Injection-molded products may to have a variety of defects in production. Failing to detect and fix the defects may reduce product quality and lead to safety issues. An injection-molded product defect detection model, IMP-DETR, is proposed to address the challenges of diversity, small size, and complex background in injection-molded products. The model constructs a feature extraction backbone network with the iRMB module to extract key information and reduce interference from irrelevant backgrounds while maintaining lightweight. The SOFP feature fusion network is used to capture rich texture information from small objects to improve the detection performance of fuzzy and small-sized defects. Additionally, the Conv3XC-Fusion module is designed to resolve the problem of integrating multi-scale features, improving the stability of detection. Due to the lack of publicly available datasets for injection-molded product defects, we constructed a custom dataset containing 2500 defect images. The experimental results indicate that the mAP of the IMP-DETR model reaches 82.4%. Compared to other benchmark object detection models, IMP-DETR demonstrates superior detection performance and a smaller model size, which is suitable for application in real scenarios.

In this paper, we tackle the challenging problem of multi-target cross localization within a novel distributed high-frequency surface wave radar (HFSWR). This system consists two transmitter-receiver pairs, each of which can correspondingly generate a set of Range-Doppler measurements. By associating the Range-Doppler measurements from the same target, the individual target location can be derived using the associated measurements. However, when multiple targets are involved, it is unknown which measurements originate from the same target, leading to ambiguities in the measurement-to-measurement association. False associations can result in the ghosts, ultimately degrading the overall system performance. To deal with the ghosts, we propose a three-stage deghosting method that incorporates the geometric characteristics of the system, the velocity features of the targets and the motion features of the targets across multiple frames. ̵‌The simulation results demonstrate the effectiveness of our proposed method compared with the other investigated deghosting algorithms. Our proposed approach exhibits robust target detection capability while significantly reducing both the OSPA error and the cardinality error.

Satellite panel is an important part of satellite, and its modeling accuracy and reliability are very important for satellite design and manufacturing. However, how to improve the accuracy and reliability of satellite panel modeling has become a key issue to be solved urgently in the field of satellite technology. In this research, based on the sandwich sandwich theory, a new finite element model for satellite panels is constructed, which can numerically express the multi-layer structure, material properties and their interactions of satellite panels, and more accurately predict and simulate the behavior of satellite panels under various conditions. In the process of model building, Lanczos method is used to analyze the modal of the satellite solar panel model, and the frequency coincidence analysis between the analysis results and the real mode is carried out. The results show that the frequency error of the first five modes is less than 5%, which indicates that the established finite element model of satellite solar panel has high accuracy. It can well capture the modal characteristics of satellite solar panel in the vibration process. Using Modal confidence and MAC (Modal Assurance Criteria) to assess, conform to the degree of its MAC value is above 0.9, conform to the Modal correlation standard. To sum up, the satellite panels finite element modeling method is appropriate, has the high accuracy and reliability, can better meet the needs of design and manufacture of satellite, promote the sustainable development of satellite technology and progress

IPMSM drive systems which are driven by matrix converters have many advantages, including one-stage power conversion, high efficiency, bidirectional power transfer, near unity input power factor, and low input current harmonics. However, the DC-link voltage of the matrix-converter is varied from 0.866 to because a large electrolytic capacitor is not used. These voltage variations deteriorate the dynamic current and speed responses and reduce the adjustable speed range of the IPMSM drive systems. In addition, the variations of the external load and inertia are very large for matrix-converter based drive systems. To solve this problem, two adaptive controllers are investigated in this paper for matrix-converter PMSM drive systems. From the experimental results, the proposed two adaptive speed controllers are superior to the PI speed controller, including faster transient responses, better tracking abilities, and greater robustness. As a result, the proposed matrix-converter based IPMSM drive system is suitable in many industrial applications.

This paper introduces a millimeter-wave Integrated Substrate Gap Waveguide (ISGW) filtering antenna featuring four adjustable radiation nulls, with two located in each of its upper and lower stop bands. These nulls are tunable by altering the dimensions of stepped-impedance resonators, complementary U-slots, and passive coplanar parasitic patches. The antenna offers the benefits of individually adjustable radiation nulls and the flexible modification of selectivity and gain curve roll-off. Simulation results indicate the antenna operates at a central frequency of 25.4 GHz, with a relative bandwidth of 14.3% (23.76–27.04 GHz), and attains an average gain of 7.6 dBi.

