Search Results (530)

This study introduces a sensorless fault diagnosis method for efficiently detecting bearing faults in induction motors. The proposed method eliminates the need for torque sensors, frequency sensors, thermal cameras, or real-time Fast Fourier Transform (FFT) tools. Induction motors are commonly utilized in a variety of industrial applications, including fans, pumps, and home appliances, due to their straightforward construction, affordability, and robust reliability. Traditional bearing fault diagnosis methods often rely on additional hardware such as vibration or thermal sensors. Additionally, approaches employing Artificial Intelligence (AI) and real-time FFT processing require advanced and expensive hardware capabilities. However, many V/f control systems are primarily intended for cost-effective and simple implementations, making resource-intensive approaches undesirable. Therefore, such methods present limitations for these use cases. To address these challenges, this paper presents a sensorless detection technique that estimates torque via a flux observer, removing the dependence on external sensors. The estimated torque is processed using an offline FFT to identify amplitude changes within bearing fault frequency bands. Here, the FFT-based frequency analysis is performed offline and is used to design a targeted band-pass filter (BPF). The torque signal, after passing through the BPF, undergoes a straightforward threshold-based logic to assess the existence of faults. Compared to AI- or data-driven approaches, the proposed method provides a lightweight, interpretable, and sensorless solution without the need for additional training or high-end processors. Despite its straightforward approach, the technique achieves effective detection of bearing faults across various components and speeds, making it ideal for embedded and economically constrained motor applications. Full article

Fluoride prophylaxis is a cornerstone in preventing dental caries, a disease for which orthodontic patients are at high risk due to the reduced effectiveness of home oral hygiene and increased plaque accumulation. Recent evidence defines caries as polymicrobial, involving Streptococcus mutans, Lactobacilli, and emerging species such as Selenomonas sputigena. This prospective, randomized, controlled study evaluated professional topical fluoride in the form of gel and varnish in 68 patients aged 8–17 years wearing fixed orthodontic appliances. Participants were divided into three equal groups: two intervention groups and one control group. Clinical parameters (DMFT, salivary pH, PCR%) and microbiological analyses of plaque and saliva (oral Streptococci, S. mutans, S. sputigena, Lactobacilli, total bacterial count) were assessed at baseline (T0) and after 4 months (T1), following professional hygiene and fluoride application for the intervention groups. At T1, salivary pH increased in the gel group, and PCR% decreased significantly in all groups, with the most pronounced decrease observed in the varnish group. PCR analysis showed a higher rate of S. mutans and S. sputigena negativization in intervention groups. Culture-based analyses revealed reductions in oral Streptococci and Lactobacilli in intervention groups, while levels increased in controls. Overall, both clinical and microbiological variables indicated improvements in the fluoride-treated groups compared to controls, highlighting the efficacy of professional fluoride prophylaxis in orthodontic patients. Full article

At present, the construction of China’s shared manufacturing platform is developing rapidly. However, it is still in the stage of practical exploration, facing numerous challenges, such as difficulties in resource integration, immature business models, and a weak digital foundation. This paper takes Changzhou Zhiyun Tiangong’s “Super Virtual Factory” as an example, utilizing the grounded theory to conduct a case study on this shared manufacturing platform. Using a ‘condition-action-result’ framework, this paper explores the value co-creation (VCC) mechanism in a shared manufacturing ecosystem. We analyze how digital intelligence convergence (DIC) and supply chain collaboration (SCC) facilitate the digital intelligence transformation of consumption, production capacity, and products. The study finds that consumer insight, technological drive, government support, enterprise challenges, and the Changzhou home appliance industry cluster are the internal driving forces for the shared manufacturing ecosystem to carry out industrial ecological VCC; DIC and SCC are the two key elements for digital intelligence technology empowerment. Digital intelligence technology is empowered from three aspects—technology, resources, and structure—enabling organizational members with capability and authority while achieving “decentralization” of industrial chains. Finally, digital intelligence empowerment enables the shared manufacturing ecosystem to achieve VCC of the industrial ecosystem, thereby establishing a VCC model for the digital intelligence empowerment shared manufacturing ecosystem. The results of the study not only help enrich the theory of VCC in shared manufacturing platforms but also provide practical insights for the digital intelligence transformation of traditional manufacturing enterprises. Full article

