A Method for SINS Alignment with Large Initial Misalignment Angles Based on Kalman Filter with Parameters Resetting

In the initial alignment process of strapdown inertial navigation system (SINS), large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles

LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network

Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent years thanks to its applicability to a plethora of real-world applications. The existing HMTC algorithms largely focus on the design of classifiers, such as the local, global, or a combination of them. However, very few studies have focused on hierarchical feature extraction and explore the association between the hierarchical labels and the text. In this paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels. Besides, hierarchical information is shared across levels while preserving the hierarchical label-based information. Separate local and global document embeddings are obtained and used to facilitate the respective local and global classifications. In our experiments, LA-HCN outperforms other state-of-the-art neural network-based HMTC algorithms on four public HMTC datasets. The ablation study also demonstrates the effectiveness of the proposed label-based attention module as well as the novel local and global embeddings and classifications. By visualizing the learned attention (words), we find that LA-HCN is able to extract meaningful information corresponding to the different labels which provides explainability that may be helpful for the human analyst.Comment: code is available at https://github.com/XinyiZ001/LA-HC

H∞ filter for flexure deformation and lever arm effect compensation in M/S INS integration

ABSTRACTOn ship, especially on large ship, the flexure deformation between Master (M)/Slave (S) Inertial Navigation System (INS) is a key factor which determines the accuracy of the integrated system of M/S INS. In engineering this flexure deformation will be increased with the added ship size. In the M/S INS integrated system, the attitude error between MINS and SINS cannot really reflect the misalignment angle change of SINS due to the flexure deformation. At the same time, the flexure deformation will bring the change of the lever arm size, which further induces the uncertainty of lever arm velocity, resulting in the velocity matching error. To solve this problem, a H∞ algorithm is proposed, in which the attitude and velocity matching error caused by deformation is considered as measurement noise with limited energy, and measurement noise will be restrained by the robustness of H∞ filter. Based on the classical “attitude plus velocity” matching method, the progress of M/S INS information fusion is simulated and compared by using three kinds of schemes, which are known and unknown flexure deformation with standard Kalman filter, and unknown flexure deformation with H∞ filter, respectively. Simulation results indicate that H∞ filter can effectively improve the accuracy of information fusion when flexure deformation is unknown but non-ignorable

Neural Multi-network Diffusion towards Social Recommendation

Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommendation suffer from serious problems of generalization and oversmoothness, because of the underexplored negative sampling method and the direct implanting of the off-the-shelf GNN models. In this paper, we propose a succinct multi-network GNN-based neural model (NeMo) for social recommendation. Compared with the existing methods, the proposed model explores a generative negative sampling strategy, and leverages both the positive and negative user-item interactions for users' interest propagation. The experiments show that NeMo outperforms the state-of-the-art baselines on various real-world benchmark datasets (e.g., by up to 38.8% in terms of NDCG@15)

Effects of doe-litter separation on intestinal bacteria, immune response and morphology of suckling rabbits

[EN] Gut development is stimulated by exposure to microorganisms, especially early-life microbial exposure. This study aimed to investigate whether doe-litter separation, which is performed in many rabbit farms, affects this exposure and therefore inhibits the development of intestinal system in suckling rabbits. Immediately after parturition, Rex rabbit does (n=16) were adjusted to 8 kits per litter and divided into doe-litter separation (DLS) group and doe-litter together (DLT) group based on the conditions of the does. One healthy kit per litter was selected and sacrificed at 7 d, 14 d, 21 d and 28 d of age, and the number of total bacteria, Escherichia coli and Bacteroides-Prevotella, expression of interleukin 6 (IL-6) and interleukin 10 (IL-10) in duodenum and caecum were investigated by real-time polymerase chain reaction. The morphological parameters of duodenum and vermiform appendix were also measured. Our results showed that doe-litter separation affected the number of intestinal bacteria. At 7 d of age, except for caecal Escherichia coli, the number of the investigated bacteria was decreased by doe-litter separation (P<0.05). But 1 wk later, only the number of total bacteria and Bacteroides-Prevotella in caecal content (P<0.05) and Escherichia coli in duodenal content from DLS kits (P<0.05) were still lower than those from DLT kits. After being provided with supplementary food for 7 d, DLS kits had fewer total bacteria in caecal content (P<0.05) and fewer E. coli in duodenal content (P<0.01) than DLT kits. After growing to 28 d of age, kits in DLS group still tended to have fewer total bacteria in caecal content, and expression of IL-10 and secretion of secretory IgA (sIgA) in vermiform appendix in DLS group was obviously lower than kits in DLT group (P<0.05). The villus height:crypt depth ratio in duodenum at 3rd wk and 4th wk was decreased by DLS (P<0.05). Kits in DLS group had shorter villus height (P<0.05), higher crypt depth (P<0.05) and shorter vermiform appendix (P<0.05) at the end of the trial. Furthermore, separating kits from the doe had a negative effect on their average daily gain at 3rd wk and 4th wk (P<0.05). Limiting the microbiological contact with the mother during suckling period affected the kits’ intestinal flora and could negatively affect the development of intestinal digestive and immune system and growth performance of kits.This study was funded by Key Science and Technology Programme of Shaanxi Province (No, 2013K02-18).Zhang, Y.; Cui, H.; Sun, D.; Liu, L.; Xu, X. (2018). Effects of doe-litter separation on intestinal bacteria, immune response and morphology of suckling rabbits. World Rabbit Science. 26(1):71-79. https://doi.org/10.4995/wrs.2018.5917SWORD717926

