Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.

Detection and treatment of water inflow in karst tunnel：A case study in Daba tunnel

In a karst tunnel, fissures or cracks that are filled with weathered materials are a type of potential water outlet as they are easily triggered and converted into groundwater outlets under the influence of high groundwater pressure. A terrible water inrush caused by potential water outlets can seriously hinder the project construction. Potential water outlets and water sources that surrounding the tunnel must be detected before water inflow can be treated. This paper provides a successful case of the detection and treatment of water inflow in a karst tunnel and proposes a potential water outlet detection(PWOD) method in which heavy rainfall(>50 mm/d) is considered a trigger for a potential water outlet. The Daba tunnel located in Hunan province, China, has been constructed in a karst stratum where the rock mass has been weathered intensely by the influence of two faults. Heavy rain triggered some potential water outlets, causing a serious water inrush. The PWOD method was applied in this project for the treatment of water inflow, and six potential water outlets in total were identified through three heavy rains. Meanwhile, a geophysical prospecting technique was also used to detect water sources. The connections between water outlets and water sources were identified with a 3-D graphic that included all of them. According to the distribution of water outlets and water sources, the detection area was divided into three sections and separately treated by curtain grouting.

Leakage Detection in Water Distribution Network Based on a New Heuristic Genetic Algorithm Model

The leakage control is an important task, because it is associated with some problems such as economic loss, safety concerns, and environmental damages. The pervious methods which have already been devised for leakage detection are not only expensive and time consuming, but also have a low efficient. As a result, the global leakage detection methods such as leak detection based on simulation and calibration of the network have been considered recently. In this research, leak detection based on calibration in two hypothetical and a laboratorial networks is considered. Additionally a novel optimization method called step-by-step elimination method (SSEM) combining with a genetic algorithm (GA) is introduced to calibration and leakage detection in networks. This method step-by-step detects and eliminates the nodes that provide no contribution in leakage among uncertain parameters of calibration of a network. The proposed method initiates with an ordinary calibration for a studied network, follow by elimination of suspicious nodes among adjusted parameters, then, the network is re-calibrated. Finally the process is repeated until the numbers of unknown demands are equal to the desired numbers or the exact leakage locations and values are determined. These investigations illustrate the capability of this method for detecting the locations and sizes of leakages.

Fluorescence Quenching of a Novel Eu(Ⅲ) Coordination Compound for the Detection of Trace Amounts of Water： Synthesis, Structure and Photoluminescent Property

A novel luminescent coordination compound Eu（TTA）3（DEDAF）（1, TTA = 1,1,1-trifluoro-3-（2-thenoyl）acetone, DEDAF = 9,9-diethyl-4,5-diazafluoren） has been synthesized and fully characterized by infrared spectrum, elemental analysis, UV-vis spectrum, etc. X-ray single-crystal diffraction analysis reveals that compound 1 shows a mononuclear structure with the europium atom in coordinating to one DAF and three TTA ligands. The mononuclear structure units are assembled into a 3-D polymer by hydrogen bonds and π-π interactions. Photoluminescent property of 1 was investigated in detail at room temperature. Complex 1 emits strong red luminescence. However, it could be quenched even by small amount of water. The fluorescence intensity at 614 nm decreases linearly with the water content increasing（vol% in acetonitrile） in the range of 0.025~0.2% under 278 nm excitation. Thermogravimetric analysis has also been studied, which demonstrates good thermal stability of 1.

Bioinspired Adaptive,Elastic,and Conductive Graphene Structured Thin-Films Achieving High-Efficiency Underwater Detection and Vibration Perception

Underwater exploration has been an attractive topic for understanding the very nature of the lakes and even deep oceans.In recent years,extensive efforts have been devoted to developing functional materials and their integrated devices for underwater information capturing.However,there still remains a great challenge for water depth detection and vibration monitoring in a high-efficient,controllable,and scalable way.Inspired by the lateral line of fish that can sensitively sense the water depth and environmental stimuli,an ultrathin,elastic,and adaptive underwater sensor based on Ecoflex matrix with embedded assembled graphene sheets is fabricated.The graphene structured thin film is endowed with favourable adaptive and morphable features,which can conformally adhere to the structural surface and transform to a bulged state driven by water pressure.Owing to the introduction of the graphene-based layer,the integrated sensing system can actively detect the water depth with a wide range of 0.3-1.8 m.Furthermore,similar to the fish,the mechanical stimuli from land(e.g.knocking,stomping)and water(e.g.wind blowing,raining,fishing)can also be sensitively captured in real time.This graphene structured thin-film system is expected to demonstrate significant potentials in underwater monitoring,communication,and risk avoidance.

