A method based on cloud point extraction was developed to determine phthalate esters including di-ethyl-phthalate （DEP）, di- （2-ethylhexyl）-phthalate （DEHP） and di-cyclohexyl-phthalate （DCP） in environmental w...A method based on cloud point extraction was developed to determine phthalate esters including di-ethyl-phthalate （DEP）, di- （2-ethylhexyl）-phthalate （DEHP） and di-cyclohexyl-phthalate （DCP） in environmental water samples using high-performance liquid chromatography separation and ultraviolet detection （HPLC-UV）. The non-ionic surfactant Triton X-114 was chosen as extraction solvent. The parameters affecting extraction efficiency, such as concentrations of Triton X-114 and Na2SO4, equilibration temperature, equilibration time and centrifugation time were evaluated and optimized. Under the optimum conditions, the method can achieve preconcentration factors of 35, 88, 111 and detection of limits of 2.0, 3.8, 1.0 ng/ml for DEP, DEHP and DCP in 10-ml water sample, respectively. The proposed method was successfully applied to the determination of trace amount of phathalate esters in effluent water of the wastewater treatment plant and the lixivium of plastic fragments.展开更多
A novel approach was developed for the determination of ultratrace amounts of copper in water samples by using electrothermal atomic absorption spectrometry （ETAAS） after cloud point extraction （ CPE ）. 1-（ 2-Pyr...A novel approach was developed for the determination of ultratrace amounts of copper in water samples by using electrothermal atomic absorption spectrometry （ETAAS） after cloud point extraction （ CPE ）. 1-（ 2-Pyridylazo ） -2- naphthol was used as the chelating reagent and Triton X-114 as the mieellar-forming surfactant. CPE was conducted in a pH 8. 0 medium at 40 ℃ for 10 rain. After the separation of the phases by contrifugafion, the surfactant-rieh phase was diluted with 1 mL of a methanol solution of 0. 1 mol/L HNO3. Then 20μL of the diluted surfactant-rieh phase was injected into the graphite furnace for atomization in the absence of any matrix modifier. Various experimental conditions that affect the extraction and atomization processes were optimized. A detection limit of 5 ng/L was obtained after preconeentration. The linear dynamic range of the copper mass concentration was found to be 0-2.0 ng/mL, and the relative standard deviation was found to be less than 3. 1% for a sample containing 1.0 ng/mL Cu （ Ⅱ ）. This developed method was successfully applied to the determination of uhratraee amounts of Cu in drinking water, tap water, and seawater samples.展开更多
Cloud point extraction (CPE) with Tergitol TMN-6 was applied for the extraction of trace amounts of palladium (Pd(Ⅱ)), platinum (Pt(Ⅳ)), and gold (Au(Ⅲ)) in the soil of industrial sewage. Ammonium pyrolysine dithio...Cloud point extraction (CPE) with Tergitol TMN-6 was applied for the extraction of trace amounts of palladium (Pd(Ⅱ)), platinum (Pt(Ⅳ)), and gold (Au(Ⅲ)) in the soil of industrial sewage. Ammonium pyrolysine dithiocarbamate (APDC) was adopted as the chelating agent prior to CPE and then was detected by atomic absorption spectrometry (AAS). Different parameters such as the concentration of surfactants, chelating agent and salt, sample pH, equilibration temperature and time, centrifugation time and rates, and the effect of foreign ions were studied. Under optimum conditions, the low limits of detections are 1.4, 2.8 and 1.2 ng·ml^-1 and the enrichment factors are 21, 12, and 24 for Pd(Ⅱ), Pt(Ⅳ), and Au(Ⅲ, respectively. The relative standard deviations vary from 0.6% to 1.0% (n=11). All correlation coefficients of the calibration curves are >0.9960. The proposed method was successfully applied for the determination of Pd(Ⅱ), Pt(Ⅳ), and Au(Ⅲ) in the real soil of industrial sewage samples.展开更多
Cloud point extraction （CPE） processes with two silicone surfactants, Dow Coming DC-190 and DC-193, were studied as preconcentration and treatment for the water polluted by three trace polycyclic aromatic hydrocarbo...Cloud point extraction （CPE） processes with two silicone surfactants, Dow Coming DC-190 and DC-193, were studied as preconcentration and treatment for the water polluted by three trace polycyclic aromatic hydrocarbons （PAHs）： anthracene, phenanthrene and pyrene. For all cases, the volumes of surfactant-rich phase obtained by two silicone surfactants were very small, i.e. a lower water content in the surfactant-rich phase was obtained. For example, less than 3% of the initial solution was obtained in a 1% （by mass） surfactant solution, which was much smaller than that of TX-114 in the same surfactant concentration. And TX-114 is known as a high compact surfactant-rich phase among most nonionic surfactants, thus the comparison showed that an excellent enrichment was ensured in the analysis application by the CPE process with the silicone surfactants, and the lower