Loss Calculation Research Articles

A Dynamic Hysteresis-Based Iron Loss Calculation Method and Its Application in a PWM-Fed IPMSM

A Dynamic Hysteresis-Based Iron Loss Calculation Method and Its Application in a PWM-Fed IPMSM

Fast power flow calculation for distribution networks based on graph models and hierarchical forward-backward sweep parallel algorithm

IntroductionIn response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a hierarchical forward-backward sweep parallel algorithm.MethodsFirstly, Neo4j is used to describe the distribution network structure as a simple graph model composed of nodes and edges. Secondly, a hierarchical forward-backward sweep method is adopted to perform power flow calculations on the graph model network. Finally, during the computation of distribution network subgraphs, the method is combined with the Bulk Synchronous Parallel (BSP) computing model to quickly complete the line loss analysis.Results and DiscussionResults from the IEEE 33-node test system demonstrate that the proposed method can calculate network losses quickly and accurately, with a computation time of only 0.175s, which is lower than the MySQL and Neo4j graph methods that do not consider hierarchical parallel computing.

Customized Pore Creation Strategies for Hyperelastic, Robust, Insulating Multifunctional MXene Aerogels for Microwave Absorption.

The construction of heterogeneous microstructure and the selection of multicomponents have turned into a research hotspot in developing ultralight, multifunctional, high-efficiency electromagnetic wave absorbing (EMA) materials. Although aerogels are promising materials to fulfill the above requirements, the increase in functional fillers inevitably leads to the deterioration of intrinsic properties. Tuning the electromagnetic properties from the structural design point of view remains a difficult challenge. Herein, we design customized pore creation strategies via introducing sacrificial templates to optimize the conductive path and construct the discontinuous dielectric medium, increasing dielectric loss and achieving efficient microwave absorption properties. A 3D porous composite (MEM) was crafted, which encapsulated an EVA/FeCoNi (EVA/MNPs) framework with Ti3C2Tx MXene coating by employing a direct heated cross-linking and immersion method. Controllable adjustment of the conductive network inside the porous structure and regulation of the dielectric character are achieved by porosity variation. Eventually, the MEM-5 with a porosity of 66.67% realizes RLmin of -39.2 dB (2.2 mm) and can cover the entire X band. Moreover, through off-axis electronic holography and the calculation of conduction loss and polarization loss, the dielectric property is deeply investigated, and the inner mechanism of optimization is pointed out. Thanks to the inherent characteristic of EVA and the porous structure, MEM-5 showed excellent thermal insulating and superior compressibility, which can maintain 60 °C on a 90-100 °C continuous heating stage and reached a maximum compressive strength of 60.12 kPa at 50% strain. Conceivably, this work provides a facile method for the fabrication of highly efficient microwave absorbers applied under complex conditions.

Physics-informed neural network classification framework for reliability analysis

Reliability analysis is crucial for quantitatively evaluating structural safety amidst uncertainties, laying the foundation for reliability-based design optimization aimed at augmenting structural reliability and reducing economic expenditures, thereby offering substantial engineering benefits. However, performing reliability analysis on intricate structures, especially those requiring time-intensive finite element models, presents a formidable challenge. This study develops an innovative physics-informed neural network classification (PINNC) model to tackle this issue. In the PINNC model, the loss associated with the structural output state (i.e., safety or failed state) is defined as classification loss. Additionally, the structural output value (i.e., actual structural response) is treated as critical physical information, with its associated loss termed as physical loss. To facilitate a separate calculation of physical loss and classification loss, a parametric sigmoid activation function is used, establishing a link between the structural output value and the structural output state. The total loss is calculated as a weighted sum of both physical and classification losses. Unlike conventional neural network classification models that solely focus on classification loss, the PINNC model integrates both physical and classification losses, markedly improving the model’s efficacy in structural reliability analysis. Moreover, an adaptive framework is developed to incrementally include samples proximal to the limit state surface as new training data, thereby reinforcing the PINNC model’s computational precision. The effectiveness of the proposed PINNC framework in overcoming reliability analysis challenges is demonstrated through various applications.

