This article proposes a cooperative energy market model for an active Distribution Network (DN) by using the theory of Generalized Nash Bargaining (GNB). The proposed energy market has three types of participants: the DN operator, buyers, and sellers. Two energy trading manners are allowed at the same time: 1) each of buyers/sellers trades energy with the DN operator; 2) buyers and sellers trade energy in a Peer-to-Peer (P2P) manner and pay network usage fees to the DN operator. In the market, the DN operator actively manages voltage and reactive power (Volt-VAR) via controlling on-load tap changers of transformers and shunt capacitors. The payments among participants are subject to price constraints. The GNB problem for the proposed market is formulated and then decomposed into two subproblems: social welfare maximization problem (P1) and energy trading problem (P2). Both P1 and P2 are nonconvex problems. Next, linearization techniques are employed and a grid propagation algorithm is developed, transforming P1 and P2 into their equivalent Mixed-Integer Linear Programming (MILP) problems. In simulation, the proposed market model is compared with other GNB-based market models. The results show that the proposed one can significantly increase social welfare through Volt-VAR control and also can maximize the extent of fairness of profit allocation under the price constraints.

Mobile Edge Computing (MEC) networks have recently developed to integrate renewable sources for providing sustainable and low-cost computation/energy services. However, one challenging problem is how to guarantee the stable and effective services for numerous devices under highly intermittent renewable generations and variable computation/energy demands. In this paper, we consider to co-deploy the edge servers and energy storage at micro Base Station (BS) in each edge sub-network. In the considered scenario, we first determine the number of edge servers and energy storage units at different sub-edge networks in a long-term period. Then, we jointly design the offloading and demand response policies for all micro BSs in a centralized manner. Considering the variabilities of renewable energy and computation/energy demands, the system cost is minimized while meeting the energy demand-supply balance and renewable portfolio standards. Furthermore, a distributed optimization problem is formulated to find the optimal offloading parameters, the amount of charging/discharging energy and utilized renewable energy of the micro BS without handing over all operational information. Numerical results show that the proposed deployment and policies can provide effective offloading and energy saving strategies for the proposed mobile edge network.

Wearable electroencephalogram (EEG) devices have shown great potential in enabling real-time monitoring of subtle changes in brain activity, providing new possibilities for the assessment and management of chronic pain. However, recognizing pain-related biomarkers from EEG data remains a complex, multi-task challenge. Most existing research focuses on single-task approaches and rarely addresses this issue within a unified framework. In this paper, we propose a novel deep neural network (DNN) model called multiscale frequency domain MetaFormer (MFDFormer), which is designed to simultaneously predict the presence, type, and intensity of chronic pain. The proposed MFDFormer comprises two primary sub-networks: multiscale feature extractor (MFE) and frequency domain MetaFormer (FDFormer) encoder. The MFE extracts diverse EEG features through convolutions with different kernel sizes, while a self-attention mechanism is integrated into MFE to emphasize the importance of inter-dependency among these features. The FDFormer encoder refines the output MFE features using causal convolutions to capture local patterns and projects them into a higher-dimensional representation domain. Additionally, it incorporates spatial and temporal frequency domain learners in parallel to effectively capture the spatial-temporal information of EEG data. Based on the publicly available BFCP dataset, the proposed MFDFormer demonstrates superior performance over state-of-the-art algorithms, achieving accuracies of 97.59%, 96.02%, and 83.48% in pain or non-pain state classification (P/NSC), pain type classification (PTC), and pain intensity classification (PIC) tasks, respectively. This paper proposes a unified, end-to-end DNN-based framework for multi-task chronic pain recognition, providing a reliable solution with the potential to advance pain diagnosis and management in IoT and smart wearable applications.

Long-term and mobile healthcare applications have increased the use of single-channel electroencephalogram (EEG) systems. However, electromyography (EMG) artifacts often disturb EEGs. The lack of spatial correlation, diversity of waveforms, and time-varying overlap make eliminating EMG interference from a single-channel EEG difficult. To overcome these challenges, we create dual-stream attention-based temporal convolution network (DSATCN), a dual-stream learning model that makes use of multilevel and multiscale temporal dependencies in different frequency bands to perform robust EEG reconstruction. The first DSATCN stream extracts low-frequency band EEG features with reduced EMG interference. The second stream selectively combines the high-level features of the first stream with its own low-level features to refine the EEG reconstruction across the entire frequency band, lowering the risk of overfitting. Both streams employ a novel attention-based temporal convolution network (ATCN) to adaptively separate the overlapping features of EEGs and EMGs. The ATCN has multiple stages to represent various temporal dependencies at different levels. Each stage consists of multiscale dilated convolutions and fast Fourier transform modulations, which efficiently enrich the receptive fields and establish global self-attention mechanisms. The stages’ outputs are merged by relaxed attentional feature fusion modules, which bridge semantic gaps between features at various levels. Extensive experimental results on three semi-simulated data sets containing 318 700 samples show that the proposed model significantly outperforms the existing methods in EEG reconstruction accuracy. And its computational cost meets the criteria for real-time processing. Our code is available at https://github.com/BaenRH/DSATCN .

Unmanned aerial vehicles as an important role for 5G and beyond networks are becoming more and more popular and have been equipped with various sensors to enable diverse emerging applications, e.g. locating sound‐emitting targets. Multi‐channel blind source separation algorithm has been applied into the unmanned aerial vehicles and micro aerial vehicles, where underdetermined mixture blind source separation is a challenging problem, i.e. the number of sources is more than the number of microphones. An optimization underdetermined blind source separation algorithm to separate the multi‐channel audio mixture signals recorded by an unmanned aerial vehicle is proposed. In the algorithm, firstly a hierarchical clustering to estimate channel as the mixing matrix initialization is employed, while using direction of arrival permutation algorithm to deal with the permutation alignment and update the mixing matrix using multiplication update method. Then the model parameters are estimated using improved expectation‐maximization update rules for the fast convergence. Finally, the frequency‐domain sources are estimated through Wiener filtering and time‐domain sources are obtained via inverse short‐time Fourier transform. Experimental results covering synthetic and real‐recorded speech source mixtures show that the proposed algorithm achieves better separation results than the state‐of‐the‐art methods.