In modern power systems, the randomness and uncertainty of the generation and loads greatly increase the balance discrepancy between the power supply and demand, which introduces major potential security hazards to the stable operation of power systems. In particular, out-of-bounds frequencies emerge. To control the system frequencies, a frequency control method based on the state space model for power systems under small, uncertain disturbances is proposed in this paper. Considering the general model of the power system frequency response with the aim of improving the input‒output finite-time stability of the system, the frequency deviation of the power system is controlled in a closed loop. The Monte Carlo simulation of different systems shows that the proposed method can effectively control the frequency deviation of the power system. When the closed-loop system is disturbed by uncertain power fluctuations, its frequency will always remain within the allowable range to ensure the secure and stable operation of the power system.

In this letter, we proposed and experimentally demonstrated a simple and efficient design method for high-voltage Floating Field Ring (FFR). Three floating FFRs is firstly used to determine the influence of rings’ distance (d) on the voltage drops of the highest voltage to second ring (Vn+1) and the second to the first ring (Vn). Then, two voltage curves (curve for Vn+1 and Vn) based on d are drawn. Then, for a target breakdown voltage, by iteration between the curves for Vn+1 and Vn, number of FFRs and corresponding distances can be found. Based on this method, an 8.0 kV PN diode with 110 FFRs is designed and fabricated. The measured breakdown voltage is 7840 V, which reaches 93% of the designed value. This method provides a simple and efficient design for high-voltage SiC devices, especially for ultra-high voltage applications where the number of rings exceeds dozens.

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, we provide insights into the transformative potential of ML in shaping the future of power system asset management.

Occluded person Re-identification (Re-ID) is to identify a particular person when the person’s body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. In this paper, we propose a novel Attention Map-Driven Network (AMD-Net) for occluded person Re-ID. In AMD-Net, human parsing labels are introduced to supervise the generation of partial attention maps, while we suggest a Spatial-frequency Interaction Module (SIM) to complement the higher-order semantic information from the frequency domain. Furthermore, we propose a Taylor-inspired Feature Filter (TFF) for mitigating background disturbance and extracting fine-grained features. Moreover, we also design a part-soft triplet loss, which is robust to non-discriminative body partial features. Experimental results on Occluded-Duke, Occluded-Reid, Market-1501, and Duke-MTMC datasets show that our method outperforms existing state-of-the-art methods. The code is available at: https://github.com/ISCLab-Bistu/SA-ReID.

The type parameter helps in the scattering mechanism analysis and scattering center identification. However, current approximate solution methods based on spectral estimation are noise-sensitive and have poor accuracy. In this letter, a high-precision approach for the type parameter based on the alternating direction method of multipliers (ADMM) is proposed. We use logarithmic transformation to separate the type parameter and the amplitude from the coupling term, and initially obtain the closed-form solution of the type parameter. Then, the regularization term of l2-norm is used for denoising. Finally, a joint optimization model based on ADMM is constructed to effectively estimate the type parameter. Simulation results confirm the high accuracy of the proposed approach.

Nikel Cobalt layered double hydroxides (NiCo LDHs) have emerged as ideal electrode materials for supercapacitor due to their high specific surface area and excellent cycling stability. Morphology control plays a unique role in regulating the performance of NiCo LDHs, but there are rare reports to regulate the morphology during energy storage. Herein, the morphology of NiCo-LDHs electrode is optimized for enhancing energy storage by simple activation process with different t concentrations of the electrolyte. During the activation process, electrochemical morphology reconstructed occurs on the electrode surface. With2 M KOH electrolyte the NiCo-LDH electrode transforms from nanosheets to nanoflower, which aids in reducing the distance of ion transport. The reconstructed NiCo-LDH (NiCo-LDH-2) exhibits an ultra-high specific capacitance of 5428 F g-1 at a current density of 1 A g-1, outperforming most of NiCo LDHs. Even at a high current density of 10 A g-1, the capacitance retention rate remains above 77.6% after 1000 charge-discharge cycles. The strategy proposed in the study, which involves concentration-controlled morphology optimization for energy storage enhancement, holds great practical significance for the field of supercapacitors.

