• Journal of Korea Water Resources Association
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• v.35 no.2
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• pp.149-159
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• 2002

The monthly water quality data measured at 19 stations located in the Sapkyo reservoir watershed were clustered into 2 to 7 clusters and factor analysis was conducted to characterize the water quality, using the information obtained from cluster analysis. The result of cluster analysis shows that Sapkyo reservoir and each stream (Sapkyo stream, Muhan stream and Kokkyo stream) in Sapkyo reservoir watershed hove their own water quality characteristics. The result of water quality analysis indicates that the concentration of suspended solids from Sapkyo reservoir is much higher than those of other streams, and which is probably because of increment of phytoplankton biomass with rich nutrient flowing Into Sapkyo reservoir from the upper stream of watershed. Furthermore, the concentrations of biochemical oxygen demand and chemical oxygen demand were 3.5 to 4.8 times and 1.7 to 2.5 times those of other streams, respectively. The overall water quality of Sapkyo reservoir watershed was considered to exceed eutrophic condition. Based on factor analysis, the water quality characteristics of Sapkyo stream and Muhan stream were closely related with farm land and residence. The water quality of Kokkyo stream was influenced by superabundant organic matter flowing from Chonan city and district wastewater treatment plant located in the upper stream of Kokkyo stream. The water quality factor influencing Sapkyo reservoir was closely related with water quality factors of other three streams.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.374-381
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• 2023

The travel time of flash floods along mountain streams is mainly governed by reach-average velocity, rather than by the point velocity of the locations of interest. Reach-average velocity is influenced by various factors such as stream geometry, streambed materials, and the hydraulic roughness of streams. In this study, the reach-average velocity in mountain streams was measured for storm periods using rhodamine dye tracing. The point cloud data obtained from a LiDAR survey was used to extract the average hydraulic roughness height, such as R_a, R_max, and R_z. The size distribution of the streambed materials (D₅₀, D₈₄) was also considered in the estimation of the roughness height. The field experiments revealed that the reach-average velocities had a significant relationship with flow discharges (v = 0.5499Q^0.6165 ), with an R² value of 0.77. The root mean square error in the roughness height of the R_a-based estimation (0.45) was lower than those of the other estimations (0.47-1.04). Among the parameters for roughness height estimation, the R_a -based roughness height was the most reliable and suitable for developing the reach-average velocity equation for estimating the travel time of flood waves in mountain streams.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.7
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• pp.501-510
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• 2011

In this study, water pollution sources of 14 major tributaries of Han river and characteristics of water quality for each target streams were evaluated based on water quality data in 2007.1-2009.12 (14 data sets) using a statistical package, SPSS-17.0. Cluster analysis over time and space for each stream resulted in 4 groups for the spatial variations in which type and density of pollution sources in the basins showed the greatest impact on grouping. Moreover, cluster analysis for the time variation in which rainfall, temperature and eutrophication were shown to contribute to the clustering, produced 2 groups, from summer to fall (July-Oct.) and from winter to early summer (Nov.-June). Four factors were found as responsible for the data structure explaining 71-90% of the total variance of the data set depending on the streams and they were organic matter, nutrients, bacterial contamination. Factor analysis showed main factors (water pollutants) changed according to the season with different pattern for each stream. This study demonstrated that water quality of each stream could produce useful outcomes when factor and pollution source of basin were evaluated together.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.473-482
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• 2019

There have already been studies analyzing the trajectories of objects from data streams of moving objects. Among those studies, there are also studies to discover groups of objects that move together, called companion groups. Most studies to discover companion groups use existing clustering techniques to find groups of objects close to each other. However, these clustering-based methods are often difficult to find the right companion groups because the number of clusters is unpredictable in advance or the shape or size of clusters is hard to control. In this study, we propose a new method that discovers companion groups based on the distance specified by the user. The proposed method does not apply the existing clustering techniques but periodically determines the groups of objects close to each other, by using a technique that efficiently finds the groups of objects that exist within the user-specified distance. Furthermore, unlike the existing methods that return only companion groups and their trajectories, the proposed method also returns their appearance and disappearance time. Through various experiments, we show that the proposed method can detect companion groups correctly and very efficiently.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1045-1054
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• 2020

