Concurrency and Computation-Practice & Experience最新文献

With the rapid development of Artificial Intelligence (AI) technology, the athletics field is undergoing profound changes. This transformation is reflected not only in the ways of training and competition but also in the overall enhancement of athletes' performance and the efficiency of event management. The introduction of AI has made data analysis feasible, enabling coaches and athletes to gain deeper insights and make more informed decisions. This paper reviews the current applications of AI in the athletics domain and its enhancement of athlete performance and competitive strategies, focusing on the practical applications of AI in event management, performance analysis, injury detection, and personalized training. Furthermore, AI systems support coaches in intelligent analysis by integrating historical data with real-time data, thereby improving the efficiency of tactical decision-making. However, despite the significant achievements in AI applications, a series of challenges remain, including the lack of high-quality datasets, insufficient model interpretability, and ethical and privacy issues. In light of these challenges, we also propose viewpoints on future development directions aimed at promoting the intelligent transformation of the athletics industry.

In practical engineering problems, uncertainties due to prediction errors and fluctuations in equipment efficiency often lead to constrained many-objective optimization problem with interval parameters (ICMaOPs). These problems pose significant challenges for evolutionary algorithms, particularly in balancing solution convergence, diversity, feasibility, and uncertainty. To address these challenges, a personalized indicator-based evolutionary algorithm (PI-ICMaOEA) specifically designed for ICMaOPs is proposed. The PI-ICMaOEA integrates a comprehensive quality indicator that encapsulates convergence, diversity, uncertainty, and feasibility factors, converting multiple objectives in high-dimensional search spaces into a single evaluative metric. Each factor's weight is personalized assigned based on individual performance, objective dimension, and the evolving conditions of the population. By prioritizing individuals with excellent indicator values for mating and environmental selection, PI-ICMaOEA effectively enhances selection pressure in high-dimensional spaces. Comparative simulations demonstrate that PI-ICMaOEA is highly competitive, offering a robust solution for balancing convergence, diversity, uncertainty, and feasibility in ICMaOPs.

Nowadays, vehicular ad-hoc networks (VANETs) offer increased convenience to drivers and enable intelligent traffic management. However, the public wireless transmission channel in VANETs brings challenges related to security vulnerabilities and privacy leakage, in addition, vehicles produced by different manufacturers may use different cryptosystems such as certificateless cryptosystems (CLCs) and identity-based cryptosystems (IBC). To address privacy leakage during cross-cryptosystem communication in VANETs, we propose a lattice-based heterogeneous signcryption scheme named LHS-C2I. The scheme facilitates secure multi-cryptosystem bidirectional communication as CLC-based vehicles to IBC-based vehicles and IBC-based vehicles to CLC-based vehicles. The confidentiality and authenticity of LHS-C2I help to prevent the users from privacy leakage during cross-cryptosystem communication and to authenticate the message integrity and the sender's identity legitimacy. The proposed scheme is proven to achieve Indistinguishability under Chosen Ciphertext Attack (IND-CCA2) and Existential Unforgeability against Adaptive Chosen Messages Attack (EUF-CMA) within the random oracle model. Performance analysis demonstrates that LHS-C2I outperforms existing schemes in terms of computational overhead, communication overhead, and overall security features. It is particularly well-suited for scenarios requiring secure communication across different cryptosystems in VANETs.

Healthcare systems are highly sensitive to cyberattacks as these systems possess most of the sensitive information compared to other systems relying on internet facilities. Due to the stronger security merits and efficiency of blockchain, it is integrated with the healthcare sector to ensure reliable data transfer. However, to improve the reliability and efficiency of the integrated system, a permissioned blockchain-based security framework combining several techniques is proposed. To enable storing and validating blocks containing medical data on the blockchain, the miner is administered using the delegated proof of stake (DPoS) consensus protocol. This protocol is efficient in choosing the miner from the list of participants. Then, the blocks are created using recursive indexing with an Even–Rodeh (RI-ER) coding hashing scheme. This algorithm is an indexing scheme that is much more efficient than the normal hashing algorithms. The validation process is carried out by the miner using the hash values provided to the users. By using the kidney disease dataset from Kaggle, the performance of the proposed method is evaluated. The performance analysis proved the effectiveness of the proposed approach compared to other schemes.

