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<abstract><p>In this study, we explore the precise trajectory tracking control problem of autonomous underwater vehicle (AUV) under the disturbance of the underwater environment. First, a model-free adaptive control (MFAC) is designed based on data-driven ideology and a full-form dynamic linearization (FFDL) method is utilized to online estimate time-varying parameter pseudo gradient (PG) to establish an equivalent data model of AUV motion. Second, the iterative extended state observer (IESO) scheme is designed to combine with FFDL-MFAC. Because the proposed novel controller is able to learn from repeated iterations, the proposed novel controller can estimate and compensate the model approximation error produced by external environmental unknown disturbance. Third, three-dimensional motion is decoupled into horizontal and vertical and a multi closed-loop control structure is designed that exhibits faster convergence rate and reduces sensitivity to parameter jumps than single closed-loop system. Finally, two simulation scenarios are designed featuring non external disturbance and Gaussian noise of signal-to-noise ratio of 90 dB. The simulation results reveal the superiority of FFDL. Furthermore, we adpot the technical parameters data of T-SEA I AUV to conduct numerical simulation, aunderwater trajectory as the tracking scenario and set waves to 0.5 m and current to 0.2 m/s to simulate Lv.2 ocean conditions of "International Ocean State Standard". The simulation results demonstrate the effectiveness and robustness of the proposed tracking control algorithm.</p></abstract>

Feature selection is an important tool to deal with high dimensional data. In unsupervised case, many popular algorithms aim at maintaining the structure of the original data. In this paper, we propose a simple and effective feature selection algorithm to enhance sample similarity preservation through a new perspective, topology preservation, which is represented by persistent diagrams from the context of computational topology. This method is designed upon a unified feature selection framework called IVFS, which is inspired by random subset method. The scheme is flexible and can handle cases where the problem is analytically intractable. The proposed algorithm is able to well preserve the pairwise distances, as well as topological patterns, of the full data. We demonstrate that our algorithm can provide satisfactory performance under a sharp sub-sampling rate, which supports efficient implementation of our proposed method to large scale datasets. Extensive experiments validate the effectiveness of the proposed feature selection scheme.

Objective The treatment and incidence of femoral neck fracture (FNF) in older patients is controversial. We investigated the new AO (Arbeitsgemeinschaft für Osteosynthese) classification in patients with FNF by age to determine the proportions of stable fracture and change trends according to patients’ age. Methods We divided patients with FNF hospitalized in Xi'an Honghui Hospital from 2018 to 2020 into five groups according to age: young (<50 years), middle-aged (50–59 years), young-elderly (60–69 years), middle-elderly (70–79 years), and very elderly (≥80 years) groups. We retrospectively collected data of patients’ sex, admission date, fracture side, mechanism of injury, and new AO classification. Results In total, 2071 patients were included for analysis, with 1329 women (64.2%); 1106 patients (53.4%) had left-side fracture. The main mechanism of injury was falling. In the young-elderly, middle-elderly, and very-elderly groups, 33.3%, 29.2%, and 24.1% had stable fracture type, respectively). The proportion of patients with FNF did not show a change trend by age during the 3-year investigation period. Conclusion In our study, the proportion of older patients with FNF did not increase, and as many as a third of patients with FNF aged 50 to 70 years had stable fracture.