Multi-objective optimization of laminated composite shells for minimum mass/cost and maximum buckling pressure with failure criteria under external hydrostatic pressure

In the present study, Multi-objective optimization of composite cylindrical shell under external hydrostatic pressure was investigated. Parameters of mass, cost and buckling pressure as fitness functions and failure criteria as optimization criterion were considered. The objective function of buckling has been used by performing the analytical energy equations and Tsai-Wu and Hashin failure criteria have been considered. Multi-objective optimization was performed by improving the evolutionary algorithm of NSGA-II. Also the kind of material, quantity of layers and fiber orientations have been considered as design variables. After optimizing, Pareto front and corresponding points to Pareto front are presented. Trade of points which have optimized mass and cost were selected by determining the specified pressure as design criteria. Finally, an optimized model of composite cylindrical shell with the optimum pattern of fiber orientations having appropriate cost and mass is presented which can tolerate the maximum external hydrostatic pressure.

     

Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot

Human mastication is a complex and rhythmic biomechanical process which is regulated by a brain stem central pattern generator (CPG). Masticatory patterns, frequency and amplitude of mastication are different from person to person and significantly depend on food properties. The central nervous system controls the activity of muscles to produce smooth transitions between different movements. Therefore, to rehab human mandibular system, there is a real need to use the concept of CPG for development of a new methodology in jaw exercises and to help jaw movements recovery. This paper proposes a novel method for real-time trajectory generation of a mastication rehab robot. The proposed method combines several methods and concepts including kinematics, dynamics, trajectory generation and CPG. The purpose of this article is to provide a methodology to enable physiotherapists to perform the human jaw rehabilitation. In this paper, the robotic setup includes two Gough–Stewart platforms. The first platform is used as the rehab robot, while the second one is used to model the human jaw system. Once the modeling is completed, the second robot will be replaced by an actual patient for the selected physiotherapy. Gibbs–Appell’s formulation is used to obtain the dynamics equations of the rehab robot. Then, a method based on the Fourier series is employed to tune parameters of the CPG. It is shown that changes in leg lengths, due to the online changes of the mastication parameters, occur in a smooth and continuous manner. The key feature of the proposed method, when applied to human mastication, is its ability to adapt to the environment and change the chewing pattern in real-time parameters, such as amplitudes as well as jaw movements velocity during mastication.     

Unsupervised adaptive neural-fuzzy inference system for solving differential equations

There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential equations. The presented solution of differential equation consists of two parts; the first part satisfies the initial/boundary condition and has no adjustable parameter whereas the second part is an ANFIS which has no effect on initial/boundary conditions and its adjustable parameters are the weights of ANFIS. The algorithm is applied to solve differential equations and the results demonstrate its accuracy and convince us to use ANFIS in solving various differential equations.     

Solving the Problem of Forbidden States in Discrete Event Systems: A Novel Systematic Method for Reducing the Number of Control Places

This paper deals with the problem of forbidden states in Discrete Event Systems modelled by non‐safe Petri Nets. To avoid these states, some Generalized Mutual Exclusion Constraints can be assigned to them. These constraints limit the weight sum of tokens in some places and can be enforced on the system using control places. When the number of these constraints is large, a large number of control places should be added to the system. In this paper, a method is presented to assign the small number of constraints to forbidden states using some states which cover the forbidden states. So, a small number of control places are added to the system leading to obtaining a maximally permissive controller.     

Adaptive simulation of two dimensional hyperbolic problems by Collocated Discrete Least Squares Meshless method

An adaptive refinement technique is presented in this paper and used in conjunction with the Collocated Discrete Least Squares Meshless (CDLSM) method for the effective simulation of two-dimensional shocked hyperbolic problems. The CDLSM method is based on minimizing the least squares functional calculated at collocation points chosen on the problem domain and its boundaries. The functional is defined as the weighted sum of the squared residuals of the differential equation and its boundary conditions. A Moving Least Squares (MLS) method is used here to construct the meshless shape functions. An error estimator based on the value of functional at nodal points used to discretize the problem domain and its boundaries is developed and used to predict the areas of poor solutions. A node moving strategy is then used to refine the predicted zones of poor solutions before the problem is resolved on the refined distribution of nodes. The proposed methodology is applied to some two dimensional hyperbolic benchmark problems and the results are presented and compared to the exact solutions. The results clearly show the capabilities of the proposed method for the effective and efficient solution of hyperbolic problems of shocked and high gradient solutions.     

Hamshahri: A standard Persian text collection

The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far.     

On Density-Based Data Streams Clustering Algorithms: A Survey简

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the nmnber of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density- based methods in the clustering process and at the same time overcoming the constraints, which are put out by data streanFs nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms＇ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.     

Natural killer or T-lymphocyte cells: Which is the best immune therapeutic agent for cancer? An optimal control approach

Therapeutic vaccines are being developed as a promising new approach to treatment for cancer patients. There are still many unanswered questions about which kind of therapeutic vaccines are the best for the cancer treatments? In this paper we consider a mathematical model, in the form of a system of ordinary differential equations (ODE), this system is an example from a class of mathematical models for immunotherapy of the tumor that were derived from a biologically validated model by Lisette G. de Pillis. The problem how to schedule a variable amount of which vaccines to achieve a maximum reduction in the primary cancer volume is consider as an optimal control problem and it is shown that optimal control is quadratic with 0 denoting a trajectory corresponding to no treatment and 1 a trajectory with treatment at maximum dose along that all therapeutics are being exhausted. The ODE system dynamics characterized by locating equilibrium points and stability properties are determined by using appropriate Lyapunov functions. Especially we attend a parametric sensitivity analysis, which indicates the dependency of the optimal solution with respect to disturbances in model parameters.