Safety monitoring of power operations in substations is crucial for accident prevention. However, traditional methods such as wearable devices and video surveillance suffer from limitations including high costs and reliance on lighting conditions. A solution that integrates millimeter-wave radar with deep learning ensures operational compliance by high precision gesture detection. This paper proposes RoPEHAR, a human activity recognition system based on millimeter-wave radar, which combines traditional transformer architecture with rotary positional encoding, specifically designed for human posture recognition in indoor industrial environments. To reduce interference from the coupling of human and instrument signals and noise in electrical scenarios, RoPEHAR introduces a hybrid filtering pipeline that combines hierarchical SNR denoising with enhanced DBSCAN clustering to accurately segment the point cloud data of limbs and instruments. The core innovation lies in the introduction of a spatiotemporal Informer, Roformer. It enhances the 3D-space vector information about data points through dynamic rotary positional encoding. This system effectively models limb motion trajectories. Experiments demonstrate that RoPEHAR achieves high-precision performance, reaching an accuracy of 95.8%, enabling real-time and reliable activity recognition for substation safety monitoring.

Accurate and scalable indoor localization for unmanned aerial vehicles (UAVs) is essential for Internet of Things (IoT) applications such as autonomous logistics, infrastructure inspection, and emergency response in GPS-denied environments. However, traditional methods often struggle with cost, deployment complexity, and sensitivity to environmental dynamics, limiting their practicality for large-scale IoT scenarios. This paper presents a method in which Channel State Information (CSI) from low-cost IoT sensors enables robust, device-free 3D UAV localization while optimizing accuracy, sensor adaptability, and data efficiency. We propose CiUAV, leveraging CSI captured by ESP32-S3 sensors, with a Robust CSI Signal Enhancement (RCSE) framework integrating Dynamic AGC Compensation (DAC) and Adaptive Noise Suppression and Outlier Removal (ANSOR), alongside a Sensor-in-Sample (SiS) multi-objective optimization model for adaptive multi-sensor fusion. Experimental evaluations in realistic indoor settings achieve a 3D root mean squared error (RMSE) of 0.2659 meters, outperforming baselines by up to 35% in accuracy and 50% in data efficiency. CiUAV offers a lightweight, scalable, and infrastructure-compatible solution for future IoT-enabled UAV systems.

Ensuring personnel safety during power station inspections is a critical yet challenging task due to inherent hazards in such environments. Traditional monitoring methods, including wearable devices and video surveillance, suffer from user discomfort, limited visibility, and high deployment costs. To overcome these limitations, this article proposes PowerHAR, a device-free framework for recognizing power inspection activities based on WiFi channel state information (CSI) acquired from custom-designed ESP32 internet of things (IoT) sensors. PowerHAR introduces a spatio-temporal feature distribution-based power operation recognition network, comprising a transformer-based preprocessing module capable of effectively handling variable-length CSI sequences, and a spatio-temporal extraction module that integrates convolutional operations with multihead self-attention mechanisms for comprehensive feature fusion. By leveraging mutual CSI sensing among distributed sensors, PowerHAR provides robust and accurate recognition of power inspection activities without requiring additional hardware infrastructure. Experimental validation demonstrates that PowerHAR significantly surpasses existing baseline methods, confirming its high reliability and practicality in safety-critical industrial scenarios.

Balanced in-core power levels in nuclear power plants (NPPs) are critical for safety, whereas power tilt disrupts this balance, reducing safety margins and posing risks. Early warning for power tilt offers an effective way of optimizing monitoring. Due to abnormal-sample scarcity and security concerns, common data-driven models train on the simulated data generated by simulators. However, achieving a satisfactory effect in practices is difficult because simulators imperfectly emulate reality. Thus, we propose a power tilt-oriented early warning method called simulation–reality spatial–temporal model (SR-STM). Motivated by the physical model in NPPs, a knowledge-guided hierarchical graph is designed to characterize spatial correlations among local power levels for SR-STM’s input. The SR-STM uses a lightweight spatial–temporal network (LST-Net) as a feature extractor, balancing precision, and efficiency. To bridge sim-real interdomain discrepancies, SR-STM utilizes node-alignment adversarial learning (NAAL) for fine weight tuning in subdomain, and eigenvalue-based scale alignment (ESA) for sim-real feature proximity. Forecasting local power levels using the SR-STM, dynamic metrics and alarm limits are calculated and compared to perform the early warning task. The online experimental prototype verifies that SR-STM surpasses various state-of-the-art methods in terms of early warning and sim-real cross-domain tasks.

