Motion artifacts (MA) significantly compromise the quality of photoplethysmographic (PPG) signals in wearable devices, hindering the accurate extraction of vital sign parameters. To address the limitations of existing methods in cross-user MA detection and the low accuracy of signal reconstruction, this paper proposes FlexiMA, a two-stage co-optimization framework that operates without reference signals. This framework is designed to tackle two key tasks: MA detection and MA removal. In the detection phase, a task-adaptive diffusion model, IMD-XResNet1D, is developed by integrating meta-learning and diffusion mechanisms, enabling efficient modeling of artifact-related features in new users. In the removal phase, an adversarial variational autoencoder (AVAE) is employed to achieve high-fidelity reconstruction of PPG signals. Experiments conducted on multi-user, multi-scenario datasets demonstrate that FlexiMA achieves 99.68% accuracy in MA detection and maintains a cross-user generalization performance of 99.32%. In the MA removal task, it attains 87.85% waveform structure similarity with an interbeat interval (IBI) error of 4.60 ms, indicating reliable signal reconstruction. These results verify the robustness and adaptability of FlexiMA in dynamic and complex environments, highlighting its strong potential for deployment in wearable applications.

Power production is a complex process that involves multiple interactions, which require rich semantic knowledge to categorize and evaluate. Utilizing high-level image understanding to accurately identify risks is significant in ensuring staff safety and grid stability. In this work, we propose image captioning and semantic text similarity analysis (IC-STSA), a risk identification method for complex power operation scenarios the employs image captioning and semantic text similarity analysis. First, image caption is used to transform an image at the site into the text that best describes the visual data. Next, a safe power production (SPP) knowledge base is established, after which semantic text similarity analysis is introduced to transform the risk identification into a text classification problem. By comparing the image caption information and the SPP knowledge base and identifying similarities, we achieve refined descriptions of operation scenarios and thereby risk identification. In the final step, actual images are used to verify the findings of the IC-STSA. Results show the proposed method performs well, generating accurate, targeted descriptions, and risk identification.

Blood pressure (BP) is a critical physiological parameter closely associated with severe diseases such as heart failure and kidney damage. Current methods either require additional or dedicated hardware, or closing touching to the devices, causing discomfort and inconvenience. Therefore, a convenient, contactless BP measurement solution is highly desired. In this work, we present Ubicon-BP, a ubiquitous, device-free, and contactless BP detection application. Ubicon-BP calculates BP based on the pulse transit time (PTT), a key feature that is medically proven correlated with BP. However, using smartphone sensors to contactless calculate PTT is non-trivial since it requires a micro-second level precision for cardiac event detection. To address this issue, we propose leveraging the acoustic sensors in smartphone to detect vibrations caused by heart valve movements, as well as camera sensors to measure finger pulses. To accurately measure heartbeat signal that are susceptible to motion, we first improve the sensing granularity of acoustic signals and then introduce the IQ-MVED model to eliminate motion interference. Furthermore, when recovering pulse signals from video signals, issues such as poor generalization performance arise. Consequently, we propose the TS-CAN and meta-learning models to obtain personalized pulse signals. Finally, we transform the extracted time-frequency features from the recovered heartbeats and pulse signals to the corresponding BP. Comprehensive testing involving 50 subjects reveal a standard deviation error of$ 4.27 \; \text{mmHg}$for diastolic pressure and$ 6.36 \; \text{mmHg}$for systolic pressure, respectively.

Icing monitoring system within internet of things is developed to provide multimodal data for assessing the degree of icing on transmission lines. Nevertheless, existing methods relying solely on either sensor data or images demonstrate limited precision and inferior fault tolerance. This paper proposes a correlation-based multimodal feature fusion approach to integrate both sensor data and imaging data, thereby enhancing the monitoring of icing severity on transmission lines and addressing the aforementioned problems. The inherent correlation characteristics present in both sensor data and images, as well as the correlation between these two modalities are thoroughly analyzed, to gain a comprehensive understanding of icing characteristics in multimodal data. Specifically, the squeeze-excitation module along with convolutional neural networks are combined to extract the temporal and spatial correlation inherent in sensor data, as well as the spatial and channel correlation inherent in images. Subsequently, the covariance matrix is used to capture the correlation between these two modalities. Moreover, with the assistance of the weight determination structure, this correlation is mapped to the fusion weights. The four-stage training strategy is introduced to guide the network training. The essentiality of the staged training approach and the necessity of correlation extraction in perceiving icing severity are validated in experiments.

The idea of Internet of Things (IoT) has revolutionized the smart grids by providing the opportunity for remote control and monitoring of the distributed generations (DGs), switches, renewable sources and electrical loads. This study investigates the smart grid concept along with IoT in the automation and optimal energy management of these grids incorporating the high uncertainties of the renewable energy sources and plug-in hybrid electric vehicles (PHEVs) charging demand. A private blockchain technology is developed to make secure data transactions. A novel stochastic structure according to the scenario generation is developed to capture the power randomness of these sources. It is tried to assess the switching effect on the energy management based on remote control in the IoT bed. Moreover, a new optimizer using the social spider algorithm (SSA) is proposed which helps in scheduling the smart grid in an economic way. In addition, a modified method is developed which can suitably enhance the SSA convergence and thus get into more optimal switching and scheduling programs. The proposed model is investigated using an IEEE test system.

Partial discharge (PD) is a widely adopted method for the internal insulation detection of electrical transformers. The impact of the refraction and diffraction of the ultrasonic signal in the oil, winding, and core in the transformer makes the localization of the PD source inside the transformer a nontrivial task. The refraction and diffraction factors can introduce additional complexity to the localization equations that can hardly be solved. This article proposes an algorithmic solution based on the time difference of arrival algorithm and semidefinite relaxation convex optimization to improve the PD source localization accuracy. The refraction and diffraction error, measurement error, and their relationship in the process of PD signal propagation are fully considered. The proposed solution is extensively assessed through simulation, testbed, and field experiments, and the results confirm that the proposed solution outperforms the Chan and particle swarm optimization algorithm with the localization error of about 0.1 m.

In the process of UAV line inspection, there may be raindrops on the camera lens. Raindrops have a serious impact on the details of the image, reducing the identification of the target transmission equipment in the image, reducing the accuracy of the target detection algorithm, and hindering the practicability of UAV line inspection technology in cyber‐physical energy systems. In this paper, the principle of raindrop image formation is studied, and a method of raindrop removal based on generation countermeasure network is proposed. In this method, the attention recurrent network is used to generate the raindrop attention map, and the context code decoder is used to generate the raindrop image. The experimental results show that the proposed method can remove the raindrops in the image and repair the background image of raindrop coverage area and can generate a higher quality raindrop removal image than the traditional method.