Grabbing Path Extraction of Deep-Sea Manganese Nodules Based on Improved YOLOv5

In an effort to enhance the efficiency and accuracy of deep-sea manganese nodule grasping behavior by a manipulator, a novel approach employing an improved YOLOv5 algorithm is proposed for the extraction of the shortest paths to manganese nodules targeted by the manipulator. The loss function of YOLOv5s has been improved by integrating a dual loss function that combines IoU and NWD, resulting in better accuracy for loss calculations across different target sizes. Additionally, substituting the initial C3 module in the network backbone with a C2f module is intended to improve the flow of gradient information while reducing computational demands. Once the geometric center of the manganese nodules is identified with the improved YOLOv5 algorithm, the next step involves planning the most efficient route for the manipulator to pick up the nodules using an upgraded elite strategy ant colony algorithm. Enhancements to the ACO algorithm consist of implementing an elite strategy and progressively decreasing the number of ants in each round. This method reduces both the number of iterations and the time required for each iteration, while also preventing the occurrence of local optimal solutions. The experimental findings indicate that the improved YOLOv5s detection algorithm boosts detection accuracy by 2.3%. Furthermore, when there are fewer than 30 target planning points, the improved algorithm requires, on average, 24% fewer iterations than the ACO algorithm to determine the shortest path. Additionally, the speed of calculation for each iteration is quicker while still providing the optimal solution.

Elevating Detection Performance in Optical Remote Sensing Image Object Detection: A Dual Strategy with Spatially Adaptive Angle-Aware Networks and Edge-Aware Skewed Bounding Box Loss Function.

In optical remote sensing image object detection, discontinuous boundaries often limit detection accuracy, particularly at high Intersection over Union (IoU) thresholds. This paper addresses this issue by proposing the Spatial Adaptive Angle-Aware (SA3) Network. The SA3 Network employs a hierarchical refinement approach, consisting of coarse regression, fine regression, and precise tuning, to optimize the angle parameters of rotated bounding boxes. It adapts to specific task scenarios using either class-aware or class-agnostic strategies. Experimental results demonstrate its effectiveness in significantly improving detection accuracy at high IoU thresholds. Additionally, we introduce a Gaussian transform-based IoU factor during angle regression loss calculation, leading to the development of Edge-aware Skewed Bounding Box Loss (EAS Loss). The EAS loss enhances the loss gradient at the final stage of angle regression for bounding boxes, addressing the challenge of further learning when the predicted box angle closely aligns with the real target box angle. This results in increased training efficiency and better alignment between training and evaluation metrics. Experimental results show that the proposed method substantially enhances the detection accuracy of ReDet and ReBiDet models. The SA3 Network and EAS loss not only elevate the mAP of the ReBiDet model on DOTA-v1.5 to 78.85% but also effectively improve the model's mAP under high IoU threshold conditions.

Improved YOLOv8 for Dangerous Goods Detection in X-ray Security Images

X-ray security images face significant challenges due to complex backgrounds, item overlap, and multi-scale target detection. Traditional methods often struggle to accurately identify objects, especially under cluttered conditions. This paper presents an advanced detection model, called YOLOv8n-GEMA, which incorporates several enhancements to address these issues. Firstly, the generalized efficient layer aggregation network (GELAN) module is employed to augment the feature fusion capabilities. Secondly, to tackle the problems of overlap and occlusion in X-ray images, the efficient multi-scale attention (EMA) module is utilized, effectively managing the feature capture and interdependencies among overlapping items, thereby boosting the model’s detection capability in such scenarios. Lastly, addressing the diverse sizes of items in X-ray images, the Inner-CIoU loss function uses auxiliary bounding boxes at varying scale ratios for loss calculation, ensuring faster and more effective bounding box predictions. The enhanced YOLOv8 model was tested on the public datasets SIXRay, HiXray, CLCXray, and PIDray, where the improved model’s mean average precision (mAP) reached 94.4%, 82.0%, 88.9%, and 85.9%, respectively, showing improvements of 3.6%, 1.6%, 0.9%, and 3.4% over the original YOLOv8. These results demonstrate the effectiveness and universality of the proposed method. Compared to current mainstream X-ray images of dangerous goods detection models, this model significantly reduces the false detection rate of dangerous goods in X-ray security images and achieves substantial improvements in the detection of overlapping and multi-scale targets, realizing higher accuracy in dangerous goods detection.

AC loss calculation of no-electrical-insulation HTS magnets using a field-circuit coupling method

Abstract No-electrical-insulation (NEI) magnets are gradually exhibiting significant appeal due to their robust thermal stability and elevated mechanical strength. However, when exposed to AC conditions, these magnets will suffer more significant AC losses in dynamic electromagnetic devices, such as motors and maglev systems. Presently, the numerical methods for predicting the electromagnetic and loss behavior of large-scale NEI magnets entail high computation costs due to the substantial degrees of freedom or complicated modeling strategies. Thus, we propose a fully finite element method, referred to as the field-circuit coupling method, to efficiently assess the overall behavior of NEI magnets while preserving adequate accuracy. This method couples the T-A formula and the single-turn equivalent circuit through a global voltage, to avoid the costly and complicated inductance calculations, and to simultaneously consider the induced current. By further integrating the homogenization method, the calculation speed can be increased up to ten times. Additionally, we study the critical current, and the electromagnetic and loss behavior of the NEI magnets based on the proposed model. We identify some measurement methods that offer more precise estimations of the critical current and the turn-to-turn contact resistance of NEI magnets. Meanwhile, the results indicate the severe impact of high AC fields on the losses, and emphasize the importance of a reliable shielding structure for operational safety. Finally, the influence of turn-to-turn contact resistivity on the loss behavior is also investigated, which can provide valuable insights for the design of NEI magnets in dynamic electromagnetic devices.

