• Bulletin of the Korean Chemical Society
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• v.27 no.10
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• pp.1604-1608
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• 2006

A simple and efficient cloud point extraction-spectrofluorimetric method for the determination of thiamine in human urine is proposed. The procedure is based on the oxidation of thiamine with ferricyanide to form thiochrome, its extraction to Triton X-114 micelles and spectrofluorimetric determination. The variables affecting oxidation of thiamine, extraction and phase separation were studied and optimized. Under the experimental conditions used, the calibration graphs were linear over the range 2.5-1000 ng $mL^{-1}$. The limit of detection was 0.78 ng $mL^{-1}$ of thiamine and the relative standard deviation for 5 replicate determinations of thiamine at 400 ng $mL^{-1}$ concentration level was 2.42%. Average recoveries between 93-107% were obtained for spiked samples. The proposed method was applied to the determination of thiamine in human urine.

• Bulletin of the Korean Chemical Society
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• v.27 no.11
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• pp.1780-1784
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• 2006

Cloud point extraction was used to extract mefenamic acid (MF) from human urine, and spectrofluorimetry and spectrophotometry were used to analyze extracted MF. The variables affecting extraction and phase separation, i.e. HCl and Triton X-114 concentration, temperature and time of equilibration, were optimized. Under the experimental conditions used the limit of detection for extraction of 25 mL of sample was 0.006 and 0.045 mg $L^{-1}$, with relative standard deviations of 2.52 and 1.45% (n = 5) for spectrofluorimetric or spectrophotometric methods, respectively. Good recoveries in the range of 95-107% were obtained for spiked samples. The proposed methods were applied to the determination of MF in human urine.

• Journal of the Korean Chemical Society
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• v.59 no.5
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• pp.407-412
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• 2015

A preconcentration procedure for determination of palladium by laser thermal lens spectrometry (TLS) is proposed. It is based on cloud point extraction of palladium(II) ions as 2-(3,5-dichloro-2-pyridylazo)-5-dimethylaminoaniline (3,5-diCl-PADMA) complexes using octylphenoxypolyethoxyethanol (Triton X-114) as surfactant. The effects of various experimental conditions such as pH, concentration of ligand and surfactant, equilibration temperature and time on cloud point extraction were studied. Under the optimized conditions, the calibration graph was linear in the range of 0.15~6 ng mL⁻¹, and the detection limit was 0.04 ng mL⁻¹ with an enrichment factor of 22. The sensitivity was enhanced by 846 times when compared with the conventional spectrophotometric method. The recovery of palladium was in the range of 96.6%~104.0%. The proposed method was applied to the determination of palladium in water samples.

• Bulletin of the Korean Chemical Society
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• v.25 no.8
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• pp.1133-1136
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• 2004

The need for highly reliable methods for the determination of trace metals is recognized in analytical chemistry and environmental science. A method based on the cloud-point extraction (CPE) technique for the trace analysis of Pb and Cu in water samples is described in this study. The analytes in the initial aqueous solution are complexed with pyrogallol, and 0.1%(w/v) Triton X-114 is added as surfactant. Following phase separation at $50^{\circ}C$, based on the cloud point of the mixture and dilution of the surfactant-rich phase with acidified methanolic solution, the enriched analytes are determined by flame atomic absorption spectrometry. After optimization of the complexation and extraction conditions, the enrichment factors of Pb and Cu were found to be 72 and 85, respectively. Under optimum conditions, the preconcentration of 60 mL of samples in the presence of 0.1%(w/v) Triton X-114 permitted the detection of 0.4 ${\mu}gL^{?1}$ of Pb and 0.05 ${\mu}gL^{?1}$ of Cu. The proposed method was applied successfully to the determination of Pb and Cu in water samples.

• Bulletin of the Korean Chemical Society
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• v.32 no.8
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• pp.2637-2642
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• 2011

A new ultrasonic-assisted micellar extraction and cloud-point pre-concentration method was developed for the determination of major saikosaponins, namely saikosaponins -A, -C and -D, in Radix Bupleuri by high performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD). The non-ionic surfactant Genapol X-080 (oligoethylene glycol monoalkyl ether) was chosen as the extraction additive and parameters affecting the extraction efficiency were optimized. The highest yield was obtained with 10% (w/v) Genapol X-080, a liquid/solid ratio of 200:1 (mL/g) and ultrasonic-assisted extraction for 40 min. In addition, the optimum cloud-point pre-concentration was reached with 10% sodium sulfate and equilibration at $60^{\circ}C$ for 30 min. Separation was achieved on an Ascentis Express C18 column (100 ${\times}$ 4.6 mm i.d., 2.7 ${\mu}M$) using a binary mobile phase composed of 0.1% acetic acid and acetonitrile. Saikosaponins were detected by ELSD, which was operated at a $50^{\circ}C$ drift tube temperature and 3.0 bar nebulizer gas ($N_2$) pressure. The water-based solvent modified with Genapol X-080 showed better extraction efficiency compared to that of the conventional solvent methanol. Recovery of saikosaponins ranged from 93.1 to 101.9%. An environmentally-friendly extraction method was successfully applied to extract and enrich major saikosaponins in Radix Bupleuri.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• Journal of IKEEE
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• v.27 no.4
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• pp.486-493
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• 2023

In this paper, we propose an automatic hand gesture area extraction and recognition technique using FMCW radar-based point cloud and LSTM. The proposed technique has the following originality compared to existing methods. First, unlike methods that use 2D images as input vectors such as existing range-dopplers, point cloud input vectors in the form of time series are intuitive input data that can recognize movement over time that occurs in front of the radar in the form of a coordinate system. Second, because the size of the input vector is small, the deep learning model used for recognition can also be designed lightly. The implementation process of the proposed technique is as follows. Using the distance, speed, and angle information measured by the FMCW radar, a point cloud containing x, y, z coordinate format and Doppler velocity information is utilized. For the gesture area, the hand gesture area is automatically extracted by identifying the start and end points of the gesture using the Doppler point obtained through speed information. The point cloud in the form of a time series corresponding to the viewpoint of the extracted gesture area is ultimately used for learning and recognition of the LSTM deep learning model used in this paper. To evaluate the objective reliability of the proposed technique, an experiment calculating MAE with other deep learning models and an experiment calculating recognition rate with existing techniques were performed and compared. As a result of the experiment, the MAE value of the time series point cloud input vector + LSTM deep learning model was calculated to be 0.262 and the recognition rate was 97.5%. The lower the MAE and the higher the recognition rate, the better the results, proving the efficiency of the technique proposed in this paper.

• Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.202-212
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• 2014

To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.