For citrus trees cultivated by dwarf dense planting, the fruits are randomly distributed in space, which poses difficulties for mechanized picking. To improve picking efficiency, a citrus picking system based on dual robot collaboration is designed and a global scanning picking scheme that scans the entire fruit tree to achieve orderly fruit picking is proposed in this research. The dual-robot calibration with the iterative method and closed-form method is completed in the research. The picking problem is attributed to the single traveling salesman problem, and the trajectory planning and the picking task are completed with genetic algorithm in the research. The picking experiments are designed in the research. As a result, in the picking experiments, the average time for planning the picking sequence of citrus was 0.1184 seconds. In each group of citrus picking experiments, the total picking time averaged 158.9 seconds, the picking success rate is 82%. The picking results show that the built dual-machine system can effectively complete the picking task. The proposed dual-robot picking system can provide a reference for the establishment of other picking robot system.

This article presents the new hybrid switched-capacitor-based transformerless DC-DC converters with a high step-up capability and common ground features. The proposed converter provides low voltage stress across semiconductor devices to utilize MOSFETs with lower Rdson and low voltage ratings. In addition, the diodes in switched-capacitor cells with low voltage stress can reduce the reverse recovery power loss of the converter. Thus, the overall efficiency of the proposed converter can be increased. The converter circuit, operating principle, and design guideline of the proposed converter are discussed. To confirm the performance of the proposed converter, a detailed comparison study with the conventional hybrid switched-capacitor converter is also presented. Finally, a laboratory prototype with 200 W, 25 V to 200 V is set up to correct the effectiveness of the proposed converter

Non-conventional renewable energy sources integration into distribution systems, data science, and enabling technological infrastructures present formidable challenges in transforming distribution systems, particularly in accommodating active demand. With escalating energy demand emphasizing the need for dependable tracking and predictive methodologies. These methodologies are essential for managing Distributed Energy Resources (DERs) and digital infrastructure. Effectively monitoring active demand requires a comprehensive understanding of the transactional system concept, encompassing digital infrastructure, and decentralized demand. Despite the increasing prominence of metaheuristic techniques in demand response integration, existing literature predominantly focuses on specific techniques rather than providing a characterization of dynamic transaction integration for active demand. Technological advancements, such as smart meters and communication systems, propel the evolution of the demand concept from rudimentary consumption measurement to active consumer involvement. This paper reviews the evolutionary trajectories of essential concepts in the energy sector, namely active demand, DERs, and transactive systems. Through in-depth analysis, Simultaneously, the paper systematically examines prevalent metaheuristic techniques in the literature, specifically focusing on their role in integrating and predicting the behaviors of active demand and DERs. The paper presents a methodology that, if utilized as a roadmap, would facilitate the evidence of stages required for the integration of DERs .

Automatic Violence Detection and Classification (AVDC) with deep learning has garnered significant attention in computer vision research. This paper presents a novel approach to combining a custom Deep Convolutional Neural Network (DCNN) with a Gated Recurrent Unit (GRU) in developing a new AVDC model called BrutNet. Specifically, we develop a time-distributed DCNN (TD-DCNN) to generate a compact 2D representation with 512 spatial features per frame from a set of equally-spaced frames of dimension 160×90 in short video segments. Further to leverage the temporal information, a GRU layer is utilised, generating a condensed 1D vector that enables binary classification of violent or non-violent content through multiple dense layers. Overfitting is addressed by incorporating dropout layers with a rate of 0.5, while the hidden and output layers employ rectified linear unit (ReLU) and sigmoid activations, respectively. The model is trained on the NVIDIA Tesla K80 GPU through Google Colab, demonstrating superior performance compared to existing models across various video datasets, including hockey fights, movie fights, AVD, and RWF-2000. Notably, our model stands out by requiring only 3.416 million parameters and achieving impressive test accuracies of 97.62%, 100%, 97.22%, and 86.43% on the respective datasets. Thus, BrutNet exhibits the potential to emerge as a highly efficient and robust AVDC model in support of greater public safety, content moderation and censorship, computer-aided investigations, and law enforcement.