Neutron detectors in nuclear power plants (NPPs) are critical for system stability, yet their malfunctions may lead to false alerts and misdiagnoses. Multidetectors deployed in diverse positions vary with the nuclear reactor states contained spatial-temporal variations of neutron fluxes. Existing methods seldom concurrently consider intricate spatial-temporal correlations and gradual state variations among detectors. This study proposes a detector-oriented fault detection and isolation method named the spatial-temporal state adaptation model (ST-SAM). The method introduces a local-global spatial-temporal network that captures the potential interdependencies within the detector topology. To minimize cross-state discrepancies in reactors, ST-SAM integrates three submodules: a signal reconstructor to enhance the specific-state variation representation; a correlation alignment to mitigate interstate feature discrepancies; and an adversarial discriminator to extract spatial-temporal state-invariant features. Leveraging the parallel detection strategy, ST-SAM effectively detects and isolates faulty detectors, preventing fault propagation on subsequent diagnosis. Experiments on ex-core and in-core neutron detectors in real-world NPPs with simulated faults verify that the ST-SAM outperforms various state-of-the-art methods in terms of signal reconstruction and fault detection.

Safety monitoring of power operations in power stations is crucial for preventing accidents and ensuring stable power supply. However, conventional methods such as wearable devices and video surveillance have limitations such as high cost, dependence on light, and visual blind spots. WiFi-based human pose estimation is a suitable method for monitoring power operations due to its low cost, device-free, and robustness to various illumination conditions. In this paper, a novel Channel State Information (CSI)-based pose estimation framework, namely PowerSkel, is developed to address these challenges. PowerSkel utilizes self-developed CSI sensors to form a mutual sensing network and constructs a CSI acquisition scheme specialized for power scenarios. It significantly reduces the deployment cost and complexity compared to the existing solutions. To reduce interference with CSI in the electricity scenario, a sparse adaptive filtering algorithm is designed to preprocess the CSI. CKDformer, a knowledge distillation network based on collaborative learning and self-attention, is proposed to extract the features from CSI and establish the mapping relationship between CSI and keypoints. The experiments are conducted in a real-world power station, and the results show that the PowerSkel achieves high performance with a PCK@50 of 96.27%, and realizes a significant visualization on pose estimation, even in dark environments. Our work provides a novel low-cost and high-precision pose estimation solution for power operation.

For intelligent inspection of power lines, a core task is to detect components in aerial images. Currently, deep supervised learning, a data-hungry paradigm, has attracted great attention. However, considering real-world scenarios, labeled data are usually limited, and the utilization of abundant unlabeled data is rarely investigated in this field. This study deploys a pretrained model for power line component detection based on a self-supervised pretraining approach, which exploits useful information from unannotated data. Concretely, we design a new masking strategy based on the structural characteristic of power lines to guide the pretraining process with meaningful semantic content. Meanwhile, a Siamese architecture is proposed to extract complete global features by using dual reconstruction with semantic targets provided by the proposed masking strategy. Then, the knowledge distillation is utilized to enable the pretrained model to learn both domain-specific and general representations. Moreover, a feature pyramid mechanism is adopted to capture multiscale features, which can benefit the detection task. Experimental results show that the proposed approach can successfully improve the performance of a variety of detection frameworks for power line components, and outperforms other self-supervised pretraining methods.

