The estimation of the pose of surgical instruments is important in Robot-assisted Minimally Invasive Surgery (RMIS) to assist surgical navigation and enable autonomous robotic task execution. The performance of current instrument pose estimation methods deteriorates significantly in the presence of partial tool visibility, occlusions, and changes in the surgical scene. In this work, a vision-based framework is proposed for markerless estimation of the 6DoF pose of surgical instruments. To deal with partial instrument visibility, a keypoint object representation is used and stable and accurate instrument poses are computed using a PnP solver. To boost the learning process of the model under occlusion, a new mask-based data augmentation approach has been proposed. To validate our model, a dataset for instrument pose estimation with highly accurate ground truth data has been generated using different surgical robotic instruments. The proposed network can achieve submillimeter accuracy and our experimental results verify its generalisability to different shapes of occlusion.

A novel approach is proposed in this paper to reconstruct the far-field radiation pattern from the phaseless electric field of an antenna scanned on a single near-field sphere. It adopts the dipole equivalence approach to project the near-field electric field into a spherically distributed array of electric dipoles. We introduce a term representing the residue from the linearly correlated portion in the least-square problem associated with the dipole equivalence, namely the linear correlation residue (LCR). It is demonstrated that by iterative search to minimize the LCR using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), near-field phase distributions can be found efficiently from the magnitude-only near-field, and the far-field radiation pattern can be computed. Two representative case studies are given here to validate the proposed method. Results demonstrate good agreements between computations and simulations.

High Resolution Range Profiles (HRRP) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of deep learning based HRRP recognition, these methods needs a large amount of training samples to generate good performance, which could be a severe challenge under non-cooperative circumstances. Currently, deep learning based models treat HRRP as sequences, which may lead to ignorance of the internal relationship of range cells. This letter introduces HRRPGraphNet, whose pivotal innovation is the transformation of HRRP data into a novel graph structure, utilizing a range cell amplitude-based node vector and a range-relative adjacency matrix. This graph-based approach facilitates both local feature extraction via one-dimensional convolution layers, global feature extraction through a graph convolution layer and a attention module. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet’s superior accuracy and robustness, particularly in limited training sample environments, underscoring the potential of graph-driven innovations in HRRP-based RATR. Codes are available at: https://github.com/MountainChenCad/HRRPGraphNet.

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.

The current overhead work robot for transmission line faces issues related to its compact structure and high target localization requirements. To address these challenges, this paper proposes a lightweight bolt detection algorithm based on improved YOLOv8 (You Only Look Once v8) model. Firstly, the C2f module in the feature extraction network is integrated with the Self-Calibrated Convolution (SCConv) module, and the model is streamlined by reducing spatial and channel redundancies of the network through the SRU and CUR mechanisms in the module. Secondly, the P2 Small Object Detection Layer is introduced into the neck structure and the BiFPN network structure is incorporated to enhance the bidirectional connection paths, thereby promoting the upward and downward propagation of features. It improves the accuracy of the network for bolt-small target detection. The experimental results show that, compared to the original YOLOv8 model, the proposed algorithm demonstrates superior performance on a self-collected dataset. In this paper, the mAP accuracy is improved by 9.9%, while the number of model parameters and the model size is reduced by 0.973×106 and 1.7MB, respectively. The improved algorithm improves the accuracy of the bolt detection while reducing the computation complexity to achieve more lightweight model.

This brief investigates the multi-point fault repair problem in active distribution networks, establishing resilience assessment metrics and constructing a fault repair mathematical model with road network coupling. An improved genetic algorithm is proposed, utilizing a comparison crossover operator and population information entropy. Compared to traditional algorithms, the comparison crossover operator preserves high-quality genes, while population information entropy maintains diversity, preventing the algorithm from converging on local optima. Simulation analysis demonstrates that the proposed fault repair method can restore normal power supply to distribution network lines within a short period and offers significant advantages in fault repair tasks.

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.

This paper introduces a novel resource allocation algorithm, Priority-Aware Federated Resource Allocation (PAFRA), tailored for Power Line Communication (PLC) systems. Utilizing a federated learning framework, PAFRA optimizes the distribution of limited spectrum resources among multiple nodes within a residential environment. The algorithm employs a priority-based time slot allocation to manage subchannel conflicts and uses a Double Deep Q-Network (DDQN) for local training at each node, incorporating state, action, and reward configurations to refine transmission power and subchannel selections. Extensive simulations demonstrate that PAFRA significantly enhances system throughput across various Signal-to-Noise Ratio (SNR) levels, outperforming existing adaptive resource allocation strategies and random allocation methods. The findings highlight PAFRA’s ability to achieve superior performance in dynamic PLC environments, illustrating its potential to optimize network efficiency while adhering to strict regulatory emission standards.

