Search Results (985)

Ground penetrating radar (GPR) image inversion is of great significance for interpreting GPR data. In practical applications, the complexity and nonuniformity of underground structures bring noise and clutter interference, making GPR inversion problems more challenging. To address these issues, this study proposes a two-stage GPR image inversion network called MHInvNet based on multi-scale dilated convolution (MSDC) and hybrid attention gate (HAG). This method first denoises the B-scan through the first network MHInvNet1, then combines the denoised B-scan from MHInvNet1 with the undenoised B-scan as input to the second network MHInvNet2 for inversion to reconstruct the distribution of the permittivity of underground targets. To further enhance network performance, the MSDC and HAG modules are simultaneously introduced to both networks. Experimental results from simulated and actual measurement data show that MHInvNet can accurately invert the position, shape, size, and permittivity of underground targets. A comparison with existing methods demonstrates the superior inversion performance of MHInvNet. Full article

This paper presents an efficient wideband data recovery circuit (DRC) for forward data transfer in the over-coupled mode of dynamic SWPDT systems. In the over-coupled mode, where the operating frequency varies, conventional DRCs often become ineffective due to their limited operating frequency range. To address this issue, we propose a wideband DRC using amplitude shift keying (ASK) with on–off keying (OOK) modulation. The proposed circuit also eliminates the need for diodes and averaging circuits, which are typically required in traditional designs. The proposed circuit achieves data recovery by passing the OOK-modulated signal through a proposed Voltage-to-Time Converter (VTC), followed by a comparator and inverter. Implemented in 180 nm CMOS technology, the circuit occupies an area of 2440 μm² and a power consumption of 52.08 μW. The circuit can operate across a wide range of carrier frequencies. It was tested and validated with OOK-modulated signals at 5 MHz, 50 MHz, and 150 MHz, confirming its versatility and robustness. The prototype circuit enables wireless data transmission in critically coupled, weakly coupled, and over-coupled modes of WPT systems, achieving a 2 Mb/s data rate without requiring receiver repositioning. Full article

The DC bus current ripple is a critical performance parameter affecting the operation of current source inverters (CSIs). In high-power applications, CSIs often operate at lower switching frequencies to minimize losses. However, maintaining low levels of DC bus current ripple necessitates the use of large inductors on the DC side, which increases the size, weight, and cost of the system. This paper first explores the inherent limitations of conventional CSI designs. Subsequently, it proposes a hierarchical coordinated switching modulation strategy based on the H7 current source inverter (H7-CSI) to address the issue of DC bus current ripple. By segmenting the zero-vector states, the proposed method fully utilizes the modulation freedom of the H7-CSI, achieving high-frequency chopping effects on the DC-side current. Experimental results show that the new modulation strategy reduces the amplitude of DC bus current ripple to 43% of that achieved by conventional CSIs under similar switching loss conditions. Furthermore, the dynamic performance of the proposed scheme remains consistent with conventional CSIs. Spectrally, this method exhibits improved performance in the low-frequency range and slightly degraded performance in the high-frequency range, although the latter remains within acceptable limits. Full article

This paper presents a carrier waves phase shifting method to reduce the dc-link capacitor current for a dual three-phase permanent magnet synchronous motor drive system. Dc-link capacitors absorb the ripple current generated at the input due to the harmonics of the pulse width modulation (PWM). The size, cost, reliability, and lifetime of the dc-link capacitor are negatively affected by this ripple current flowing through it. The proposed method is especially appropriate for common dc-link capacitors for a dual inverter system driving two PMSMs. In this paper, the input current of each inverter is analyzed using Double Fourier Analysis, and the harmonic components of the dc-link capacitor current are determined. The carrier wave phase shifting method is proposed to reduce the magnitude of the harmonics and thus reduce the dc-link capacitor current. Furthermore, the optimum angle between the carrier waves for the maximum reduction in the dc-link capacitor current is analyzed and simulated for different scenarios considering the speed and load torque of the PMSMs. The proposed method is verified through experiments and PMSMs are driven by three-phase voltage source inverters (VSIs) modulated with Space Vector Pulse Width Modulation (SVPWM), which is the most common PWM strategy. The proposed method reduces the dc-link capacitor current by 60%, thereby significantly decreasing the required dc-link capacitance, the volume of the drive system, and its cost. Full article