This paper presents an intelligent hybrid framework for short-term energy consumption forecasting and real-time energy management in photovoltaic (PV)-integrated smart homes with Vehicle-to-Home (V2H) systems, tailored to the Tunisian context. The forecasting module employs an Attention-based Long Short-Term Memory (LSTM) neural network, whose hyperparameters (learning rate, hidden units, temporal window size) are optimized using the Harris Hawks Optimization (HHO) algorithm. Simulation results show that the proposed LSTM-HHO model achieves a Root Mean Square Error (RMSE) of 269 Wh, a Mean Absolute Error (MAE) of 187 Wh, and a Mean Absolute Percentage Error (MAPE) of 9.43%, with R² = 0.97, substantially outperforming conventional LSTM (RMSE: 945 Wh, MAPE: 51.05%) and LSTM-PSO (RMSE: 586 Wh, MAPE: 28.72%). These accurate forecasts are exploited by the Energy Management System (EMS) to optimize energy flows through dynamic appliance scheduling, HVAC load shifting, and coordinated operation of home and EV batteries. Compared with baseline operation, PV self-consumption increased by 18.6%, grid reliance decreased by 25%, and household energy costs were reduced by 17.3%. Cost savings are achieved via predictive and adaptive control that prioritizes PV utilization, shifts flexible loads to surplus periods, and hierarchically manages distributed storage (home battery for short-term balancing, EV battery for extended deficits). Overall, the proposed LSTM-HHO-based EMS provides a practical and effective pathway toward smart, sustainable, and cost-efficient residential energy systems, contributing directly to Tunisia’s energy transition goals. Full article

This paper outlines the process of developing a low-cost system for home appliance control via real-time hand gesture classification using Computer Vision and a custom lightweight machine learning model. This system strives to enable those with speech or hearing disabilities to interface with smart home devices in real time using hand gestures, such as is possible with voice-activated ‘smart assistants’ currently available. The system runs on a Raspberry Pi 5 to enable future IoT integration and reduce costs. The system also uses the official camera module v2 and 7-inch touchscreen. Frame preprocessing uses MediaPipe to assign hand coordinates, and NumPy tools to normalise them. A machine learning model then predicts the gesture. The model, a feed-forward network consisting of five fully connected layers, was built using Keras 3 and compiled with TensorFlow Lite. Training data utilised the HaGRIDv2 dataset, modified to consist of 15 one-handed gestures from its original of 23 one- and two-handed gestures. When used to train the model, validation metrics of 0.90 accuracy and 0.31 loss were returned. The system can control both analogue and digital hardware via GPIO pins and, when recognising a gesture, averages 20.4 frames per second with no observable delay. Full article

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as ‘Watch_TV’, ‘Sleep’, ‘Work_On_Computer’, and ‘Cook_Dinner’, is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models—XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)—are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation. Full article

A significant portion of home energy consumption is due to concealed faults and the inefficient usage of home appliances, usually because of user ignorance and a lack of proactive maintenance strategies. In this paper, ENACT, a digital-twin-based system, is proposed as the solution that facilitates better user understanding, encourages sustainable maintenance practices for appliances, and provides prescriptive maintenance recommendations. With the integration of smart plugs, behavioral analysis, and a 3D spatial interface, ENACT offers real-time device monitoring while providing context-aware suggestions. The system was installed in 20 households over a 12-month period, with users engaging with both 2D and 3D models of their surroundings. The quantitative results, including an average System Usability Scale score of 80.5, and qualitative feedback demonstrated intense user engagement, with strong evidence of mindset shifts towards proactive maintenance behavior. The findings confirm that digital twin technologies, when combined with targeted guidance, can significantly improve appliance lifespans, energy efficiency, and user empowerment within homes. Full article

In response to the demand for rapid matte finishing on large-format 304 stainless steel surfaces, this study utilized four fiber laser devices (output wavelength: 1064 nm, output power: 100 W, maximum modulation frequency: 4 kHz) to simultaneously perform surface matte finishing experiments on 304 stainless steel, with the aim of fabricating anti-reflective micro-nano structures. During the experiments, by systematically investigating the influence of parameters—including laser power, scanning speed, frequency, and idle speed of a single laser head—on the matte finishing process, the optimal processing parameters for a single laser head were determined as follows: laser power of 20 W, scanning speed of 11,000 mm/s, and frequency of 80 kHz. For large-area high-speed laser matte finishing, the delay of laser on/off was adjusted to compensate for the galvanometer’s motion trajectory, thereby ensuring uniform ablation at both the start and end positions of the processing path. Furthermore, in the context of large-area rapid multi-head laser matte finishing on 304 stainless steel, the overlapping of surface regions processed by different galvanometers was achieved by calibrating the motion start and end points of each galvanometer. The optimal overlapping parameters were successfully obtained. This study provides technical support for environmentally friendly matte finishing of stainless steel and offers valuable insights for its application in the stainless steel home appliance industry. Full article

