Search Results (65)

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

Unmanned aerial vehicles are increasingly used in civil Search and Rescue operations due to their rapid deployment and wide-area coverage capabilities. However, detecting missing persons from aerial imagery remains challenging due to small object sizes, cluttered backgrounds, and limited onboard computational resources, especially when managed by civil agencies. In this work, we present a comprehensive methodology for optimizing YOLO-based object detection models for real-time Search and Rescue scenarios. A two-stage transfer learning strategy was employed using VisDrone for general aerial object detection and Heridal for Search and Rescue-specific fine-tuning. We explored various architectural modifications, including enhanced feature fusion (FPN, BiFPN, PB-FPN), additional detection heads (P2), and modules such as CBAM, Transformers, and deconvolution, analyzing their impact on performance and computational efficiency. The best-performing configuration (YOLOv5s-PBfpn-Deconv) achieved a mAP@50 of 0.802 on the Heridal dataset while maintaining real-time inference on embedded hardware (Jetson Nano). Further tests at different flight altitudes and explainability analyses using EigenCAM confirmed the robustness and interpretability of the model in real-world conditions. The proposed solution offers a viable framework for deploying lightweight, interpretable AI systems for UAV-based Search and Rescue operations managed by civil protection authorities. Limitations and future directions include the integration of multimodal sensors and adaptation to broader environmental conditions. Full article

Today’s power grids are being modernized with the integration of new technologies, making them increasingly efficient, secure, and flexible. One of these technologies, which is beginning to make great contributions to distribution systems, is solid-state transformers (SSTs), motivating the present technical and economic study of local level 2 distribution systems in Colombia. Taking into account Resolution 015 of 2018 issued by the Energy and Gas Regulatory Commission (CREG), which establishes the economic and quality parameters for the remuneration of electricity operators, the possibility of using these new technologies in electricity networks, particularly distribution networks, was studied. The methodology for developing this study consisted of creating a reference framework describing the topologies implemented in local distribution systems (LDSs), followed by a technical and economic evaluation based on demand management and asset remuneration through special construction units, providing alternatives for the digitization and modernization of the Colombian electricity market. The research revealed the advantages of SST technologies, such as reactive power compensation, surge protection, bidirectional flow, voltage drops, harmonic mitigation, voltage regulation, size reduction, and decreased short-circuit currents. These benefits can be leveraged by distribution network operators to properly manage these types of technologies, allowing them to be better prepared for the transition to smart grids. Full article

Abnormal operations, such as faults occurring in an electrical power system (EPS), disrupt its balanced operation, posing potential hazards to human lives and the system’s equipment. Effective monitoring, control, protection, and coordination are essential to mitigate these risks. The complexity of these processes is further compounded by the presence of intermittent distributed energy resources (DERs) in active distribution networks (ADNs) with bidirectional power flow, which introduces a fast-changing dynamic aspect to the system. The deployment of phasor measurement units (PMUs) within the EPS as highly responsive equipment can play a pivotal role in addressing these challenges, enhancing the system’s resilience and reliability. However, synchrophasor measurement-based studies and analyses of power system phenomena may be hindered by the absence of PMU blocks in certain simulation tools, such as PSCAD, or by the existing PMU block in Matlab/Simulink R2021b, which exhibit technical limitations. These limitations include providing only the positive sequence component of the measurements and lacking information about individual phases, rendering them unsuitable for certain measurements, including unbalanced and non-symmetrical fault operations. This study proposes a new reliable PMU model in Matlab and tests it under normal and abnormal conditions, applying real-time simulation and controller-hardware-in-the-loop (CHIL) techniques. Full article