DC-YOLOv5-based target detection algorithm for cervical vertebral maturation.

The cervical vertebral maturation (CVM) method is essential to determine the timing of orthodontic and orthopedic treatment. In this paper, a target detection model called DC-YOLOv5 is proposed to achieve fully automated detection and staging of CVM. A total of 1800 cephalometric radiographs were labeled and categorized based on the CVM stages. We introduced a model named DC-YOLOv5, optimized for the specific characteristics of CVM based on YOLOv5. This optimization includes replacing the original bounding box regression loss calculation method with Wise-IOU to address the issue of mutual interference between vertical and horizontal losses in Complete-IOU (CIOU), which made model convergence challenging. We incorporated the Res-dcn-head module structure to enhance the focus on small target features, improving the model's sensitivity to subtle sample differences. Additionally, we introduced the Convolutional Block Attention Module (CBAM) dual-channel attention mechanism to enhance focus and understanding of critical features, thereby enhancing the accuracy and efficiency of target detection. Loss functions, precision, recall, mean average precision (mAP), and F1 scores were used as the main algorithm evaluation metrics to assess the performance of these models. Furthermore, we attempted to analyze regions important for model predictions using gradient Class Activation Mapping (CAM) techniques. The final F1 scores of the DC-YOLOv5 model for CVM identification were 0.993, 0.994 for mAp0.5 and 0.943 for mAp0.5:0.95, with faster convergence, more accurate and more robust detection than the other four models. The DC-YOLOv5 algorithm shows high accuracy and robustness in CVM identification, which provides strong support for fast and accurate CVM identification and has a positive effect on the development of medical field and clinical diagnosis.

Analisis Efektivitas Mesin Bordir Komputer Menggunakan Metode Overall Equipment Effetivenes (Oee) dan Six Big Loses pada UD. Jadi Gumilang Sukorejo

The garment company UD. Jadi Gumilang is a home-based industry located in Mojotengah Village, Sukorejo District, Pasuruan Regency, East Java. UD. Jadi Gumilang engages in the production of bags, clothes, uniforms, and embroidery, which are produced only when there are customer orders. Frequent damage to the computerized embroidery machine occurs because operators do not perform routine maintenance as per the garment's regulations. To address this issue, the Overall Equipment Effectiveness (OEE) value was calculated to analyze the major factors causing downtime. OEE itself is the result of multiplying the variables of Availability (90%), Performance (95%), and Quality (99%). After calculating the OEE value, the six big losses values were calculated to identify the highest losses that cause low OEE values. Then, from the highest loss values, a fishbone diagram (cause-and-effect diagram) analysis was conducted to identify the factors causing damage to minimize the recurrence of similar issues in the future. The results of the study show that out of the 2 computerized embroidery machines, one machine has an OEE value below 85%, which is the computerized embroidery machine 1 (84.64%). From this value, the six big losses calculations reveal that the largest loss is idling and minor stoppages losses, amounting to 20.02%. After conducting a fishbone diagram analysis, the factors causing idling and minor stoppages losses were identified as Human, Machine, Method, and Raw Material. Suggestions to prevent idling and minor stoppages losses include frequently cleaning the production area after production, regularly lubricating the machine to prevent wear, always checking the machine's condition before use, and others.

AC Loss Calculation of Racetrack-Shaped HTS-Excitation Magnet for Electrical Machine Considering End Part

AC Loss Calculation of Racetrack-Shaped HTS-Excitation Magnet for Electrical Machine Considering End Part

Numerical simulation of pressure loss and flow characteristics in combined elbow pipes for solid-liquid two-phase flow.