For telecom operators, customer service is integral to their business. Traditional service systems, responsible for managing large amounts of data and complex knowledge bases, need more time retrieval processes and a lack of precision, hindering their ability to respond quickly to customer requests. To address these issues, this paper uses the LangChain programming framework to create a customized Large Language Model (LLM) specifically for the customer service context of telecom operators. It also uses reinforcement learning to improve the performance of the models and reduce the production of incorrect information. Experimental results show that the acceptance of our model’s recommended knowledge has increased from 15% to 70%, confirming its reliable operation in resource-constrained environments.

Edge computing has been proved an efficient approach to provisioning computation offloading service to vehicles on road through Road-Side Units (RSUs). However, the traffic volume on road is highly dynamic, while RSU-based edge servers are static in terms of geographical location and computation capacity. To address this problem, this paper proposes a mobile edge server placement strategy using cruising UAVs along the roads based on the genetic algorithm. We first build a mathematical model to characterize the deployment cost of these UAV-mounted servers and their routes. Next, we design a heuristic UAV-mounted edge server deployment scheme based on K-medoid clustering and genetic algorithms. Experimental results verify that our proposed UAV deployment scheme satisfies the offloading demand of IoV nodes while reducing the total deployment cost by 17.05% to 48.94% compared with existing popular approaches.

In order to maximize the promotion effect of renewable energy policies, this paper proposes a capacity allocation optimization method of wind power generation, solar power and energy storage in power grid planning under different policy objectives. Firstly, based on the policy quantification, grey relation analysis is used to calculate the correlation degree of the policy indicators on the planning capacity of renewable energy. Further, a multi-objective capacity estimation model is comprehensively presented. Some highly correlated policy indicators are transformed into the special constraints. And the economy and the stability of the power grid are integrated as the objective function. Meanwhile, the carbon trading and punishment for wind power and solar power abandonment is considered. Finally, the proposed model is solved by NSGA-Ⅱ-PSO algorithm. The novelty of the algorithm is that the crossover operation of NSGA-Ⅱ is replaced by the position updating of particle swarm. The calculation result of the case study can effectively evaluate the optimal planning capacity of renewable energy under different policies, while ensure the economic and the stability of the power system. The paper can provide the reasonable basis and the valid analytical method for the policy formulation and the renewable energy development.

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.

Targets with rotating components generate micro-motion (MM) modulation effect in addition to the main body. Extracting MM parameters is challenging due to interference from the target’s main body, necessitating the separation of modulation signals. This letter proposes a robust complex local mean decomposition (RCLMD) method with self-adaptive sifting stopping, aiming at the problem of component redundancy due to multiple iterations during break and the loss of modulation components during the separation process. The proposed method sets the objective function and self-adaptive stopping criterion, combined with the modulation signal characteristics, enhancing the accuracy and efficiency of MM component extraction. Simulation experiments indicate that at a low signal-to-noise ratio (SNR) of 3 dB, the separation effect of RCLMD is still 14.72\% higher than that of the conventional complex local mean decomposition (CLMD) method, and the separation efficiency is improved by 54.92\%. Furthermore, the measured radar signals verify the effectiveness of the proposed method in real scenarios.

Power system unreliability tracing model allocates the system’s reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model’s allocation results are reasonable and more accurate than the traditional model.

Welcome to this Special Issue of Wiley’s Healthcare Technology Letters (HTL) journal dedicated to the 2023 edition of the Augmented Environments for Computer-Assisted Interventions (AE-CAI), Computer Assisted and Robotic Endoscopy (CARE), and Context-aware Operating Theatres (OR 2.0) joint workshop. We are pleased to present the proceedings of this exciting scientific gathering held in conjunction with the Medical Image Computing and Computer-Assisted Interventions (MICCAI) conference on October 8th, 2023 in Vancouver, British Columbia, Canada. We hope that you will enjoy reading this Special Issue and we look forward to your continuing support and participation in future editions of the AE-CAI, CARE and OR 2.0 workshops. Their continued success demands our ongoing commitment and support, and we hope to welcome you all to the next edition of the workshop at MICCAI 2024 in Marrakesh, Marocco.