With the increasing significance of deep-sea resource development, Forward-Looking Sonar (FLS) has become an essential technology for real-time environmental mapping and navigation in deep-sea mining vehicles (DSMV). However, FLS images often suffer from a limited field of view, uneven imaging, and complex noise sources, making single-frame images insufficient for providing continuous and complete environmental awareness. Existing mosaicking methods typically rely on external sensors or controlled laboratory conditions, often failing to account for the high levels of uncertainty and error inherent in real deep-sea environments. Consequently, their performance during sea trials tends to be unsatisfactory. To address these challenges, this study introduces a robust FLS image mosaicking framework that functions without additional sensor input. The framework explicitly models the noise characteristics of sonar images captured in deep-sea environments and integrates bidirectional cyclic consistency filtering with a soft-weighted feature refinement strategy during the feature-matching stage. For image fusion, a radial adaptive fusion algorithm with a protective frame is proposed to improve edge transitions and preserve structural consistency in the resulting panoramic image. The experimental results demonstrate that the proposed framework achieves high robustness and accuracy under real deep-sea conditions, effectively supporting DSMV tasks such as path planning, obstacle avoidance, and simultaneous localization and mapping (SLAM), thus enabling reliable perceptual capabilities for intelligent underwater operations. Full article

With the increasing global focus on sustainable development, circulating fluidized bed (CFB) boilers, as highly efficient and low-pollution combustion equipment, play an important role in energy production and environmental protection. However, the combustion efficiency and emission control of CFB boilers still face challenges, and there is an urgent need for multi-objective optimization through advanced technologies to support the goal of sustainable development. This study proposes an intelligent framework integrating Bidirectional Temporal Convolutional Network (BiTCN) and Hybrid Dung Beetle Optimizer (HDBO) for multi-objective optimization of combustion efficiency and NOx/SO₂ emissions in CFB boilers. The BiTCN model captures bidirectional temporal dependencies between dynamic parameters (e.g., air-coal ratio, bed temperature) and target variables through operational data analysis. Three key improvements are implemented in DBO: (1) Chaotic initialization via sequential pattern mining (SPM) enhances population diversity and spatial coverage; (2) The osprey optimization algorithm (OOA) hunting mechanism replaces the original rolling update strategy, improving global exploration; (3) t-Distribution perturbation is applied to foraging beetles in later iterations, leveraging its “sharp peak and thick tail” characteristics to dynamically balance exploitation and exploration. Experimental results demonstrate 0.5–1% combustion efficiency improvement and 15.1%/30% reductions in NOx/SO₂ emissions for a typical CFB boiler. Full article

The protection of personally identifiable information (PII) is being increasingly demanded by customers and governments via data protection regulations. Private and public organizations store and exchange through the Internet a large amount of data that include the personal information of users, employees, and customers. While discovering PII from a large unstructured text corpus is still challenging, a lot of research work has focused on identifying methods and tools for the detection of PII in real-time scenarios and the ability to discover data exfiltration attacks. In those research attempts, natural language processing (NLP)-based schemas are widely adopted. Our work combines NLP with deep learning to identify PII in unstructured texts. NLP is used to extract semantic information and the syntactic structure of the text. This information is then processed by a pre-trained Bidirectional Encoder Representations from Transformers (BERT) algorithm. We achieved high performance in detecting PII, reaching an accuracy of 99.558%. This represents an improvement of 7.47 percentage points over the current state-of-the-art model that we analyzed. However, the experimental results show that there is still room for improvement to obtain better accuracy in detecting PII, including working on a new, balanced, and higher-quality training dataset for pre-trained models. Our study contributions encourage researchers to enhance NLP-based PII detection models and practitioners to transform those models into privacy detection tools to be deployed in security operation centers. Full article

Temporal random noise (TRN) in uncooled infrared detectors significantly degrades image quality. Existing denoising techniques primarily address fixed-pattern noise (FPN) and do not effectively mitigate TRN. Therefore, a novel TRN denoising approach based on total variation regularization and low-rank tensor decomposition is proposed. This method effectively suppresses temporal noise by introducing twisted tensors in both horizontal and vertical directions while preserving spatial information in diverse orientations to protect image details and textures. Additionally, the Laplacian operator-based bidirectional twisted tensor truncated nuclear norm (bt-LPTNN), is proposed, which is a norm that automatically assigns weights to different singular values based on their importance. Furthermore, a weighted spatiotemporal total variation regularization method for nonconvex tensor approximation is employed to preserve scene details. To recover spatial domain information lost during tensor estimation, robust principal component analysis is employed, and spatial information is extracted from the noise tensor. The proposed model, bt-LPTVTD, is solved using an augmented Lagrange multiplier algorithm, which outperforms several state-of-the-art algorithms. Compared to some of the latest algorithms, bt-LPTVTD demonstrates improvements across all evaluation metrics. Extensive experiments conducted using complex scenes underscore the strong adaptability and robustness of our algorithm. Full article

