• Bulletin of the Korean Chemical Society
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• v.18 no.3
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• pp.316-318
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• 1997

A general and universal detection method, which can be used in high performance liquid chromatography (HPLC) and flow injection analysis (FIA) system for the determination of any metal ions complexing with ethylenediaminetetraacetic acid (EDTA), is demonstrated. Pulsed amperometric detection scheme is applied in a flow-through thin layer electrochemical cell at an Au working electrode. Fluctuation of peak current level at the same flow rate of carrier solution is minimized at this solid working electrode, whereas not at a dropping mercury electrode. Removal of dissolved oxygen can be omitted with this detection method, which is a required step for cathodic detection methods. Also, a group of metal ions can be determined selectively and indirectly with this detection scheme.

• ETRI Journal
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• v.45 no.2
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• pp.329-337
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• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_1
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• pp.217-223
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• 2023

With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniq ues, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.142.2-142.2
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• 2015

Plasma etch endpoint detection (EPD) of SiO2 and PR layer is demonstrated by plasma impedance monitoring in this work. Plasma etching process is the core process for making fine pattern devices in semiconductor fabrication, and the etching endpoint detection is one of the essential FDC (Fault Detection and Classification) for yield management and mass production. In general, Optical emission spectrocopy (OES) has been used to detect endpoint because OES can be a simple, non-invasive and real-time plasma monitoring tool. In OES, the trend of a few sensitive wavelengths is traced. However, in case of small-open area etch endpoint detection (ex. contact etch), it is at the boundary of the detection limit because of weak signal intensities of reaction reactants and products. Furthemore, the various materials covering the wafer such as photoresist (PR), dielectric materials, and metals make the analysis of OES signals complicated. In this study, full spectra of optical emission signals were collected and the data were analyzed by a data-mining approach, modified K-means cluster analysis. The K-means cluster analysis is modified suitably to analyze a thousand of wavelength variables from OES. This technique can improve the sensitivity of EPD for small area oxide layer etching processes: about 1.0 % oxide area. This technique is expected to be applied to various plasma monitoring applications including fault detections as well as EPD.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• Korean Journal of Heritage: History & Science
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• v.57 no.2
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• pp.102-115
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• 2024

Painted Cultural heritage uses various materials such as paper, silk, wood, soil, and lime as a base layer to draw on using ink sticks and express lines or colors using various colorants. The importance of underdrawings is emphasized when it comes to replication and preservation, as they can reveal the original drawing. Investigations using infrared have been extensively conducted to detect underdrawings. However, there has been a paucity of research on the influence of underdrawing detection according to the base layer. In this study, the effect of the base layer materials on underdrawing detection in painted cultural heritage was confirmed using an infrared camera and hyperspectral camera (900 to 1700 nm). The study samples marked '檢' with ink below the color layer (cinnabar, orpiment, malachite, azurite, white lead, and red lead) by the base layer materials: Paper (Dakji, indigo/Dakji), silk (silk, silk/white lead), wood (celadonite/wood), soil (celadonite/soil), and lime. The difference in the effect on underdrawing detection was minimal for paper and silk, and no significant differences were found between Dakji and indigo/Dakji, or between silk and silk/white lead. However, we found that celadonite/wood, celadonite/soil, and lime have a significant impact on underdrawing detection. In particular, for wood and soil painted with celadonite, underdrawings were not detected for all six color layers. In the case of lime, it was found that all color layers except malachite had a more positive effect on underdrawing detection. The findings of this study will aid in selecting the appropriate method for underdrawing analysis in the restoration of painted cultural heritage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2213-2231
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• 2013

Compressed sensing is a novel technology used in the field of wireless communication and sensor networks for channel estimation, signal detection, data gathering, network monitoring, and other applications. It plays a significant role in highly secure, real-time, well organized, and cost-effective data communication in smart-grid (SG) systems, which consist of multi-tier network standards that make it challenging to synchronize in power management communication. In this paper, we present a multi-layer communication model for SG systems and propose compressed-sensing based data transmission at every layer of the SG system to improve data transmission performance. Our approach is to utilize the compressed-sensing procedure at every layer in a controlled manner. Simulation results demonstrate that the proposed monitoring devices need less transmission power than conventional systems. Additionally, secure, reliable, and real-time data transmission is possible with the compressed-sensing technique.

• Journal of the Korean Ceramic Society
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• v.39 no.7
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• pp.622-627
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• 2002

Multi-layer composites consisting of silicon nitride, silicon nitride-silicon carbide and boron nitride-alumina layers were prepared fly stacking the corresponding ceramic tapes. The composites demonstrated self-diagnostic capability and non-catastrophic failure behavior. The composites consisting of many thin layers exhibited high strength and stepwise increase of the electrical resistance during the flexure test. The strength of the composite with too thick silicon nitride layers was low and the electrical resistance was abruptly increased to the detection limit of the digital multi-meter during the test. An extensive crack branching was observed in the weak (BN + Al$_2$O$_3$)layer.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.16 no.2
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• pp.113-119
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• 2003

We report the electrical properties of copper(II)-phthalocyanine(Cu-Pc) as a hole injaction layer in organic light-emitting diode (OLED). OLEDs were constructed by the following material structure : indium tin oxaide (ITO)/ CuPc/ triphenyl-diamine (TPD)/ tris-(8-hydroxyquinoline)aluminum (Alq3)/4-(Dicyanomethlene)-2-methyl-6-(4-dimethylaminostyryl)-4H-pyran (DCM)/ Al. we observed that the change of recombination zone by using a DCM detection thin layer (6 ${\AA}$) in a Alq$_3$ emitting layer. layer. Recombination zone was moved toward the cathode as the hole mobility increased due to the heat-treatment temperature of cupc layer increased.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.163-171
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• 2016

Lane detection is a widely researched topic. Although simple road detection is easily achieved by previous methods, lane detection becomes very difficult in several complex cases involving noisy edges. To address this, we use a Convolution neural network (CNN) for image enhancement. CNN is a deep learning method that has been very successfully applied in object detection and recognition. In this paper, we introduce a robust lane detection method based on a CNN combined with random sample consensus (RANSAC) algorithm. Initially, we calculate edges in an image using a hat shaped kernel, then we detect lanes using the CNN combined with the RANSAC. In the training process of the CNN, input data consists of edge images and target data is images that have real white color lanes on an otherwise black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) of 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged to form a deep structure. Our proposed lane detection algorithm successfully eliminates noise lines and was found to perform better than other formal line detection algorithms such as RANSAC