With rapid advancements in computer hardware and numerical modeling methods, Computational Fluid Dynamics (CFD) has gained prominence in simulating complex flows. As parallel computation becomes an industry standard, the computational efficiency of simulations has become critical. The flow around a Vertical Axis Wind Turbine (VAWT), characterized by complex dynamics and challenging rotating geometry, serves as an intriguing case for CFD studies. This study employs the open-source CFD solvers SU2 and OpenFOAM to simulate the incompressible, unsteady, and turbulent flow around an H-type Darrieus VAWT in two dimensions. Spatial and temporal discretization parameters are examined to balance computational cost and accuracy, revealing notable effects on power predictions. Simulations conducted under identical conditions allow for a comparison of the predictions and parallel performances of SU2 and OpenFOAM across three distinct tip speed ratios (TSRs). The findings show that discretization parameters behave differently at various TSRs. While power predictions from SU2 and OpenFOAM generally align with experimental data and with each other, discrepancies arise at lower TSRs, with thrust predictions showing better consistency. Although OpenFOAM provides a faster solution across all parallel configurations, SU2 demonstrates superior parallel scalability, achieving higher speedup and efficiency.

In aviation, petroleum and chemical industries and other special areas of production, since the liquid in containers mostly are flammable, explosive, volatile, and corrosive mixtures, the accurate measurement of the liquid level is essential to the real-time control and production process. In this study, an approximation algorithm based on echo pressure model for measuring the liquid level in special field is proposed in this paper. According to the model, a complete lateral incident ultrasonic measurement system is constructed for the inductive liquid level detection technology in special applications, an algorithm model of the echo pressure is established, which provides the determination of the liquid level relying on the characteristic curve of the echo pressure. The model in this study converts complex interaction problems of sound fields into the echo pressure calculation, which quickly determines performance parameters of sound fields, and a virtual reflection method is used to calculate the sound pressure of the receiving transducer in different states, which reduces computational complexity, finally, through the simulation and experiment, the algorithm is validated. The results are consistent with expectations, and the algorithm can be further applied in practice.

Smooth signed distance surface reconstruction remains a popular technique for generating watertight surfaces from discrete point clouds. However, it frequently encounters issues with geometric detail loss when reconstructing complicated models. In this paper, we introduce a novel reconstruction technique for multi-scale smooth signed distance surfaces based on Gaussian curvature. Initially, the point cloud data is fitted using the moving least squares to calculate the Gaussian curvature. After that, a curvature-adaptive octree is constructed based on the Gaussian curvature, which can dynamically adjust the local resolution. Geometric information can be captured more effectively, improving the accuracy of surface reconstruction. Finally, implicit functions are adopted to perform global fitting, and the zero-level set is obtained through the octree isosurface extraction algorithm. In solving the iterative linear system, multi-thread techniques are implemented for parallel computation to enhance the execution performance of the algorithm. Experimental results demonstrate that the curvature-adaptive octree based on Gaussian curvature, can effectively capture complex geometric details, and the algorithm accomplishes high-precision surface reconstruction at different scales. Furthermore, multi-thread technology enhances local and global computing performance, ensuring the algorithm's effectiveness in processing large-scale data.

Industrial processes are specialized and intricate systems. Current intelligent fault diagnosis methods do not take into account the interactions between individual units and variables, instead using only the temporal or Euclidean geometric space characteristics of industrial process data. How to utilize the complex relationship between variables for fault diagnosis remains an issue to be solved. This study proposed a fault diagnosis framework based on the deep spatiotemporal fusion graph convolutional network (DSTFGCN) for graph representation learning of correlations between variables. First, the maximum information coefficient was introduced to represent the complex correlation between variables in the graph signal construction process. Second, to effectively extract spatiotemporal features from the data, the graph convolutional network (GCN) and the convolutional neural network (CNN) were introduced into the DSTFGCN for mining complex spatial features in the data, and the long short-term memory (LSTM) network was employed to capture the evolution of multivariate time series. Consequently, the fault detection and false-positive rates of the proposed model were, respectively, 94.45% and 0.22% in the Tennessee Eastman Process (TEP), whereas the rates were, respectively, 99.61% and 0.07% on the Three-Phase Flow Facility (TPFF) datasets. These experimental results demonstrate the excellent performance and robustness of the proposed model, compared to those of both machine learning and deep learning models.