The concentration of dissolved gases in transformer oil can be utilized to diagnose faults in transformers. However, substandard online monitoring data may lead to inaccurate fault diagnosis outcomes, resulting in severe repercussions. Hence, this study presents a novel approach for anomaly detection in dissolved gas online monitoring data in transformer oil using stacking ensemble learning. Firstly, a sliding time window is employed to preprocess the monitoring data and generate a dataset consisting of time series monitoring data. Subsequently, evaluation metrics and diversity measures are applied to select distinct base learners and a meta-learner for the stacking model. This approach amalgamates the strengths and disparities of various learners. Lastly, comparative analysis of case studies demonstrates the effectiveness of the proposed method in distinguishing different types of anomalies in dissolved gas online monitoring data, exhibiting superior performance in terms of accuracy, F1 score, and area under curve(AUC).

Accurate indoor pedestrian localization and tracking are crucial in many practical applications. One efficient yet low-cost sensing scheme is the integration of inertial measurement unit and WiFi received signal strength (RSS) due to the popularity of smart devices and WiFi networks. Many approaches have been proposed to enhance the localization performance. However, they heavily rely on prerequisites, including prior knowledge (e.g., map information) and beacon corrections, which degrades the generalization of the approaches and their accuracy in complex environments. To address this issue, in this article, we propose a novel localization approach named VariFi, which incorporates variational inference techniques to estimate the location of pedestrian. Variational inference is applied in this work, whose inference network can produce accurate estimates as its parameters are optimized in terms of the reconstruction loss and regularization loss in real time. A signal map is constructed to provide a conditional RSS distribution at any given location, which is further applied to generate the reconstruction loss based on the real measurements. Also, a filtering mechanism is designed to reduce local optimum cases in optimization by utilizing the prior estimate and RSS fingerprinting estimate. In addition, VariFi can be further applied to conduct online optimization following the existing localization approaches. We conduct experiments, including static localization and trajectory estimation scenarios to validate the performance of our approach. The trajectory estimation results show that our approach outperforms the mainstream approaches in terms of both localization accuracy and robustness, respectively. Furthermore, the combination of existing approaches and VariFi has also been validated effectively in the experiments of two environments, where VariFi has the ability to bring enhanced localization accuracy.

Low-resolution infrared-based human activity recognition (HAR) attracted enormous interests due to its low cost and private. In this article, a novel semi-supervised cross-domain neural network (SCDNN) based on$8\times8$low-resolution infrared sensor is proposed for accurately identifying human activity despite changes in the environment at a low cost. The SCDNN consists of feature extractor, domain discriminator, and label classifier. In the feature extractor, the unlabeled and minimal labeled target domain data are trained for domain adaptation to achieve a mapping of the source domain and target domain data. The domain discriminator employs the unsupervised learning to migrate data from the source domain to the target domain. The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data. Experimental results show that the proposed method achieves 92.12% accuracy for recognition of activities in the target domain by migrating the source and target domains. The proposed approach adapts superior to cross-domain scenarios compared to the existing deep learning methods, and it provides a low cost yet highly adaptable solution for cross-domain scenarios.

Detecting the components in aerial images is a crucial task in automatic visual inspection for power lines. Currently, deep learning models guided by external knowledge have achieved promising performances compared to directly applying the benchmark detectors. However, the component relationship, as human commonsense knowledge for object reasoning, is rarely investigated in this field. This study presents a graph-based relation guided network for power line component detection, which exploits correlations of regions, images, and categories. The visual relation module is employed to learn region-to-region relationship and enhance the visual features of each proposal that may contain components. Meanwhile, two guidance modules are proposed to capture image-to-region correlation and distinctively facilitate the category classification and position regression, which has not been considered in previous methods. Moreover, the category graphs built in these two modules are able to explore category-to-category dependencies that can further promote the network ability. Experimental results demonstrate that the proposed method can achieve more accurate and reasonable component detection compared to previous methods, which verifies the effectiveness of the proposed model incorporated with relation knowledge.

