A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme

Graph Fuzzy System: Concepts, Models and Algorithms

Fuzzy systems (FSs) have enjoyed wide applications in various fields, including pattern recognition, intelligent control, data mining and bioinformatics, which is attributed to the strong interpretation and learning ability. In traditional application scenarios, FSs are mainly applied to model Euclidean space data and cannot be used to handle graph data of non-Euclidean structure in nature, such as social networks and traffic route maps. Therefore, development of FS modeling method that is suitable for graph data and can retain the advantages of traditional FSs is an important research. To meet this challenge, a new type of FS for graph data modeling called Graph Fuzzy System (GFS) is proposed in this paper, where the concepts, modeling framework and construction algorithms are systematically developed. First, GFS related concepts, including graph fuzzy rule base, graph fuzzy sets and graph consequent processing unit (GCPU), are defined. A GFS modeling framework is then constructed and the antecedents and consequents of the GFS are presented and analyzed. Finally, a learning framework of GFS is proposed, in which a kernel K-prototype graph clustering (K2PGC) is proposed to develop the construction algorithm for the GFS antecedent generation, and then based on graph neural network (GNNs), consequent parameters learning algorithm is proposed for GFS. Specifically, three different versions of the GFS implementation algorithm are developed for comprehensive evaluations with experiments on various benchmark graph classification datasets. The results demonstrate that the proposed GFS inherits the advantages of both existing mainstream GNNs methods and conventional FSs methods while achieving better performance than the counterparts.Comment: This paper has been submitted to a journa

Ligand and structure-based approaches for the exploration of structure–activity relationships of fusidic acid derivatives as antibacterial agents

Introduction: Fusidic acid (FA) has been widely applied in the clinical prevention and treatment of bacterial infections. Nonetheless, its clinical application has been limited due to its narrow antimicrobial spectrum and some side effects.Purpose: Therefore, it is necessary to explore the structure–activity relationships of FA derivatives as antibacterial agents to develop novel ones possessing a broad antimicrobial spectrum.Methods and result: First, a pharmacophore model was established on the nineteen FA derivatives with remarkable antibacterial activities reported in previous studies. The common structural characteristics of the pharmacophore emerging from the FA derivatives were determined as those of six hydrophobic centers, two atom centers of the hydrogen bond acceptor, and a negative electron center around the C-21 field. Then, seven FA derivatives have been designed according to the reported structure–activity relationships and the pharmacophore characteristics. The designed FA derivatives were mapped on the pharmacophore model, and the Qfit values of all FA derivatives were over 50 and FA-8 possessed the highest value of 82.66. The molecular docking studies of the partial target compounds were conducted with the elongation factor G (EF-G) of S. aureus. Furthermore, the designed FA derivatives have been prepared and their antibacterial activities were evaluated by the inhibition zone test and the minimum inhibitory concentration (MIC) test. The derivative FA-7 with a chlorine group as the substituent group at C-25 of FA displayed the best antibacterial property with an MIC of 3.125 µM. Subsequently, 3D-QSAR was carried on all the derivatives by using the CoMSIA mode of SYBYL-X 2.0.Conclusion: Hence, a computer-aided drug design model was developed for FA, which can be further used to optimize FA derivatives as highly potent antibacterial agents

A New Cooperative Anomaly Detection Method for Stacker Running Track of Automated Storage and Retrieval System in Industrial Environment

Considering the complexity and the criticality of the stacker equipment, in order to solve the problem that the stop accuracy of the stacker reduces or even fails to work due to abrasion of the running rail, this paper proposes a cooperative detection method based on Pulse Coupling Neural Network (PCNN) and wavelet transform theory to detect the abnormal points of the stacker running rail in industrial environment by analyzing the variation signals. First of all, considering the fact that the data is mixed up with noises because of the environment at the site and the possibility of the data acquisition equipment breaking down, a noise reduction method for the vibration signal data of stacker is constructed based on PCNN. Then, the basic theory of wavelet transform is introduced and then the rules of judging anomaly points on stackers’ running tracks are discussed based on wavelet transform. In addition, a cooperative detection method based on PCNN and wavelet transform theory is carried out based on the space-time distribution feature of the vibration of the stacker orbits in the industrial environment. Then the rationality of the proposed algorithm is verified by simulation through data provided by State Grid Measuring Center of China. This paper constructs a model of the abnormal point detection of the stackers in an industrial environment. The experimental simulation and example simulation show that the cooperative detection method based on PCNN and wavelet transform theory can effectively detect and locate the anomaly points of the stacker running tracks. The expansibility in engineering applications is promising. Lastly, some conclusions are discussed

Cooling Characteristic of a Wall Jet for Suppressing Crossflow Effect under Conjugate Heat Transfer Condition

The leading edge is the critical portion for a gas turbine blade and is often insufficiently cooled due to the adverse effect of Crossflow in the cooling chamber. A novel internal cooling structure, wall jet cooling, can suppress Crossflow effect by changing the coolant flow direction. In this paper, the conjugate heat transfer and aerodynamic characteristics of blades with three different internal cooling structures, including impingement with a single row of jets, swirl cooling, and wall jet cooling, are investigated through RANS simulations. The results show that wall jet cooling combines the advantages of impingement cooling and swirl cooling, and has a 19–54% higher laterally-averaged overall cooling effectiveness than the conventional methods at different positions on the suction side. In the blade with wall jet cooling, the spent coolant at the leading edge is extracted away through the downstream channels so that the jet could accurately impinge the target surface without unnecessary mixing, and the high turbulence generated by the separation vortex enhances the heat transfer intensity. The Coriolis force induces the coolant air to adhere to the pressure side’s inner wall surface, preventing the jet from leaving the target surface. The parallel cooling channels eliminate the common Crossflow effect and make the flow distribution of the orifices more uniform. The trailing edge outlet reduces the entire cooling structure’s pressure to a low level, which means less penalty on power output and engine efficiency