Outlier Detection for Water Supply Data Based on Joint Auto-Encoder

With the development of science and technology,the status of the water environment has received more and more attention.In this paper,we propose a deep learning model,named a Joint Auto-Encoder network,to solve the problem of outlier detection in water supply data.The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data,and then reconstructs the input data effectively into an output.The outliers are detected based on the network’s reconstruction errors,with a larger reconstruction error indicating a higher rate to be an outlier.For water supply data,there are mainly two types of outliers:outliers with large values and those with values closed to zero.We set two separate thresholds,and,for the reconstruction errors to detect the two types of outliers respectively.The data samples with reconstruction errors exceeding the thresholds are voted to be outliers.The two thresholds can be calculated by the classification confusion matrix and the receiver operating characteristic(ROC)curve.We have also performed comparisons between the Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data set and the MNIST data set.As a result,our model has proved to outperform the vanilla Auto-Encoder and some other outlier detection approaches with the recall rate of 98.94 percent in water supply data.

Development and evaluation of a DNA microarray assay for the simultaneous detection of nine harmful algal species in ship ballast and seaport waters

Rapid,high-throughput and reliable methods are urgently required to accurately detect and monitor harmful algae,which are responsible for algal blooms,such as red and green tides. In this study,we successfully developed a multiplex PCR-based DNA microarray method capable of detecting nine harmful algal species simultaneously,namely A lexandrium tamarense,Gyrodinium instriatum,Heterosigma akashiwo,Karenia mikimotoi,Prorocentrum donghaiense,Prorocentrum minimum,Ulva compressa,Ulva ohnoi and Ulva prolifera. This method achieved a limit of detection(LOD) of 0.5 ng of genomic DNA(orders of magnitude of the deci-nanogram range) in the tested algae cultures. Altogether,230 field samples from ship ballast waters and seaport waters were used to evaluate the DNA microarray. The clinical sensitivity and specificity of the DNA microarray assay in detecting field samples were 96.4% and 90.9%,respectively,relative to conventional morphological methods. This indicated that this high-throughput,automatic,and specific method is well suited for the detection of algae in water samples.

Leak Detection in Water Distribution Systems Using Bayesian Theory and Fisher’s Law

A leak detection method based on Bayesian theory and Fisher’s law was developed for water distribution systems. A hydraulic model was associated with the parameters of leaks (location, extent). The randomness of parameter values was quantified by probability density function and updated by Bayesian theory. Values of the parameters were estimated based on Fisher’s law. The amount of leaks was estimated by back propagation neural network. Based on flow characteristics in water distribution systems, the location of leaks can be estimated. The effectiveness of the proposed method was illustrated by simulated leak data of node pressure head and flow rate of pipelines in a test pipe network, and the leaks were spotted accurately and renovated on time.

THE STUDY ON NOVEL MICROELECTRODE ARRAY CHIPS FOR THE DETECTION OF HEAVY METALS IN WATER POLLUTION

Qualitative and quantitative analysis of trace heavy metals in aqueous environment are rapidly assuming significance along with the rapid development of industry.In this paper,gold microelectrode array(MEA)plated with mercury film was used for simultaneous voltammetric detection of zinc,cadmium,lead and copper ions in water.The electrochemical behavior and the actual surface area of the MEA were investigated by cyclic voltammetry in K_(3)[Fe(CN)_(6)].Electrochemical impedance spectrum(EIS)was utilized to examine the deposition of mercury on the electrode surface.Based on anodic stripping voltammetry,mercury film
Au MEA was applied to the detection of heavy metals in artificial analyte,where good calibrate linearity was obtained for cadmium,lead and copper ions,but with zinc exhibiting poor linearity.

Sensitive Determination of Metal Ions in Drinking Water by Capillary Electrophoresis Coupled with Contactless Conductivity Detection Using 18-Crown-6 Ether and Hexadecyltrimethylammonium Bromide as Complexing Reagents

A simple, economical, and sensitive capillary electrophoresis (CE) method integrated with capacitively coupled contactless conductivity detection was developed for the determination of metal ions such as K+, Na+, Mg2+, Sr2+, Ca2+ in drinking water. 18-Crown-6 ether and Hexadecyltrimethylammonium Bromide (CTAB) were employed as complexing reagents. The effects of electrolyte additives, citric acid buffer solution, and other separation conditions of CE were comprehensively investigated and carefully optimized. The best results were obtained in a running buffer solution composed of citric acid (12 mM), 18-crown-6 ether (0.2 mM), and CTAB (0.015 mM) at pH 3.5. Under these conditions, a complete separation of five metal ions was successfully achieved in less than 12 min. The limits of detection for the optimal procedure were determined to be in the range of 0.02 - 0.2 mg·L-1. The repeatability with respect to migration times and peak areas, expressed as relative standard deviations, was better than 2.3% and 5.1%, respectively. Evaluation of the efficiency of the methodology indicated that it was reliable for the determination of metal ions in six different brands of drinking water samples.

