• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.163-174
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• 2006

In distributed wireless sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. We propose a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes input as a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a standard text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Bayesian classifier and SVM, and for unsupervised, we tested Jaccard, TFIDF Jaro and Jaro Winkler. In our experiments, SVM and TFIDF outperformed other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.15 no.5
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• pp.103-114
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• 2012

The purpose of this research is classifying stream into different types depending on various factor from the perspective of stream corridor restoration and using it as basic data, which are used to consider efficient management and planning for the healthy stream according to the characteristic by types. In this study, 130 points of location of the Nakdong river basin which consist of various geographic factors have been chosen and hierarchical cluster analysis has been carried out in these points by using biological and physiochemical factors whose health can be considered to be predicted and evaluated. As a result of cluster analysis, there were three divided types. Type A whose biology and water quality are considered the best was the highest in forest area percentage so that it was classified into natural stream. Type B was classified into a rural region stream with a mixture of urban and agricultural region. Type C, with the most damaged water quality and biology health had the most urban region surface area and was named as urban region stream. Moreover, an overall restoration strategy according to characteristic by stream types was set. By the results of correlation analysis on factors, water quality showed a high correlation with biological properties and was affected by surrounding land usage. In evaluation of streams, it proves the need to consider not only other habitat's geographical and biological factors but also the water quality and land usage factors. There needs to be further research on stream ecosystem functionality factors and structural aspects by using a more objective and total evaluation result in selecting additional index and various other specific classification methods by stream types and its restoration strategies.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.615-625
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• 2008

In this study, classification of streams was conducted for Ji Stream, a tributary to the Geum River and Yo Stream, a tributary to the Seomjin River, and in addition, channel evolution model to the same streams was applied. The classification approaches suggested by Rosgen and Korea Institute of Construction Technology (KICT) were conducted. The channel evolution model suggested by Schumm et al. (1984) was applied. Based on the application results of Rosgen approach, Ji Stream and Yo stream show the characteristics of mountainous stream with pebbles. The application results of channel evolution model indicated that the current condition of Ji Stream and Yo Stream is a state of equilibrium, balancing the sediment supply and sediment transport capacity. The results of this study can be used as a fundamental data for water control project, river restoration and appropriate channel planning.

• IE interfaces
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• v.22 no.2
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• pp.126-134
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• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.713-727
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• 2018

This study is to develop an assessment system for stream physical environments by considering stream characteristics. Comprehensively, the descriptions of and steam classification, assessing reach selection, contents of assessment categories and indexes are summarized. Since the physical structure of stream is results of reaction by stream power, streams were classified into three types (as high gradient stream, mid gradient stream and low gradient stream) according to the slope of channel, the grain size of bed material and the characteristics of channel topography. The scale of assessment reach was selected based on 10 or 25 times of channel width according to typical characteristics such as interval of step or riffle and sinuosity in each stream type. The assessment indexes were organized into common indicators such as channel stability, flow status, cross-section shape, bank stability, channel alteration and stream crossing structure, and characteristic indicators by stream type such as effective habitats, bed embeddedness, diversity of flow and frequency of step or riffle. To evaluate the applicability, the assessment system was applied to 9 streams and the results were analyzed and presented.

• Journal of Korean Society on Water Environment
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• v.30 no.3
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• pp.319-328
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• 2014

Dorang - the Korean term for headwater streams - occupy a large portion of the total stream length in a basin, and contribute significantly towards the quantitative and qualitative characteristics, and the ecosystem, of the main river. The Ministry of Environment of South Korea has supported the investigation of the status of Dorang in the nation's four major basins, since 2007. Without a widely accepted academic or legal definition of Dorang, however, there are limits to understand the distribution of Dorang at the national scale and to systematically compile a Dorang database. This paper, through a review of the stream classification system and Korean legal system delineating streams, defines Dorang as 1st and 2nd order streams according to the Strahler ordering method, in a 1:25,000 geographical scale. Analysis of the Geum River basin, with this definition, reveals that the total length of Dorang is 20,622.4 km (73.6% of total stream length), and the number of Dorang catchments is 23,639 (71.3% of the basin area). Further analysis of the geomorphological characteristics of Dorang catchments shows that the average total stream length is 1.1 km, average catchment length is 1.2 km, average drainage area is $0.4km^2$, and average drainage density is 3.08/km.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.179-193
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• 2021

This study examines the use of satellite images for river classification and determination of stream reach, which is the first priority in the river environment assessment system. In the river environment assessment system used in South Korea, it is proposed to set a stream reach by using 10 or 25 times the width of the river based on the result of river classification. First, river classification for the main stream section of Cheongmi stream was performed using various river-related data. The maximum likelihood method was applied for land cover classification. In this study, Sentinel-2 satellite imagery, which is an open data technology with a resolution of 10 m, was used. A total of four satellite images from 2018 was used to consider various flow conditions: February 2 (daily discharge = 2.39 m³/s), May 23 (daily discharge = 15.51 m³/s), June 2 (daily discharge = 3.88 m³/s), and July 7 (daily discharge = 33.61 m³/s). The river widths were estimated from the result of land cover classification to determine stream reach. The results of the assessment reach classification were evaluated using indicators of stream physical environments, including pool diversity, channel sinuosity, and river crossing shape and structure. It is concluded that appropriate flow conditions need to be considered when using satellite images to set up assessment segments for the river environment assessment system.