This paper introduces a voltage-mode DC-DC buck converter designed to address the challenges of high-frequency operation. The proposed comparator-less Reactive Ramp Generator (RRG) topology mitigates the issues associated with comparator delays, achieving a fast load transient response. By eliminating all comparators from the buck converter’s control circuit, we prevent potential delay-induced malfunctions, thereby enhancing overall operational reliability. The rapid response of the RRG, enabled by a short feedback loop, allows for swift output voltage regulation during load transients. Replacing comparators in the PWM controller with inverters effectively removes delay issues without adding complexity. Since the proposed design retains the conventional voltage-mode transfer function, standard type-3 compensation is readily applicable. Operating with a 3.3 V input, the buck converter provides an output range from 0.65 V to 3.0 V, achieving a settling time of 0.802 µs for load changes from 200 mA to 1 A, and 1.27 µs for load changes from 1 A to 200 mA. The proposed architecture achieves a peak efficiency of 92.78% at 2.4 V and 600 mA. Full article

Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model’s focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(P), recall(R), mAP_0.5, and mAP_0.50:0.95 achieved are 89.44%, 88.82%, 92.99%, and 80.38%. Compared with the YOLO v8n, CSGD-YOLO improves performance in terms of accuracy, model size, parameter number, and floating-point operation counts by 1.39, 1.43, 1.77, and 2.95 percentage points, respectively. Therefore, CSGD-YOLO outperforms existing mainstream target detection models in detection performance and model complexity, making it suitable for detecting corn seed germination status and providing a reference for rapid germination rate detection. Full article

Automated kiwi harvesting hinges on the seamless deployment of a detection model and the accurate detection of kiwifruits. However, practical challenges, such as the limited computational resources on harvesting robots and occlusions among fruits, hinder the effectiveness of automated picking. To address these issues, this paper introduces EDT-YOLOv8n, a lightweight and efficient network architecture based on YOLOv8n. The proposed model integrates the Effective Mobile Inverted Bottleneck Convolution (EMBC) module to replace the C2f modules, mitigating the channel information loss and bolstering generalization. Additionally, the DySample upsampler, an ultra-lightweight and effective dynamic upsampler, improves feature extraction and resource efficiency when compared to traditional nearest-neighbor upsampling. Furthermore, a novel Task Align Dynamic Detection Head (TADDH) is implemented, incorporating group normalization for a more efficient convolutional structure and optimizing the alignment between the classification and localization tasks. The experimental results reveal that the proposed EDT-YOLOv8n model achieves higher precision (86.1%), mAP0.5 (91.5%), and mAP0.5-0.95 (65.9%), while reducing the number of parameters, the number of floating-point operations, and the model size by 15.5%, 12.4%, and 15.0%, respectively. These improvements demonstrate the model’s effectiveness and efficiency in supporting kiwifruit localization and automated harvesting tasks. Full article

Finite Control Set Model Predictive Control emerges as a promising method for controlling power electronics inverters, outperforming traditional linear techniques. However, implementing Finite Control Set Model Predictive Control on conventional processors faces a significant computational burden due to its repetitive nature. This paper presents a novel approach that utilizes LabVIEW & Field Programmable Gate Arrays to address this computational bottleneck. By capitalizing on the inherent parallelism and suitability of Field Programmable Gate Arrays for discrete control problems, substantial computational advantages are achieved for Finite Control Set Model Predictive Control. The use of LabVIEW, a well-established platform in industrial and commercial solutions, ensures that this work is relevant not only academically but also for real-world industrial applications of FCS-MPC in power electronics and motor drives. This research successfully demonstrates the application of Finite Control Set Model Predictive Control for controlling the current of a motor-like load for a three-phase Voltage Source Inverter system in LabVIEW. To simplify the traditionally complex Field Programmable Gate Arrays programming process, user-friendly toolkits such as LabVIEW Control Design & Simulation, LabVIEW Real-Time, and LabVIEW FPGA Module are employed. This LabVIEW-based integration facilitates the execution of both concurrent and sequential Field Programmable Gate Arrays algorithms, leading to efficient Field Programmable Gate Arrays resource management and user-defined restrictions on maximum switching frequency, obviating the need for resource-intensive control methods for fast switches such as SiC and GaN IGBTs. The proposed controller is validated using an off-the-shelf computer turned into a real-time system but also on Field Programmable Gate Arrays for comparison purposes. Full article

