Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional approaches commonly collect training and testing samples from the same patient due to inter-individual variability. However, the challenging problem of domain shift between various subjects remains unsolved, resulting in a low conversion rate to the clinic. In this work, a domain adaptation (DA)-based model is proposed to circumvent this issue. The short-time Fourier transform (STFT) is employed to extract the time-frequency features from raw EEG data, and an autoencoder is developed to map these features into high-dimensional space. By minimizing the inter-domain distance in the embedding space, this model learns the domain-invariant information, such that the generalization ability is improved by distribution alignment. Besides, to increase the feasibility of its application, this work mimics the data distribution under the clinical sampling situation and tests the model under this condition, which is the first study that adopts the assessment strategy. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can minimize the domain gap effectively compared with previous approaches.

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G new radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28-GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160-MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve submeter-level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems.

Boosted by a strong solar power market, the electricity grid is exposed to risk under an increasing share of fluctuant solar power. To increase the stability of the electricity grid, an accurate solar power forecast is needed to evaluate such fluctuations. In terms of forecast, solar irradiance is the key factor of solar power generation, which is affected by atmospheric conditions, including surface meteorological variables and column integrated variables. These variables involve multiple numerical time-series and images. However, few studies have focused on the processing method of multiple data types in an inter-hour direct normal irradiance (DNI) forecast. In this study, a framework for predicting the DNI for a 10-min time horizon was developed, which included the nondimensionalization of multiple data types and time-series, development of a forecast model, and transformation of the outputs. Several atmospheric variables were considered in the forecast framework, including the historical DNI, wind speed and direction, relative humidity time-series, and ground-based cloud images. Experiments were conducted to evaluate the performance of the forecast framework. The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41% and a normalized root mean square error (nRMSE) of 20.53%, and outperforms the persistent model with an improvement of 34% in the nRMSE.

The photovoltaic (PV) cell temperature strongly affects the performance and efficiency of the entire PV module. Thus, the accurate estimation of the cell temperature plays an important role in the health monitoring and energy assessment of PV systems. This article proposes a multi-state dynamic thermal model for PV modules, considering the heat-transfer mechanisms between the module and its environments, as well as between layers. The proposed model is benchmarked against field measurements at Aalborg University, Denmark. The results demonstrate the effectiveness of the model to characterize the internal behavior of the PV module under varying weather conditions. The performance of the proposed thermal model is also compared with prior-art models, i.e., two benchmark models, a one-state thermal model and two typical empirical equation-based models. The comparison further confirms that the estimation of cell temperature using the developed model is more accurate, presenting a reliable prediction of power production for further monitoring and diagnosis.

Ultra-short-term photovoltaic (PV) power forecasting holds significant importance in enhancing grid stability. Most PV power forecasting methods based on satellite images rely on pixel-level predictions, which are inefficient and redundant. Meanwhile, current deep-learning approaches struggle to establish correlations between large-scale cloud features and PV generation patterns. In this paper, an end-to-end model based on multimodal learning is proposed for directly obtaining multi-step PV power forecasts from satellite images and time series. To capture cloud dynamics and features within the region of interest (RoI), ConvLSTM-RICNN is utilized to encode satellite images. To mitigate the impact of noise and missing data in PV power, a robust fusion approach named DCCA-LF is introduced. This approach integrates deep canonical correlation analysis (DCCA) into late fusion (LF) to strengthen cross-modal feature alignment. The proposed model is verified using publicly available data from BP Solar in Alice Springs and Himawari-8, from January 1, 2020, to October 8, 2022. Comparison with current state-of-the-art research shows that the suggested model achieves the best RMSE and MAE with minimal complexity across all cloud conditions. Moreover, the proposed approach is robust to noise and missing data.