water content obtained in the surfactant-rich phase is also important in the large scale water treatment. The influences of additives and phase separation methodology on the recovery of PAHs were discussed. Comparing with DC-193, DC-190 has a lower cloud point and a higher recovery （near 100%） of all the three PAHs in same surfactant concentration, which was required for application as a preconcentration process prior to HPLC system. However the DC-190 solution is hard to be phase separated only by heating, whereas DC-193 has a relative higher phase separating speed by heating, but a high cloud point （around 360K） limits its application. Due to the phase separation by heating is the only method of CPE suitable to the large scale water treatment, the mixtures of two silicone surfacrants solutions were investigated in this study. A solution containing 1% of mixed DC-190 and DC-193 （in the ratio of 90 ： 10） removed anthracene, phenanthrene and pyrene near 100% with a relative low cloud point and quick phase separating speed.展开更多
A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-...A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-hydroxyquinoline and Triton X-100 were used as the ligand and surfactant respectively. Nickel（Ⅱ） can form a hy-drophobic complex with 8-hydroxyquinoline, the complex can be extracted into the small volume surfactant rich phase at the cloud point temperature（CPT） for GFAAS determination. The factors affecting the cloud point extraction, such as pH, ligand concentration, surfactant concentration, and the incubation time were optimized. Under the optimal conditions, a detection limit of 12 ng/L and a relative standard deviation（RSD） of 2.9% were obtained for Ni（Ⅱ） determination. The enrichment factor was found to be 25. The proposed method was successfully applied to the determination of nickel（Ⅱ） in certified reference material and different types of water samples and the recovery was in a range of 95%―103%.展开更多
A method for the determination of trace mercury in water samples by hydride generation atomic absorption spectrophotometry after cloud point extraction was proposed in the present work. The effects of pH, concentratio...A method for the determination of trace mercury in water samples by hydride generation atomic absorption spectrophotometry after cloud point extraction was proposed in the present work. The effects of pH, concentration of surfactant, and equilibration time on cloud point extraction were discussed. The enhancement factor of 20 and the detection limit of 0.039 μg/L were obtained for mercury with relative standard deviation of 4.8% （n = 11）.展开更多
A new method was developed for the determination of sodium copper chlorophyll(SCC) by cloud point extraction preconcentration and spectrophotometry, for which Triton X-114 was selected as a nonionic surfactant. Severa...A new method was developed for the determination of sodium copper chlorophyll(SCC) by cloud point extraction preconcentration and spectrophotometry, for which Triton X-114 was selected as a nonionic surfactant. Several factors affecting the extraction efficiency of SCC and its subsequent determination, including the p H of the sample solution, salt and surfactant concentrations, and equilibration temperature and time, were studied and optimized. The extraction efficiency approached 99.4%.The calibration graph under the optimum conditions was linear in the concentration range of 3–220 mg/L with correlation coefficients> 0.9997(n = 8). The limit of detection for the analytes was 0.6 mg/L(S/N = 3). The proposed method is inexpensive, simple, and accurate for the extraction and determination of SCC in food samples.展开更多
With quantum chemical parameters, topological indexes, and physical chemistry parameters as descriptors, a quantitative structure-property relationship（QSPR） has been found for the cloud points of four series of non...With quantum chemical parameters, topological indexes, and physical chemistry parameters as descriptors, a quantitative structure-property relationship（QSPR） has been found for the cloud points of four series of nonionic surfactants（ a total of 65 surfactants）. The best-regressed model includes six descriptors, and the correlation coefficient of multiple determination is as high as 0. 962.展开更多
