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Traditional transmission line fault diagnosis approaches ignore local structure feature information during feature extraction and cannot concentrate more attention on fault samples, which are difficult to diagnose. To figure out these issues, an enhanced feature extraction-based attention temporal convolutional network (EATCN) is developed to diagnose transmission line faults. The proposed EATCN suggests a new comprehensive feature-preserving (CFP) technique to maintain the global and local structure features of original process data during dimension reduction, where the local structure-preserving technique is incorporated into the principal component analysis model. Furthermore, to diagnose transmission line faults more effectively, a CFP-based attention TCN scheme is constructed to classify the global and local structure features of a fault snapshot dataset. To be specific, to cope with the gradient disappearance problem and improve learning capability, a skip connection attention (SCA) network is developed by incorporating a skip connection structure and two fully connected layers into the existing attention mechanism. By combining the developed SCA network with the conventional TCN's residual blocks, an EATCN-based diagnosis model is then constructed to dynamically pay attention to various imported global and local structure features. Detailed experiments on the datasets of the simulated power system are performed to test the effectiveness of the developed EATCN-based fault diagnosis scheme. [ABSTRACT FROM AUTHOR]

Radix aconiti carmichaeli is a widely used traditional Chinese medicine that has been found to be effective in treating cardiovascular diseases and metabolic disorders. Patients with these diseases often experience a heat generation disorder, which is characterized by chilliness and can worsen the progression of the disease. This study established an in vitro screening model combining the examination of cellular mitochondrial membrane potential and mitochondrial temperature to screen drugs with thermogenic activity. After differentiation and determination of the content of characteristic metabolites of the drug-containing serum blood components, it was found that Fuziline (FZL) is the key thermogenic property in Radix aconiti carmichaeli, responsible for its thermogenic effects with a high relative importance of 33%. Experiments were conducted to evaluate the thermogenic activity of Radix aconiti carmichaeli and FZL in vivo by assessing temperature changes in various organs, including the rectum, liver, and brown adipose tissue. Moreover, the effects of intracellular β3-adrenergic receptor (β3-AR) agonistic effects were evaluated using transient β3-AR transfection and dual-luciferase assay systems. The molecular mechanism by which FZL promotes thermogenesis and improves mitochondrial function was investigated by verifying the β-adrenergic receptors (β-AR) downstream signaling pathway. The results suggest that FZL activates β-AR nonselectively, which in turn activates the downstream cAMP-PKA signaling pathway and leads to an increase in liver glycogenolysis and triglyceride hydrolysis, accompanied by enhancing mitochondrial energy metabolism. Consequently, the liver and brown adipose tissue receive energy to generate heat. In summary, these findings provide insight into the therapeutic application of Radix aconiti carmichaeli for metabolic disorders associated with heat generation disorders. [ABSTRACT FROM AUTHOR]

CO2 adsorption is one of the promising CCS technologies, and activated coke is a solid adsorbent with excellent adsorption properties. In this study, activated coke was prepared by using bituminous coal and coconut shells activated with KOH or CaCl2 in a physically activated atmosphere and modified with ammonia. The effect of the active agent impregnation ratio on the physicochemical properties of activated coke was investigated by N2 adsorption isotherms, scanning electron microscopy (SEM) and Fourier transform infrared spectrometry (FTIR). The CO2 adsorption performance of activated coke was tested, and the effect of nitrogen-containing functional groups on CO2 adsorption was investigated by experiments and simulations. The results showed that the specific surface area of activated coke reached 629.81 m2/g at a KOH impregnation ratio of 0.5 and 610.66 m2/g at a CaCl2 impregnation ratio of 1. The maximum CO2 adsorption capacity of activated coke reached 71.70 mg/g and 90.99 mg/g for conventional power plant flue gas and oxy–fuel combustion flue gas, respectively. After ammonia modification, the CO2 adsorption capacity of activated coke was further increased. Simulations showed that pyrrole and pyrrole functional groups changed the polarity of graphene and established weak interactions with CO2. [ABSTRACT FROM AUTHOR]

The efficient utilization of energy in rich fuel detonation processes and the effective control method of soot are important topics in combustion research. In this paper, we numerically study the detonation wave behavior of acetylene–air systems in rich fuel condition by using a reduced reaction mechanism. The non-stiff terms of the governing equations are solved explicitly using the gas kinetic scheme, and the stiff terms are solved implicitly. Our results show that the acetylene pyrolysis is the dominant reaction process. The oxidation reaction is exploited to initiate the reaction induction process, providing the required energy to overcome the potential energy barrier. The secondary detonation structure is due to the stable interaction of the transverse waves and the combined action of the vinyl reaction system, thus effectively improve the energy release rate and providing a powerful solution for the fuel-rich high-energy release of advanced heat engines. The area of the unreacted pocket increases with the acetylene concentration, resulting in an irregular wave-front and detonation cell. The reflected shock wave impacting on the wall induces the secondary reaction of the detonation products. The concentration of polycyclic aromatic hydrocarbons decreases significantly and regenerates near the wall. Our approach provides an effective tool for controlling detonation soot and the preparation of carbon particles. [ABSTRACT FROM AUTHOR]

Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning. Finally, to validate the effectiveness of the proposed method, experiments are conducted on linear, S-shaped, and circular gate frame trajectories with both single and multiple robotic fish. Analysis reveals that the visual navigation method proposed in this paper can stably traverse various types of gate frame trajectories. Compared to end-to-end learning and purely unsupervised image reconstruction, the proposed control strategy demonstrates superior performance, offering a new solution for the intelligent navigation of robotic fish in complex environments. [ABSTRACT FROM AUTHOR]

The attainment of accurate motion control for robotic fish inside intricate underwater environments continues to be a substantial obstacle within the realm of underwater robotics. This paper presents a proposed algorithm for trajectory tracking and obstacle avoidance planning in robotic fish, utilizing nonlinear model predictive control (NMPC). This methodology facilitates the implementation of optimization-based control in real-time, utilizing the present state and environmental data to effectively regulate the movements of the robotic fish with a high degree of agility. To begin with, a dynamic model of the robotic fish, incorporating accelerations, is formulated inside the framework of the world coordinate system. The last step involves providing a detailed explanation of the NMPC algorithm and developing obstacle avoidance and objective functions for the fish in water. This will enable the design of an NMPC controller that incorporates control restrictions. In order to assess the efficacy of the proposed approach, a comparative analysis is conducted between the NMPC algorithm and the pure pursuit (PP) algorithm in terms of trajectory tracking. This comparison serves to affirm the accuracy of the NMPC algorithm in effectively tracking trajectories. Moreover, a comparative analysis between the NMPC algorithm and the dynamic window approach (DWA) method in the context of obstacle avoidance planning highlights the superior resilience of the NMPC algorithm in this domain. The proposed strategy, which utilizes NMPC, demonstrates a viable alternative for achieving precise trajectory tracking and efficient obstacle avoidance planning in the context of robotic fish motion control within intricate surroundings. This method exhibits considerable potential for practical implementation and future application. [ABSTRACT FROM AUTHOR]

The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is achieved by the robots' interaction with the environment, even in situations when the environment is unfamiliar. Consequently, we provide a refined deep reinforcement learning algorithm that builds upon the soft actor-critic (SAC) algorithm, incorporating the concept of maximum entropy for the purpose of path planning. The objective of this strategy is to mitigate the constraints inherent in conventional reinforcement learning, enhance the efficacy of the learning process, and accommodate intricate situations. In the context of reinforcement learning, two significant issues arise: inadequate incentives and inefficient sample use during the training phase. To address these challenges, the hindsight experience replay (HER) mechanism has been presented as a potential solution. The HER mechanism aims to enhance algorithm performance by effectively reusing past experiences. Through the utilization of simulation studies, it can be demonstrated that the enhanced algorithm exhibits superior performance in comparison with the pre-existing method. [ABSTRACT FROM AUTHOR]

A realistic and visible dynamic simulation platform can significantly facilitate research on underwater robots. This paper uses the Unreal Engine to generate a scene that resembles real ocean environments, before building a visual dynamic simulation platform in conjunction with the Air-Sim system. On this basis, the trajectory tracking of a biomimetic robotic fish is simulated and assessed. More specifically, we propose a particle swarm optimization algorithm-based control strategy to optimize the discrete linear quadratic regulator controller for the trajectory tracking problem, as well as tracking and controlling discrete trajectories with misaligned time series through introducing a dynamic time warping algorithm. Simulation analyses of the biomimetic robotic fish following a straight line, a circular curve without mutation, and a four-leaf clover curve with mutation are carried out. The obtained results verify the feasibility and effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

An experimental study was carried out on the ignition characteristics of the HAN/(Emim)(EtSO4) (hydroxylammonium nitrate and 1-ethyl-3-methyl-imidazolium ethyl sulfate) dual-mode ionic liquid monopropellant in chemical propulsion mode in model thrusters. Firstly, a model thruster with a detachable convergent nozzle was designed and fabricated. Secondly, catalytic ignition experiments at different flow rates were carried out in atmosphere and in high chamber pressure environment, respectively, using a model thruster, with and without the convergent nozzle. During the catalytic ignition process, measurement methods such as thermocouple, pressure sensor, and flue gas analyzer were employed to obtain the temperature at different depths of the catalytic bed, the pressure of the combustion chamber, and the concentration variations of gaseous products CO, CO2, CH4, SO2, NO, and NO2. Then the three characteristic stages of water evaporation, HAN decomposition, and (Emim)(EtSO4) combustion were analyzed at the initiation time, and the reaction characteristics in the process of the catalytic ignition were analyzed. In addition, the composition and concentration of the combustion products at equilibrium were theoretically calculated. The effects of temperature and pressure on the concentrations of five main gaseous products were studied. Finally, the exhaust gas of the three groups of catalytic ignition experiments under different pressure environments was separately collected and measured with gas chromatography (GC) when the experiments approached equilibrium, the result of which roughly agrees with the theoretical calculations. These results are of great significance for exploring the chemical propulsion of the dual-mode ionic liquid propellant and understanding its physical catalytic combustion mechanisms. [ABSTRACT FROM AUTHOR]