Abstract Digital imaging and image‐processing techniques have revolutionized the way of power line inspection in recent years. Massive images are captured and utilized for further processing to maintain the reliability, safety, and sustainability of power transmission. For power line inspection, the component region in the delivered images is required to be centered, large, and clear enough. In this paper, a component‐oriented image quality assessment method is proposed to automatically predict image quality according to the demand of power line inspection. The proposed method considers two factors: spatial characteristic evaluation and sharpness evaluation. The spatial characteristic evaluation utilizes YOLOv3 to evaluate whether the component region is sufficiently centered and large, which enables the observer to quickly find the target. The sharpness evaluation employs ResNet to evaluate the clarity of component and makes the condition monitoring more accurately. For final quality assessment, a multi‐stage filtering strategy is presented to aggregate these two factors and obtain high quality inspection images. The experimental results indicate that the high‐quality images can be accurately identified to satisfy the requirements of power line inspection. The proposed quality assessment method enhances the efficiency for further data analysis of aerial images.

Aiming at measuring tree barriers in transmission line corridors, a binocular vision ranging method is proposed to measure the distance between the transmission lines and trees. Based on the principle of binocular vision ranging, the binocular camera is calibrated using a marked checkerboard as a calibration board. Then, the SAD region matching algorithm is applied to the preprocessed images, and the disparity map is acquired. Finally, the distance between the tree and the transmission line can obtain according to the three-dimensional coordinate information of two target points. The results of the experiments show that the proposed binocular vision-based method can achieve the measurement error within ±30cm. The precision can meet the need of measuring the distance between trees and the transmission lines, which is an effective way for warning tree barriers in transmission line corridors.

Intelligent inspection in the substation transformer using optical character recognizer has been developing rapidly. Character segmentation from the text line of data plate is an important step for localization and recognition of electrical equipment. However, on-site character segmentation is challenging if the data plate contains multiple languages, especially when the width between Chinese and non-Chinese character differs significantly and the complex environments cause the light reflection and fading. This paper proposes a new method, based on analyzing the connected component and Chinese character's structure, to segment characters from multi-language data plate of substations. The proposed method uses the combination of the HSV color space and multi-scale MSRCP to reduce the effect of illumination and complex background. The proposed method utilized the width of each kind character, the interval between characters and the relationship within the left-right structure Chinese character to improve the segmentation accuracy. Experimental results show that the text lines from the data plate in substation transformer, including Chinese, English, Roman numerals, Arabic numerals and symbols, can be segmented correctly. Results show that the proposed method outperforms two existing character segmentation methods and achieves 99.4% precision in the multi-language data plate dataset.

In the era of Internet of Things (IoT), human activity recognition is becoming the vital underpinning for a myriad of emerging applications in smart home and smart buildings. Existing activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose DeepSense, a device-free human activity recognition scheme that can automatically identify common activities via deep learning using only commodity WiFi-enabled IoT devices. We design a novel OpenWrt-based IoT platform to collect Channel State Information (CSI) measurements from commercial IoT devices. Moreover, an innovative deep learning framework, Autoencoder Long-term Recurrent Convolutional Network (AE-LRCN), is proposed. It consists of an autoencoder module, a convolutional neural network (CNN) module and a long short-term memory (LSTM) module, which aims to sanitize the noise in raw CSI data, extract high-level representative features and reveal the inherent temporal dependencies among data for accurate human activity recognition, respectively. All the hyperparameters in AE-LRCN are fine-tuned end-to-end automatically. Extensive experiments are conducted in typical indoor environments and the experimental results demonstrate that DeepSense outperforms existing methods and achieves a 97.6% activity recognition accuracy without human intervention.

Device-free activity recognition is appealing in smart home applications. It not only is convenient, but also causes no privacy concern, as compared to other activity recognition techniques such as the vision based technique. Existing WiFi-based methods have achieved high accuracy in static circumstances but have limitations in adapting changes in environment and activities locations. In this paper, we propose a fine-grained adaptive location-independent activity recognition system (FALAR) which leverages WiFi signals to characterize and recognize common activities regardless of inconsistency of mutative surroundings. FALAR applies fine-grained channel state information (CSI) to achieve accurate recognitions. To address the issue of environmental changes, we present a Kernel Density Estimation (KDE) based motion extraction method and a coarse-to-fine search strategy for speedy processing. After a denoising scheme, we introduce Class Estimated Basis Space Singular Value Decomposition (CSVD) to efface the static path in the background, and use nonnegative matrix factorization to distinguish various activities by looking into the signal profiles. We evaluate FALAR using two commodity WiFi routers in a typical office environment. Our results show that it achieves remarkable performance.