The transport of solid-liquid two-phase flow is widely used in water conservancy, environmental protection, and municipal engineering. Accurate pressure loss calculation is crucial for hydraulic transport pipelines, particularly in the case of bends, valves, and other deformation parts. These factors directly impact the energy consumption and the investment of the system. This paper employed the Euler-Euler multiphase flow model to investigate the characteristics of solid-liquid two-phase flow in vertically positioned combined elbows. The model was initially validated using data from the literature. Subsequently, based on the validated model, an investigation was conducted to determine the relationship between pressure loss and various factors, including flow velocity, combined angle, particle concentration, and particle size. Finally, the changes in velocity distribution, particle concentration, and turbulent kinetic energy were analyzed. The results indicate that the pressure loss increases with the flow velocity, tends to decrease with the combined angle, and increases with the particle concentration. The relationship between pressure loss and particle size is more complex. The velocity distribution, particle concentration, and turbulent kinetic energy exhibit the variations caused by different factors.

HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small objects in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-Level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small objects. Finally, we propose the Shape Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes, and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conducted comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.

Dense Pedestrian Detection Based on GR-YOLO.

In large public places such as railway stations and airports, dense pedestrian detection is important for safety and security. Deep learning methods provide relatively effective solutions but still face problems such as feature extraction difficulties, image multi-scale variations, and high leakage detection rates, which bring great challenges to the research in this field. In this paper, we propose an improved dense pedestrian detection algorithm GR-yolo based on Yolov8. GR-yolo introduces the repc3 module to optimize the backbone network, which enhances the ability of feature extraction, adopts the aggregation-distribution mechanism to reconstruct the yolov8 neck structure, fuses multi-level information, achieves a more efficient exchange of information, and enhances the detection ability of the model. Meanwhile, the Giou loss calculation is used to help GR-yolo converge better, improve the detection accuracy of the target position, and reduce missed detection. Experiments show that GR-yolo has improved detection performance over yolov8, with a 3.1% improvement in detection means accuracy on the wider people dataset, 7.2% on the crowd human dataset, and 11.7% on the people detection images dataset. Therefore, the proposed GR-yolo algorithm is suitable for dense, multi-scale, and scene-variable pedestrian detection, and the improvement also provides a new idea to solve dense pedestrian detection in real scenes.

Spatio-temporal task pricing for shared electric micro-mobility battery-swapping platform with reinforcement learning

Spatial crowdsourcing has emerged in shared electric micro-mobility platforms, compensating occasional drivers (ODs) per task of swapping micro-mobility batteries. As ODs autonomously select tasks only when satisfied with predetermined compensation and travel distance, a traditional uniform pricing strategy results in possible low task completion. To resolve the imbalance between ODs and tasks, this study introduces a spatio-temporal pricing strategy where task prices differ by region and time interval. Considering the daily variations in task distribution and OD availability, the goal is to minimise the platform costs equal to the sum of total OD wages and penalties for uncompleted tasks. The reinforcement learning approach with proximal policy optimisation (PPO) is implemented to generate real-time continuous task prices. A domain-specific masking technique is incorporated to improve the learning process by disregarding the data from inactive grids in loss calculations. Computational results show that the PPO agent strategically raises prices in regions with insufficient ODs according to the OD density level. Further comparison with the mixed integer programming model with perfect information on ODs' willingness-to-accept parameters demonstrates the superior capability of our algorithm in navigating the uncertainties of OD task acceptance. A sensitivity analysis provides insights into the decision of system parameters.

DESIGN OF PUMPING INSTALLATIONS: DEVELOPMENT OF AN EXCEL WORKBOOK FOR HYDRAULIC MACHINES LECTURES

Nowadays, pumps are used worldwide in a very wide range of facilities and for countless purposes, including HVAC, domestic and commercial buildings, district energy, industrial processes and water treatment, municipal wastewater and water supply, agriculture and irrigation, among others. Accordingly, the number of applications is also very diverse. The purpose of present contribution is to introduce an Excel Workbook that represents a friendly easy to use tool that enables the design of pumping systems. It was first thought for Hydraulic Machines Master lectures, but its use might be looked at in a wider perspective. The Workbook includes 17 worksheets (Figure 1), all linked to each other, addressing different aspects of the design. Special attention is paid to the major and minor head losses calculation, the cavitation phenomenon, the use of dimensionless coefficients to determine the rotation speed to obtain a specific operating point and to the calculation of the system curve. Energy efficiency represents today an important goal in every pumping facility; therefore, one of the objectives of this tool is to enable the user to quantify both the shaft power and the efficiency of different operating points thus allowing a sustained definition of the best solution.

