PSG College of Technology

About: PSG College of Technology is a based out in . It is known for research contribution in the topics: Machining & Thin film. The organization has 3174 authors who have published 3575 publications receiving 40690 citations.

Multi-Agent Path Finding with Delay Probabilities.

Abstract: Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search. We make the following contributions to solve the MAPF problem with imperfect plan execution with small average makespans: First, we formalize the MAPF Problem with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the use of robust plan-execution policies for valid MAPF-DP plans to control how each agent proceeds along its path. Second, we discuss 2 classes of decentralized robust plan-execution policies (called Fully Synchronized Policies and Minimal Communication Policies) that prevent collisions during plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP solver (called Approximate Minimization in Expectation) that generates valid MAPF-DP plans.

Buckling and vibration of axially functionally graded nonuniform beams using differential transformation based dynamic stiffness approach

Abstract: The free vibration of axially functionally graded (FG) non-uniform beams with different boundary conditions is studied using Differential Transformation (DT) based Dynamic Stiffness approach. This method is capable of modeling any beam (Timoshenko or Euler, centrifugally stiffened or not) whose cross sectional area, moment of Inertia and material properties vary along the beam. The effectiveness of the method is confirmed by comparing the present results with existing closed form solutions and numerical results. In FG beams, flexural rigidity and mass density may take majority of functions including polynomials, trigonometric and exponential functions (converted to polynomial expressions). DT based Dynamic stiffness approach is proved to be a versatile and simple approach compared to many other methods already proposed.

LD2FA-PSO: A novel Learning Dynamic Deterministic Finite Automata with PSO algorithm for secured energy efficient routing in Wireless Sensor Network

Abstract: Wireless Sensor Networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Energy efficiency and secured data transmission are considered as the most important design goals for WSN. As the complexity of computer networks increased, the erudition of network-based attacks drew the attention of various researchers from several sectors. As a result of this, many Intrusion Detection System (IDS) have been deployed in such a way that it resolves various aspects of network security such as DoS, worms, viruses, malware, etc. Automation of IDS has been proposed in order to improve the energy efficient routing in Wireless Sensor Network in a secured manner. The proposed work incorporates the Deterministic Finite Automata (DFA) and Particle Swarm Optimization (PSO) for intrusion detection and the data transmission is done in a secured manner by determining and following the optimized route. A novel LD2FA (Learning Dynamic Deterministic Finite Automata) has been proposed so that the dynamic nature of the network is cultured. In turn, LD2FA - PSO provides the information about the node, packet and route inspection for detection and elimination of intruders so that the data transmission is done in an energy efficient manner through the optimal path. Routing through optimal path improves the overall performance of the sensor network and it has been examined through various metrics such as energy consumption, throughput, network lifetime, alive and dead nodes. The performance is evaluated by simulating in MATLAB with sensor nodes ranging from 100–700 in a region of 500 × 500 m^2 and comparing the obtained experimental results with the existing PSO, Greedy Load Balanced Clustering Algorithm (GLBCA), Genetic Algorithm (GA) based clustering, Least Distance Clustering (LDC), cluster-based IDS, light weight IDS. It is observed that, LD2FA-PSO obtained 16% increase in throughput than cluster-based IDS, almost 70% increase in throughput than light weight IDS, 6% and 32% improvement in network lifetime than PSO and GLBCA respectively, almost 30% and 54% improvement in network lifetime than GA and LDC respectively. The energy consumed is almost 3% and 6% lesser than PSO and GA, and 13% higher energy is consumed than LDC. The alive sensor node becomes nil at 2600th round which is better than cluster-based IDS and light weight IDS.