Haitao Xiao

A distributed localized decision self-health monitoring system in WSN developed for bridge diagnoses

In Japan, Bridge is an important part of public transportation infrastructures. As a result, the monitoring of bridge health is accordingly vital important for the government and related institutions. In bridge diagnoses system, we develop a wireless sensor network to measure and gather the vibration data of bridge. The problem is: in past field bridge diagnosis experiment, node failure and data packets loss always occurred in the WSN. Furthermore, when we deploy all the nodes of WSN to bridge in the first time, it is always difficult to set the location of nodes in order to ensure the quality of link is extremely reliable. In order to solve above mentioned problem we propose a distributed data aggregation active monitoring method and a distributed localized decision monitoring method. Based on the two methods we build a self-health monitoring system for WSN: DLDMS (Distributed Localized Decision Monitoring System). We apply this system to the field experiments at Kitakyushu city in Japan and successfully obtain the health status of WSN.

A novel bridge structure damage diagnosis algorithm based on statistical pattern recognition

This paper presents a structure damage detection algorithm based on statistical pattern recognition to analyze the acquired data to evaluate the health level of bridge. In this algorithm a novel statistical pattern recognition damage detection algorithm including a new damage sensitive index DSPR is proposed to determine the severity and location of damages. This paper also presents simulation and experiment, including a detection experiment of making artificial damage to a real bridge, to show that our design choices are indeed quite effective.

A new low-power wireless sensor network for real-time bridge health diagnosis system

With the development of wireless sensor networks (WSNs), it is applied in various fields, such as bridge health diagnosis. In previous bridge health diagnosis system, most of researches use general network models which cannot real-time acquire data or have high power consumption. In order to adapt the network characters, this paper presents a new type Low-power WSN, named LW, for real-time bridge health diagnosis system. Comparing with the WSN of previous bridge diagnosis systems, the contributions of this paper are two aspects. First, a low-power node module board to reduce the power consumption of hardware is designed. Next, an energy efficient and real-time data collection algorithm based on time table (TDMA) is designed to make WSN able to collect data efficiently. This paper also presents several simulations and experiments to show that our design choices are indeed quite effective.

Low energy consumption method based on multi-objective PSO for illumination distribution optimization

Due to the rapid development of intelligent lighting technologies, those intelligences, just like smooth dimming control, communication and networking, lumen control, predictive failure and maintenance, daylight harvesting, etc., which offers us a opportunity to incorporate non-traditional capabilities into our lighting design and makes the intelligent lighting solutions more attractive. Nowadays, in many special situations in designing of intelligent lighting systems(meticulous detection in specified area, occupancy sensing and lighting in office, etc.), saving energy and offering a accurate, stable illumination on a fixed range of area at the same time becomes a charming challenge for designers and researchers. In this paper, we provide a low energy consumption method by using MPSO algorithm and simple illumination calculation method, to simulate the optimized illumination distribution results on a specified space in virtual scene.

Surface illumination controlling system for surface checking of automatic vehicle by using PSO algorithm

In manufacturing process of automobile, people always provide steady illumination during the time when automobile moves on the assembly line. Automobile is moving slowly on the assembly line for surface detection, painting or other operations in the factory, for lower power consumption and giving a satisfaction sufficient to meet a requirement of illumination on the specified surface of automobile, we consider an surface illumination controlling system by establishing a virtual scene for these works. To achieve this target, we provide (i) Illumination Environment Simulation; (ii) Illumination Model; (iii) Particle Swarm Optimization Algorithm. The performance of the proposed algorithm shows that we achieved illumination rendering on the surface of automatic vehicle.

A Clustering Routing and Coverage Algorithm for WSN Based on Brief Artificial Fish-School Optimization

This paper describes a novel clustering routing and coverage algorithm, referred as Brief Artificial Fish-School Optimization Clustering Routing and Coverage (BAFSOCRC), for a wireless sensor network. Based on the detailed analysis of the cluster head selection algorithm and the deficiencies of LEACH algorithm, I propose an algorithm based on Brief Artificial Fish-School Optimization (BAFSO). The key point of the clustering routing algorithm is applying BAFSO algorithm. This algorithm can be divided into two parts: The clustering routing algorithm of WSN based on BAFSO (BAFSOCR) and the coverage of WSN based on BAFSO (BAFSOC). In the BAFSOCR, it can achieve the balance of the network energy consumption, improve the energy efficiency and prolong life cycle of the whole network. In the BAFSOC, it can achieve the distribution optimization in a higher speed with a lower cost, and increase the efficiency of the algorithm.