This study investigates the dynamic performance and degradation behavior of corrugated cardboard used as protective packaging for home appliances subjected to random vibrations during transportation. Simulated vibration tests were conducted on fully packaged refrigerators to assess the mechanical response of cardboard and expanded polystyrene (EPS) supports under prolonged vibration excitation. Relaxation tests were performed to characterize time-dependent stress decay in the absence of vibration, while cantilever beam experiments quantified dynamic stiffness degradation during vibration exposure. The vibration-induced damage was evaluated by monitoring the decrease in support stiffness over time, revealing a distinct exponential reduction that correlated with increasing excitation levels. Statistical load count analyses, based on auto-spectral methods and Basquin’s power law, were used to model fatigue behavior and predict service life. The findings demonstrated that corrugated cardboard exhibited comparable performance to EPS in maintaining support stiffness while offering the advantage of environmental sustainability. These results provide quantitative evidence supporting the use of cardboard as an effective and eco-friendly alternative to polymer-based packaging materials, contributing to the development of optimized packaging solutions with enhanced vibration durability. Full article

In this study, a sensorless speed control method is proposed to enhance the speed control performance under load variations by utilizing a discrete-time flux observer in a V/f control environment. Due to their simple structure, low cost, and high reliability, induction motors are widely used in various fields, such as fans, pumps, and home appliances. Among the control methods for induction motors, V/f control operates as an open-loop system, without using speed sensors. It is mainly applied in industrial environments where fast dynamic performance is not required, due to its simple implementation and low cost. However, in cases of load variations or low-speed operation, it suffers from performance degradation and control limitations due to flux variations. To overcome these issues, this paper proposes a method that uses a discrete-time flux observer to estimate the stator flux. We calculate the rotor speed based on the estimated flux, and then improve V/f control performance by adding a compensation signal to the reference frequency, which signal is generated through a PI controller based on the difference between the estimated rotor speed and the reference speed. The proposed method is validated through MATLAB/Simulink-based simulations and experiments using a 5.5 kW induction motor M−G set, confirming that compared to conventional V/f control, the speed maintenance capability and overall robustness against load variations are enhanced. This study presents a practical solution to effectively improve the performance of existing V/f control systems without adding external sensors. Full article

Background: Presurgical nasoalveolar molding (NAM) improves outcomes in infants with cleft lip and palate by guiding alveolar segment alignment and enhancing nasal symmetry prior to primary lip repair. However, traditional NAM protocols require frequent clinical visits and specialized expertise, limiting access for families in rural and low-resource settings. Objective: This retrospective clinical study evaluated the feasibility and clinical outcomes of a digitally guided NAM approach using thermoformed clear aligners in infants with unilateral complete cleft lip and palate. Material and Methods: Twenty-five neonates residing in rural regions were treated over a 20-week pre-surgical period using a digital workflow that included intraoral scanning, 3D model design, and sequential aligner fabrication. The protocol minimized the number of in-office visits while engaging caregivers in home-based appliance management. Anatomical changes were assessed using 3D models at baseline and at treatment completion. Results: Significant reductions were observed in anterior cleft width (mean decrease: 5.38 mm, 95% CI: –7.58 to –3.18, p < 0.001) and posterior cleft width (mean decrease: 3.39 mm, 95% CI: –4.79 to –1.99, p < 0.001). Intermolar distance increased by 1.23 mm (p = 0.036), while intercanine width remained stable (p = 0.515), indicating preservation of maxillary arch form. Surgeons reported improved nasal symmetry and tissue alignment at the time of lip repair. Conclusions: This digitally planned NAM clear aligner protocol demonstrated clinical feasibility and effectiveness in reducing cleft width during the pre-surgical period. Findings should be interpreted with caution, given the retrospective design, lack of a control group, and absence of objective nasal outcome measures. Further studies are recommended to assess long-term outcomes and broader implementation potential. Full article

