Search Results (12,005)

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. Full article

Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network (CNNs) and long short-term memory network (LSTM) with a generative adversarial network (GAN). Firstly, a reliability mechanism based on principal component analysis (PCA) is designed to solve the problem of data bias caused by multiple monitoring devices. Then, the CNN-LSTM network is used to predict time series data, and the GAN is used to expand fault data samples to solve the problem of an unbalanced data distribution. Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by $5.5\%$, $4.8\%$, $7.8\%$, and $9.3\%$, with at least a $160\%$ improvement in the fault recall rate. Full article

In this paper, an automatic phase-shifting interferometer has been developed using a homemade liquid-crystal phase shifter, which demonstrates a low-cost, fully automated technical solution for measuring the phase information of optical waves in devices. Conventional phase-shifting interferometers usually rely on PZT piezoelectric phase shifters, which are complex, require additional half-inverse and half-transparent optics to build the optical path, and are expensive. To overcome these limitations, we used a laboratory-made liquid-crystal waveplate as a phase shifter and integrated it into a Mach–Zehnder phase-shifting interferometer. The system is controlled by an STM32 microcontroller and self-developed measurement software, and it utilizes a four-step phase-shift interferometry algorithm and the CPULSI phase-unwrapping algorithm to achieve automatic phase measurements. Phase test experiments using a standard plano-convex lens and a homemade liquid-crystal grating as test objects demonstrate the feasibility and accuracy of the device by the fact that the measured focal lengths are in good agreement with the nominal values, and the phase distributions of the gratings are also in good agreement with the predefined values. This study validates the potential of liquid-crystal-based phase shifters for low-cost, fully automated optical phase measurements, providing a simpler and cheaper alternative to conventional methods. Full article

The advancement of Internet of Things (IoT) technology has enabled battery-powered devices to be deployed across a wide range of applications; however, it also introduces challenges such as high energy consumption and security vulnerabilities. To address these issues, adiabatic logic circuits offer a promising solution for achieving energy efficiency and enhancing the security of IoT devices. Adiabatic logic circuits are well suited for energy harvesting systems, especially in applications such as sensor nodes, RFID tags, and other IoT implementations. In these systems, the harvested bipolar sinusoidal RF power is directly used as the power supply for the adiabatic logic circuit. However, adiabatic circuits require a peak detector to provide bulk biasing for pMOS transistors. To meet this requirement, a diode-connected MOS transistor-based voltage doubler circuit is used to convert the sinusoidal input into a usable DC signal. In this paper, we propose a novel adiabatic logic design that maintains low power consumption while optimizing energy and current fluctuations across various input transitions. By ensuring uniform and complementary current flow in each transition within the logic circuit’s functional blocks, the design reduces energy variation and enhances resistance against power analysis attacks. Evaluation under different clock frequencies and load capacitances demonstrates that the proposed adiabatic logic circuit exhibits lower fluctuation and improved security, particularly at load capacitances of 50 fF and 100 fF. The results show that the proposed circuit achieves lower power dissipation compared to conventional designs. As an application example, we implemented an ultrasonic transmitter circuit within a LoRaWAN network at the end-node sensor level, which serves as both a communication protocol and system architecture for long-range communication systems. Full article

This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article

Tracking the state of the health of electrical insulation in high-power electric machines has always been a topic of great interest due to the high cost of downtime associated with unexpected failures. Over the years, there have been continuous efforts to develop and improve upon methods for testing and categorizing the health and expected lifetime of stator insulation. Methods such as partial discharge, surge, and dissipation factor testing are common examples. With the increasing use of high-specific-power electric machines in new applications such as traction and wind power generation, coupled with the increasing use of wide-bandgap semiconductor device-based inverters, some traditional methods for insulation health tracking may need adjustments or be combined with newer methods to remain accurate and useful. This paper outlines a review of the traditional insulation health tracking methods and newer methods and improvements that have been proposed to address the concerns and shortcomings of traditional methods. Full article

Timely and accurate identification of plant diseases is critical to mitigating crop losses and enhancing yield in precision agriculture. This paper proposes AgriFusionNet, a lightweight and efficient deep learning model designed to diagnose plant diseases using multimodal data sources. The framework integrates RGB and multispectral drone imagery with IoT-based environmental sensor data (e.g., temperature, humidity, soil moisture), recorded over six months across multiple agricultural zones. Built on the EfficientNetV2-B4 backbone, AgriFusionNet incorporates Fused-MBConv blocks and Swish activation to improve gradient flow, capture fine-grained disease patterns, and reduce inference latency. The model was evaluated using a comprehensive dataset composed of real-world and benchmarked samples, showing superior performance with 94.3% classification accuracy, 28.5 ms inference time, and a 30% reduction in model parameters compared to state-of-the-art models such as Vision Transformers and InceptionV4. Extensive comparisons with both traditional machine learning and advanced deep learning methods underscore its robustness, generalization, and suitability for deployment on edge devices. Ablation studies and confusion matrix analyses further confirm its diagnostic precision, even in visually ambiguous cases. The proposed framework offers a scalable, practical solution for real-time crop health monitoring, contributing toward smart and sustainable agricultural ecosystems. Full article