This paper presents a prediction-based dynamic range (DR) compensation technique in continuous-time (CT) delta -sigma modulator (DSM) with digital-domain noise coupling (DNC) structure. A digital prediction technique is proposed to address the issue of maximum stable amplitude (MSA) loss caused by DNC. The proposed prediction method has been shown to significantly extend the MSA of CT DSM with DNC. Moreover, through the extension of MSA, the DR and SNDR is significantly improved. A CTDSM with proposed compensation achieves a SNDR of 85.1dB at -1dBFS and a DR of 89dB.

Future communication networks are widely considered to be able to provide both high-speed communication service and reliable sensing service. Integrated sensing and communication (ISAC) has been a novel trend due to its possible hardware gain and spectrum gain by the integration of signal communication and sensing capabilities. Appropriate resource allocation strategy is essential for ensuring the quality of both communication and sensing services in terms of ISAC. Existing ISAC-based resource allocation schemes mainly focus on the coexistence of sensing and communication. However, in multi-user ISAC networks, the services required by users are diverse, including sensing-only, communication-only and dual-function cases. To this end, we proposes a resource allocation strategy based on user quality of service(QoS). Specifically, the sum rate of cellular network is maximized via optimizing spectrum resource selection and transmit power, while meeting the sensing QoS. We achieve the optimal solution to the entire problem by first demonstrating that the communication rate in this paper is a monotonically increasing function of sensing power, allowing us to obtain the optimal power allocation scheme. Subsequently, we employ a matching method to obtain the optimal spectrum sharing scheme.

Integrated sensing and communication (ISAC) is a promising technique for unmanned aerial vehicle (UAV)-empowered 6G networks. In this letter, we propose a novel adaptable ISAC mechanism. Firstly, we investigate both the communication and sensing channel model under Doppler and beam-squint effect for the UAV ISAC with the wideband massive multiple-input multiple-output (MIMO) configuration. Then, we propose an adaptive sensing method by the joint designing the signal echo sensing and beam-squint sensing under practical environment. Specifically, when the echo power is larger than the minimum detectable power, the proposed method could realize the effective sensing by utilizing the system model without feedback. Otherwise, the proposed method could exploit the beam-squint for sensing with the feedback of the sub-carrier index. Finally, various simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Indoor navigation networks serve as the foundation for indoor pedestrian navigation services. However, current graph-based topological network models cannot easily represent complex indoor structures in a manner consistent with user behaviour and cognitive patterns, and thus cannot support fine-grained indoor navigation analysis services. To enhance the rationality of the model, we considered the influences of morphological and functional characteristics of indoor spaces on pedestrian movement and proposed a method for classifying navigation spaces. This method, based on convex space segmentation, defines morphological and functional characteristic functions, classifying indoor navigation spaces as either corridors or open spaces, which suitable for representing as median axis models or visual graphs, respectively. These categories supported the creation of topological network models for route planning analysis. Indoor map experiments confirmed the effectiveness of our method in identifying various types of corridors and open spaces, that addressing the limitations of width-based parameters，even in complex indoor settings.

In this paper, we introduce an innovative temporal consistency enhancement approach, which enables image-based models on video data by leveraging a deep-learning-based Kalman Filter. More specifically, we propose a novel Bi-direction Kalman filter strategy, utilizing forward and backward processing to capitalize on higher-quality pose estimations near the camera, enhancing the robustness and precision of vehicle tracking across varying distances and conditions. Then, rather than using the conventional mathematical motion model, we propose a learnable motion model, dubbed Future State Predictor, to represent the complex, non-linear motion patterns observed in vehicles. The experimental results demonstrate that our approach enhances pose accuracy and temporal consistency, which allows us to handle the challenging occluded/distant vehicles.