Cloud point extraction (CPE) has been used for the preconcentration of cadmium, after the formation of a complex with 1, 5-bis(di-2-pyridylmethylene) thiocarbonohydrazide (DPTH), and further determination by flame ato...Cloud point extraction (CPE) has been used for the preconcentration of cadmium, after the formation of a complex with 1, 5-bis(di-2-pyridylmethylene) thiocarbonohydrazide (DPTH), and further determination by flame atomic absorption spectrometry (FAAS) using Triton X-114 as surfactant. The main factors affecting the CPE, such as concentration of Triton X-114 and DPTH, pH, equilibration temperature and incubation time, were optimized for the best extract efficiency. Under the optimum conditions i.e., pH 5.4, [DPTH] = 6x10-3%, [Triton X-114] = 0.25% (v/v), an enhancement factor of 10.5 fold was reached. The lower limit of detection (LOD) obtained under the optimal conditions was 0.95 μg L?1. The precision for 8 replicate deter- minations at 20 and 100 μgL?1 Cd were 2.4 % and 2 % relative standard deviation (R.S.D.). The calibration graph using the preconcentration method was linear with a correlation coefficient of 0,998 at levels close to the detection limit up to at least 200 μgL?1. The method was successfully applied to the determination of cadmium in water, environmental and food samples and in a BCR-176 standard reference material.展开更多
2-(pyridine-2-yl)-N-p-chlorohydrazinecarbothioamide (HCPTS) was synthesized, characterized and successfully applied for the preconcentration of Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II) in wat...2-(pyridine-2-yl)-N-p-chlorohydrazinecarbothioamide (HCPTS) was synthesized, characterized and successfully applied for the preconcentration of Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II) in water, blood, and urine samples prior to graphite furnace atomic absorption determination (GFAAS);Hg was determined by cold vapor technique. Under the optimum experimental conditions (i.e. pH = 8, 10–4 M of HCPTS, 0.05% w/v of Triton X-114), calibration graphs were linear in the range of 0.02 to 200 ng?mL–1 for Co(II), Cd(II), Pb(II) and Ni(II);0.03 to 200 ng?mL–1 for Cu(II);0.07 to 200 ng?mL–1 for Fe(II) and Zn(II) and 0.02 to 150 ng?mL–1 for Hg(II). The enrichment factors were 43, 51, 41, 46, 54, 40, 45 and 52 for Cu(II), Ni(II),Zn (II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II), respectively. The limit of detection were found to be 0.019, 0.094, 0.0514, 0.052, 0.0165, 0.047, 0.068 and 0.041 ng?mL–1 for Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II), respectively. The developed method was applied to the determination of these metal ions in water, blood and urine samples with satisfactory results.展开更多
In this study the potential of cloud point extraction formed by a non-ionic surfactant was used in order to separate polyphenols from industrial residues of camu-camu. The effects of operational conditions of the clou...In this study the potential of cloud point extraction formed by a non-ionic surfactant was used in order to separate polyphenols from industrial residues of camu-camu. The effects of operational conditions of the cloud point extraction(CPE) on the polyphenol recovery and volumetric ratio were investigated. The results showed a maximum recovery of 95.71% that was obtained using 7.0 wt% Triton X-114, native pH(3.25), and 80 wt%polyphenol extract at 30 °C. The use of cloud point extraction was successful to recover the polyphenols from agroindustrial residue since it is a simple as well as of low-cost technique.展开更多
3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
Evaluating rock mass quality using three-dimensional(3D)point clouds is crucial for discontinuity extraction and is widely applied in various industrial sectors.However,the utilization of this method in geological sur...Evaluating rock mass quality using three-dimensional(3D)point clouds is crucial for discontinuity extraction and is widely applied in various industrial sectors.However,the utilization of this method in geological surveys remains limited.Notable limitations of current research include the scarcity of validation using simple geometric shapes for discontinuity extraction methods,and the lack of studies that target both planar and linear discontinuity.To address these gaps,this study proposes a workflow for identifying discontinuity planes and traces in rock outcrops from photogrammetric 3D modeling,employing the Compass and Facets plugins in the open-source CloudCompare software.Prior to field application,the efficacy of the extraction methods was first evaluated using experimental datasets of a cube and an isosceles triangular prism generated under laboratory-controlled conditions.This validation demonstrated exceptional accuracy,with the dip and dip direction(DDD)of extracted structures consistently within±2°of the actual values.Following this rigorous laboratory validation,this methodology was applied to a more complex natural rock outcrop(Miocene–Pliocene deposits in Japan),demonstrating its applicability in realistic geological settings for identifying structures.The results showed that the dip and dip direction trends of the extracted bedding planes and faults were consistent with field measurements,achieving a time reduction of approximately 40%compared to traditional methods.In conclusion,through strictly controlled initial verification and subsequent successful application to a complex natural setting,this study confirmed that the proposed workflow can effectively and efficiently extract discontinuous geological structures from point clouds.展开更多
Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva...Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.展开更多
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a...The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks.展开更多
To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determ...To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:https://github.com/HuaTThanhIT2327Tqu/datasetv2.展开更多
针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编...针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编码,采用最大池化与平均池化叠加的方法将点云的显著特征与细节特征映射为柱体特征;其次,针对算法对伪图特征关注与利用不充分的问题,引入坐标注意力(coordinate attention,CA)机制和残差连接的伪图特征提取模块(attention and residual second block,ARSB),将深层与浅层特征图进行融合,优化算法梯度,增强算法对有效目标的关注度.试验结果表明:改进算法对全局点云检测精度较高,平均精度优于PointPillars、稀疏到稠密3D目标检测器(STD)等点云目标检测算法,在汽车类别上的检测精度优势明显,检测速度较快,符合实时性要求.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for...This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for feature extraction from the PC.The effects of different neighborhood sizes(k=5,10,20,50,and 100)have been evaluated to assess their impact on classification performance.After that,17 geometric and spatial features were extracted from the PC.Next,ensemble methods,AdaBoost.M2,random forest,and decision tree,have been compared with Artificial Neural Networks to classify the main discontinuity sets.The McNemar test indicates that the classifiers are statistically significant.The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95%when using a neighborhood size of k=100,while recall,F-score,and Cohen's Kappa also demonstrate high success.SHapley Additive exPlanations(SHAP),an Explainable AI technique,has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification.The analysis reveals that features such as normal vectors,verticality,and Z-values have the greatest influence on identifying main discontinuity sets,while linearity,planarity,and eigenvalues contribute less,making the model more transparent and easier to understand.After classification,individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets.Finally,the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus(RANSAC).Two real-world datasets(obtained from SfM and LiDAR)and one synthetic dataset were used to validate the proposed method,which successfully identified rock discontinuities and their orientation parameters(dip angle/direction).展开更多
基金Projected supported by the National Basic Research Program (973)of China (No. 2003CB415001)the Pilot Program of KnowledgeInnovation Program of Chinese Academy of Sciences (No. KZCX3-SW-431).
文摘A method based on cloud point extraction was developed to determine phthalate esters including di-ethyl-phthalate （DEP）, di- （2-ethylhexyl）-phthalate （DEHP） and di-cyclohexyl-phthalate （DCP） in environmental water samples using high-performance liquid chromatography separation and ultraviolet detection （HPLC-UV）. The non-ionic surfactant Triton X-114 was chosen as extraction solvent. The parameters affecting extraction efficiency, such as concentrations of Triton X-114 and Na2SO4, equilibration temperature, equilibration time and centrifugation time were evaluated and optimized. Under the optimum conditions, the method can achieve preconcentration factors of 35, 88, 111 and detection of limits of 2.0, 3.8, 1.0 ng/ml for DEP, DEHP and DCP in 10-ml water sample, respectively. The proposed method was successfully applied to the determination of trace amount of phathalate esters in effluent water of the wastewater treatment plant and the lixivium of plastic fragments.
基金the Analysis and Testing Foundation of Zhejiang Province(No 04045)
文摘A novel approach was developed for the determination of ultratrace amounts of copper in water samples by using electrothermal atomic absorption spectrometry （ETAAS） after cloud point extraction （ CPE ）. 1-（ 2-Pyridylazo ） -2- naphthol was used as the chelating reagent and Triton X-114 as the mieellar-forming surfactant. CPE was conducted in a pH 8. 0 medium at 40 ℃ for 10 rain. After the separation of the phases by contrifugafion, the surfactant-rieh phase was diluted with 1 mL of a methanol solution of 0. 1 mol/L HNO3. Then 20μL of the diluted surfactant-rieh phase was injected into the graphite furnace for atomization in the absence of any matrix modifier. Various experimental conditions that affect the extraction and atomization processes were optimized. A detection limit of 5 ng/L was obtained after preconeentration. The linear dynamic range of the copper mass concentration was found to be 0-2.0 ng/mL, and the relative standard deviation was found to be less than 3. 1% for a sample containing 1.0 ng/mL Cu （ Ⅱ ）. This developed method was successfully applied to the determination of uhratraee amounts of Cu in drinking water, tap water, and seawater samples.