To address the issue of deformation mismatch between the exterior wall panels and the resilient frame structure under large deformations, two novel flexible devices (FDs) with different working principles are proposed in this paper. These FDs enable the exterior wall panels to achieve cooperative deformation with frame columns or beams under horizontal loads, thus improving the seismic performance of the frame structure with additional exterior wall panels. This study begins by explaining the specific design thought of the FDs based on examining the deformation characteristics of frame structures. Then, a series of low-cycle loading tests are conducted on frame specimens to demonstrate the effectiveness of the FDs. The experimental results indicate that the FDs can improve the interaction between the exterior wall panels and the main frame, reduce plastic damage to the wall panels, and increase the peak load-bearing capacity of the overall structure by approximately 17–21%. In addition, a refined finite element modeling method for the proposed FDs is presented using the ABAQUS software, providing a basis for further research on frame structures with additional exterior wall panels. Full article

Objectives: To develop a microfluidic paper-based analytical device (μPAD) that identifies myeloperoxidase (MPO) levels in the saliva of healthy patients and those with periodontal disease. Materials and Methods: A platform similar to a 96-well plate was printed on Watman^® chromatography paper to run the experimental analysis with unstimulated saliva samples were collected from two groups of patients: those with periodontal health (H, n = 15) and established periodontitis (PD, n = 15). Then, three types of chromophore substrates were pipetted into the wells of the prototype: (1) Guaiacol; (2) Guaiacol, 4,4 ′-diaminodifenilsulfon (DAB) and hydrogen peroxide in Tris-HCl buffer; and (3) 3,3′,5,5′-Tetramethylbenzidine (TMB), followed by saliva samples. The reaction images were analyzed by numbering according to the intensity scale. Results: The comparative results of the reactions using μPAD demonstrated that both the H and PD groups were compatible with each other without differences among the chromophore substrates (p > 0.05). However, the protocol with TMB showed a faster reaction and better color difference when comparing 15.62 ng/mL and 7.81 ng/mL of MPO in the plate embedded with Guaiacol; 1000 ng/mL and 62.5 ng/mL on the Guaiacol and DAB plate; and 62.5 ng/mL of TMB. The average detectable concentrations of MPO in saliva using TMB were H = 21.2 ± 10.4 ng/mL and PD = 28.9 ± 12.8 ng/mL (p = 0.08). Conclusions: The developed microfluidic paper-based analytical device has been tested for identifying the myeloperoxidase saliva levels of healthy patients and those with periodontal disease. This rapid test demonstrated its possible applicability mainly when associated with the TMB chromophore, but further studies are required with different biomarkers to explore this promising diagnostic platform. Full article

For the airfoil in freestream, the pressure difference between the upper and lower surfaces and the variations in pressure gradients are significant at its leading edge area. Under reasonable deflections, the active leading edge can effectively change airfoil aerodynamic loads, which helps to improve the rotor aerodynamic performance. In this paper, a modeling method for an airfoil with an active leading edge was developed to calculate its aerodynamic loads. The pitch motion of the rotor blade and the leading edge deflections were taken into account. Firstly, simulations of steady and unsteady flow for the airfoil with an active leading edge were conducted under different boundary conditions and with different leading edge deflection movement. Secondly, the rational function approximation (RFA) was employed to establish the relationship between aerodynamic loads and airfoil/active leading edge deflections. Then, coefficient matrices of the RFA approach were identified based on a limited number of high-fidelity computational fluid dynamics (CFD) results. Finally, an aerodynamic model of the airfoil with an active leading edge was developed, and its accuracy was validated by comparing it to the high-fidelity CFD results. Comparative results reveal that the developed model can calculate the aerodynamic loads of an airfoil with an active leading edge accurately and efficiently when applied appropriately. The modeling method can be used in aerodynamic load calculations and the aeroelastic coupling analysis of a rotor with active control devices. Full article

Circulating current control is a critical part of the Modular Multilevel Converter (MMC) control system. Existing control methods rely on arm current information obtained from complex current measurement devices. However, these devices are susceptible to failures, which can lead to distorted arm currents, increased peak arm current values, and higher losses. In extreme cases, this can result in system instability. This paper first analyzes four typical arm current measurement faults, i.e., constant gain faults, amplitude deviation faults, phase shift faults, and stuck faults. Then, a Kalman Filter (KF)-based fault detection method is proposed, which allows for the simultaneous monitoring status of all six arm current measurements. Moreover, to facilitate fault detection, the Moving Root Mean Square (MRMS) value of the observation residual is defined, which effectively detects faults while suppressing noise. The entire fault detection process takes less than 20 ms. Finally, the feasibility and effectiveness of the proposed method are validated through MATLAB/Simulink simulations and experimental results. Full article