Frequency response analysis is widely used for the offline diagnosis of winding deformations in power transformers. To apply it to a working transformer, the magnitude of signals of the response current, which is of the order of microamperes, needs to be measured by using Rogowski coil sensors against the load current of the order of thousands of amperes. The saturation of the power frequency magnetic field in current sensors must be inhibited to ensure the accurate measurement of response currents with such a small magnitude. The authors of this paper propose a method to suppress the power frequency magnetic field by using a sensing system involving a special connection of three-phase current sensors based on the rule that the sum of the three-phase power frequency load currents of the transformer is close to zero. Each sensor consists of two secondary-side coils: a measuring coil and an anti-saturation coil. The anti-saturation coils are connected in parallel with one anothes through small inductors to eliminate the power frequency magnetic field in the cores of the sensors. We use theoretical analysis to derive the solution to this system. The results of experiments to verify the proposed method showed that it enables the sensors to function with a transformer carrying a load current of 2333 A, while incurring a relative error in the response current that is smaller than 2%.

Secure and reliable electricity supply is a prerequisite for the development of smart cities, and the trustworthy and efficient transmission of electrical data is the foundation for the safe and stable operation of the power grid. This paper introduces a real-time data transmission blockchain technique based on parallel proof of work algorithm. The new block generation progress of proposed blockchain is divided into five subroutines: hash pointer computation, real-time data pudding, signature value iteration, interruption, block header assembly. The real-time data pudding and signature value iteration are parallel processed, which brings the effect of decreasing energy loss of blockchain system, and upgrades the speed of new block generation and the bandwidth of data storing on blockchain. Computer simulation shows the proposed strategy can be effectively applied in real-time electrical data transmission application, raising the data transmission reliability with no harm to real-time data transfer function. This strategy provides a solution to guarantee data transmission safety in the digital conversion of power grid.

In order to ensure the power safety of important power customers, a new evaluation of power consumption status of important power customers based on the AHP(Analytic Hierarchy Process)-TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution) algorithm is proposed by fully mining and applying the power big data. Firstly, a power consumption big data analysis platform based on the Hadoop architecture is built to provide a high-performance platform support for big data analysis. Secondly, nine evaluation indexes are constructed from the three dimensions of voltage, load and synthesis, which objectively and scientifically describes the power consumption status of important power customers. Finally, the AHP-TOPSIS algorithm is used to evaluate and analyze the voltage, load and comprehensive indicators respectively, thus, obtaining the evaluation values of three kinds of indicators. The power consumption status scores of important power customers are determined by the variable weight weighted summation. The rationality and feasibility of the method and algorithm are proved by example analysis and field verification. This method helps to promote the transformation from post fault emergency repair to warning beforehand. It has the multiple effects of ensuring safe power consumption, supporting accurate patrolling and active emergency repair serving.

Grease in the normal operation of the RV reducer has a role that can not be ignored, for the variable working conditions of the RV reducer, the performance of the lubricant changes directly affect its reliable operation. Therefore, the study of the rheological properties of the grease has become the focus of the study of RV reducer performance.In this paper, SK-1A grease is taken as the research object, and its rheological characteristics under wide temperature range working conditions (-20℃~40℃) are investigated through rheological experiments, to analyze the potential influence of SK-1A grease on the performance of RV reducer.In addition, to better study the rheological properties of grease under different working conditions, the Elman neural network model was used to predict the rheological properties of grease based on the rheological experiments of grease, and the results were compared with those of the BP neural network and the RBF neural network.The prediction accuracy of the Elman neural network model was assessed using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in a cross-validation approach.The results show that the viscoelastic properties of SK-1A grease generally show a decreasing trend as the temperature rises in a wide temperature range, and the degree of entanglement of soap fibers decreases more obviously, but its fluidity is more stable.The results of the three neural network prediction models show that the Elman neural network model used for the prediction of grease rheological properties shows high prediction accuracy, and the model can provide a valuable theoretical reference for the accurate prediction of the rheological properties of grease affected by complex multifactor.

