The giant panda, a rare and iconic species endemic to China, has attracted significant attention from both domestic international researchers due its crucial ecological role, unique cultural value, distinct evolutionary history. While substantial progress been made in the field of individual identification, behavior recognition remains underdeveloped, facing challenges such as lack dynamic temporal features insufficient extraction behavioral characteristics. To address these challenges, we...

HCIC is a interval variable selection method based on correlation analysis and Bayesian theory, developed with domain knowledge to capture physically relevant feature structures, thereby improving the performance of selection.

Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose significant challenges training under the current Federated Learning (FL) paradigm. Existing approaches efficient FL often leverage model parameter dropout. manipulating individual parameters is not only inefficient meaningfully reducing communication overhead when models, but may also be detrimental to scaling efforts...

Purpose This study explores the relationship between physical exercise and older people’s subjective well-being mediating role of a sense meaning in life self-esteem by using structural equation modeling (SEM) approach, order to provide some suggestions for improving well-being. Methods In this study, cross-sectional survey was conducted offline simple random method collection, Physical Activity Rating Scale (PARS-3), Subjective Well-being (SWB), Meaningfulness Life (MLQ), Self-Esteem (SES)...

Wearable exoskeletons play an important role in people's lives, such as helping stroke and amputation patients to carry out rehabilitation training so on. How make the exoskeleton accurately judge human action intention is basic requirement ensure that it can complete corresponding task. Traditional control signals include pressure values, joint angles acceleration which only reflect current motion information of lower limbs cannot be used predict motion. The electromyography (EMG) signal...

Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of applications. Since complex systems often involve multiple plants (possibly belonging to different companies or subsidiaries) with sensitive data collected and stored a distributed manner, collaborative diagnostic model training needs leverage federated learning (FL). As scale models are large communication channels such not exclusively used for FL training, existing deployed...

Unmanned aerial vehicle (UAV) swarm coordinated confrontation is a hot topic in academic research at home and abroad, dynamic maneuver decision-making one of the most important fields for UAV countermeasures. Aiming complexity, uncertainty cooperative confrontation, concepts such as relative advantage degree coefficient are introduced, game theory used framework to construct non-zero-sum cluster model, finally convert it into an optimization problem. On this basis, using Nash equilibrium...

Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, real-world scenarios, models are usually deployed on resource-limited devices, e.g., FPGAs, and often quantized hard-coded non-modifiable parameters for acceleration. In light of this, existing methods infeasible since they heavily depend computation-intensive backpropagation updating that may be not supported. To address we propose test-time...

This paper addresses the study of novel constructions variational analysis and generalized differentiation that are appropriate for characterizing robust stability properties constrained set-valued mappings/multifunctions between Banach spaces important in optimization theory its applications. Our tools revolve around newly introduced concept ε-regular normal cone to sets associated coderivative notions mappings. Based on these constructions, we establish several characterizations central...

Drilling fluids are critical in oil and gas well drilling, particularly deep shale drilling. In recent years, applying nanoparticles as additives drilling has received widespread attention to address the various challenges associated with This study focused on performance of three nanoparticle-enhanced oil-based (OBDFs), carbon nanotubes (CNTs), silicon dioxide (SiO2), aluminums oxide (Al2O3) terms improving thermal capacity cooling efficiency. The potential improve management capability was...

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power computational techniques to analyze educational data. With increasing complexity and diversity data, Deep Learning have shown significant advantages in addressing challenges associated with analyzing modeling this This survey aims systematically review state-of-the-art EDM Learning. We begin by providing brief introduction Learning, highlighting their relevance context modern education. Next, we...

This paper addresses the study of novel constructions variational analysis and generalized differentiation that are appropriate for characterizing robust stability properties constrained set-valued mappings/multifunctions between Banach spaces important in optimization theory its applications. Our tools revolves around newly introduced concept $\varepsilon$-regular normal cone to sets associated coderivative notions mappings. Based on these constructions, we establish several...

In the die-sinking electrical discharge machining (EDM), a large energy for roughing (several hundred amperes with few microseconds) and low finishing (a tens to hundreds of nanoseconds) pose challenge pulse power generator. This paper proposes power-electronic-based generator, which adopts multi-phase interleaved buck converters as main circuit, high output voltage circuit gap breakdown. The control current waveform follow given reference by frequency switching mode operation, ripple is...

Existing animal individual identification methods mostly use only single images and cannot effectively leverage complementary features in video frames. To further improve the robustness accuracy of animals like red pandas that have complex body deformation or pose variations, we propose this paper a deep network to learn hybrid feature representation adaptively aggregates local global for panda identification. The is obtained by finding discriminative patches each frame aggregating across...

This letter presents a 60-element single-layer rectangular radial line helical array (RLHA). A new probe is designed to achieve RLHAs with higher number of elements, advantages low coupling coefficient, simple structure and easy fabrication. Moreover, the has reflection coefficient high power capacity, making it well-suited for application in high-power RLHAs. Compared typical limited element count, proposed RLHA demonstrated lower loss aperture efficiency application. To address this,...