In recent years, home energy management systems (HEMSs) have emerged as critical components within the concept of smart cities and grids. Within HEMSs, load flow analysis represents one of the fundamental tools for smart grid studies, forming the basis for a wide range of advanced applications, including state estimation, fault diagnosis, and optimal power flow computation. To achieve high levels of load flow accuracy and reliability, HEMSs must incorporate detailed models of all electrical elements found in modern residential units, including appliances, wiring, and energy resources. This paper proposes a load flow solution for smart home networks, featuring detailed models of wiring, appliances, and on-site generation systems. Moreover, a detailed appliance model derived from smart meter data, capable of representing both static-load devices and complex appliances with time-varying consumption profiles, is introduced. Additionally, a measurement-based validation of residential electrical wiring model is presented. The proposed models and calculation procedures have been verified by comparing the simulated results with the literature, yielding a deviation of approximately 0.7%. Analyses of a large-scale network have shown that this approach is up to six times faster compared to state-of-the-art procedures. The developed solution demonstrates practical applicability for use in industry-grade smart power management software. Full article

To cope with the increasing global demand of energy and significant energy wastage caused by the use of different home appliances, smart load monitoring is considered a promising solution to promote proper activation and scheduling of devices and reduce electricity bills. Instead of installing a sensing device on each electric appliance, non-intrusive load monitoring (NILM) enables the monitoring of each individual device using the total power reading of the home smart meter. However, for a high-accuracy load monitoring, efficient artificial intelligence (AI) and deep learning (DL) approaches are needed. To that end, this paper thoroughly reviews traditional AI and DL approaches, as well as emerging AI models proposed for NILM. Unlike existing surveys that are usually limited to a specific approach or a subset of approaches, this review paper presents a comprehensive survey of an ensemble of topics and models, including deep learning, generative AI (GAI), emerging attention-enhanced GAI, and hybrid AI approaches. Another distinctive feature of this work compared to existing surveys is that it also reviews actual cases of NILM system design and implementation, covering a wide range of technical enablers including hardware, software, and AI models. Furthermore, a range of new future research and challenges are discussed, such as the heterogeneity of energy sources, data uncertainty, privacy and safety, cost and complexity reduction, and the need for a standardized comparison. Full article

This paper presents the design and implementation of an Intelligent Home Energy Management System in a smart home. The system is based on an economically decentralized hybrid concept that includes photovoltaic technology, a proton exchange membrane fuel cell, and a hydrogen refueling station, which together provide a reliable, secure, and clean power supply for smart homes. The proposed design enables power transfer between Vehicle-to-Home (V2H) and Home-to-Vehicle (H2V) systems, allowing electric vehicles to function as mobile energy storage devices at the grid level, facilitating a more adaptable and autonomous network. Our approach employs Double Deep Q-networks for adaptive control and forecasting. A Multi-Agent System coordinates actions between home appliances, energy storage systems, electric vehicles, and hydrogen power devices to ensure effective and cost-saving energy distribution for users of the smart grid. The design validation is carried out through MATLAB/Simulink-based simulations using meteorological data from Tunis. Ultimately, the V2H/H2V system enhances the utilization, reliability, and cost-effectiveness of residential energy systems compared with other management systems and conventional networks. Full article

This article presents the results of a performance comparison of four forecasting methods for prediction of electric power quality parameters (PQPs) in small-scale off-grid environments. Forecasting PQPs is crucial in supporting smart grid control and planning strategies by enabling better management, enhancing system reliability, and optimizing the integration of distributed energy resources. The following methods were compared: Bagging Decision Tree (BGDT), Boosting Decision Tree (BODT), and the K-Nearest Neighbor (KNN) algorithm with $k-5$ and $k-10$ nearest neighbors considered by the algorithm when making a prediction. The main goal of this study is to find a relation between the input variables (weather conditions, first and second back steps of PQPs, and consumed power of home appliances) and the power quality parameters as target outputs. The studied PQPs are the amplitude of power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), Power Factor ($PF$), and Power Load ($PL$). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for $TH{D}_u$, $TH{D}_i$, and $PF$. Only BODT obtained a good forecasting result for $PL$. The KNN (k = 5) algorithm obtained a good result for $PF$ prediction. The KNN (k = 10) algorithm predicted acceptable results for U and $PF$. The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees. Full article