衡阳师范学院

The Internet of Things (IoT) has gained significant popularity through wearable technologies. Several IoT devices are used in educational institutions, smart homes, and security management. Their widespread use in sports training applications using IoT (WS-IoT)-based wearable sensors is noteworthy. Meanwhile, artificial intelligence (AI) has evolved rapidly, and its applications have expanded across many diverse fields. Over the past few years, China has placed greater emphasis on physical education to enhance students' physical fitness and overall capabilities. A significant number of colleges and universities have adopted artificial intelligence to transform traditional single-mode sports training methods. This paper introduces a continuous monitoring system for sports individuals using the WS-IoT. The goal of coaches is to maximize the results of athlete training across various sports events using AI technology and their sports expertise. In this study, we examine the use of AI technology in sports and assess the practical implications of its implementation. To achieve the objective function, we used a Bayesian deep classifier and deep learning algorithms that utilize adaptive optimization. Utilizing artificial intelligence in Chinese educational institutions improves training efficiency and effectiveness. The system was then rigorously tested and analyzed in terms of the quality-of-service parameters, including communication collisions and packet loss rates. It is shown that packet loss rates increase with increasing packet numbers, but collision rates remain unchanged. Furthermore, a practical application of the system in colleges and universities showed that 14.4% of the students were extremely satisfied with the use of teaching materials, 24.5% were satisfied with the materials, 30.1% were satisfied with the training methods, and 17.9% were dissatisfied.

Exciting achievements have been made in audio-driven face-talking head generation. However, while existing methods excel at generating a speaking head from a frontal identity image, generating a speaking head with a head pose does not yield satisfactory results when the identity is based on a side image of the face. To address this limitation, a concise and effective approach is proposed in this work. Our method generates efficient talking head videos using a side face image as the identity. It uses facial features and head posture to predict frontal keypoints, during which facial expression features are added as additional features. The method achieves impressive head motion effects and maintains identity information consistency during motion. Additionally, a Transform-based lip-sync expert discriminator is designed to guide the lip synchronization of the generated video. A cross-attention is employed to jointly learn the front and back frames, extracting contextual information that improves lip synchronization over long sequences. Our method is tested on several datasets, such as VoxCeleb2 and LRS2, and compared with multiple advanced methods, demonstrating its outstanding performance through experiments.

CO2 absorption by the organic amine solvent is currently the most promising method for CO2 capture, while this process suffers from high solvent regeneration heat duty. The catalyst-assisted regeneration is an effective mean to reduce the energy consumption of amine solution regeneration. However, the catalysts reported so far still have shortcomings with insufficient performance, difficult preparation, and high cost. In this study, four solid acid catalysts, Fe2(SO4)3/Fe2O3, NiSO4/NiO, CuSO4/CuO, Zr(SO4)2/ZrO2 were prepared using a simple one-step synthesis method. These catalysts were then utilized to catalyze CO2 desorption at 88 degrees C in a rich 5 M monoethanolamine (MEA) solution to lower the regeneration heat duty. All the prepared catalysts showed superior ability to promote CO2 desorption, with the NiSO4/NiO catalyst presenting the best activity. Notably, the NiSO4/ NiO catalyst enabled a 360 % enhancement in the maximum CO2 desorption rate, a 118 % increase in the CO2 desorption amount, and a 46 % reduction in the relative energy penalty. After 18 absorption-desorption tests, NiSO4/NiO proved remarkable stability. The NiSO4/NiO catalyst contains both B and L acids, and the addition of sulfate also enhances the acidity of the metal oxides, thereby accelerating the CO2 desorption. A potential NiSO4/ NiO-catalyzed reaction mechanism is proposed based on FT-IR characterization and DFT calculations. This work provides a simple and inexpensive strategy to facilitate the rich amine solution regeneration process with low temperature and energy consumption.

Alkaline water electrolysis is a promising strategy for hydrogen and oxygen production, but developing efficient non-precious metal electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) remains a significant challenge. To achieve low cost and efficient hydrogen evolution, we prepared the FeP-CoVO heterostructure using the morphological control and defect engineering and the urea oxidation is introduced to instead of OER at the anode. In 1 M KOH, the FeP-CoVO electrode requires overpotentials of 209 and 281 mV for HER and OER at current densities of 100 cm-2, respectively. Furthermore, after OER, the micro-morphology of the catalyst are restructured and there are the transition of cobalt oxidation state and iron oxyhydroxide species reconstructed. For the urea oxidation reaction (UOR), the FeP-CoVO also demonstrates high activity (with the overpotential 1.346 V at 50 cm-2) and high durability. For the application of a dual-electrode electrolyzer, the low cell voltages is 1.417 V at a current density of 10 cm-2for overall urea oxidation. For the development of water and urea electrolysis technologies, this work provides critical insights into designing efficient and low-cost non-precious metal catalysts.

Currently, many color image encryption algorithms suffer from weak inter-channel interactions and lack sensitivity in their permutation and diffusion mechanisms. Therefore, this paper presents a novel 4D hyperchaotic system along with two types of 3-layer Peano curves (3LPC), based on which an efficient and secure cross-channel color image encryption scheme is proposed. Firstly, we incorporate the absolute value term and implement nonlinear coupling within the hyperchaotic system, which is proven to have complex dynamic behavior and a wide hyperchaotic interval through attractor diagrams, Lyapunov exponent spectrums, bifurcation diagrams, Poincar & eacute; section diagrams, and NIST test. Secondly, to overcome the challenges of cross-channel coverage and low utilization in current space-filling curves used for image encryption, we creatively propose the 3LPC that corresponds to the three-channel physical structure of color images. Based on different application scenarios, we design two distinct adaptation strategies to efficiently meet the encryption requirements of various stages in the encryption process. Finally, we propose a cross-channel technique with cross-channel random permutation and bidirectional spatial diffusion. By optimizing the 3LPC, the RGB channels of the color image are interconnected, ensuring that the influence of pixel encryption extends beyond individual channels to encompass the entire image structure. Meanwhile, the chaotic matrix generated by the 4D hyperchaotic system guarantees randomness in the encryption process, significantly increasing key sensitivity and key space. Simulation results and security analyses demonstrate that the proposed encryption scheme exhibits excellent permutation and diffusion properties, effectively resisting a range of illegal attacks.