The emergence of the Internet of Things (IoT) technologies has greatly enhanced the lives of individuals with disabilities by leveraging radio frequency identification (RFID) systems to improve autonomy and access to essential services. However, these advancements also pose significant security risks, particularly through side-channel attacks that exploit weaknesses in the design and operation of RFID tags and readers, potentially jeopardizing sensitive information. To combat these threats, several solutions have been proposed, including advanced cryptographic protocols built on cryptographic algorithms such as elliptic curve cryptography. While these protocols offer strong protection and help minimize data leakage, they often require substantial computational resources, making them impractical for low-cost RFID tags. Therefore, it is essential to focus on the efficient implementation of cryptographic algorithms, which are fundamental to most encryption systems. Cryptographic algorithms primarily depend on various finite field operations, including field multiplication, field inversion, and field division. Among these operations, field multiplication is especially crucial, as it forms the foundation for executing other field operations, making it vital for the overall performance and security of the cryptographic framework. The method of implementing field multiplication operation significantly influences the system’s resilience against side-channel attacks; for instance, implementation using unidirectional systolic array structures can provide enhanced error detection capabilities, improving resistance to side-channel attacks compared to traditional bidirectional multipliers. Therefore, this research aims to develop a novel unidirectional systolic array structure for the Dickson basis multiplier, which is anticipated to achieve lower space and power consumption, facilitating the efficient and secure implementation of computationally intensive cryptographic algorithms in RFID systems with limited resources. This advancement is crucial as RFID technology becomes increasingly integrated into various IoT applications for individuals with disabilities, including secure identification and access control. Full article

A matrix converter (MC) converts an AC source voltage into a variable-voltage variable-frequency AC output voltage (direct AC-AC) without an intermediate DC-link capacitance. By eliminating the traditional DC-link capacitor, MCs can achieve higher power densities and reliability when compared to conventional AC-DC-AC converters. MCs also offer the following characteristics: total semiconductor solution, sinusoidal input and output currents, bidirectional power flow and controllable input power factor. This paper reviews the history, recent developments and commercialization of MCs and discusses several technical requirements and challenges, including bidirectional switches, wide bandgap (WBG) opportunities using GaN and SiC, overvoltage protection, electromagnetic interference (EMI) and ride-through in motor drive applications. MC design solutions and operation are discussed, including a comparison of control and modulation techniques as well as the detailed development of space vector modulation (SVM) to provide a deep insight into the control implementation and results. The paper concludes with compelling motor drive innovation opportunities made possible by advanced MCs including fully integrated and multiphase systems. For conventional MCs, size reductions of 30% are reported, as well as efficiencies of 98% and low input current total harmonic distortion of 3–5%. Full article

Effective management of malignant weeds is critical to soybean growth. This study focuses on addressing the critical challenges of targeted spraying operations for malignant weeds such as Cirsium setosum, which severely threaten soybean yield in soybean fields. Specifically, this research aims to tackle key issues in plant protection operations, including the precise identification of weeds, the lightweight deployment of segmentation models, real-time requirements for spraying operations, and the generalization ability of models in diverse field environments. To address these challenges, this study proposes an improved weed instance segmentation model based on YOLOv8s-Seg, named FCB-YOLOv8s-Seg, for targeted spraying operations in soybean fields. The FCB-YOLOv8s-Seg model incorporates a lightweight backbone network to accelerate computations and reduce model size, with optimized Squeeze-and-Excitation Networks (SENet) and Bidirectional Feature Pyramid Network (BiFPN) modules integrated into the neck network to enhance weed recognition accuracy. Data collected from real soybean field scenes were used for model training and testing. The results of ablation experiments revealed that the FCB-YOLOv8s-Seg model achieved a mean average precision of 95.18% for bounding box prediction and 96.63% for segmentation, marking an increase of 5.08% and 7.43% over the original YOLOv8s-Seg model. While maintaining a balanced model scale, the object detection and segmentation accuracy of this model surpass other existing classic models such as YOLOv5s-Seg, Mask-RCNN, and YOLACT. The detection results in different scenes show that the FCB-YOLOv8s-Seg model performs well in fine-grained feature segmentation in complex scenes. Compared with several existing classical models, the FCB-YOLOv8s-Seg model demonstrates better performance. Additionally, field tests on plots with varying weed densities and operational speeds indicated an average segmentation rate of 91.30%, which is 6.38% higher than the original model. The proposed algorithm shows higher accuracy and performance in practical field instance segmentation tasks and is expected to provide strong technical support for promoting targeted spray operations. Full article

