Trajectory Planning with Pose Feedback for a Dual-Arm Space Robot

In order to obtain high precision path tracking for a dual-arm space robot, a trajectory planning method with pose feedback is proposed to be introduced into the design process in this paper. Firstly, pose error kinematic models are derived from the related kinematics and desired pose command for the end-effector and the base, respectively. On this basis, trajectory planning with pose feedback is proposed from a control perspective. Theoretical analyses show that the proposed trajectory planning algorithm can guarantee that pose error converges to zero exponentially for both the end-effector and the base when the robot is out of singular configuration. Compared with the existing algorithms, the proposed algorithm can lead to higher precision path tracking for the end-effector. Furthermore, the algorithm renders the system good anti-interference property for the base. Simulation results demonstrate the effectiveness of the proposed trajectory planning algorithm

Aggregated Multi-GANs for Controlled 3D Human Motion Prediction

Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate the body parts to explore various future possibilities. Undoubtedly, this greatly limits the usefulness and applicability of motion prediction. In this paper, we propose a generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion. Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways. To this aim, a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented. The local GANs game in lower dimensions, while the global GAN adjusts in high dimensional space to avoid mode collapse. Extensive experiments show that our method outperforms state-of-the-art. The codes are available at https://github.com/herolvkd/AM-GAN

A spectral-spatial feature extraction method with polydirectional CNN for multispectral image compression

Convolutional neural networks (CNN) has been widely used in the research of multispectral image compression, but they still face the challenge of extracting spectral feature effectively while preserving spatial feature with integrity. In this article, a novel spectral-spatial feature extraction method is proposed with polydirectional CNN (SSPC) for multispectral image compression. First, the feature extraction network is divided into three parallel modules. The spectral module is employed to obtain spectral features along the spectral direction independently, and simultaneously, with two spatial modules extracting spatial features along two different spatial directions. Then all the features are fused together, followed by downsampling to reduce the size of the feature maps. To control the tradeoff between the rate loss and the distortion, the rate-distortion optimizer is added to the network. In addition, quantization and entropy encoding are applied in turn, converting the data into bit stream. The decoder is structurally symmetric to the encoder, which is convenient for structuring the framework to recover the image. For comparison, SSPC is tested along with JPEG2000 and three-dimensional (3-D) SPIHT on the multispectral datasets of Landsat-8 and WorldView-3 satellites. Besides, to further validate the effectiveness of polydirectional CNN, SSPC is also compared with a normal CNN-based algorithm. The experimental results show that SSPC outperforms other methods at the same bit rates, which demonstrates the validity of the spectral-spatial feature extraction method with polydirectional CNN

4-Nonylphenol induces autophagy and attenuates mTOR-p70S6K/4EBP1 signaling by modulating AMPK activation in Sertoli cells

The estrogenic chemical 4-nonylphenol (NP) is known to impair testicular devolopment and spermatogenesis in rodents. The objective of this study was to explore the effects of NP on autophagy induction and AMPK-mTOR signaling pathway in Sertoli cells (SCs), which are the “nursemaid cells” for meiosis of spermatocytes. In this study we exposed 7-week-old male rats to NP by intra-peritoneal injection at 0, 20, 50 or 100 mg/kg body weight/2 days for 20 consecutive days. Our results showed that exposure to NP dose-dependently induces the formation of autophagosomes in SCs, increases the expression of Beclin-1, the conversion of LC3-I to LC3-II and the mRNA expression of Atg3, Atg5, Atg7 and Atg12 in testis, and these effects are concomitant with the activation of AMPK, and the suppression of TSC2-mTOR-p70S6K/4EBP1 signaling cascade in testis. Furthermore, 10 µM Compound C or AMPKα1 siRNA pre-treatment effectively attenuated autophagy and reversed AMPK-mTOR-p70S6K/4EBP1 signaling in NP-treated SCs. Co-treatment with 1 mM AICAR remarkably strengthened NP-induced autophagy and mTOR inhibition in SCs. Together, these data suggest that NP stimulates Sertoli cell autophagy and inhibits mTOR-p70S6K/4EBP1 activity through AMPK activation, which is the potential mechanism responsible for the regulation of testis function and differentiation following NP exposure