In response to pressure from all sides, governments around the world have com-mitted to decarbonising their economies within the next thirty years. Taken literally, this would involve eliminating the use of naturally occurring hydrocarbons such as coal, natural gas, and oil. However, based on their analysis of data drawn from ac-ademic papers, the Australian Federal Government, energy industry bodies, and manufacturers of electricity generating equipment, the authors believe at this time, such elimination of fossil fuel use is simply not possible. That said, by moving to a connected energy island (CEI) power generation topology, the data suggest we can at once: create a sustainably robust energy supply, move Australia closer to its environmental targets, and underpin the country’s future economic well-being.

In this paper, Short-term predicting of load and spinning reserve is first performed using a combination of ANFIS and meta-heuristic algorithms including Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The ANFIS-PSO combination is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. As a DL method, LSTM network can provide good accuracy for load and spinning reserve forecasting. In the optimal ANFIS-PSO method, the average error value is low, but the error variance is high, on the contrary, in the LSTM method, the average error value is high, and the error variance is low. Therefore, we use the combination of ANFIS-PSO and LSTM to reduce the average error and error variance to an acceptable level. The weighted average method is as follows: the accuracy of each Method is obtained in the training step, then the predicted value for each data in the test step is calculated in each Method, then they are multiplied, and after that added together, finally will be divided to the total accuracy of two methods. The results obtained from the weighted average Method show the success of the proposed Method.

TSP is one of the most famous problems in graph theory, as well as one of the typical NP-hard problems in combinatorial optimization. Its applications range from how to plan the most reasonable and efficient road traffic to how to better set up nodes in the Internet environment to facilitate information flow, among others. Reinforcement learning has been widely regarded as an effective tool for solving combinatorial optimization problems. This paper attempts to solve the TSP problem using different reinforcement learning algorithms and evaluated the performance of three RL algorithms (Q-learning, Sarsa, and Double Q-Learning) under different reward functions, ε-greedy decay strategies, and running times. The results show that the Double Q-Learning algorithm is the best algorithm, as it could produce results closest to the optimal solutions, and by analyzing the results, better reward strategies and epsilon-greedy decay strategies are obtained.

Today’s healthcare system relies on MRI (medical resonance imaging) for early diagnosis and treatment planning. For open MRI systems to achieve resolutions of about a hundred microns, a high voltage is required, as well as a specialized power supply. NP0 (Negative-Positive-Zero) ceramic is selected for the fabrication of adjustable capacitors. Specifically, it stands for which is a classification based on the temperature coefficient of capacitance (TCC) of the ceramic material used in the capacitor. NP0 capacitors have a TCC of 0 ±30 ppm/°C, which means that their capacitance value does not change significantly with temperature and frequency. They are known for their stability and low losses, making them ideal for applications that require high accuracy and reliability, such as timing circuits for RF applications. In this paper, MgTiO-CaTiO ceramic is used to make an adjustable capacitor with desired properties for MRI systems. To enhance the dielectric properties of MgTiO3 ceramics,CaTiO3 was added in varying concentrations. After pressing and sintering, the resulting samples were tested using a vector network analyzer in the frequency range of 10 MHz to 130 MHz.The adjustable capacitor fabricated using high co-fired NP0 ceramic may have been used for MRI applications such as tuning circuits and matching networks, where precise capacitance values and low loss are critical[1]. MRI systems with resonance frequencies of 128 MHz require trimmers with ceramic cores.

5G network slicing is a promising solution to prioritize time-critical protection communication in wireless networks. However, recent trends indicate that a 5G slice could encompass all smart grid applications lacking the necessary granularity. At the same time, while substation communication standards recommend prioritization of protection communication traffic to improve reliability, these recommendations are only for wired connections. Therefore, this paper investigates traffic shaping and uplink (UL) bitrate adaptation of video stream based on existing commercial solutions as methodologies for prioritizing the protection communication in a 5G slice. These methodologies are validated in an experimental setup combining controller-hardware-in-the-loop (CHIL) simulation with a quality of service (QoS) measurement system. The system under test consists of commercial 5G networks, commercial intelligent electronic devices (IEDs), and merging units to validate the methodologies on three smart grid applications: fault location, line differential, and intertrip protection. The results show improvement in protection communication when traffic shaping and UL bitrate adaptation are applied. Traffic shaping even improves prioritization with a wired connection.