Bismuth (Bi) can be considered for use as a green substitute for lead in bearing applications. However, accelerated Bi oxidation can occur during operation, creating a brittle surface and resulting in premature seizure failure. Thus, the aim of this study was to evaluate the influence of engine oil degradation, especially nitration processes, on the oxidation of Bi. Tailor-made artificially aged oils with different degrees of nitration were produced and utilized in static bearing oxidation tests. By means of X-ray photoelectron spectroscopy (XPS), the Bi surfaces were analyzed regarding their chemical compositions after the tests. The results were correlated with the respective oil conditions determined via conventional parameters as well as high-resolution mass spectrometry. The findings obtained revealed a direct correlation between the amount of Bi-oxide and the nitration values of the oil, proving there was a positive impact of nitration products on the oxidation of the Bi surfaces. A comparison with the Bi content in the oils demonstrated a protective effect of the oxide layer as the Bi content declined with an increase in nitration. Overall, valuable insight into understanding the impact of oil condition on engine parts is given, and the importance of testing engine parts with aged lubricants is emphasized. Full article

A direct bidirectional current discharge path between the input/output (I/O) and ground (GND) is essential for the robust protection of charging device models (CDM) in the tightly constrained design parameters of advanced low-voltage (LV) processes. Dual-direction silicon controlled rectifiers (DDSCRs) serve as ESD protection devices with high efficiency unit area discharge, enabling bidirectional electrostatic protection. However, the high trigger voltage of conventional DDSCR makes it unsuitable for ASICs used for the preamplification of biomedical signals, which only operate at low supply voltage. To address this issue, a self-biased triggered DDSCR (STDDSCR) structure is proposed to further reduce the trigger voltage. When the ESD pulse comes, the external RC trigger circuit controls the PMOS turn-on by self-bias, and the current release path is opened in advance to reduce the trigger voltage. As the ESD pulse voltage increases, the SCR loop opens to establish positive feedback and drain the amplified current. Additionally, the junction capacitance is decreased through high-resistance epitaxy and low-concentration P-well injection to further lower the trigger voltage. The simulation results of LTspice and TCAD respectively demonstrate that ESD devices can clamp transient high voltages earlier, with low parasitic capacitance and leakage current suitable for ESD protection of high-speed ports up to 1.5 V under normal operating conditions. Full article

The electrical protection of active distribution networks is crucial for ensuring reliable, safe, and flexible operations. However, protecting these networks presents several challenges due to the emergence of bi-directional power flows, network reconfiguration capabilities, and changes in fault current levels resulting from the integration of inverter-based resources. This paper introduces an innovative protection strategy for active distribution networks, leveraging the principles of distributed coordination and multi-agent systems. The proposed strategy consists of two stages. The first stage involves a fault detection algorithm that relies solely on local measurements, while the second stage uses agent classification to compute the optimal operating time based on a dynamic matrix representation of the fault path, combined with a simplified distributed optimization problem. The coordination process is formulated as a set of linear optimization problems, simplifying the solution. The proposed protection strategy is validated in a real-time simulation environment using a modified CIGRE MV European grid as a case study, considering low-impedance symmetric fault scenarios and topological changes. The results demonstrate that the protection scheme exhibits robust performance, enhancing the adaptability of the protection equipment while ensuring suitable sensitivity and operational speed. Full article

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG). Full article