• Journal of Electrical Engineering and Technology
• /
• 제10권4호
• /
• pp.1877-1885
• /
• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

• 대한안전경영과학회지
• /
• 제24권4호
• /
• pp.61-70
• /
• 2022

In spite of lab safety act for over 10 years, over 100 safety accidents in the laboratory have been constantly occurring. The ideal safety management system is to prevent accidents by differential classifying and managing laboratory regulatory materials according to the risk level. In order to approach this system, in-depth interviews with safety managers were first conducted to identify the current status of safety management in domestic university laboratories. And then through comparative analysis of safety management systems in domestic and foreign laboratories, a new regulatory substance classification standard based on the analysis of the hazards and the classification of risk grades, and a safety management system are proposed. From this study, it will contribute to the creation of a safe laboratory environment by differential classification and management laboratory regulatory materials based on the risk level.

• 한국측량학회지
• /
• 제29권4호
• /
• pp.429-438
• /
• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• 대한안전경영과학회지
• /
• 제14권3호
• /
• pp.143-150
• /
• 2012

In Korea more than 38,000 types of hazardous material(HAZMAT) are distributed, accordingly the accidents during transportation are also increasing. The agencies related to HAZMAT such as Environment Ministry, National Emergency Management Agency and National Police Agency have their own regulations. However, the classification criteria of HAZMAT are different to each other, which causes many problems in response to transportation accidents. In this study the classification standard of HAZMAT and the classification code using CAS number are suggested to manage HAZMAT efficiently. Through efficient management and standard classification of HAZMAT, the rapid and systematic response to transportation accidents related to HAZMAT is expected to be possible.

• 한국전자거래학회지
• /
• 제7권3호
• /
• pp.49-60
• /
• 2002

Many researchers have studied component technologies with concept, methodology and implementation for partial business domain, however there are rarely researches for component classification to manage these systematically. In this paper, we suggest a component classification model, which can make component reusability higher and can derive higher productivity of software development. We take four focuses generalization, abstraction, technology and size. The generalization means which category a component belongs to. The abstraction means how specific a component encapsulates its inside. The technology means which platform for hardware environment a component can be plugged in. The size means the physical component volume.

• Nuclear Engineering and Technology
• /
• 제54권4호
• /
• pp.1336-1342
• /
• 2022

Risk-informed safety classification of structures, systems and components (SSCs) is very important for ensuring the safety and economic efficiency of nuclear power plants (NPPs). However, previous methods for safety classification of SSCs do not take the plant operating modes or the operational process of SSCs into consideration, thus cannot concentrate on the safety and economic efficiency accurately. In this contribution, a new method for safety classification of SSCs based on the categorization of plant operating modes is proposed, which considers the NPPs operating history to improve the economic efficiencies while maintaining the safety. According to the time duration of plant configurations in plant operating modes, average importances of SSCs are accessed for an NPP considering the operational process, and then safety classification of SSCs is performed for plant operating modes. The correctness and effectiveness of the proposed method is demonstrated by application in an NPP's safety classification of SSCs.

• Journal of Information Science Theory and Practice
• /
• 제5권3호
• /
• pp.31-47
• /
• 2017

The narrow-down approach, separately composed of search and classification stages, is an effective way of dealing with large-scale hierarchical text classification. Recent approaches introduce methods of incorporating global, local, and path information extracted from web taxonomies in the classification stage. Meanwhile, in the case of utilizing path information, there have been few efforts to address existing limitations and develop more sophisticated methods. In this paper, we propose an expansion method to effectively exploit category path information based on the observation that the existing method is exposed to a term mismatch problem and low discrimination power due to insufficient path information. The key idea of our method is to utilize relevant information not presented on category paths by adding more useful words. We evaluate the effectiveness of our method on state-of-the art narrow-down methods and report the results with in-depth analysis.

• 반도체디스플레이기술학회지
• /
• 제8권3호
• /
• pp.39-44
• /
• 2009

Local Binary Pattern (LBP) is becoming a popular tool for various machine vision applications such as face recognition, classification and background subtraction. In this paper, we propose a new extension of LBP, called the Diagonal LBP (DLBP), to handle the image-based gender classification problem arise in interactive display systems. Instead of comparing neighbor pixels with the center pixel, DLBP generates codes by comparing a neighbor pixel with the diagonal pixel (the neighbor pixel in the opposite side). It can reduce by half the code length of LBP and consequently, can improve the computation complexity. The Support Vector Machine is utilized as the gender classifier, and the texture profile based on DLBP is adopted as the feature vector. Experimental results revealed that our approach based on the diagonal LPB is very efficient and can be utilized in various real-time pattern classification applications.

• Journal of Positioning, Navigation, and Timing
• /
• 제12권2호
• /
• pp.149-155
• /
• 2023

In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권7호
• /
• pp.1858-1872
• /
• 2023

With the advance of radar technologies, the availability of synthetic aperture radar (SAR) images increases. To improve application of SAR images, a management system for SAR images is proposed in this paper. The system provides trainable land cover classification module and display of SAR images on the map. Users of the system can create their own classifier with their data, and obtain the classified results of newly captured SAR images by applying the classifier to the images. The classifier is based on convolutional neural network structure. Since there are differences among SAR images depending on capturing method and devices, a fixed classifier cannot cover all types of SAR land cover classification problems. Thus, it is adopted to create each user's classifier. In our experiments, it is shown that the module works well with two different SAR datasets. With this system, SAR data and land cover classification results are managed and easily displayed.