硅灰混凝土的研究及其在公路道面的应用

 了提高道路路面混凝土的抗磨能力，采用硅灰代替部分水泥的方法，对硅灰混凝土的性能进行了较系统的试验研究。成果表明其抗压强度、抗弯强度、抗磨强度等指标都得到明显提高，全面达到和超过了规范要求。并已在丹江口水利枢纽上坝公路中得到应用，实践证明硅灰混凝土技术可靠、经济合理、易于施工。特别是抗磨度的提高，对延长公路路面的使用寿命具有重要意义。     

Extraction of voltage harmonics using multi-layer perceptron neural network

This paper presents a harmonic extraction algorithm using artificial neural networks for Dynamic Voltage Restorers (DVRs). The suggested algorithm employs a feed forward Multi Layer Perceptron (MLP) Neural Network with error back propagation learning to effectively track and extract the 3rd and 5th voltage harmonics. For this purpose, two different MLP neural network structures are constructed and their performances compared. The effects of hidden layer, supervisors and learning rate are also presented. The proposed MLP Neural Network algorithm is trained and tested in MATLAB program environment. The results show that MLP neural network enable to extract each harmonic effectively.     

Single machine scheduling with unequal release date using neuro-dominance rule

A neuro-dominance rule (NDR) for single machine total weighted tardiness problem with unequal release date is presented by the author. To obtain the NDR, backpropagation artificial neural network (BPANN) has been trained using 10,000 data and also tested using 10,000 another data. Inputs of the trained BPANN are starting date of the first job (t), processing times (p_i and p_j), due dates (d_i and d_j), weights of the jobs (w_i and w_j) and r_i and r_j release dates of the jobs. Output of the BPANN is a decision of which job should precede. Training set and test set have been obtained using Adjusted Pairwise Interchange method. The proposed NDR provides a sufficient condition for local optimality. It has been proved that if any sequence violates the NDR then violating jobs are switched according to the total weighted tardiness criterion. The proposed NDR is compared to a number of competing heuristics (ATC, COVERT, EDD, SPT, LPT, WDD, WSPT, WPD, CR, FCFS) and meta heuristics (simulated annealing and genetic algorithms) for a set of randomly generated problems. The problem sizes have been taken as 50, 70, 100. NDR is applied 270,000 randomly generated problems. Computational results indicate that the NDR dominates the heuristics and meta heuristics in all runs. Therefore, the NDR can improve the upper and lower bounding schemes.     

A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study

In this study, a hybrid intelligent solution system including neural networks, genetic algorithms and simulated annealing has been proposed for the inverse kinematics solution of robotic manipulators. The main purpose of the proposed system is to decrease the end effector error of a neural network based inverse kinematics solution. In the designed hybrid intelligent system, simulated annealing algorithm has been used as a genetic operator to decrease the process time of the genetic algorithm to find the optimum solution. Obtained best solution from the neural network has been included in the initial solution of genetic algorithm with randomly produced solutions. The end effector error has been reduced micrometer levels after the implementation of the hybrid intelligent solution system.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

Coordinating metaheuristic agents with swarm intelligence

Coordination of multi agent systems remains as a problem since there is no prominent method suggests any universal solution. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems using metaheuristic algorithms. An idea for coordinating metaheuristic agents borrowed from swarm intelligence is introduced in this paper. This swarm intelligence-based coordination framework has been implemented as swarms of simulated annealing agents collaborated with particle swarm optimization for multidimensional knapsack problem. A comparative performance analysis is also reported highlighting that the implementation has produced much better results than the previous works.     

A simulated annealing algorithm for multi-agent systems: a job-shop scheduling application

In this paper, a parallel implementation of the modular simulated annealing algorithm for classical job-shop scheduling is presented. The implementation is for a multi agent system running on the distributed resource machine, which is a novel, scalable, distributed virtual machine based on Java technology. The problems tackled are well known, difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit time. The empirical results obtained show that the method proposed is successful in comparison with a sequential version of modular simulated annealing algorithm and other methods described in the literature.     

WSANFlow: An Interface Protocol Between SDN Controller and End Devices for SDN-Oriented WSAN

When dealing with a Wireless Sensor and Actuator Network (WSAN) structure, one of the challenging problems is lack of flexibility in such network operations as establishment, management, and configuration. Software-defined Networking (SDN) is a promising technology for a simpler, more flexible, and less overworked network structure. Integration of SDN as a solution into the existing WSAN structures seems to be a strong candidate of deployment solutions for next generation WSAN systems. In order to get enhanced performance results for WSAN systems, we proposed an interface protocol, referred to as WSANFlow, which is responsible for all the communications between SDN controller (SDNC) and SDN-oriented end devices. The SDNC in this approach has the network intelligence and is capable of handling all the control and management operations related to the network. Thus, advanced communication operations can be managed and efficiently optimized efficiently by the SDN controller and then, subsequently, corresponding instructions can be delivered to end devices using the proposed WSANFlow protocol. In the study, we analyzed the proposed framework performance, in terms of power consumption ratio, throughput, and end to end delay metrics. Then, we compared the results with those of a ZigBee-based counterpart for different workloads such as; light, heavy and heavier load which modelizes a video stream of mild parameters. The results show that not only has the overall performance of the existing WSAN system been enhanced, but also control and management operations have been simplified by the proposed model.