Different Detection and Treatment Methods for <i>Entamoeba histolytica</i>and <i>Entamoeba dispar</i>in Water/Wastewater: A Review

Entamoeba histolytica is an anaerobic parasitic protozoan and well known as a human pathogen, while its close relative, Entamoeba dispar, also possesses similar characteristics as an infectious agent. These microorganisms are generally transmitted in fecal-contaminated water. However, E. dispar present in industrial wastewater is also capable of creating biofilms that can cause adverse impacts in piping networks. Therefore, it is important to detect both of these protozoan species in water and to find a cost-effective technique for inactivation or management control. This review article summarizes the available detection methods in water and wastewater matrices along with feasible disinfection techniques.

Scanning electron microscopy coupled with energydispersive X-ray spectrometry for quick detection of sulfuroxidizing bacteria in environmental water samples

Detection of sulfur-oxidizing bacteria has largely been dependent on targeted gene sequencing technology or traditional cell cultivation, which usually takes from days to months to carry out. This clearly does not meet the requirements of analysis for time-sensitive samples and/or complicated environmental samples. Since energy-dispersive X-ray spectrometry(EDS) can be used to simultaneously detect multiple elements in a sample, including sulfur, with minimal sample treatment, this technology was applied to detect sulfur-oxidizing bacteria using their high sulfur content within the cell. This article describes the application of scanning electron microscopy imaging coupled with EDS mapping for quick detection of sulfur oxidizers in contaminated environmental water samples, with minimal sample handling. Scanning electron microscopy imaging revealed the existence of dense granules within the bacterial cells, while EDS identified large amounts of sulfur within them. EDS mapping localized the sulfur to these granules. Subsequent 16S rRNA gene sequencing showed that the bacteria detected in our samples belonged to the genus Chromatium, which are sulfur oxidizers. Thus, EDS mapping made it possible to identify sulfur oxidizers in environmental samples based on localized sulfur within their cells, within a short time(within 24 h of sampling). This technique has wide ranging applications for detection of sulfur bacteria in environmental water samples.

Automatic Sensing and Detection for Subway Tunnel Pathologies

Subway tunnels often suffer from surface pathologies such as cracks,corrosion,fractures,peeling,water and sand infiltration,and sudden hazards caused by foreign object intrusions.Installing a mobile visual pathology sensing system at the front end of operating trains is a critical measure to ensure subway safety.Taking leakage as the typical pathology,a tunnel pathology automatic visual detection method based on Deeplabv3+(ASTPDS)was proposed to achieve automatic and high-precision detection and pixel-level morphology extraction of pathologies.Compared with similar methods,this approach showed significant advantages and achieved a detection accuracy of 93.12%,surpassing FCN and U-Net.Moreover,it also exceeded the recall rates for detecting leaks of FCN and U-Net by 8.33%and 8.19%,respectively.

Rapid detection of coliforms in drinking water of Arak city using multiplex PCR method in comparison with the standard method of culture(Most probably Number)

Objective:To analyse molecular detection of coliforms and shorten the time of PCR.Methods:Rapid detection of coliforms by amplification of lacZ and uidA genes in a multiplex PCR reaction was designed and performed in comparison with most probably number(MPN)method for 16 artificial and 101 field samples.The molecular method was also conducted on isolated coliforms from positive MPN samples;standard sample for verification of microbial method certificated reference material;isolated strains from certificated reference material and standard bacteria.The PCR and electrophoresis parameters were changed for reducing the operation time.Results:Results of PCR for lacZ and uidA genes were similar in all of standard,operational and artificial samples and showed the 876 bp and 147 bp bands of lacZ and uidA genes by multiplex PCR.PCR results were confirmed by MPN culture method by sensitivity 86%(95%CI:0.71-0.93).Also the total execution time,with a successful change of factors,was reduced to less than two and a half hour.Conclusions:Multiplex PCR method with shortened operation time was used for the simultaneous detection of total coliforms and Escherichia coli in distribution system of Arak city.It's recommended to be used at least as an initial screening test,and then the positive samples could be randomly tested by MPN.

