Analysis of Effluent Outfall Problems and Their Classification and Regulation Countermeasures

Effluent outfalls are an important exit for pollutants discharged from the source flowing into environmental water bodies,as well as an important guarantee for the ecological environment of natural water bodies.In response to main problems of large and diverse effluent outfalls,as well as their monitoring analysis,tracing and regulation in China,classification and regulation countermeasures were proposed based on the characteristics of effluent outfalls.It is suggested that a comprehensive management and control system should be built by improving the management and control system,upgrading monitoring techniques and strengthening social supervision and public education,so as to provide a scientific basis for the supervision and management of effluent outfalls in China and help promote the improvement of water quality and the sustainable development and utilization of water resources.

Monitoring and analysis of nonlinear dynamic damage of transport roadway supported by composite hard rock materials in Linglong Gold Mine

The study concentrates mainly on the development of failure process incomposite rock mass. By use of acoustic emission (AE), convergence inspection, pressure monitoring,level measurement techniques and the modem signal analysis technology, as well as scan electronmicroscopy (SEM) experiment, various aspects of nonlinear dynamic damage of composite rock masssurrounding the transport roadway in Linglong gold mine are discussed. According to the monitoringresults, the stability of the rock mass can be synthetically evaluated, and the intrinsic relationbetween the damage and the characteristic parameters of acoustic emission can be determined. Thelocation of the damage of rock mass can also be detected based on the acoustic emission couplemonitoring signals. Finally, the key factors which influence the stability of the transport roadwaysupported by composite hard rock materials are found out.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Monitoring result analyses of high slope of five-step ship lock in the Three Gorges Project

The construction of the double-lane five-step ship lock of the Three Gorges Project （TGP） wascommenced in 1994, the excavation of the ship lock was completed by the end of 1999, and the ship lockwas put in operation in June 2003. The side slopes of the ship lock are characterized by great height（170 m）, steepness （70 m in height of upright slope）, and great length （over 7000 m in total length）. Inassociation with the ship lock, the surrounding rocks in slope have a high potential to deform, withwhich the magnitude of deformation is restricted. Monitoring results show that the deformation of thefive-step ship lock high slopes of the TGP primarily occurred in excavation period, and deformationtended to be stable and convergent during operation period, suggesting the allowable ranges of deformation.At present, the slopes and lock chambers are stable, and the ship lock works well under normaloperation condition, enabling the social and economic benefits of the TGP. 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.

Tropical Cyclone Monitoring and Analysis Techniques: A Review

Tropical cyclones(TCs),including tropical depressions and different categories of typhoons,hurricanes,and cyclonic storms,mostly originate over the oceans in the absence of direct observations.Thus,detailed monitoring and analysis of TCs has always been an unsolved problem.In the recent 20 years,great changes have taken place in domestic and foreign TC monitoring techniques,imposing a significant impact on TC operations and research.Some new technologies and products gradually emerge to support operations,with improved monitoring accuracy.In this paper,the progress on TC monitoring and analysis via meteorological satellites,radars,and airplanes in China and the world is reviewed,compared,and summarized,with special focuses on multisatellite fusion observations,in situ aircraft measurements,and some unconventional observation equipment such as rockets,saildrones,and underwater gliders.On this basis,the paper points out future directions for improving TC monitoring and analysis in aid of better TC forecast and early warning.

Effects of modeling means on properties of monitoring models of spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.

Features of long-term health monitored strains of a bridge with wavelet analysis

This paper analyses the five years’ monitored strains collected from a long-term health monitoring system installed on a bridge with wavelet transform.In the analysis,the monitored strains are pre-processed,features of the monitored data are summarized briefly.The influences of the base functions on the results of wavelet analysis are studied simultaneously.The results show that the db wavelet is a good mother wavelet function in the analysis,and the order N should be larger than 20,but less than 46 in decomposing the monitored strains of the bridge.According to the strain variation features of concrete bridge,the proper decomposition level is 4 in the wavelet multi-resolution analysis.With the present method,the strains caused by random loads and daily sunlight can be accurately extracted from the monitored strains.The decomposed components of the monitored strains show that the amplitudes of the strains caused by random loads,daily sunlight,and annual temperature effect,are about 5 με,25 με,and 50 με respectively.The structural response under random load is smaller than the other parts.

Damage detection with image processing: a comparative study

Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by image processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.

Improving Scalability of Cloud Monitoring Through PCA-Based Clustering of Virtual Machines

Cloud computing has recently emerged as a leading paradigm to allow customers to run their applications in virtualized large-scale data centers. Existing solutions for monitoring and management of these infrastructures consider virtual machines （VMs） as independent entities with their own characteristics. However, these approaches suffer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can bc addressed by leveraging the similarity among VMs behavior in terms of resource usage patterns. In this paper we propose an automated methodology to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. The innovative contribution of the proposed methodology is the use of the statistical technique known as principal component analysis （PCA） to automatically select the most relevant information to cluster similar VMs. We apply the methodology to two case studies, a virtualized testbed and a real enterprise data center. In both case studies, the automatic data selection based on PCA allows us to achieve high performance, with a percentage of correctly clustered VMs between 80% and 100% even for short time series （1 day） of monitored data. Furthermore, we estimate the potential reduction in the amount of collected data to demonstrate how our proposal may address the scalability issues related to monitoring and management in cloud computing data centers.

Performability analysis of avionics system with multilayer HM/FM using stochastic Petri nets

The integrated modular avionics （IMA） architecture is an open standard in avionics industry, in which the number of functionalities implemented by software is greater than ever before. In the IMA architecture, the reliability of the avionics system is highly affected by the software applications. In order to enhance the fault tolerance feature with regard to software application failures, many industrial standards propose a layered health monitoring/fault management （HM/FM） scheme to periodically check the health status of software application processes and recover the malfunctioning software process whenever an error is located. In this paper, we make an analytical study of the HM/FM system for avionics application software. We use the stochastic Petri nets （SPN） to build a formal model of each component and present a method to combine the components together to form a complete system model with respect to three interlayer query strategies. We further investigate the effectiveness of these strategies in an illustrative system.

A water quality model applied for the rivers into the Qinhuangdao coastal water in the Bohai Sea, China

The water quality of all rivers into the Qinhuangdao coastal water was below the grade V in 2013. In this study, an inte- grated MIKE 1 l water quality model is applied to deal with the water environment in the rivers into the Qinhuangdao coastal water. The model is first calibrated with the field measured chemical oxygen demand （COD） concentrations. Then the transport of the COD in the rivers into the Qinhuangdao coastal water is computed based on the model in the water environmental monitoring process. Numerical results show that the COD concentration decreases dramatically in the estuaries, from which we can determine the positions of long-term monitoring stations to monitor the river pollutions into the coastal water. Furthermore, different scenarios about the inputs of the point sources and the non-point sources are simulated to discuss the model application in the water enviro- nmental control, and simplified formula are derived for assessing the water quality and the environmental management of rivers.