Experimental and numerical investigation on overall performance of a radial inflow turbine for 100kw microturbine

ABSTRACT This paper describes a study on overall performance of the radial inflow turbine for a 100kW microturbine by means of experimental and numerical investigations. All tests are performed with a turbine inlet temperature of 773K and an atmospheric exit pressure, and the rotor rotational speed is ranged from 20000 to 50000 rpm. In addition, the overall performance and the energy loss characteristics for each component of the radial inflow turbine are investigated by 3D Reynolds-averaged Navier-Stokes solutions. The volute, the whole passages of nozzle vanes and rotor, and the exhaust diffuser are meshed with multi-block structured grid. The results of numerical simulation agreed well, as a whole, with that of the experiment both for stage mass flow rate and stage total-static efficiency, which achieved the desired requirements of the design. Based on the results of numerical simulation, the losses of components and exit velocity are analyzed respectively at the off-design conditions. At turbine design point, the losses of volute, nozzle, rotor, exit velocity and exhaust diffuser are about 1.5%, 25%, 43%, 19.5%, 11% of the total loss, respectively, and the rotor incidence angle is basically at optimized value of -23.6°. The losses of rotor and exit velocity change significantly when stage expansion ratio or the rotational speed of rotor altered. In addition, the loss of volute is relative small in the nozzled radial inflow turbine, and then the effect of volute can be neglected in the process of thermal aerodynamic design

Disc Thickness and Spacing Distance Impacts on Flow Characteristics of Multichannel Tesla Turbines

Tesla turbines are a kind of unconventional bladeless turbines, which utilize the viscosity of working fluid to rotate the rotor and realize energy conversion. They offer an attractive substitution for small and micro conventional bladed turbines due to two major advantages. In this study, the effects of two influential geometrical parameters, disc thickness and disc spacing distance, on the aerodynamic performance and flow characteristics for two kinds of multichannel Tesla turbines (one-to-one turbine and one-to-many turbine) were investigated and analyzed numerically. The results show that, with increasing disc thickness, the isentropic efficiency of the one-to-one turbine decreases a little and that of the one-to-many turbine reduces significantly. For example, for turbine cases with 0.5 mm disc spacing distance, the former drops less than 7% and the latter decreases by about 45% of their original values as disc thickness increases from 1 mm to 2 mm. With increasing disc spacing distance, the isentropic efficiency of both kinds of turbines increases first and then decreases, and an optimal value and a high efficiency range exist to make the isentropic efficiency reach its maximum and maintain at a high level, respectively. The optimal disc spacing distance for the one-to-one turbine is less than that for the one-to-many turbine (0.5 mm and 1 mm, respectively, for turbine cases with disc thickness of 1 mm). To sum up, for designing a multichannel Tesla turbine, the disc spacing distance should be among its high efficiency range, and the determination of disc thickness should be balanced between its impacts on the aerodynamic performance and mechanical stress

An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design

This paper proposes an integrated design and optimization approach for radial inflow turbines consisting of an automated preliminary design module and a flexible three-dimensional multidisciplinary optimization module. The latter was constructed by an evolution algorithm, a genetic algorithm-assisted self-learning artificial neural network and a dynamic sampling database. The 3-D multidisciplinary optimization approach was validated by the original T-100 turbine and the T-100re turbine obtained from the automated preliminary design approach, for maximizing the total-to-static efficiency and minimizing the rotor weight while keeping the mass flow rate constant and stress limitation satisfied. The validation results indicate that the total-to-static efficiency is 89.6%, increased by 1.3%, and the rotor weight is reduced by 0.14 kg (14.6%) based on the T-100re turbine, while the efficiency is 88.2%, increased by 2.2% and the weight is reduced by 0.49 kg (37.4%) based on the original T-100 turbine. Moreover, the T-100re turbine shows better performance at the preliminary design stage and conserves this advantage to the end, though both the aerodynamic performance of the T-100 and the T-100re turbine are improved after 3-D optimization. At the same time, it is implied that the preliminary design plays an essential role in the radial inflow turbine design process, and it is hard for only 3-D optimization to get a further performance improvement

Effects of Surface Roughness on Windage Loss and Flow Characteristics in Shaft-Type Gap with Critical CO2

To investigate the effects of surface roughness on windage loss and flow characteristics in a shaft-type gap, the skin friction coefficient (Cf) and flow versus Reynolds number (Re) at different surface roughness (Ra) and radius ratio (η) values were investigated. The results showed that Cf decreased as Re increased, and the rate of decrease was constant at low Re but reduced at high Re. The growing relative deviations between the coefficients of smooth and rough walls with Ra indicated that Cf was influenced by rough walls when Re > 102. Moreover, Cf and the variation rate increased with η and were easily influenced by Ra for larger η at low Re, since the interaction between wall roughness and fluid influences windage loss. In addition, the flow field implied the flow had transitioned to Taylor-Couette flow, Taylor vortexes occurred when Re > 102, and the number of vortexes increased with increasing Ra and were reduced with increasing η. The velocity was divided into three regions and the pressure rose from the rotational to stationary walls, but decreased with growing η as a whole. This paper improves the research exploring windage loss and will help design smaller supercritical CO2 power devices