In this letter, we focus on the efficient channel estimation problem for millimeter wave (MMW) systems with massive antenna arrays and RF constraints, aiming at achieving a fast and high resolution angle-of-arrival/angle-of-departure (AoA/AoD) estimation. We first propose a presentation of antenna array with virtual elements (AAVE) by appending additional virtual antenna elements into the original antenna array. On the basis of the AAVE structure, we explore the channel sparsity in the angular domain and develop an efficient angle estimation algorithm by using compressive sensing theories. We then proposed a training design and prove that the sensing matrix in the proposed training can guarantee the accurate detection of angles with a high probability. Both the analytical and simulation results show that, without changing the physical antenna arrays, the proposed approach can achieve not only a lower overhead, but also a significantly higher resolution in angles estimation, compared to the existing algorithms.
Background: Elderly patients undergoing hip fracture repair surgery are at increased risk of delirium due to aging, comorbidities, and frailty. But current methods for identifying the high risk of delirium among hospitalized patients have moderate accuracy and require extra questionnaires. Artificial intelligence makes it possible to establish machine learning models that predict incident delirium risk based on electronic health data.Methods: We conducted a retrospective case-control study on elderly patients (≥65 years of age) who received orthopedic repair with hip fracture under spinal or general anesthesia between June 1, 2018, and May 31, 2019. Anesthesia records and medical charts were reviewed to collect demographic, surgical, anesthetic features, and frailty index to explore potential risk factors for postoperative delirium. Delirium was assessed by trained nurses using the Confusion Assessment Method (CAM) every 12 h during the hospital stay. Four machine learning risk models were constructed to predict the incidence of postoperative delirium: random forest, eXtreme Gradient Boosting (XGBoosting), support vector machine (SVM), and multilayer perception (MLP). K-fold cross-validation was deployed to accomplish internal validation and performance evaluation.Results: About 245 patients were included and postoperative delirium affected 12.2% (30/245) of the patients. Multiple logistic regression revealed that dementia/history of stroke [OR 3.063, 95% CI (1.231, 7.624)], blood transfusion [OR 2.631, 95% CI (1.055, 6.559)], and preparation time [OR 1.476, 95% CI (1.170, 1.862)] were associated with postoperative delirium, achieving an area under receiver operating curve (AUC) of 0.779, 95% CI (0.703, 0.856).The accuracy of machine learning models for predicting the occurrence of postoperative delirium ranged from 83.67 to 87.75%. Machine learning methods detected 16 risk factors contributing to the development of delirium. Preparation time, frailty index uses of vasopressors during the surgery, dementia/history of stroke, duration of surgery, and anesthesia were the six most important risk factors of delirium.Conclusion: Electronic chart-derived machine learning models could generate hospital-specific delirium prediction models and calculate the contribution of risk factors to the occurrence of delirium. Further research is needed to evaluate the significance and applicability of electronic chart-derived machine learning models for the detection risk of delirium in elderly patients undergoing hip fracture repair surgeries.
Electric load forecasting (ELF) is vitally beneficial for electrical power planning and economical running in smart grid. However, the medium-term load forecasting has been rarely studied. In addition, existing ELF models mainly consider the impact of limited external factors, which are usually difficult to forecast accurately. In this paper, the characteristics of the electric loads are analyzed and used as a guideline for the design of the proposed methods. To fully exploit the quasi-periodicity with different time ranges, i.e., year, quarter, month, and week, two deep learning methods, time-dependency convolutional neural network (TD-CNN), and cycle-based long short-term memory (C-LSTM) network are proposed to improve the forecasting performance of short-term load forecasting and MTLF with a little payload of computational complexity. Both of them only utilize the historical electric load and can mine the underlying load patterns by extracting the long-term global integrated features and short-term local similar features. By representing the loads as pixels and rearranging them into a 2-D photograph, TD-CNN transforms the temporal correlation of load series into the spatial correlation and keeps the long-term memory. Specifically, the convolutional kernel with special size targeted to load data is designed to extract the local pattern with similar characteristic, while the pooling layer is removed in order to keep the finer features. Moreover, in order to extract the temporal correlation between the long-term sequences with lower complexity, the proposed C-LSTM method generates a new short series from the original long load series without information loss. The LSTM is then applied to model the dynamical relationship of the load series with shorter time steps. The experimental results show that the proposed methods outperform the existing method with greatly reduced computation complexity, whose training time is about two-to-five times shorter than the existing method. INDEX TERMS MTLF, long-term historical load data, spatial correlation, CNN, LSTM.
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