Number of Family Members, a New Influencing Factor to Affect the Risk of Melamine-Associated Urinary Stones

Melamine is a new risk of urinary stones. Gansu province is a heavily affected area and has large population and underdeveloped economy. We hypothesized that number of family members and family income may play significant roles in the formation of urinary stones. A case-control study was performed among 190 infants. Results showed that the case group had less numbers of family members than the control (4.4 vs. 5.6, respectively). The multivariate logistic regression analysis indicated that number of family members was an independent influencing factor associated with urinary stones (OR, 0.606; 95% CI, 0.411-0.893; P = 0.011). Family income, however, did not exhibit a significant difference. Observed results suggested that number of family members was a new and significant influencing factor to affect the risk of melamine-associated urinary stones

Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

An increasing number of high performance computing parallel applications leverages the power of the cloud for parallel processing. How to schedule the parallel applications to improve the quality of service is the key to the successful host of parallel applications in the cloud. The large scale of the cloud makes the parallel job scheduling more complicated as even simple parallel job scheduling problem is NP-complete. In this paper, we propose a parallel job scheduling algorithm named MEASY. MEASY adopts migration and consolidation to enhance the most popular EASY scheduling algorithm. Our extensive experiments on well-known workloads show that our algorithm takes very good care of the quality of service. For two common parallel job scheduling objectives, our algorithm produces an up to 41.1% and an average of 23.1% improvement on the average response time; an up to 82.9% and an average of 69.3% improvement on the average slowdown. Our algorithm is robust even in terms that it allows inaccurate CPU usage estimation and high migration cost. Our approach involves trivial modification on EASY and requires no additional technique; it is practical and effective in the cloud environment

GANs with Multiple Constraints for Image Translation

Unpaired image translation is a challenging problem in computer vision, while existing generative adversarial networks (GANs) models mainly use the adversarial loss and other constraints to model. But the degree of constraint imposed on the generator and the discriminator is not enough, which results in bad image quality. In addition, we find that the current GANs-based models have not yet been implemented by adding an auxiliary domain, which is used to constrain the generator. To solve the problem mentioned above, we propose a multiscale and multilevel GANs (MMGANs) model for image translation. In this model, we add an auxiliary domain to constrain generator, which combines this auxiliary domain with the original domains for modelling and helps generator learn the detailed content of the image. Then we use multiscale and multilevel feature matching to constrain the discriminator. The purpose is to make the training process as stable as possible. Finally, we conduct experiments on six image translation tasks. The results verify the validity of the proposed model

Research on Routing Equalization Algorithm of Inter-Satellite Partition for Low-Orbit Micro-Satellites

Low-orbit micro-satellite technology has developed rapidly in recent years due to its advantages of low time delay, low cost and short research period. However, among the existing inter-satellite routing algorithms, the classical flooding and greedy algorithms and their derivatives also have some limitations. The path delay calculated by the flooding algorithm is small but the calculation is large, while the greedy algorithm is the opposite. In this paper, a balanced inter-satellite routing algorithm based on partition routing is proposed. This paper presents the simulation experiments for the following indexes of the classic inter-satellite routing algorithms and the balanced partition routing algorithm: computation complexity, single-node computation pressure, routing path delay, path delay variance (data in Topo table satisfy μ =5, σ2=10). The results reveal that the balanced partition routing algorithm achieves better performance. In this paper, two optimization directions of the balanced partition routing algorithm are simulated under conditions that the data in the Topo table satisfy μ =5, σ2= 6, σ2=10 and σ2=15, respectively, when comparing their performance indicators. The experiments show that these two optimization methods can be adapted to various application scenarios and can further reduce the hardware cost of satellite nodes