Intelligent occupancy sensing is becoming a vital underpinning for various emerging applications in smart homes, such as security surveillance and human behavior analysis. However, prevailing approaches mainly rely on video camera, ambient sensors, or wearable devices, which either requires arduous deployment or arouses privacy concerns. In this paper, we present a novel real-time, device-free, and privacy-preserving WiFi-enabled Internet of Things platform for occupancy sensing, which can promote a myriad of emerging applications. It is designed to achieve an optimal tradeoff between performance and scalability. Our system empowers commercial off-the-shelf WiFi routers to collect channel state information (CSI) measurements and provides an efficient cloud server for computing via a lightweight communication protocol. To demonstrate the usefulness of our platform, an occupancy detection system is developed by exploiting the CSI curve of human presence. Furthermore, we also design an innovative activity recognition system based on our platform and machine learning techniques with high availability and extensibility. In the evaluation, the experimental results show that our platform enables these applications efficiently, with the accuracy of 96.8% and 90.6% in terms of occupancy detection and recognition, respectively.

Accurate Location Based Service (LBS) is one of the fundamental but crucial services in the era of Internet of Things (IoT). WiFi fingerprinting-based Indoor Positioning System (IPS) has become the most promising solution for indoor LBS. However, the offline calibrated received signal strength (RSS) radio map is unable to provide consistent LBS with high localization accuracy under various environmental dynamics. To address this issue, we propose TKL-WinSMS as a systematic strategy, which is able to realize robust and adaptive indoor localization in dynamic indoor environments. We developed a WiFi-based Non-intrusive Sensing and Monitoring System (WinSMS) that enables WiFi routers as online reference points by extracting real-time RSS readings among them. With these online data and labeled source data from the offline calibrated radio map, we further combine the RSS readings from target mobile devices as unlabeled target data, to design a robust localization model using an emerging transfer learning algorithm, namely transfer kernel learning (TKL). It is able to learn a domain-invariant kernel by directly matching the source and target distributions in the reproducing kernel Hilbert space instead of the raw noisy signal space. The resultant kernel can be used as input for the SVR training procedure. In this manner, the trained localization model can inherit the information from online phase to adaptively enhance the offline calibrated radio map. Extensive experiments were conducted and demonstrated that the proposed TKL- WinSMS is able to improve the localization accuracy by at least 26% compared with existing solutions under various environmental interferences.

WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.

In the era of Internet of Things, WiFi fingerprinting based indoor positioning system (IPS) has been recognized as the most promising IPS for indoor location-based service. Fingerprinting-based algorithms critically rely on a fingerprint database built from machine learning methods, and extreme machine learning (ELM) is preferred for its fast training speed. However, traditional WiFi based IPS usually requires a central server to collect and process data, which is tremendously vulnerable to server breakdown and communication link failure. To address this issue, we propose Consensus-based Parallel ELM (CPELM) to enhance the robustness by distributing the data on different computation nodes. Specifically, each node keeps updating the corresponding terms in the ELM regression equation as a weighted average of those from neighboring nodes based on the distributed consensus iterative scheme. Upon the agreement of the regression equation within the network, the output weight of ELM can be calculated on some nodes and propagated to other nodes. Extensive simulation with real data has demonstrated that CPELM is able to produce same level of localization accuracy as centralized ELM without incurring additional computational cost, and in the meanwhile provides more robustness to the entire IPS in case of server breakdown and link failures.

