• Bulletin of the Korean Chemical Society
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• 제33권7호
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• pp.2224-2228
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• 2012

This study describes the ability of capillary electrochromatography (CEC) for the determination of imidacloprid and carbendazim in tomato samples. A novel liquid crystal crown ether modified hybrid silica monolithic column was synthesized, characterized and developed as separation column for the first time. Baseline separation of imidacloprid and carbendazim could be achieved using a mobile phase containing 90% (v/v) 20 mmol/L phosphate buffer (pH 7.0) and 10% (v/v) acetonitrile. The matrix matched calibration curves were linear with correlation coefficient $r^2$ > 0.9998 in the range of 0.20-10.00 mg/L. The limits of detection for imidacloprid and carbendazim were 0.061 and 0.15 mg/kg, respectively, which were below the maximum residue limits established by the European Union as well as Codex Alimentarius. Average recoveries for imidacloprid and carbendazim varied from 101.6-108.0% with relative standard deviations lower than 6.3%. This method was applied to the analysis of tomatoes collected from local markets.

• Smart Structures and Systems
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• 제17권1호
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• pp.135-147
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• 2016

This paper describes a smart structural system, which uses smart materials for real-time monitoring and active control of bolted-joints in steel structures. The goal of this research is to reduce the possibility of failure and the cost of maintenance of steel structures such as bridges, electricity pylons, steel lattice towers and so on. The concept of the smart structural system combines impedance based health monitoring techniques with a shape memory alloy (SMA) washer to restore the tension of the loosened bolt. The impedance-based structural health monitoring (SHM) techniques were used to detect loosened bolts in bolted-joints. By comparing electrical impedance signatures measured from a potentially damage structure with baseline data obtained from the pristine structure, the bolt loosening damage could be detected. An outlier analysis, using generalized extreme value (GEV) distribution, providing optimal decision boundaries, has been carried out for more systematic damage detection. Once the loosening damage was detected in the bolted joint, the external heater, which was bonded to the SMA washer, actuated the washer. Then, the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. Additionally, temperature variation due to the heater was compensated by applying the effective frequency shift (EFS) algorithm to improve the performance of the diagnostic results. An experimental study was conducted by integrating the piezoelectric material based structural health monitoring and the SMA-based active control function on a bolted joint, after which the performance of the smart 'self-monitoring and self-healing bolted joint system' was demonstrated.

• Environmental Analysis Health and Toxicology
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• 제26권
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• pp.2.1-2.7
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• 2011

Objectives: N-Nitrosodimethylamine (NDMA) is classified as a probable human carcinogen by the United States Environmental Protection Agency (US EPA) and is formed during the chlorination of municipal drinking water. In this study, selected nitrosamines were measured in chlorinated drinking water collected from Chuncheon, Kangwon-do, Republic of Korea, and a risk assessment for NDMA was conducted. Methods: Twelve water samples were collected from 2 treatment plants and 10 household taps. Samples were analyzed for 6 nitrosamines via solid-phase extraction cleanup followed by conversion to dansyl derivatives and high-performance liquid chromatography-fluorescence detection (HPLC-FLD). Considering the dietary patterns of Korean people and the concentration change of NDMA by boiling, a carcinogenic risk assessment from ingestion exposure was conducted following the US EPA guidelines. Results: NDMA concentrations ranged between 26.1 and 112.0 ng/L. NDMA in water was found to be thermally stable, and thus its concentration at the end of boiling was greater than before thermal treatment owing to the decrease in water volume. The estimated excess lifetime carcinogenic risk exceeded the regulatory baseline risk of $10^{-5}$. Conclusions: This result suggests that more extensive studies need to be conducted on nitrosamine concentration distributions over the country and the source of relatively high nitrosamine concentrations.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 춘계학술대회
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• pp.175-178
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• 1996

The purpose of this thesis is the propose of various signal processing algorithm for the ECG(electrocardiogram) and the design of realtime automated ECG analyzer feasible with these algorithms. The algorithms are composed of (1)filtering procedure fer the estimation and removal of baseline drift, 60Hz power line interference, and muscle artifacts (2)detection procedure of QRS complex and P wave (3)typification procedure for the pattern classification according to the morphologies (4) selection of representative beat, significant point and wave boundary decision procedure and (5) parameter extraction and diagnosis procedure. All verifications are carried out between the algorithms proposed in this paper and other algorithms already proposed by many researchers, for the objective comparison in each procedure. The efficiency of proposed algorithms are demonstrated with the aid of internationally validated CSE database and the performances of filtering procedure are compared on artificial noise signal as well as actual ECG signals with appropriate noise components. for the comparison on the performance of designed automated ECG analyzer, the diagnosis results were compared with ECG analyzer manufactered by Fukuda denshi in Japan.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 하계종합학술대회논문집
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• pp.269-272
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• 2000