Calculation of Productivity Loss at Batching Plant Due to Production Decline

The Batching Plant owned by PT Semen Indogreen Sentosa was established in the Binjai-Langsa Section on the Trans Sumatera Toll Road Project specifically to handle the demand for ready-mix concrete in the project. However, several obstacles in the project have caused a decrease in production from the batching plant, necessitating an evaluation of productivity loss due to these issues. The productivity loss calculation was conducted by sampling one of the batching plants over a 1-month working period using three approaches/methods: Zink’s Method, Thomas Baseline Technique, and Ibbs Statistical Approach. The calculation results from these three approaches provided a range of minimum daily productivity values, which should be 4.40 w-h/m3 - 2.48 w-h/m3, which can then be used as a reference for evaluation purposes, claims, etc.

Methods for Determining Losses and Parameters of Cylindrical-Rotor Medium-Power Synchronous Generators

This paper presents the analytical and numerical application of a method to determine the parameters and power losses in the core of two medium-power synchronous generators. These generators are used as emergency power sources powered by diesel engines, gas engines, and gas turbines. They cover peak electricity demand but can also be used in traction drives. This article presents a new numerical method for determining losses in the generator core based on the use of a time-stepping solution using the FEM method and calculating these losses using analytical formulas. In calculating the losses for the FEM method, approximations of the loss characteristics of the sheet were used with a wide range of induction values and frequencies. This method is specific to the solution used and was adapted from the authors’ previous work on losses in induction machines. A one-phase winding with alternating voltage was supplied to determine the basic parameters in the form of synchronous reactance. Also, an important novelty is the introduction of a new method of determining the saturation state of the magnetic circuit, which significantly affects the machine parameters. The obtained results were used in analytical calculations and implemented in a computer program that allows for the calculation of electromagnetic parameters, operating characteristics, and core losses, taking into account additional losses, total losses, and efficiency, as well as machine parameters in unsteady operating states and the current characteristics of a three-phase symmetrical short circuit at the machine terminals. The calculations obtained were verified experimentally by measurements of real machines.

Performance Investigation of 17 Level Reduced Switch Count Multilevel Inverter

The primary objective of this paper is to introduce a 17-level multilevel inverter with only eight power switches and three diodes that can be suitable for electric vehicle applications. This setup utilizes three distinct unequal DC sources to create the 17 levels of output voltage waveforms. The modes of operations of the proposed topology have been discussed in detail. The calculation of conduction losses, switching losses, efficiency, total standing voltage, and cost function per level for the suggested inverter has been elaborated. Due to more semiconductor power switches, diodes, capacitors, driver circuits, and DC sources in typical inverters, switching losses, costs, and harmonic distortion are increased. The nearest-level control technique has been utilized to control the switching elements of the recommended configuration. The performance comparison of various multilevel inverter topologies has also been discussed. The suggested multilevel inverter provides a higher efficiency of 98.60%, cost function of 3.6 for a weight coefficient of 1, improved power quality, and higher reliability. A reduction in the power switches significantly reduces the convolution of switching circuitry. The total harmonic distortion produced by this inverter is 3.49%, which comes under the IEEE standard of 5%.

Modeling, design and validation of DC-DC landsman converter for low-power electric vehicle battery charging applications

As the world moves towards sustainable development, the reduction of air pollution is considered an important factor. Consequently, the transportation sector is moving from conventional internal combustion engine vehicles to electric vehicles (EVs). To encourage EV usage, governments are working towards installing more public charging systems. These systems invariably use a DC-DC converter to ensure the supply of an appropriate voltage to charge the battery. In developing countries, the majority of users prefer electric bikes and electric rickshaws, which require low power. Therefore, it becomes necessary to use low-power converters suitable to charge these batteries. In such systems, non-isolated DC-DC converters play an important role. This work is intended to explore the use of the Landsman converter for EV battery charging. The motivation behind the work is explained, and the state of the art of EV battery charging research is reviewed to show the research gap. The operation of the proposed converter is explained in detail with its dynamic loop and nodal equations. The gain equation and the memory elements design are detailed with the corresponding equations. The model of the proposed converter is provided with the loss calculation. The system is simulated on MATLAB R2022b software, and the simulation results are provided to show the behavior of voltage and current in each component. To validate the design, a 100 W hardware prototype is implemented using discrete components to supply a resistive load, and to charge a 48 V Li-ion battery. The corresponding results are provided to validate the design and operation. A comparison of the proposed converters’ performance parameters with other non-isolated converters is provided. The topology is seen to provide an instantaneous efficiency of 88% during the prototype testing for resistive load, and an efficiency of 89.6% while charging a 48 V Li-ion battery, wherein, the efficiency magnitudes are the experimental values obtained through real-time measurements on Keysight IntegraVision Power Analyzer PA2203A, during the testing of the hardware prototype, as presented in section . Consequently, Landsman converter is seen to be an attractive converter for EV battery charging applications.