基金supported by the National Natural Science Foundation of China(No.20961012)the Medical Neurobiology Key Laboratory of Kunming University of Science and Technology,Basic and Applied Research Project in Yunnan Province(No.2008ZC082M)+3 种基金the Analysis and Testing Foundation of Kunming University of Science and Technology(No.2010121)Innovation Fund for Smalland Medium Technology Based Firms(No.11C26215305936)Natural and Science Foundation of Yunnan Province(No.2010ZC027)Focus Fund of Department of Education in Yunnan Province(No.2010Z016)
文摘Cloud point extraction (CPE) with Tergitol TMN-6 was applied for the extraction of trace amounts of palladium (Pd(Ⅱ)), platinum (Pt(Ⅳ)), and gold (Au(Ⅲ)) in the soil of industrial sewage. Ammonium pyrolysine dithiocarbamate (APDC) was adopted as the chelating agent prior to CPE and then was detected by atomic absorption spectrometry (AAS). Different parameters such as the concentration of surfactants, chelating agent and salt, sample pH, equilibration temperature and time, centrifugation time and rates, and the effect of foreign ions were studied. Under optimum conditions, the low limits of detections are 1.4, 2.8 and 1.2 ng·ml^-1 and the enrichment factors are 21, 12, and 24 for Pd(Ⅱ), Pt(Ⅳ), and Au(Ⅲ, respectively. The relative standard deviations vary from 0.6% to 1.0% (n=11). All correlation coefficients of the calibration curves are >0.9960. The proposed method was successfully applied for the determination of Pd(Ⅱ), Pt(Ⅳ), and Au(Ⅲ) in the real soil of industrial sewage samples.
文摘Cloud point extraction （CPE） processes with two silicone surfactants, Dow Coming DC-190 and DC-193, were studied as preconcentration and treatment for the water polluted by three trace polycyclic aromatic hydrocarbons （PAHs）： anthracene, phenanthrene and pyrene. For all cases, the volumes of surfactant-rich phase obtained by two silicone surfactants were very small, i.e. a lower water content in the surfactant-rich phase was obtained. For example, less than 3% of the initial solution was obtained in a 1% （by mass） surfactant solution, which was much smaller than that of TX-114 in the same surfactant concentration. And TX-114 is known as a high compact surfactant-rich phase among most nonionic surfactants, thus the comparison showed that an excellent enrichment was ensured in the analysis application by the CPE process with the silicone surfactants, and the lower water content obtained in the surfactant-rich phase is also important in the large scale water treatment. The influences of additives and phase separation methodology on the recovery of PAHs were discussed. Comparing with DC-193, DC-190 has a lower cloud point and a higher recovery （near 100%） of all the three PAHs in same surfactant concentration, which was required for application as a preconcentration process prior to HPLC system. However the DC-190 solution is hard to be phase separated only by heating, whereas DC-193 has a relative higher phase separating speed by heating, but a high cloud point （around 360K） limits its application. Due to the phase separation by heating is the only method of CPE suitable to the large scale water treatment, the mixtures of two silicone surfacrants solutions were investigated in this study. A solution containing 1% of mixed DC-190 and DC-193 （in the ratio of 90 ： 10） removed anthracene, phenanthrene and pyrene near 100% with a relative low cloud point and quick phase separating speed.
基金Supported by the National Natural Science Foundation of China(No.20075009)
文摘A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-hydroxyquinoline and Triton X-100 were used as the ligand and surfactant respectively. Nickel（Ⅱ） can form a hy-drophobic complex with 8-hydroxyquinoline, the complex can be extracted into the small volume surfactant rich phase at the cloud point temperature（CPT） for GFAAS determination. The factors affecting the cloud point extraction, such as pH, ligand concentration, surfactant concentration, and the incubation time were optimized. Under the optimal conditions, a detection limit of 12 ng/L and a relative standard deviation（RSD） of 2.9% were obtained for Ni（Ⅱ） determination. The enrichment factor was found to be 25. The proposed method was successfully applied to the determination of nickel（Ⅱ） in certified reference material and different types of water samples and the recovery was in a range of 95%―103%.