Simultaneous Detection of Phenols and Anilines in Oilfield Waste Water

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A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

For surface defects in electronic water pump shells,the manual detection efficiency is low,prone to misdetection and leak detection,and encounters problems,such as uncertainty.To improve the speed and accuracy of surface defect detection,a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods.In this method,the MobileNetV3 module replaces the backbone network of YOLOv5s,depth-separable convolution is introduced,the parameters and calculations are reduced,and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy.A dataset of electronic pump shell defects is established,and the performance of the improved method is evaluated by comparing it with that of the original method.The results show that the parameters and FLOPs are reduced by 49.83%and 61.59%,respectively,compared with the original YOLOv5s model,and the detection accuracy is improved by 1.74%,which is an indication of the superiority of the improved method.To further verify the universality of the improved method,it is compared with the results using the original method on the PASCALVOC2007 dataset,which verifies that it yields better performance.In summary,the improved lightweight method can be used for the real-time detection of electronic water pump shell defects.

Height Detection and Analysis of Water Flowing Fractured Zone of Coal Face

Taking 91105 working face as the research object, the observation method of water flowing fractured zone and the layout of mining holes were determined by analyzing the field geological structure. It was shown that the fractured zone height and the ratio given by the measured method were 52.33 and 12.46, respectively. By the numerical simulation method with the software of UDEC, the fractured zone height and the ratio were 42.5 and 10.12. By comparison of measured height data and UDEC numerical simulation, there were some differences between the measured height and the calculated results of UDEC numerical simulation method. The method of simulation can be used as the technical basis for the design of waterproof coal pillar in the future.

Spatial-Temporal Event Detection Method with Multivariate Water Quality Data

With the rapid development of the society,water contamination events cause great loss if the accidents happen in the water supply system.A large number of sensor nodes of water quality are deployed in the water supply network to detect and warn the contamination events to prevent pollution from speading.If all of sensor nodes detect and transmit the water quality data when the contamination occurs,it results in the heavy communication overhead.To reduce the communication overhead,the Connected Dominated Set construction algorithm-Rule K,is adopted to select a part fo sensor nodes.Moreover,in order to improve the detection accuracy,a Spatial-Temporal Abnormal Event Detection Algorithm with Multivariate water quality data(M-STAEDA)was proposed.In M-STAEDA,first,Back Propagation neural network models are adopted to analyze the multiple water quality parameters and calculate the possible outliers.Then,M-STAEDA algorithm determines the potential contamination events through Bayesian sequential analysis to estimate the probability of a contamination event.Third,it can make decision based on the multiple event probabilities fusion.Finally,a spatial correlation model is applied to determine the spatial-temporal contamination event in the water supply networks.The experimental results indicate that the proposed M-STAEDA algorithm can obtain more accuracy with BP neural network model and improve the rate of detection and the false alarm rate,compared with the temporal event detection of Single Variate Temporal Abnormal Event Detection Algorithm(M-STAEDA).

Detection of early gastric cancer using hydro-stomach CT:Blinded vs unblinded analysis

AIM:To evaluate the difference in diagnostic performance of hydro-stomach computed tomography(CT) to detect early gastric cancer(EGC) between blinded and unblinded analysis and to assess independent factors affecting visibility of cancer foci.METHODS:Two radiologists initially blinded and then unblinded to gastroscopic and surgical-histological findings independently reviewed hydro-stomach CT images of 110 patients with single EGC.They graded the visibility of cancer foci for each of three gastric segments(upper,middle and lower thirds) using a 4-point scale(1:definitely absent,2:probably absent,3:probably present,and 4:definitely present).The sensitivity and specificity for detecting an EGC were calculated.Intraobserver and interobserver agreements were analyzed.The visibility of an EGC was evaluated with regard to tumor size,invasion depth,gastric segments,histological type and gross morphology using univariate and multivariate analysis.RESULTS:The respective sensitivities and specificities [reviewer 1:blinded,20%(22/110) and 98%(215/220);unblinded,27%(30/110) and 100%(219/220)/reviewer 2:blinded,19%(21/110) and 98%(216/220);unblinded,25%(27/110) and 98%(215/220)] were not significantly different.Although intraobserver agreements were good(weighted κ = 0.677 and 0.666),interobserver agreements were fair(blinded,0.371) or moderate(unblinded,0.558).For both univariate and multivariate analyses,the tumor size and invasion depth were statistically significant factors affecting visibility.CONCLUSION:The diagnostic performance of hydrostomach CT to detect an EGC was not significantly different between blinded and unblinded analysis.The tumor size and invasion depth were independent factors for visibility.