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The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, a new abnormal data detection method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed. In the proposed method, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, in which the particle swarm optimization is used to find the optimal kernels for higher detection accuracy. Besides, by using smoothing mechanism, the constrained quadratic programming problem is translated to be smooth and differentiable. Then, this problem can be solved by the conjugate gradient method, which could reduce the computational complexity. In experimental section, abundant simulation experiments were designed and the experimental results verify that the proposed SMP-SVDD method could achieve higher detection accuracy and low computational cost compared with different detection methods in different guidance stages.

IntroductionSuprapectoral tenodesis is a common technique for the treatment of long head biceps tendon lesions. However, so far, there is no gold standard treatment in all-inside arthroscopy. The purpose of the present study was to introduce and evaluate the functional outcomes of an innovative, all-inside arthroscopic high resistance loop (H-loop, high resistance to tissue cutout and 360° grasping of the tendon) technique for long head of biceps (LHB) tenodesis.MethodFrom September 2020 to March 2022, a series of cases of 32 consecutive patients (28 rotator cuff tear with LHBT pathology and 4 superior labrum anterior-posterior (SLAP) tears which including 2 type II and 2 type IV) who received LHB tenodesis using all-inside arthroscopic high resistance loop technique were included in this study. The American Shoulder and Elbow Surgeon Score (ASES), Visual Analog Scale (VAS), Simple Shoulder Test Score (SST), Constant–Murley scores, and University of California at Los Angeles Scoring System (UCLA) were used to evaluate the clinical outcomes of patients in preoperative and final follow-up. Meanwhile, postoperative complications were also observed.Result32 patients (14 women and 18 men, average age was 55.7 years) underwent all-inside arthroscopic knotless suprapectoral biceps tenodesis using the H-Loop stitch technique. The mean time of follow-up was 16.2 ± 2.6 months. The ASES, VAS, Constant–Murley, SST, and UCLA scores improved from 51.5 ± 15.8, 5.5 ± 1.6, 57.8 ± 14.7, 5.0 ± 2.8, and 16.1 ± 3.8 preoperatively, to 89.1 ± 7.5, 1.0 ± 0.8, 87.3 ± 5.5, 10.4 ± 1.5, and 31.3 ± 2.6 in the last follow-up, respectively (p ConclusionThis article presents an innovative, all-arthroscopic H-loop technique for LHB tenodesis. This technique for LHB tenodesis showed favorable functional and cosmetic outcomes, as well as high satisfaction rates. Due to its simplicity of operation and satisfactory preliminary clinical outcomes, H-loop technique is perhaps another option to choose in all-inside arthroscopic LHB tenodesis.

The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, aiming to detect abnormal data in seeker pitch angle deviation data, a method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed to detect abnormal data in guidance angle data. On the one hand, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, and the particle swarm optimization is used to find the optimal kernel, which improves the detection accuracy. On the other hand, the constrained quadratic programming problem is smooth and differentiable, and the conjugate gradient method can be applied to reduce the complexity of problem solving. Through simulation experiments, it is verified that the SMP-SVDD method has higher detection accuracy and faster calculation speed compared with different detection methods in different guidance stages.

Multimode compound guidance is the important research direction of the future intelligent ammunition development. For the fusion of seeker’s pitch and azimuth deviations in laser, millimeter wave, and infrared three-mode compound guidance, a grey relational belief degree least squares method is proposed in this study. Angle measurement models are firstly constructed for the three guidance modes. Grey relational degrees among the data of different modes are calculated to construct the grey relational matrix. And then, the grey relational matrix is used as the probability distribution between different modes. The belief degree of each mode is calculated in real time according to evidence theory in the fusion process. Finally, the least squares fusion result based on the grey relational belief degree is obtained. In simulation experiments, compared with weighted least square method, the fusion approach in this study achieves a lower root mean square error.