• 약학회지
• /
• 제37권1호
• /
• pp.1-8
• /
• 1993

A procedure for the high-performance liquid chromatographic determination of Nebramycin factors in fermentation broth of Streptoalloteichus hindustanus ATCC 31218 mutant was investigated using pre-column derivatization and LTV detection. The method is based on pre-column derivatization of Nebramycin factors with 2,4-dinitrofluorobenzene(DNFB) in the presence of Tris (hydroxymethyl)aminoethane. The chromatographic separation of derivatives of Nebramycin factors and unknown impurities is achieved using reversed-phase column (NOVA-PAK $C_{18}$, Waters Co.) and AcCN : H$_{2}$O : AcOH (53.0:46.5:0.5) as a mobile phase. The mixture of these derivatives were separated within 35 minutes and the optimum wavelength($\lambda_{max}$ ) of the UV detector was 353 nm. The linearity of response for derivatives of Nebramycin factors is demonstrated for concentrations up to 500 $\mu\textrm{g}$/ml and the relative standard deviation is less than 0.79%. Detection limit was 1.67 ng for the 10 $\mu\textrm{l}$ sample volume employed.

• 로봇학회논문지
• /
• 제14권4호
• /
• pp.301-310
• /
• 2019

A new algorithm has been propose to detect the reflected light region as disturbances in a real-time vision system. There have been several attempts to detect existing reflected light region. The conventional mathematical approach requires a lot of complex processes so that it is not suitable for a real-time vision system. On the other hand, when a simple detection process has been applied, the reflected light region can not be detected accurately. Therefore, in order to detect reflected light region for a real-time vision system, the detection process requires a new algorithm that is as simple and accurate as possible. In order to extract the reflected light, the proposed algorithm has been adopted several filter equations and clustering processes in the HSI (Hue Saturation Intensity) color space. Also the proposed algorithm used the pre-defined reflected light data generated through the clustering processes to make the algorithm simple. To demonstrate the effectiveness of the proposed algorithm, several images with the reflected region have been used and the reflected regions are detected successfully.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권12호
• /
• pp.4816-4834
• /
• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Journal of Information Processing Systems
• /
• 제19권1호
• /
• pp.109-117
• /
• 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.

• 한국화재소방학회논문지
• /
• 제2권3호
• /
• pp.11-15
• /
• 1988

This study is explained about operation function and maintenance of components for automatic fire extinguishing systems. Which is included the wetting agent, detection devices and automatic values for pre-engineered type extinguishing systems.

• 제어로봇시스템학회논문지
• /
• 제8권9호
• /
• pp.818-826
• /
• 2002

Since multipath phenomenon frequently occurs when a Global Positioning System receiver is placed in urban area crowded with large buildings, efficient mitigation of multipath effects is necessary to resolve. In this paper, we propose a new multipath detection technique that is useful in real-time positioning with a general Global Positioning System receiver. The proposed technique is based on a channelwise multipath test statistic that efficiently indicates the degree of fluctuations induced by multipath error. The proposed multipath test statistic is operationally advantageous because it does not require any specialized hardware nor any pre-computation of receiver position, it is directly related to standard $\chi$$^2$-distributions, and it can adjust the detection resolution by increasing the number of successive measurements. Simulation and experiment results verify the performance of the proposed multipath detection technique.

• 한국정보기술응용학회:학술대회논문집
• /
• 한국정보기술응용학회 2005년도 6th 2005 International Conference on Computers, Communications and System
• /
• pp.153-156
• /
• 2005

Skin region detection in images is an important process in many computer vision applications targeting humans such as hand gesture recognition and face identification. It usually starts at a pixel-level, and involves a pre-process of color spae transformation followed by a classification process. A color space transformation is assumed to increase separability between skin classes and other classes, to increase similarity among different skin tones, and to bring a robust performance under varying imaging conditions, without any complicated analysis. In this paper, we examine if the color space transformation actually brings those benefits to the problem of skin region detection on a set of human hand images with different postures, backgrounds, people, and illuminations. Our experimental results indicate that color space transfomation affects the skin detection performance. Although the performance depends on camera and surround conditions, normalized [R, G, B] color space may be a good choice in general.

• Journal of Multimedia Information System
• /
• 제6권4호
• /
• pp.235-238
• /
• 2019

In real world object detection is an active research topic for understanding different objects from images. There are different models presented in past and had significant results. In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN). Both the front and rear view of the vehicles were used for training and testing the proposed method. Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images. Using perspective transformation, the top view images were transformed into front view images. This algorithm has higher detection rate as compared to raw images. Furthermore, YOLO model has better result as compare to F-RCNN model.

• 한국식품영양과학회지
• /
• 제28권4호
• /
• pp.882-885
• /
• 1999

Along with the increasing demands for food irradiation technology, proper detection methods for controlling irradiated foods are required. Dried vegetable(chunggyungchae), which is permitted to be irradiated in Korea, was subjected to a detection study by ESR spectroscopy. Pre established threshold value was successfully applicable to the detection of 50 coded unknown samples of dried clean vege tables, both nonirradiated and electron beam irradiated. Three calibration curves obtained from the samples irradiated at 2.5~15 kGy were not practically adopted to estimate actual absorbed doses ranging from 4 to 7 kGy.

• Journal of Information Processing Systems
• /
• 제16권4호
• /
• pp.795-808
• /
• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.