Condition monitoring data can be used to assess the health of a power system component but is rarely used to assess the reliability of the power system they are part of. For high-voltage circuit breakers (HVCBs), the susceptibility to various failures can be assessed by examining trip coil current (TCC) measurements. HVCBs have two failure modes, failure to trip on command and tripping without a command, which are triggered by various failure mechanisms that in turn may depend on the technical condition of the HVCBs. The aim of this paper is to demonstrate a methodology for quantifying the impact that the technical condition of HVCBs has on the power system reliability indices. An equivalent transmission line failure rate considering protection system failures, including failures related to HVCBs, can be calculated by computing contributions from various fault types (FTs). This paper proposes a framework that can quantify the frequency of the FTs that are affected by HVCB condition. The system-level effects are then evaluated using approximate methods for power system reliability evaluation. To demonstrate the principles and benefits of this methodology, a case study is presented that incorporates HVCB condition data from the Icelandic transmission grid into an illustrative 4-bus test system; a dataset of 83 TCC measurements from 38 HVCBs from the Icelandic Transmission System Operator is used, together with an outage database of life histories of 464 HVCBs from the Icelandic transmission grid. Results indicate that the condition deterioration associated with the probability of an HVCB failing to trip on command can significantly degrade the reliability indices.

Characterizing soil moisture (SM) around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated by two different microwave imaging techniques based on spatio-temporal measurements at various frequency bands. The study investigates the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN) algorithms for moisture classification based on these images in three scenarios: before clutter removal, after clutter removal, and after applying imaging techniques such as back projection and the Born approximation. We also explore the potentials of CNN and KNN for moisture estimation around the plant roots and in the presence of pebbles. The results demonstrate the more accurate moisture estimation using CNN when it is applied after clutter reduction considering back projection algorithm (BPA) as the imaging technique. The results indicate that using the Back Projection technique for image formation, combined with CNN for classification, improves leak detection accuracy by approximately 20% compared to other methods.

This study developed a damped alternating current (DAC) testing methodology, composed of an improved DAC generator and a pulse-based distributed PD-detection technique. The improved DAC generator lacks high voltage (HV) solid-state switches and is thus a cost-efficient solution for field-testing voltage generation via a direct current–alternating current (DC-AC) conversion technique. A capacitor bank is used for power supply to achieve the requirement for high instantaneous power. In addition, the use of a distributed PD detection technique enables high sensitivity and makes the method free from restrictions of the cable length. To reduce the construction cost of the distributed PD-detection system, a pulse synchronization technique is used. Attempts was made to reduce the weight based on the simulations, and a prototype with the total weight of 850kg was designed and developed for a 64/110kV power cable. Laboratory and field tests validated the effectiveness of the cost-efficient DAC testing methodology.

Faults in power systems occur for various reasons, such as aging or natural disasters. Detecting, locating, and promptly clearing these faults is crucial for maintaining the safety and reliability of transmission lines. Distance relays, which protect transmission lines, detect faults, estimate their location, and send the required commands. However, these relays may experience mis-detection due to manipulated impedance arising from both internal and external factors. These factors include measurement device errors, network topology changes, the presence of fault resistance, and injected currents from remote line terminals. To address this challenge, we propose an innovative adaptive protection scheme that considers fault resistance, changes in network topology, and injected current from the opposite end of the line. By estimating the equivalent circuit impedances of the network connected to the terminal of the transmission line, this protection scheme utilizes impedance estimation techniques at the line terminals and offline network information. Simulation studies (tested on the IEEE 39-bus standard network) show that the proposed scheme accurately estimates fault location and fault resistance with high precision. The simulation results demonstrate its effectiveness in improving the performance of conventional distance protection relays in both the first and second protection zones (Z_1 and Z_2).

The correct design of the generator circuit breaker (GCB) is essential for the safe and reliable operation of a power plant or generation system. The design is usually based on standardized calculation methods according to standards (IEC standard 60909-0, IEC/IEEE standard 62271-37-013), often supplemented by selected transient calculations. A non-systematic approach can often be observed here, which does not adequately take into account significant influencing variables or operating states of the generator. This article therefore systematically examines various parameters that influence the short-circuit current components of the generator and are relevant for the design of the generator circuit-breaker: Short-circuit angle, operating point, phase clearing, switching arc, and fault arc. The results of the current parameters most relevant to the dimensioning of the GCB were then compared for different calculation methods. Particular attention was paid to the influence of the switching arc and fault arc, which is modeled in a transient calculation, and its effect on the short-circuit currents is systematically recorded. This work aims to summarize all relevant variables that influence the generator short-circuit current and are relevant for the dimensioning of the GCB and to present the different results based on a short-circuit calculation according to the standard and transient calculation to create a basis for a proper dimensioning of the generator circuit breaker.