文摘A method for the determination of trace mercury in water samples by hydride generation atomic absorption spectrophotometry after cloud point extraction was proposed in the present work. The effects of pH, concentration of surfactant, and equilibration time on cloud point extraction were discussed. The enhancement factor of 20 and the detection limit of 0.039 μg/L were obtained for mercury with relative standard deviation of 4.8% （n = 11）.
文摘A new method was developed for the determination of sodium copper chlorophyll(SCC) by cloud point extraction preconcentration and spectrophotometry, for which Triton X-114 was selected as a nonionic surfactant. Several factors affecting the extraction efficiency of SCC and its subsequent determination, including the p H of the sample solution, salt and surfactant concentrations, and equilibration temperature and time, were studied and optimized. The extraction efficiency approached 99.4%.The calibration graph under the optimum conditions was linear in the concentration range of 3–220 mg/L with correlation coefficients> 0.9997(n = 8). The limit of detection for the analytes was 0.6 mg/L(S/N = 3). The proposed method is inexpensive, simple, and accurate for the extraction and determination of SCC in food samples.
基金Supported by the National Natural Science Foundation of China(Nos.20676051,20573048)Youth Foundation of SouthernYangtze University(No.006283).
文摘With quantum chemical parameters, topological indexes, and physical chemistry parameters as descriptors, a quantitative structure-property relationship（QSPR） has been found for the cloud points of four series of nonionic surfactants（ a total of 65 surfactants）. The best-regressed model includes six descriptors, and the correlation coefficient of multiple determination is as high as 0. 962.
文摘Cloud point extraction (CPE) has been used for the preconcentration of cadmium, after the formation of a complex with 1, 5-bis(di-2-pyridylmethylene) thiocarbonohydrazide (DPTH), and further determination by flame atomic absorption spectrometry (FAAS) using Triton X-114 as surfactant. The main factors affecting the CPE, such as concentration of Triton X-114 and DPTH, pH, equilibration temperature and incubation time, were optimized for the best extract efficiency. Under the optimum conditions i.e., pH 5.4, [DPTH] = 6x10-3%, [Triton X-114] = 0.25% (v/v), an enhancement factor of 10.5 fold was reached. The lower limit of detection (LOD) obtained under the optimal conditions was 0.95 μg L?1. The precision for 8 replicate deter- minations at 20 and 100 μgL?1 Cd were 2.4 % and 2 % relative standard deviation (R.S.D.). The calibration graph using the preconcentration method was linear with a correlation coefficient of 0,998 at levels close to the detection limit up to at least 200 μgL?1. The method was successfully applied to the determination of cadmium in water, environmental and food samples and in a BCR-176 standard reference material.
文摘2-(pyridine-2-yl)-N-p-chlorohydrazinecarbothioamide (HCPTS) was synthesized, characterized and successfully applied for the preconcentration of Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II) in water, blood, and urine samples prior to graphite furnace atomic absorption determination (GFAAS);Hg was determined by cold vapor technique. Under the optimum experimental conditions (i.e. pH = 8, 10–4 M of HCPTS, 0.05% w/v of Triton X-114), calibration graphs were linear in the range of 0.02 to 200 ng?mL–1 for Co(II), Cd(II), Pb(II) and Ni(II);0.03 to 200 ng?mL–1 for Cu(II);0.07 to 200 ng?mL–1 for Fe(II) and Zn(II) and 0.02 to 150 ng?mL–1 for Hg(II). The enrichment factors were 43, 51, 41, 46, 54, 40, 45 and 52 for Cu(II), Ni(II),Zn (II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II), respectively. The limit of detection were found to be 0.019, 0.094, 0.0514, 0.052, 0.0165, 0.047, 0.068 and 0.041 ng?mL–1 for Cu(II), Ni(II), Zn(II), Cd(II), Co(II), Pb(II), Fe(II), and Hg(II), respectively. The developed method was applied to the determination of these metal ions in water, blood and urine samples with satisfactory results.
