Fuzzy reliability analysis of nanocomposite ZnO beams using hybrid analytical-intelligent method

A hybrid analytical-intelligent approach is proposed for fuzzy reliability analysis of the composite beams reinforced by zinc oxide (ZnO) nanoparticle. The fuzzy reliability index corresponding to buckling failure mode of nanocomposite beam under thickness-direction external voltage is computed based on three-levels: (1) fuzzy analysis, (2) reliability analysis and (3) analytical buckling analysis. In fuzzy analysis level, an improved gravitational search algorithm has been applied to determine uncertainty interval for membership levels of reliability index. The adaptive formulation with a dynamical self-adjusting process is used for reliability analysis level based on conjugate first-order reliability method (FORM). The self-adjusting term in conjugate sensitivity vector is used to satisfy the sufficient descent condition for controlling instability of FORM formula while the proposed conjugate scalar factor is computed less than the original conjugate FORM, thus it may be provided with the efficient results for the convex problem. The new and previous sensitivity vectors obtained by conjugate and steepest descent vectors dynamically adjusted the proposed conjugate factor. In the buckling analysis level, an exponential theory in conjunction with the method of energy is utilized. Fuzzy random variables including applied voltage, the volume fraction of ZnO, thickness of beam, spring constant and shear constant of the foundation are considered in studied nanocomposite beam. Survey results indicated that the proposed method can provide stable and acceptable fuzzy membership functions for parametric study. Moreover, the ratio of length to thickness and spring constant of foundation are the more sensitive parameters which affect fuzzy reliability index significantly.

     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Unsupervised Scale-Consistent Depth Learning from Video

International Journal of Computer Vision - We propose a monocular depth estimation method SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent...     

Noise-adaptive synthetic oversampling technique

Applied Intelligence - In the field of supervised learning, the problem of class imbalance is one of the most difficult problems, and has attracted a great deal of research attention in recent...     

Mining sequential rules with itemset constraints

Applied Intelligence - Mining sequential rules from a sequence database usually returns a set of rules with great cardinality. However, in real world applications, the end-users are often...     

Chaos analysis and stability control of the MEMS resonator via the type-2 sequential FNN

Microsystem Technologies - In this paper, a fuzzy stability control algorithm based on the type-2 sequential fuzzy neural network (T2SFNN) is proposed to suppress the chaotic motion of the MEMS...     

基于UAV发动机点火系统的EMI抑制研究

UAV集电子设备于一身,其最大射频电磁干扰(EMI)源自发动机点火系统,它不同程度地影响着机载计算机、自动驾驶仪等以及不同任务载荷的正常工作;通过对某UAV发动机研制的电容放电式点火系统(CDI)的电路和布局描述,研讨了屏蔽对其二次回路影响的机理,合理确定了工程路径,并研究出一种阻尼-屏蔽法实施在CDI中,降低点火系统EMI近20dB,同时不影响其可靠性;本研究的成功,使长期困扰CDI的EMI深入抑制取得了进步。     

A self-learning particle swarm optimizer for global optimization problems

Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.     

A space and time efficient algorithm for SimRank computation

SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing approaches for conducting SimRank computation adopt an iteration paradigm. The most efficient deterministic technique yields O(n³)O\left(n^3\right) worst-case time per iteration with the space requirement O(n²)O\left(n^2\right), where n is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration takes O (min{ n ·m , n^r })O \left(\min \left\{ n \cdot m , n^r \right\}\right) time and O ( n + m )O \left( n + m \right) space, where m is the number of edges in a web-graph model and r ≤ log₂ 7. In addition, we extend the similarity transition matrix to prevent random surfers getting stuck, and devise a pruning technique to eliminate impractical similarities for each iteration. Moreover, we also develop a reordering technique combined with an over-relaxation method, not only speeding up the convergence rate of the existing techniques, but achieving I/O efficiency as well. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness of our iteration techniques.     

Modeling and controlling the mobile harbour crane system with virtual prototyping technology

Virtual simulation of the real behaviour of mobile harbour crane (MHC) without using the traditional build-and-test method is an imperative approach to the design stage that can increase the quality of the product by reducing manufacturing cost and errors. This paper introduces an engineering model that describes the mechanical behaviour of MHC, and the control design for increasing the position accuracy. Based on a concept of the MHC, a virtual mechanical model was created using SOLIDWOKS, which was then exported to the Automatic Dynamic Analysis of Mechanical System (ADAMS) environment. This model was simulated to investigate the dynamic behaviour of the MHC system. In addition, an adaptive siding mode PID controller was also developed in MATLAB/Simulink to control the crane trolley position and suppress the swing angle of the load. This co-simulation demonstrates the reliability of the mechanical and control functionalities of the developed system.