An Exponential Decay Model for the Deterministic Correlations in Axial Compressors

International audienceThe average-passage equation system (APES) provides a rigorous mathematical framework to account for the UBRI in steady state environment by introducing the deterministic correlations (DC). How to model the DC is the key in APES method. The primary purpose of this study is to develop a DC model for compressor routine design. A 3D viscous unsteady and time-averaging CFD flow solver is developed to investigate the APES technique. Steady, unsteady and time-averaging simulations are conducted on the investigation of the UBRI in the first stage of NASA 67 compressor. Based on DC characteristics and its effects on time-averaged flow, an exponential decay DC model is proposed and implemented into the time-averaging solver. Based on the unsteady simulation, the proposed model is validated by comparing DC distributions and mean flow fields. The comparison indicates that the proposed model can take into account the major part of UBRI and provide significant improvements for predicting spanwise distributions of flow properties in axial compressors, compared with the steady mixing plane method

CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. However, offline RL still bears a large burden of specifying/handcrafting extrinsic rewards for each transition in the offline data. As a remedy for the labor-intensive labeling, we propose to endow offline RL tasks with a few expert data and utilize the limited expert data to drive intrinsic rewards, thus eliminating the need for extrinsic rewards. To achieve that, we introduce \textbf{C}alibrated \textbf{L}atent g\textbf{U}idanc\textbf{E} (CLUE), which utilizes a conditional variational auto-encoder to learn a latent space such that intrinsic rewards can be directly qualified over the latent space. CLUE's key idea is to align the intrinsic rewards consistent with the expert intention via enforcing the embeddings of expert data to a calibrated contextual representation. We instantiate the expert-driven intrinsic rewards in sparse-reward offline RL tasks, offline imitation learning (IL) tasks, and unsupervised offline RL tasks. Empirically, we find that CLUE can effectively improve the sparse-reward offline RL performance, outperform the state-of-the-art offline IL baselines, and discover diverse skills from static reward-free offline data

Fine structure and distribution of antennal sensilla of stink bug Arma chinensis (Heteroptera: Pentatomidae)

Scanning electron microscopy was used to examine the morphology, ultrastructure, and distribution of antennal sensilla of the stink bug Arma chinensis. Two types of sensilla trichodea (ST1–2), four types of sensilla basiconica (SB 1– 4), one type of sensilla chaetica (SCH), one type of sensilla cavity (SCA) and one type of sensilla coeloconica (SCO) were distinguished on the antennae in both sexes. ST1 and ST2 were absent from the scape and pedicel. SB1 were absent from the scape. SB2 were distributed throughout the antennae. SB3 were located on the second pedicel and the two flagellomeres. SB4 were absent from the second flagellomere. SCH was observed on the second pedicel and the two flagellomeres. SCA and SCO occurred only on the second flagellomere. SB1 clusters occurred on the distal part of the second flagellomere. We compared the morphology and structure of these sensilla to other Heteroptera and discuss their possible functions

Experimental Investigation of Flow Control Using Blade End Slots in a Highly Loaded Compressor Cascade

International audienceA detailed experimental investigation is conducted to suppress three-dimensional (3D) corner separation by a proposed passive control method using blade end slots in a highly loaded high-speed compressor cascade. Experiments are carried out under a wide range of incidence angles at Ma=0.59 using blades with and without blade end slots, respectively. Based on the experimental results, extensive comparisons show that the proposed method using blade end slots can efficiently suppress the 3D corner separation and broaden the effective operating range in the highly loaded high-speed compressor cascade. The total pressure loss is significantly reduced under most conditions. The reduction of total pressure loss in the measurement plane is as high as 18.4%, 20.6%, 24.3% and 39.4% at the incidence angle of-1.69˚, 0˚, 2˚ and 4˚, respectively. Furthermore, spanwise distributions of the pitch-averaged total pressure loss and deviation angle as well as the 3D flow field structures are analyzed to reveal the flow control mechanisms using blade end slots. The blade end slots can generate self-adaptive high momentum jet flow through the pressure difference from blade pressure and suction surface. These jet flows from the blade end slots effect downstream along the blade suction surface and significantly increase the flow momentum in the corner region. The main secondary vortex structures are suppressed by the high momentum jet flow; the 3D corner separation is reduced, and the two-dimesionality in the mid-span region is enhanced

Greedy-based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning

Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning methods with linear value decomposition (LVD) or monotonic value decomposition (MVD) suffer from relative overgeneralization. As a result, they can not ensure optimal consistency (i.e., the correspondence between individual greedy actions and the maximal true Q value). In this paper, we derive the expression of the joint Q value function of LVD and MVD. According to the expression, we draw a transition diagram, where each self-transition node (STN) is a possible convergence. To ensure optimal consistency, the optimal node is required to be the unique STN. Therefore, we propose the greedy-based value representation (GVR), which turns the optimal node into an STN via inferior target shaping and further eliminates the non-optimal STNs via superior experience replay. In addition, GVR achieves an adaptive trade-off between optimality and stability. Our method outperforms state-of-the-art baselines in experiments on various benchmarks. Theoretical proofs and empirical results on matrix games demonstrate that GVR ensures optimal consistency under sufficient exploration

Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning

Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intrinsic rewards to enable committed exploration or use role-based learning for decomposing joint action spaces instead of directly conducting a collective search in the entire action-observation space. However, they often face challenges obtaining specific joint action sequences to reach successful states in long-horizon tasks. To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios. IIE employs a transformer model to imagine how the agents reach a critical state that can influence each other's transition functions. Then, we initialize the environment at this state using a simulator before the exploration phase. We formulate the imagination as a sequence modeling problem, where the states, observations, prompts, actions, and rewards are predicted autoregressively. The prompt consists of timestep-to-go, return-to-go, influence value, and one-shot demonstration, specifying the desired state and trajectory as well as guiding the action generation. By initializing agents at the critical states, IIE significantly increases the likelihood of discovering potentially important under-explored regions. Despite its simplicity, empirical results demonstrate that our method outperforms multi-agent exploration baselines on the StarCraft Multi-Agent Challenge (SMAC) and SMACv2 environments. Particularly, IIE shows improved performance in the sparse-reward SMAC tasks and produces more effective curricula over the initialized states than other generative methods, such as CVAE-GAN and diffusion models.Comment: The 38th Annual AAAI Conference on Artificial Intelligenc

SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios

Despite the success in 6D pose estimation in bin-picking scenarios, existing methods still struggle to produce accurate prediction results for symmetry objects and real world scenarios. The primary bottlenecks include 1) the ambiguity keypoints caused by object symmetries; 2) the domain gap between real and synthetic data. To circumvent these problem, we propose a new 6D pose estimation network with symmetric-aware keypoint prediction and self-training domain adaptation (SD-Net). SD-Net builds on pointwise keypoint regression and deep hough voting to perform reliable detection keypoint under clutter and occlusion. Specifically, at the keypoint prediction stage, we designe a robust 3D keypoints selection strategy considering the symmetry class of objects and equivalent keypoints, which facilitate locating 3D keypoints even in highly occluded scenes. Additionally, we build an effective filtering algorithm on predicted keypoint to dynamically eliminate multiple ambiguity and outlier keypoint candidates. At the domain adaptation stage, we propose the self-training framework using a student-teacher training scheme. To carefully distinguish reliable predictions, we harnesses a tailored heuristics for 3D geometry pseudo labelling based on semi-chamfer distance. On public Sil'eane dataset, SD-Net achieves state-of-the-art results, obtaining an average precision of 96%. Testing learning and generalization abilities on public Parametric datasets, SD-Net is 8% higher than the state-of-the-art method. The code is available at https://github.com/dingthuang/SD-Net

An Information Perception-Based Emotion Contagion Model for Fire Evacuation

In fires, people are easier to lose their mind. Panic will lead to irrational behavior and irreparable tragedy. It has great practical significance to make contingency plans for crowd evacuation in fires. However, existing studies about crowd simulation always paid much attention on the crowd density, but little attention on emotional contagion that may cause a panic. Based on settings about information space and information sharing, this paper proposes an emotional contagion model for crowd in panic situations. With the proposed model, a behavior mechanism is constructed for agents in the crowd and a prototype of system is developed for crowd simulation. Experiments are carried out to verify the proposed model. The results showed that the spread of panic not only related to the crowd density and the individual comfort level, but also related to people’s prior knowledge of fire evacuation. The model provides a new way for safety education and evacuation management. It is possible to avoid and reduce unsafe factors in the crowd with the lowest cost

Nitrogen addition increases the contents of glomalin-related soil protein and soil organic carbon but retains aggregate stability in a Pinus tabulaeformis forest

Background Glomalin-related soil protein (GRSP) and soil organic carbon (SOC) contribute to the formation and stability of soil aggregates, but the mechanism by which global atmospheric nitrogen (N) deposition changes soil aggregate stability by altering the distribution of GRSP and SOC in different aggregate fractions remains unknown. Methods We used a gradient N addition (0–9 g N m−2 y−1) in Pinus tabulaeformis forest for two years in northeast China and then examined the changes in SOC contents, total GRSP (T-GRSP), and easily extractable GRSP (EE-GRSP) contents in three soil aggregate fractions (macro-aggregate: >250 μm, micro-aggregate: 250–53 μm, and fine material: <53 μm) and their relationship with aggregate stability. Results (1) The soil was dominated by macro-aggregates. Short term N addition had no significant effect on mean weight diameter (MWD) and geometric mean diameter (GMD). (2) GRSP varied among aggregate fractions, and N addition had different effects on the distribution of GRSP in aggregate fractions. The EE-GRSP content in the macro-aggregates increased initially and then decreased with increasing N addition levels, having a peak value of 0.480 mg g−1 at 6 g N m−2 y−1. The micro-aggregates had the lowest EE-GRSP content (0.148 mg g−1) at 6 g N m−2 y−1. Furthermore, the T-GRSP content significantly increased in the aggregate fractions with the N addition levels. (3) The macro-aggregate had the highest SOC content, followed by the micro-aggregate and the fine material had the lowest SOC content. N addition significantly increased the SOC content in all the aggregate fractions. (4) GRSP and SOC contents were not significantly correlated with MWD. Conclusion Glomalin-related soil protein and SOC contents increased by N addition, but this increase did not enhance aggregate stability in short term, and the improvement of stability might depend on binding agents and incubation time