Wind energy systems require fault diagnosis that identifies faults despite data inconsistencies. This study addresses challenges in supervisory control and data acquisition (SCADA) systems for monitoring wind turbine conditions from imbalanced data representation and error vulnerability. It examines the efficacy of Adaptive Elite-Particle Swarm Optimization (AEPSO)-tuned Extreme Gradient Boosting (XGBoost) on an imbalanced SCADA dataset for wind turbine fault classification. The methodology integrates the resampled SCADA dataset with t-distributed Stochastic Neighbour Embedding represented deep learning features. Employing AEPSO-XGBoost classifier trained on merged SCADA and deep learning data from a physics-informed deep convolutional neural network forms the basis of the fault classification model. The AEPSO-XGBoost regressor is validated across three distinct rear bearing temperature datasets, facilitating parameter optimization and model robustness. Also, this study explores supervised and unsupervised anomaly detection models using PDCNN and AEPSO-XGBoost with rear-bearing temperature data. Findings exhibit substantial fault classification and prediction enhancements by merging resampled SCADA data with deep learning features. Moreover, results show that applying AEPSO-XGBoost can significantly improve anomaly detection metrics. Through AEPSO-XGBoost’s efficacy in enhancing fault prediction within imbalanced SCADA datasets, the study proposes an integrated framework for fault classification and anomaly detection as an innovative predictive maintenance system for wind energy systems.

This research explores the desorption mechanism and thermal stability of octadecyl trichlorosilane (OTS, CH3(CH2)17SiCl3) coating on quartz slides and in alkali-metal vapor cells. We measured systematically the morphological thermal-changes, energy dissipation diversity and anti-relaxation characteristic of OTS coatings before and after exposure to Cs atoms by Fourier transform infrared spectroscopy (FTIR), water contact angle measurement, atomic force microscopy (AFM) imaging, collision energy dissipation analysis, and free induction decay (FID) of Cs atoms. The results show that the OTS coatings exhibit the best thermal stability under the specific process conditions, and the homogeneous and dense structure makes the adsorption of alkali metal atoms more stable, which effectively reduces surface energy dissipation and prolongs the relaxation time of Cs atoms. The study provides certain reference for efficient anti-relaxation coating fabrication and coated cell application.

The single transmitter and multiple receivers (STMR) mobile radar system is a special distributed radar system, which has stronger detection and survival capabilities. However, the high-speed movement of the radar platforms easily produce the across-range cell migration (RCM) of radar echo signal, which cause envelope shift effects within the coherent processing interval (CPI). In this paper, we proposed a motion compensation method for the STMR mobile radar system in the sea environment, to solve the problem of RCM. Firstly, the target points and clutter points with the same Doppler velocity are obtained by the radon Fourier transform (RFT) method. Secondly, using the range / Doppler difference between clutter and target under multi-radar views, the target and clutter points are separated by the spatial range mapping. The simulation results show that the method can effectively improve the coherent accumulation effect of the target in the sea environment with low signal-to-clutter ratio (SCR).

The detection of defects on the copper chain in the production process of tobacco cutters is crucial for ensuring product quality. Traditional defect detection methods often rely on spatial domain image analysis, which not only has a large computational load but also performs poorly in handling high-frequency noise and complex backgrounds. To address this issue, this paper proposes a novel neural network model based on frequency domain analysis, called Frequency Domain Attention Network (FDANet). This network first utilizes Discrete Cosine Transform (DCT) to transform the image from the spatial domain to the frequency domain, effectively reducing computational complexity and improving processing speed. Subsequently, through the innovative Frequency Domain Attention Module (FDAM), the network automatically identifies and enhances key discriminative features in the frequency domain, thereby strengthening the model’s ability to identify defects. Finally, the frequency domain attention map, after feature extraction and integration, is inputted into the coupling detection head to achieve high-precision defect detection. Experimental results demonstrate that FDANet performs excellently in the task of detecting copper chain defects in tobacco cutters, showing significant improvement compared to traditional methods and verifying the effectiveness and practicality of the proposed approach.

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