Advanced Control Strategy of DFIG Wind Turbines for Power System Fault Ride Through

This paper presents an advanced control strategy for the rotor and grid side converters of the doubly fed induction generator (DFIG) based wind turbine (WT) to enhance the low-voltage ride-through (LVRT) capability according to the grid connection requirement. Within the new control strategy, the rotor side controller can convert the imbalanced power into the kinetic energy of the WT by increasing its rotor speed, when a low voltage due to a grid fault occurs at, e.g., the point of common coupling (PCC). The proposed grid side control scheme introduces a compensation term reflecting the instantaneous DC-link current of the rotor side converter in order to smooth the DC-link voltage fluctuations during the grid fault. A major difference from other methods is that the proposed control strategy can absorb the additional kinetic energy during the fault conditions, and significantly reduce the oscillations in the stator and rotor currents and the DC bus voltage. The effectiveness of the proposed control strategy has been demonstrated through various simulation cases. Compared with conventional crowbar protection, the proposed control method can not only improve the LVRT capability of the DFIG WT, but also help maintaining continuous active and reactive power control of the DFIG during the grid faults

Assessing Junior Faculty Research Productivity in the IS Field: Recommendations for Promotion and Tenure Standards for Asian Schools

We gathered information about junior faculty research productivity in the information systems (IS) field in North America and in a set of top Asian schools. Our work complements prior studies on IS faculty research productivity in several ways. First, we focused on junior faculty research productivity, which refers to publication records of current tenure-track assistant professors. To provide statistics with a greater coverage of IS researchers, we also collected information about the pre-tenure publication records of associate professors. Second, we covered IS researchers who obtained their doctoral degrees in or after the year 2000 and counted their publications until 2013 to provide the most up-to-date information about junior faculty research productivity. Third, we collected information about IS researchers’ publications in leading IS journals (based on the AIS Senior Scholar basket of journals) and in elite broader business journals (based on the Financial Times list and UT Dallas list). Finally, examining junior faculty research productivity in the IS field in Asian schools and in North America enabled us to provide recommendations for promotion and tenure standards for Asian schools in light of the research productivity and tenure standards in North America

Transurethral resection of bladder tumor-based bladder preservation therapy for refractory high risk non-muscle invasive bladder cancer: Current landscape and future directions

Bladder cancer is the most common malignant tumor of urinary system worldwide. Approximately 75% of patients with bladder cancer present with non-muscle-invasive bladder cancer (NMIBC), which is effectively managed with transurethral resection of bladder tumor (TURBT). For refractory high risk NMIBC, patients are typically treated by radical cystectomy (RC). TURBT deserves further evaluation. Growing evidence suggests that repeated TURBT-based bladder-sparing approaches may improve oncological outcomes and quality of life in highly selected patients. Novel imaging techniques and biomarkers may aid in patients selection and postoperative surveillance. With growing interest in adding immunotherapy to refractory bladder cancer, TURBT based approaches enable the bladder preservation therapy for high risk NMIBC. Here we summarize the current landscape, biomarkers for surveillance, and future directions for applying TURBT-based bladder preservation therapy