In this paper, we designed a TCP/IP based ring buffer system that can stably transfer bulk data streams in the unstable network environments. In the scheme we proposed, The observation data stream generated and output by each radio observatory's backend system as a UDP frame is stored as a UDP packet in a large capacity ring buffer via a socket buffer in the client system. Thereafter, for stable transmission to the remote destination, the packets are processed in TCP and transmitted to the socket buffer of server system in the correlation center, which packets are stored in a large capacity ring buffer if there is no problem with the packets. In case of errors such as loss, duplication, and out of order delivery, the packets are retransmitted through TCP flow control, and we guaranteed that the reliability of data arriving at the correlation center. When congestion avoidance occurs due to network performance instability, we also suggest that performance degradation can be minimized by applying parallel streams.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.558-567
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• 2010

There is a large body of ongoing research to develop efficient prediction methods for data streams. These methods provide single prediction with a fixed time interval. It is necessary to develop a method for multi-interval prediction (MIP) because different prediction results may be obtained based on different intervals in many cases. In this paper, we propose a solution for MIP based on the Hierarchical Temporal Memory (HTM) model. In order to solve the problem of MIP with HTM, we present an Integrated Hierarchical Temporal Memory (IHTM) network by introducing a new node type Zeta1LastNode to the original HTM network. Using the hierarchical characteristic of the IHTM network, different levels in the network learn and model the features of a data stream with different intervals and generate prediction results for different intervals. Performance evaluation shows that the IHTM is efficient in the memory and time consumption compared with the original HTM network in MIP.

• Journal of Broadcast Engineering
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• v.6 no.1
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• pp.82-97
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• 2001

In this paper, we describe a software implementation of a digital TV stream analyzer that can be used for analyzing and verifying digital TV bitstreams on personal computes. It accepts as input MPEG-2 transport streams (TS's) already stored on hard disks and doesn't require any special hardware. After classifying TS packets into program specific information(PSI) TS section auido, video, program clock reference (PCR) private data and null packets, it displays their contents through a graphic user interface along with the syntax elements of the TS header. Also it displays the decoded I frame nearest in time axis the TS packet currently shown This feature helps pin pointing the specific location of problematic parts in bitstreams. The bitsteam analyzer provides the compliance test of MPEG-2 Systems standard and the data injection functionality with which one can easily insert additional data to existing MPEG-2 bitstreams. Using the resulting system one can produce at low test streams for interactive broadcasting and data broadcasting for laboratory use.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.42-53
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• 2020

With the development of social network services, graph structures have been utilized to represent relationships among objects in various applications. Recently, a demand of subgraph matching in real-time graph streams has been increased. Therefore, an efficient approximate Top-k subgraph matching scheme for low latency in real-time graph streams is required. In this paper, we propose an approximate Top-k subgraph matching scheme considering data reuse in graph stream environments. The proposed scheme utilizes the distributed stream processing platform, called Storm to handle a large amount of stream data. We also utilize an existing data reuse scheme to decrease stream processing costs. We propose a distance based summary indexing technique to generate Top-k subgraph matching results. The proposed summary indexing technique costs very low since it only stores distances among vertices that are selected in advance. Finally, we provide k subgraph matching results to users by performing an approximate Top-k matching on the summary indexing. In order to show the superiority of the proposed scheme, we conduct various performance evaluations in diverse real world datasets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.370-381
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• 2012

Multi-criteria result extraction is crucial in many scientific applications that support real-time stream processing, such as habitat research and disaster monitoring. Skyline evaluation is computational intensive especially over continuous time-interval data streams where each object has its own customized expiration time. In this work, we propose TI-Sky - a continuous skyline evaluation framework. To ensure correctness, the result space needs to be continuously maintained as new objects arrive and older objects expire. TI-Sky strikes a perfect balance between the costs of continuously maintaining the result space and the costs of computing the final skyline result from this space whenever a pull-based user query is received. Our key principle is to incrementally maintain a partially precomputed skyline result space - however doing so efficiently by working at a higher level of abstraction. TI-Sky's algorithms for insertion, deletion, purging and result retrieval exploit both layers of granularity. Our experimental study demonstrates the superiority of TI-Sky over existing techniques to handle a wide variety of data sets.