Existing WiFi fingerprinting-based Indoor Positioning System (IPS) suffers from the vulnerability of environmental dynamics. To address this issue, we propose TKL-WinSMS as a systematic strategy, which is able to realize robust and adaptive localization in dynamic indoor environments. We developed a WiFi-based Non-intrusive Sensing and Monitoring System (WinSMS) that enables COTS WiFi routers as online reference points by extracting real-time RSS readings among them. With these online data and labeled source data from the offline calibrated radio map, we further combine the RSS readings from target mobile devices as unlabeled target data, to design a robust localization model using an emerging transfer learning algorithm, namely transfer kernel learning (TKL). It can learn a domain-invariant kernel by directly matching the source and target distributions in the reproducing kernel Hilbert space instead of the raw noisy signal space. By leveraging the resultant kernel as input for the SVR training, the trained localization model can inherit the information from online phase to adaptively enhance the offline calibrated radio map. Extensive experimental results verify the superiority of TKL-WinSMS in terms of localization accuracy compared with existing solutions in dynamic indoor environments.

The explosive proliferation of mobile devices and the popularity of social networks have spurred extensive demands on Location Based Services (LBSs) in recent decades. The IEEE 802.11 (WiFi) based Indoor Positioning Systems (IPSs) are gaining popularity because of the wide and ubiquitous availability of WiFi infrastructures in indoor environments. Most of IPSs are adopting the fingerprinting approach to mitigate pervasive indoor multipath effects. However, the heterogeneity of mobile devices significantly degrades the localization performance of the fingerprinting approach. In this paper, we apply the Procrustes analysis method to transform the WiFi received signal strengths (RSSs) to a new type of standard location fingerprints which are tolerant of the heterogeneity of various devices. Then, a robust indoor positioning algorithm based on the standardized location fingerprints and the weighted k nearest neighbor (WKN-N) method is proposed. Extensive experiments are carried out and show that the standardized location fingerprints and the proposed positioning system address the device heterogeneity issue satisfactorily.

Accurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. Some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibration phases, or need to install bespoke hardware such as CCTV cameras, which are privacy-intrusive by default. In this paper, we present the design and implementation of a practical end-to-end occupancy inference system, which requires minimum user effort, and is able to infer room-level occupancy accurately with commodity WiFi infrastructure. Depending on the needs of different occupancy information subscribers, our system is flexible enough to switch between snapshot estimation mode and continuous inference mode, to trade estimation accuracy for delay and communication cost. We evaluate the system on a hardware testbed deployed in a 600m2workspace with 25 occupants for 6 weeks. Experimental results show that the proposed system significantly outperforms competing systems in both inference accuracy and robustness.

Indoor positioning system (IPS) has become one of the most attractive research fields due to the increasing demands on location-based services (LBSs) in indoor environments. Various IPSs have been developed under different circumstances, and most of them adopt the fingerprinting technique to mitigate pervasive indoor multipath effects. However, the performance of the fingerprinting technique severely suffers from device heterogeneity existing across commercial off-the-shelf mobile devices (e.g., smart phones, tablet computers, etc.) and indoor environmental changes (e.g., the number, distribution and activities of people, the placement of furniture, etc.). In this paper, we transform the received signal strength (RSS) to a standardized location fingerprint based on the Procrustes analysis, and introduce a similarity metric, termed signal tendency index (STI), for matching standardized fingerprints. An analysis of the capability of the proposed STI to handle device heterogeneity and environmental changes is presented. We further develop a robust and precise IPS by integrating the merits of both the STI and weighted extreme learning machine (WELM). Finally, extensive experiments are carried out and a performance comparison with existing solutions verifies the superiority of the proposed IPS in terms of robustness to device heterogeneity.

Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset classification applications. It has been broadly embedded in many applications due to its fast speed of computation and accuracy. How to make good use of machine learning techniques in Indoor Positioning System (IPS) is a hot research topic in recent years. Some existing IPSs have already adopted ELM, but it suffers from signal variation and environmental dynamics in indoor settings. In this paper, extreme learning machine with dead zone (DZ-ELM) is proposed to address this problem. The consistency of this approach should be applied is studied. Simulations are also conducted to compare the performance of DZ-ELM and ELM. Lastly, real-world experimental results show that the proposed algorithm can not only provide higher accuracy but also improve the repeatability of IPSs.