It is need to develop and apply a human pulse diagnosis system providing a quantitative and automatic analysis in the the oriental medicine. In order to analyze quantitatively the characteristic of pulsation, each of points had to be recognized accurately notifying the existence and the position of feature point in the wave form. And getting the period of human pulse. Thus, in this paper, it is proposed the preprocessing method of human pulse and the detection method of period by Wavelet Transformation. The human pulse is seprated from each band through Wavelet Transformation and feature points can be recognized through over the fact, and then the parameter of proposed Mac-Jin parameter is measured. Commonly, Human pulse signal has often various noises which are baseline drift, high frequency noise and so on. So it is significant to remove that noises. Thus, in this paper, the one period of human pulse is deciede and the feature points are detected after doing the preprocessing by wavelet transformation. As a result, it could be confirmed that this method is effective as a real program for the auto-diagnosis of human pulse.

• 한국축산식품학회지
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• 제42권4호
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• pp.672-688
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• 2022

The objective of this study was to explore the potential of front face fluorescence spectroscopy (FFFS) as rapid, non-destructive and inclusive technique along with multi-variate analysis for predicting meat adulteration. For this purpose (FFFS) was used to discriminate pure minced beef meat and adulterated minced beef meat containing (1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) of chicken meat as an adulterant in uncooked beef meat samples. Fixed excitation (290 nm, 322 nm, and 340 nm) and fixed emission (410 nm) wavelengths were used for performing analysis. Fluorescence spectra were acquired from pure and adulterated meat samples to differentiate pure and binary mixtures of meat samples. Principle component analysis, partial least square regression and hierarchical cluster analysis were used as chemometric tools to find out the information from spectral data. These chemometric tools predict adulteration in minced beef meat up to 10% chicken meat but are not good in distinguishing adulteration level from 1% to 5%. The results of this research provide baseline for future work for generating spectral libraries using larger datasets for on-line detection of meat authenticity by using fluorescence spectroscopy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• Structural Monitoring and Maintenance
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• 제9권3호
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• pp.237-258
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• 2022

This paper studies detecting and locating loose bolts using nonlinear guided waves. The 3D Finite Element (FE) simulation is used for the prediction of guided waves' interactions with loose bolted joints. The numerical results are verified by experimentally obtained data. The study considers bolted joints consisting of two bolts. It is shown that the guided waves' interaction with surfaces of a loose bolted joint generates Contact Acoustic Nonlinearity (CAN). The study uses CAN for detecting and locating loose bolts. The processed experimentally obtained data show that the CAN is able to successfully detect and locate loose bolted joints. A 3D FE simulation scheme is developed and validated by experimentally obtained data. It is shown that FE can predict the propagation of guided waves in loose bolts and is also able to detect and locate them. Several numerical case studies with various bolt sizes are created and studied using the validated 3D FE simulation approach. It is shown that the FE simulation modeling approach and the signal processing scheme used in the current study are able to detect and locate the loose bolts in imperfect bolted joints. The outcomes of this research can provide better insights into understanding the interaction of guided waves with loose bolts. The results can also enhance the maintenance and repair of imperfect joints using the nonlinear guided waves technique.

• 대한임베디드공학회논문지
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• 제17권4호
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• pp.217-228
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• 2022

Currently, there are increasing demands for applying deep neural networks (DNNs) in the embedded domain such as classification and object detection. The DNN processing in embedded domain often requires custom hardware such as NPU for acceleration due to the constraints in power, performance, and area. Processing DNN models requires a large amount of data, and its seamless transfer to NPU is crucial for performance. In this paper, we developed a cycle-accurate NPU simulator to evaluate diverse NPU microarchitectures. In addition, we propose a novel technique for reducing the number of memory accesses when processing convolutional layers in convolutional neural networks (CNNs) on the NPU. The main idea is to reuse data with memory interleaving, which recycles the overlapping data between previous and current input windows. Data memory interleaving makes it possible to quickly read consecutive data in unaligned locations. We implemented the proposed technique to the cycle-accurate NPU simulator and measured the performance with LeNet-5, VGGNet-16, and ResNet-50. The experiment shows up to 2.08x speedup in processing one convolutional layer, compared to the baseline.

• Smart Structures and Systems
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• 제29권1호
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.