As many renewable energy sources have been waiting to be interconnected with distribution systems due to the lack of power system infrastructure in Korea, studies to solve the delayed problem for renewable energy sources required. In order to overcome these problems, this paper presents an introduction model and optimal capacity algorithm of a VPL (virtual power line) device, which is a virtual power line operation technology to manage the power system by operating an ESS installed at the coupling point of renewable energy source without additionally expanding the power system infrastructure in a conventional way; this paper also proposes an economic evaluation method to assess the feasibility of the VPL device. The optimal capacity of the VPL device is determined by solving the over-voltage problem for the customer, and the economic evaluation method for the VPL device is considered by cost and benefit elements to evaluate the feasibility of introduction model for VPL device. From the simulation result of the proposed optimal capacity algorithm and economic evaluation method based on the introduction model in the VPL device, and it was confirmed that the optimal kW capacity of VPL device was selected as the maximum value in power control values, and the optimal kWh capacity was also determined by accumulating the power control values over the time intervals; also, the proper capacity of the VPL can be more economical than the investment cost of power system infrastructure expansion in the conventional method. Full article

Over-the-air federated learning (Air-FL) has emerged as a promising paradigm that integrates communication and learning, which offers significant potential to enhance model training efficiency and optimize communication resource utilization. This paper addresses the challenge of interference management in multi-cell Air-FL systems, focusing on parallel multi-task scenarios where each cell independently executes distinct training tasks. We begin by analyzing the impact of aggregation errors on local model performance within each cell, aiming to minimize the cumulative optimality gap across all cells. To this end, we formulate an optimization framework that jointly optimizes device transmit power and denoising factors. Leveraging the Pareto boundary theory, we design a centralized optimization scheme that characterizes the trade-offs in system performance. Building upon this, we propose a distributed power control optimization scheme based on interference temperature (IT). This approach decomposes the globally coupled problem into locally solvable subproblems, thereby enabling each cell to adjust its transmit power independently using only local channel state information (CSI). To tackle the non-convexity inherent in these subproblems, we first transform them into convex problems and then develop an analytical solution framework grounded in Lagrangian duality theory. Coupled with a dynamic IT update mechanism, our method iteratively approximates the Pareto optimal boundary. The simulation results demonstrate that the proposed scheme outperforms baseline methods in terms of training convergence speed, cross-cell performance balance, and test accuracy. Moreover, it achieves stable convergence within a limited number of iterations, which validates its practicality and effectiveness in multi-task edge intelligence systems. Full article

This paper suggests a perspective-controlled solution for an integrated Infrared micro-/nanoplastic spectroscopy/photometry-based detection, from the diffraction up to the geometry etendue, with the aim of yielding a universal spectrometer/photometer. Spectrophotometry, unlike spectroscopy that shows the interaction between matter and radiated energy, is a specific form of photometry that measures light parameters in a particular range as a function of wavelength. The solution, meant for diffraction grating and geometry etendue of the display unit, is provided by a controller that tunes the grating pitch to accommodate any emitted/transmitted wavelength from a sample made of microplastics, their degraded forms and their potential retention, and ensures that all the diffracted wavelengths are concentrated on the required etendue. The purpose is not only to go below the current Infrared limit of $20\mathsf{\mu }\mathrm{m}$ microplastic size, or to suggest an Infrared spectrophotometry geometry capable of detecting micro- and nanoplastics in the range of ($1\mathrm{nm}$–$20\mathsf{\mu }\mathrm{m}$) for integrated nano- and micro-scales, but also to transform most of the pivotal components to be directly wavelength-independent. The related controlled geometry solutions, from the controlled grating slit-width up to the controlled display unit etendue functions, are suggested for a wider generic range integration. The results from image-size characterization show that the following charge-coupled devices, nanopixel CCDs, and/or micropixel CCDs of less than $100\mathrm{nm}$ are required on the display unit, justifying the Infrared micro- and nanoplastic-integrated spectrophotometry, and the investigation conducted with other electromagnetic spectrum ranges that suggests a possible universal spectrometer/photometer. Full article

The widespread integration of power electronic devices and renewable energy sources into power systems has significantly exacerbated voltage and current waveform distortion issues, where asymmetric loads—including single-phase nonlinear equipment and unbalanced three-phase power electronic installations—serve as critical harmonic sources whose inherent nonlinear and asymmetric characteristics increasingly compromise power quality. To enhance power quality management, this paper proposes a universal harmonic source modeling and operational state identification methodology integrating physical mechanisms with data-driven algorithms. The approach establishes an RL-series equivalent impedance model as its physical foundation, employing singular value decomposition and Z-score criteria to accurately characterize asymmetric load dynamics; subsequently applies Variational Mode Decomposition (VMD) to extract time-frequency features from equivalent impedance parameters while utilizing Density-Based Spatial Clustering (DBSCAN) for the high-precision identification of operational states in asymmetric loads; and ultimately constructs state-specific harmonic source models by partitioning historical datasets into subsets, substantially improving model generalizability. Simulation and experimental validations demonstrate that the synergistic integration of physical impedance modeling and machine learning methods precisely captures dynamic harmonic characteristics of asymmetric loads, significantly enhancing modeling accuracy, dynamic robustness, and engineering practicality to provide an effective assessment framework for power quality issues caused by harmonic source integration in distribution networks. Full article