A single-phase multilevel inverter with a switched-capacitor multilevel (SC-MLI) configuration is developed to provide 13-level output voltages. An improved genetic algorithm (GA) with adaptive mutation and crossover rates is employed to achieve robust harmonic mitigation by avoiding local optima and ensuring optimal performance. The topology introduces an SC-MLI that generates AC output voltage at desired levels using only two capacitors, two asymmetrical DC sources, one diode, and 11 switches. This allows the inverter to use fewer gate drivers and, hence, increases the power density of the converter. A significant challenge in the normal operation of SC-MLI circuits relates to the self-voltage balance of the capacitors, which easily becomes unstable, particularly at low modulation indices. The proposed design addresses this issue without the need for ancillary devices or complex control schemes, ensuring stable self-balanced operation across the entire spectrum of the modulation index. In this context, the harmonic mitigation technique optimized through GA applied in this inverter ensures low harmonic distortion, achieving a total harmonic distortion (THD) of 6.73%, thereby enhancing power quality even at low modulation indices. The performance of this SC-MLI is modeled under various loading scenarios using MATLAB/Simulink^® 2023b with validation performed through an Opal-RT real-time emulator. Additionally, the inverter’s overall power losses and individual switch losses, along with the efficiency, are analyzed using the simulation tool PLEXIM-PLECS. Efficiency is found to be 96.62%. Full article

This paper examines the current state of Home Energy Management Systems (HEMSs), highlighting the key role of the single-phase bidirectional rectifier (SPBR). It provides a detailed design process for the converter used in HEMSs, with a particular focus on the bidirectional charge and discharge of high-voltage batteries. The converter’s operating conditions were determined through a comprehensive evaluation of its components, which were designed and assessed to enable accurate power loss calculations. This approach ensures proper component sizing and a clear understanding of the converter’s efficiency. A specialized electronic control circuit manages two operating modes of the converter: a boost rectifier with power factor correction (PFC) and a sinusoidal pulse width modulation (SPWM) inverter. To validate the design, a 7.4 kW prototype was developed using silicon carbide (SiC) metal oxide semiconductor field effect transistors (MOSFETs). The prototype achieved a peak efficiency of nearly 98% in both modes, with a unity power factor (PF) and total harmonic distortion (THD) below 7% at full power. Full article

When designing a wireless power transfer system, overall system efficiency is one of the key parameters. The efficiency depends on several parameters, which also includes control modulation, used for excitation of the transmitter coil. However, how much the modulation technique impacts the efficiency is studied rarely. In this paper, we compare the three most popular control modulation techniques for transmitter voltage of a wireless power transfer system. First, the theory behind the methods is presented by highlighting the difference between them and their implementation. The methods are implemented, together with a linear PI control scheme, to evaluate the efficiency and performance of each of the methods. Finally, the practical tests were performed, under different output power levels, in order to evaluate the practical performance of each modulation strategy further. Full article

To address the issue of the computational intensity and deployment difficulties associated with weed detection models, a lightweight target detection model for weeds based on YOLOv8s in maize fields was proposed in this study. Firstly, a lightweight network, designated as Dualconv High Performance GPU Net (D-PP-HGNet), was constructed on the foundation of the High Performance GPU Net (PP-HGNet) framework. Dualconv was introduced to reduce the computation required to achieve a lightweight design. Furthermore, Adaptive Feature Aggregation Module (AFAM) and Global Max Pooling were incorporated to augment the extraction of salient features in complex scenarios. Then, the newly created network was used to reconstruct the YOLOv8s backbone. Secondly, a four-stage inverted residual moving block (iRMB) was employed to construct a lightweight iDEMA module, which was used to replace the original C2f feature extraction module in the Neck to improve model performance and accuracy. Finally, Dualconv was employed instead of the conventional convolution for downsampling, further diminishing the network load. The new model was fully verified using the established field weed dataset. The test results showed that the modified model exhibited a notable improvement in detection performance compared with YOLOv8s. Accuracy improved from 91.2% to 95.8%, recall from 87.9% to 93.2%, and [email protected] from 90.8% to 94.5%. Furthermore, the number of GFLOPs and the model size were reduced to 12.7 G and 9.1 MB, respectively, representing a decrease of 57.4% and 59.2% compared to the original model. Compared with the prevalent target detection models, such as Faster R-CNN, YOLOv5s, and YOLOv8l, the new model showed superior performance in accuracy and lightweight. The new model proposed in this paper effectively reduces the cost of the required hardware to achieve accurate weed identification in maize fields with limited resources. Full article