基金Supported by CAPES and Brazilian National Council for Scientific and Technological Development(CNPq)(150522/2018-5)
文摘In this study the potential of cloud point extraction formed by a non-ionic surfactant was used in order to separate polyphenols from industrial residues of camu-camu. The effects of operational conditions of the cloud point extraction(CPE) on the polyphenol recovery and volumetric ratio were investigated. The results showed a maximum recovery of 95.71% that was obtained using 7.0 wt% Triton X-114, native pH(3.25), and 80 wt%polyphenol extract at 30 °C. The use of cloud point extraction was successful to recover the polyphenols from agroindustrial residue since it is a simple as well as of low-cost technique.
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
文摘Evaluating rock mass quality using three-dimensional(3D)point clouds is crucial for discontinuity extraction and is widely applied in various industrial sectors.However,the utilization of this method in geological surveys remains limited.Notable limitations of current research include the scarcity of validation using simple geometric shapes for discontinuity extraction methods,and the lack of studies that target both planar and linear discontinuity.To address these gaps,this study proposes a workflow for identifying discontinuity planes and traces in rock outcrops from photogrammetric 3D modeling,employing the Compass and Facets plugins in the open-source CloudCompare software.Prior to field application,the efficacy of the extraction methods was first evaluated using experimental datasets of a cube and an isosceles triangular prism generated under laboratory-controlled conditions.This validation demonstrated exceptional accuracy,with the dip and dip direction(DDD)of extracted structures consistently within±2°of the actual values.Following this rigorous laboratory validation,this methodology was applied to a more complex natural rock outcrop(Miocene–Pliocene deposits in Japan),demonstrating its applicability in realistic geological settings for identifying structures.The results showed that the dip and dip direction trends of the extracted bedding planes and faults were consistent with field measurements,achieving a time reduction of approximately 40%compared to traditional methods.In conclusion,through strictly controlled initial verification and subsequent successful application to a complex natural setting,this study confirmed that the proposed workflow can effectively and efficiently extract discontinuous geological structures from point clouds.
基金supported in part by the National Key Research and Development Program of Chinaunder(Grant 2021YFB3101100)in part by the National Natural Science Foundation of Chinaunder(Grant 42461057),(Grant 62272123),and(Grant 42371470)+1 种基金in part by the Fundamental Research Program of Shanxi Province under(Grant 202303021212164)in part by the Postgraduate Education Innovation Program of Shanxi Province under(Grant 2024KY474).
文摘Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.
基金The National Natural Science Foundation of China(No.52338011)the Start-up Research Fund of Southeast University(No.RF1028624058)+1 种基金the Southeast University Interdisciplinary Research Program for Young Scholarsthe National Key Research and Development Program of China(No.2024YFC3014103).
文摘The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks.
文摘To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:https://github.com/HuaTThanhIT2327Tqu/datasetv2.
文摘针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编码,采用最大池化与平均池化叠加的方法将点云的显著特征与细节特征映射为柱体特征;其次,针对算法对伪图特征关注与利用不充分的问题,引入坐标注意力(coordinate attention,CA)机制和残差连接的伪图特征提取模块(attention and residual second block,ARSB),将深层与浅层特征图进行融合,优化算法梯度,增强算法对有效目标的关注度.试验结果表明:改进算法对全局点云检测精度较高,平均精度优于PointPillars、稀疏到稠密3D目标检测器(STD)等点云目标检测算法,在汽车类别上的检测精度优势明显,检测速度较快,符合实时性要求.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
文摘This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for feature extraction from the PC.The effects of different neighborhood sizes(k=5,10,20,50,and 100)have been evaluated to assess their impact on classification performance.After that,17 geometric and spatial features were extracted from the PC.Next,ensemble methods,AdaBoost.M2,random forest,and decision tree,have been compared with Artificial Neural Networks to classify the main discontinuity sets.The McNemar test indicates that the classifiers are statistically significant.The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95%when using a neighborhood size of k=100,while recall,F-score,and Cohen's Kappa also demonstrate high success.SHapley Additive exPlanations(SHAP),an Explainable AI technique,has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification.The analysis reveals that features such as normal vectors,verticality,and Z-values have the greatest influence on identifying main discontinuity sets,while linearity,planarity,and eigenvalues contribute less,making the model more transparent and easier to understand.After classification,individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets.Finally,the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus(RANSAC).Two real-world datasets(obtained from SfM and LiDAR)and one synthetic dataset were used to validate the proposed method,which successfully identified rock discontinuities and their orientation parameters(dip angle/direction).
