an estimated parameter for monitoring SPR processes is studied. The unknown process parameter is estimated using both the uniformly minimum variance unbiased estimator (UMVUE) and the maximum likelihood estimator (MLE). Then, the truncated run length ($RL$) properties—i.e., truncated average $RL$ ($TARL$), truncated standard deviation of $RL$ ($TSDRL$), and percentiles of the truncated $RL$—of the Shewhart TBE chart in SPR for different parameter settings are obtained through extensive numerical simulations. These truncated $RL$ properties are compared with properties of the Shewhart TBE chart with a known parameter in an SPR. The results show that the Shewhart TBE chart with an estimated parameter is more favorable in cases of an underestimation of the process parameter ${\widehat{\theta }}_0$ for the detection of upward changes or an overestimation of ${\widehat{\theta }}_0$ of the process parameter for the detection of downward changes. Full article

Unexpected degradation in servomotor-driven multi-mode industrial systems such as CNC feed drives and robotic machining cells compromises positioning accuracy, availability and operational safety, rendering early fault diagnosis and predictive maintenance essential in smart manufacturing environments. In this study, a predictive maintenance framework based on multi-sensor data fusion was developed to support condition monitoring, fault classification, and remaining useful life estimation of robot servomotors. Time- and frequency-domain features were extracted from synchronized electrical current, vibration, acoustic, and temperature signals using fixed-length sliding windows. Feature-level fusion was applied to combine complementary information from different sensor modalities. A data-driven health assessment approach was employed in which an autoencoder model trained on healthy operating data was used to generate a scalar Servomotor Health Score representing degradation progression. Fault types were identified using a Random Forest classifier, while remaining useful life was estimated in terms of operational cycles using a Gradient Boosting regression model. Experimental evaluations were carried out under repeated reference motion profiles, and representative mechanical and electrical fault conditions were introduced in a controlled manner. The results demonstrated that the proposed health score provided a smooth and monotonic degradation trend, enabling early fault detection without false alarms under healthy conditions. High classification performance was achieved for fault identification, and remaining useful life predictions showed low estimation error on previously unseen faulty servomotors. Feature contribution analysis indicated that electrical current and temperature signals provided the most robust indicators of degradation, while vibration and acoustic measurements offered complementary diagnostic information. The proposed framework was shown to be an effective and practical solution for predictive maintenance of servomotor-driven manufacturing systems such as CNC axes and robotic machining platforms operating under low-speed and variable-load conditions. Full article

The rapid evolution of Artificial Intelligence (AI) has significantly transformed the capabilities, performance, and autonomy of modern mechatronic systems. As industries transition toward intelligent and interconnected manufacturing environments, AI has emerged as a powerful enabler of real-time decision-making, adaptive control, predictive maintenance, and autonomous operation. This review provides a comprehensive analysis of AI integration within mechatronic systems, examining its influence on system performance, autonomy, and manufacturing efficiency. Key AI techniques including machine learning, deep learning, reinforcement learning, evolutionary optimization, and computer vision are evaluated in terms of their applications in control, sensing, diagnostics, and robotics. The paper also highlights advancements in AI-driven motion control, autonomous navigation, sensor fusion, and smart factory operations. Critical challenges such as data requirements, computational constraints, system interoperability, and safety concerns are discussed to identify research gaps. Finally, emerging trends and future directions, such as edge AI, digital twins, explainable AI, and fully autonomous mechatronic cells, are explored. This review consolidates current knowledge and provides insights to guide researchers and practitioners in developing next-generation intelligent mechatronic systems capable of supporting the demands of Industry 4.0 and beyond. Full article