Gonadal transcriptome analysis of paradise fish Macropodus opercularis to reveal sex-related genes.

Macropodus opercularis is an ornamental fish species endemic to China, with obvious sexual dimorphism in phenotype. To obtain the gene expression profile of the gonads of M. opercularis and explore its sex-related genes, six cDNA libraries were constructed from the sexually mature M. opercularis, and RNA-seq analysis was performed. The sequenced clean data were assembled by de novo splicing to generate 171,415 unigenes, and differentially expressed genes (DEGs) screening revealed that there were 41,638 DEGs in the gonads of M. opercularis. By comparing those DEGS in the ovary with the testis, we found 29,870 DEGs were upregulated and 11,768 DEGs were downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis showed that GO terms related to cell cycle and gamete formation were enriched, and pathway signals related to sex differences, such as FoxO signalling pathway and PI3K-Akt signalling pathway, were also detected. Reverse transcript fluorescence quantitative PCR (RT-qPCR) validation of 14 DEGs associated with sex differences showed that the RT-qPCR results were consistent with RNA-Seq analysis, and five genes, foxl2, sox3, foxo, zar1, cyp19a1, were significantly expressed in the ovaries. dmrt1, cyp11b, amh, sf1, sox9, gdf6, dmrt3, fstl1 and hsd11b2, a total of nine genes were significantly expressed in the testis. The results of this study provide a basis for the study of gonadal differentiation, developmental mechanisms and related functional genes in M. opercularis.

Multi-spectral radiation thermometry based on an Alpha spectrum-LM algorithm under the background of high temperature and intense reflection.

In order to meet the needs of multi-spectral radiation temperature measurement under high temperature background, this paper studies the problems of reflected radiation interference and spectral emissivity difficult to obtain in high temperature and intense reflection environment. First, using discrete triangular surface elements and radiation angle coefficients, an analysis model of high temperature background reflected radiation is constructed to describe the variation characteristics of high temperature background reflected radiation. Secondly, the least squares support vector machine (LSSVM) is optimized by particle swarm optimization (PSO) algorithm, and an emissivity model identification algorithm based on Alpha spectrum-Levenberg Marquarelt (LM) algorithm is proposed, which has stronger applicability and accuracy than existing emissivity model identification methods. Finally, the high temperature background radiation and the emissivity model are combined to construct and solve the multi-spectral target equation, so as to realize the reflected radiation error correction and radiation temperature measurement under the high temperature and intense reflection background. The simulation and experimental comparison with the existing methods show that the temperature measurement error of the radiation temperature measurement method proposed in this paper is below 9.5K, which can effectively correct the reflected radiation error and further improve the temperature measurement accuracy.

Improved Multiple-Model Adaptive Estimation Method for Integrated Navigation with Time-Varying Noise.

The accurate noise parameter is essential for the Kalman filter to obtain optimal estimates. However, problems such as variations in the noise environment and measurement anomalies can cause degradation of estimation accuracy or even divergence. The adaptive Kalman filter can simultaneously estimate state and noise parameters, while its performance will also be degraded in complex noise. To address the problem of estimation accuracy degradation and result divergence of the integrated navigation system in a complex time-varying noise environment, an improved multiple-model adaptive estimation (MMAE) that combines the Sage-Husa adaptive unscented Kalman filter with the MMAE is proposed in this paper. The forgetting factor is included as an unknown parameter of MMAE so that the algorithm can adjust the value of the forgetting factor according to different system states. In addition, we improve the hypothesis testing algorithm of classical MMAE to deal with the competition problem of undesirable models that severely impacts the performance of variable-parameter MMAE and enhance the algorithm's parameter identification capability. Simulation results show that this method enhances the system's robustness to noises of different statistical properties and improves the estimation accuracy of the filter in time-varying noise environments.

An Anti-Turbulence Self-Alignment Method for SINS under Unknown Latitude Conditions.

For the alignment problem of strapdown inertial navigation system (SINS) under the complex environment of unknown latitude, angular oscillation interference, and line interference, the ant colony simulated annealing algorithm of gravity vector optimization is proposed to obtain the gravity apparent motion vector optimization equation, and the polynomial fitting method is proposed to simultaneously perform latitude estimation and self-alignment in combination with the alignment principle of SINS. Simulations and experiments show that the proposed method has more robust anti-interference capability than the traditional interference-based alignment method, the latitude estimation accuracy is improved by six times, the self-alignment yaw angle error RMSE value after obtaining the latitude is within 0.7°, and the roll angle and pitch angle error values are within 0.1°.

Optimal Rotational Angular Velocity Determination Method Based on Compound Rotary Semi-Strapdown Inertial Navigation System.

Single-axis rotation modulation (SRM) still accumulates errors in the roll axis direction, which leads to the navigation accuracy not meeting the requirements of guided missiles. Compound rotation modulation (CRM) superimposes one-dimensional rotation on the basis of SRM, so that the error of the projectile in the direction of the roll axis is also modulated. However, the error suppression effect of CRM is not only affected by the error of the IMU itself, but also related to the modulation angular velocity. In order to improve the accuracy of rotary semi-strapdown inertial navigation system (RSSINS), this paper proposes an optimal rotation angular velocity determination method. Firstly, the residual error in CRM scheme is analyzed; then, the relationship between the incomplete modulation error and the modulation angular velocity in CRM is discussed; finally, a method for determining the optimal modulation angular velocity is proposed (K-value method). The analysis of the results shows that the navigation accuracy of the guided projectile is effectively improved with the rotation scheme set at the modulation angular velocity determined by the K-value method.

A Low-Cost MEMS Missile-Borne Compound Rotation Modulation Scheme.