Microwave radiometers are passive remote sensing devices that provide important observational data on the state of the oceanic and terrestrial atmosphere. Temperature retrieval accuracy is crucial for radiometer performance. However, inversions during strong convective weather or seasonal phenomena are short-lived and spatially limited, making it challenging for neural network algorithms trained on historical data to invert accurately, leading to significant errors. This paper proposes a long short-term memory (LSTM) network forecast correction model based on the temperature inversion phenomenon to resolve these large temperature inversion errors. The proposed model leverages the seasonal periodicity of atmospheric temperature profiles in historical data to form a circumferential background field, enabling the prediction of expected background profiles for the forecast day based on temporal and spatial continuity. The atmospheric profiles obtained using the radiometer retrieval are compensated with the forecast temperature inversion vector on the forecast day to obtain the final data. In this study, the accuracy of the forecast correction model was verified utilizing meteorological records for the Taizhou area from 2013 to 2017. Using a hierarchical backpropagation network based on the residual module for comparison, which had a forecast accuracy error of 0.0675 K, the error of our new model was reduced by 34% under the temperature inversion phenomenon. Meanwhile, error fluctuations were reduced by 33% compared with the residual network algorithm, improving the retrieval results’ stability in the temperature inversion state. Our results provide insights to improve radiometer remote sensing accuracy. Full article

This work presents the results of research aimed at evaluating the performance of the photovoltaic system connected to the electrical grid at the University of Brasília (UnB), Brazil. Following the guidelines established by the Brazilian Standard for Performance Monitoring and Analysis of Grid-connected Photovoltaic Systems, it was possible to evaluate the system’s performance by determining the Performance Ratio (PR) indicator. The operating temperatures were estimated using measured values of the ambient temperature and solar irradiation. These data were collected by a nearby solarimetric station. Next, the theoretical energy injected into the electrical grid was determined based on calculations of the Direct Current (DC) power at the inverter input and the Alternating Current (AC) power at the inverter output. To this end, the coefficients of the inverter efficiency curve were considered as well as a loss scenario, as recommended. With these results, as well as the information about the total photovoltaics (PV) system AC production obtained from the inverter supervisory system, it was possible to determine the average annual PR achieved and compare the theoretical and practical results obtained. The main contribution of this paper is the performance evaluation of a 125 kWp grid-connected photovoltaic system at the University of Brasília (UnB), assessed using Brazilian Standards for performance monitoring and analysis. The system, installed on the rooftop of the UED building, consists of 298 Canadian Solar HiKu CS3W-420P modules with a 15-degree north pitch angle facing geographic north. It interfaces with the grid through two three-phase inverters, model CSI-75K-T400 (74.76 kWp) and a CSI-50KTL-GI (50.4 kWp). The results showed that the system with a 50kW inverter had an average PR of 78%, while the system with a 75 kW inverter showed a PR variation from 56% to 93%. The information obtained in this work will be used to develop computational tools capable of monitoring and evaluating, in real time, the performance of photovoltaic systems and ensuring that the expected financial return is achieved through the use of preventive and corrective maintenance actions in a timely manner. Full article

Surface defect detection based on machine vision and convolutional neural networks (CNNs) is an important and necessary process that enables rubber ring manufacturers to improve production quality and efficiency. However, such automatic detection always consumes substantial computer resources to guarantee detection accuracy. To solve this problem, in this paper, a CNN optimization algorithm based on the Ghost module is presented. First, the convolutional layer is replaced with the Ghost module in CNNs so that feature maps can be generated using cheaper linear operations. Second, an optimization method is used to obtain the best replacement of the Ghost module to balance computer resource consumption and detection accuracy. Finally, an image preprocessing method that includes inverting colors is applied. This algorithm is integrated into YOLOv5, trained on a dataset of rubber ring surface defects. Compared to the original network, the network size decreases by 30.5% and the computational cost decreases by 23.1%, whereas the average precision only decreases by 1.8%. Additionally, the network’s training time decreases by 16.1% as a result of preprocessing. These results show that the proposed approach greatly helps practical rubber ring surface defect detection. Full article