Rotation modulation (RM) has been widely used in navigation systems to significantly improve the navigation accuracy of inertial navigation systems (INSs). However, the traditional single-axis rotation modulation cannot achieve the modulation of all the constant errors in the three directions; thus, it is not suitable for application in highly dynamic environments due to requirements for high precision in missiles. Aiming at the problems of error accumulation and divergence in the direction of rotation axis existing in the traditional single-axis rotation modulation, a novel rotation scheme is proposed. Firstly, the error propagation principle of the new rotation modulation scheme is analyzed. Secondly, the condition of realizing the error modulation with constant error is discussed. Finally, the original rotation modulation navigation algorithm is optimized for the new rotation modulation scheme. The experiment and simulation results show that the new rotation scheme can effectively modulate the error divergence of roll angle and improve the accuracy of roll angle by two orders of magnitude.

Roll Angular Rate Measurement for High Spinning Projectiles Based on Redundant Gyroscope System.

Precision-guided projectiles, which can significantly improve the accuracy and efficiency of fire strikes, are on the rise in current military engagements. The accurate measurement of roll angular rate is critical to guide a gun-launched projectile. However, Micro-Electro-Mechanical System (MEMS) gyroscope with low cost and large range cannot meet the requirement of high precision roll angular rate measurement due to the limitation by the current technology level. Aiming at the problem, the optimization-based angular rate estimation (OBARS) method specific for projectiles is proposed in this study. First, the output angular rate model of redundant gyroscope system based on the autoregressive integrated moving average (ARIMA) model is established, and then the conventional random error model is improved with the ARIMA model. After that, a Sage-Husa Adaptive Kalman Filter (SHAKF) algorithm that can suppress the time-varying process and measurement noise under the flight condition of the high dynamic of the projectile is designed for the fusion of dynamic data. Finally, simulations and experiments have been carried out to validate the performance of the method. The results demonstrate the proposed method can effectively improve the angular rate accuracy more than the related traditional methods for high spinning projectiles.

Optimal Design and Analysis on High Overload Buffer Structure of Passive Semi-Strapdown Inertial Navigation System.

The isolation rolling platform inside a passive semi-strapdown inertial navigation system can isolate the high-speed rotation of a projectile via bearing to provide a low rotating speed environment for the angular rate sensors inside the platform in order to further improve the accuracy by reducing its measurement range. Aiming at the problem that the internal bearing cannot withstand high overload, an optimal design method for a high overload buffer structure based on point contact spherical cap structure is proposed. Changing the materials of the spherical caps can reduce the deformation of the two spherical caps during impact and reduce the pivoting friction; at the same time, the upper and lower spherical caps are both forced to separate by the spring force after the impact and to eliminate the influence of the pivoting friction torque that is generated by the contact of two spherical caps on the stability of the isolated rolling platform. By virtue of finite element analysis and ground semi-physical simulation experiments, the feasibility of the design is verified. The experiment results show that the design can play an effectively protective role in anti-high overload, and the maximum deformation radius of the optimized point contact spherical cap structure can be reduced by 40.8%; after the upper and lower spherical caps are separated, the isolation rolling platform' capability of anti-high-speed rotation can be improved by 52% under the rotation speed of the main shaft at 10 r/s. In this way, the stability of the platform is improved, thus proving the value of the design method in engineering applications.

A New In-Flight Alignment Method with an Application to the Low-Cost SINS/GPS Integrated Navigation System.

The optimization-based alignment (OBA) methods, which are implemented by the optimal attitude estimation using vector observations-also called double-vectors-have proven to be effective at solving the in-flight alignment (IFA) problem. However, the traditional OBA methods are not applicable for the low-cost strap-down inertial navigation system (SINS) since the error of double-vectors will be accumulated over time due to the substantial drift of micro-electronic- mechanical system (MEMS) gyroscope. Moreover, the existing optimal estimation method is subject to a large computation burden, which results in a low alignment speed. To address these issues, in this article we propose a new fast IFA method based on modified double-vectors construction and the gradient descent method. To be specific, the modified construction method is implemented by reducing the integration interval and identifying the gyroscope bias during the construction procedure, which improves the accuracy of double-vectors and IFA; the gradient descent scheme is adopted to estimate the optimal attitude of alignment without complex matrix operation, which results in the improvement of alignment speed. The effect of different sizes of mini-batch on the performance of the gradient descent method is also discussed. Extensive simulations and vehicle experiments demonstrate that the proposed method has better accuracy and faster alignment speed than the related traditional methods for the low-cost SINS/global positioning system (GPS) integrated navigation system.

Optimization of a New High Rotary Missile-Borne Stabilization Platform.

The passive semi-strapdown roll stabilized platform is an inertial platform, which can isolate the rolling of a projectile body by a special mechanical device. In the passive semi-strapdown roll stabilized platform, the bearing device plays an important role in isolating the rolling of the projectile body. The smaller the friction moment of bearing, the smaller the swing angular velocity of the platform, the smaller the range of inertial sensors required, the higher the accuracy of the navigation solution. In order to further reduce the swing angular velocity of the platform and improve the navigation accuracy, the bearing nested structure that could reduce the friction torque is proposed. Combined with the working principle of the passive semi-strapdown roll stabilized platform, the mechanical calculation model of friction at the moment of bearing the nested structure was established. A series of simulation analysis and tests showed that the output stability value of the friction moment was 47% that of a single bearing; the roll rate of the platform based on the bearing nested structure decreased to 50% of that based on the single bearing structure; the position and attitude errors measured of the platform based on the bearing nested structure decreased to more than 50% of that based on the single bearing structure. It showed that the bearing nested structure could effectively reduce the friction moment, improve the axial reliability of the bearing, and provide a more stable working environment for the passive semi-strapdown roll stabilized platform.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.