• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.377-380
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• 2019

To accommodate for the difficult task of satisfying application's system timing constraints, we are developing Splash, a real time stream processing language for embedded AI applications. Splash is a graphical programming language that designs applications through data flow graph which, later automatically generates into codes. The codes are compiled and executed on top of the Splash runtime system. The Splash runtime system supports two aspects of the application. First, it supports the basic stream processing functions required for an application to operate on multiple streams of data. Second, it supports the checking and handling of the user configurated timing constraints. In this paper we explain the implementation of the first aspect of the Splash runtime system which is being developed using a real time communication middleware called DDS.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.1073-1080
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• 1996

The impact of the input and output probability distributions on the performance of neural networks to forecast two year peak stream flow (cubic meters per second) is examined for two major river basins of the US. The neural network input consisted of drainage area(square kilometers ) and elevation (meters). When data are normally distributed , the neural networks predict much better than when the data are non-normal and have larger tails in their distributions.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.341-351
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• 2008

Since the damage from the torrential rain increases recently due to climate change and global warming, the significance of flood forecasting and warning becomes important in medium and small streams as well as large river. Through the preprocess and main processes for estimating runoff, diverse errors occur and are accumulated, so that the outcome contains the errors in the existing flood forecasting and warning method. And estimating the parameters needed for runoff models requires a lot of data and the processes contain various uncertainty. In order to overcome the difficulties of the existing flood forecasting and warning system and the uncertainty problem, ANFIS(Adaptive Neuro-Fuzzy Inference System) technique has been presented in this study. ANFIS, a data driven model using the fuzzy inference theory with neural network, can forecast stream level only by using the precipitation and stream level data in catchment without using a lot of physical data that are necessary in existing physical model. Time series data for precipitation and stream level are used as input, and stream levels for t+1, t+2, and t+3 are forecasted with this model. The applicability and the appropriateness of the model is examined by actual rainfall and stream level data from 2003 to 2005 in the Tancheon catchment area. The results of applying ANFIS to the Tancheon catchment area for the actual data show that the stream level can be simulated without large error.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.11-20
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• 2012

Stream data shows a sequence of values changing continuously over time. Due to the nature of stream data, its trend is continuously changing according to various time intervals. Therefore the prediction of stream data must be carried out simultaneously with respect to multiple intervals, i.e. Continuous Multiple Prediction(CMP). In this paper, we propose a Continuous Integrated Hierarchical Temporal Memory (CIHTM) network for CMP based on the Hierarchical Temporal Memory (HTM) model which is a neocortex leraning algorithm. To develop the CIHTM network, we created three kinds of new modules: Shift Vector Senor, Spatio-Temporal Classifier and Multiple Integrator. And also we developed learning and inferencing algorithm of CIHTM network.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.453-462
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• 2010

The switch from analog to digital broadcast television is extended rapidly. The DTV can offer multiple programming choices, interactive capabilities and so on. Moreover, with the spread of Internet, the information exchange between the communities is increasing, too. These facts lead to the new TV service environment which can offer customized TV programs to personal/community users. This paper proposes a 'Customized Digital TV System for Individuals/Communities based on Data Stream Mining' which can analyze user's pattern of TV watching behavior. Due to the characteristics of TV program data stream and EPG(electronic program guide), the data stream mining methods are employed in the proposed system. When a user is watching DTV, the proposed system can control the surrounding circumstances as using the user behavior profiles. Furthermore, the channel recommendation system on the smart phone environment is proposed to utilize the profiles widely.

• Magazine of the Korean Society of Agricultural Engineers
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• v.24 no.2
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• pp.76-88
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• 1982

The stream morphological characteristics of a basin have important influence upon the analysis of runoff. In this study, the laws of stream morphology-the law of average stream fall and the law of least rate of potential energy expenditure-which were derived based on the analogy of entropy in thermodynamics are introduced and their validity is analysised with the data taken from the topographic maps covering the whole Geum River system. The first law is the Law of Average Stream Fall which states that under the dynamic equilibrium condition the ratio of average fall between any two different order stream in the same river basin in unity. The second law is the law of least rate of energy expenditure which states that all natural streams are intended to choose their own course of flow such that the rate of potential energy loss per unit mass of water this course is a minimum. The parameters representing the morphological characteristics of 13 tributaries in the Geum River system such as stream bifurcation ratio and stream concavity were Computed from the Horton-Strahler's laws and are used to check the law of average stream fall. The result showed that the law of average stream fall agrees reasonably well with law of Horton-Strahler. Concavity of a river basin is shown to be the determinative factor to the formation of a stream system. Concavity of a river basin is shown to be the determinative factor to the formation of a stream system. Based on Horton's Law and the law of average stream fall, longitudinal stream profiles can be calculated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.241-251
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• 2006

This study analyzed the topographical characteristics by extracting property factors of stream (stream order, number of stream, stream length, mean stream length) and property factors of basin (basin area, basin length, total stream length, total number of stream, basin mean width, form factor, maximum stream order, basin density, stream frequency, relief ratio, mean elevation, mean, slope, maximum elevation) from DEM (digital elevation model) and stream network generated by 1:5,000 NGIS (national geographical information system) data for the Bukhan-river basin. In addition, topographical factors for upper, mid stream and lower stream were analyzed and the mutuality of the factors by linear and nonlinear regression curve was identified.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.75-94
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• 2009

Since XML emerged as a standard for data exchange on the web, it has been established as a core component in e-Commerce and efficient query processing over XML data in ubiquitous computing environment has been also receiving much attention. Recently, the techniques were proposed whereby an XML document is fragmented into XML fragments to be streamed and the mobile clients receive the stream while processing queries over it. In processing queries over an XML fragment stream, the average access time significantly depends on the order of fragments in the stream. As such, for query performance, an efficient organization of XML fragment stream is required as well as the indexing for energy-efficient query processing due to the reduction of tuning time. In this paper, a technique of XML fragment stream organization based on query frequencies, fragment size, fragment access frequencies, and an active XML-based indexing scheme are proposed. Through implementation and performance experiments, our techniques were shown to be efficient compared with the conventional XML fragment stream organizations.

• Journal of Korea Water Resources Association
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• v.32 no.3
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• pp.291-300
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• 1999

The Snyder's synthetic unit hydrograph method is selected to apply the concept of the fractal dimension by stream order for the practicable rainfall-runoff generation, and fourth types of the Snyder's relation are derived from topographic and observed unit hydrograph data of twenty-nine basins. As a result of the analysis of twenty-nine basins and the verification of two basins, the Snyder's relation which considers the fractal dimension of the stream length and uses calculated unit hydrograph data shows the best result. The concept of the fractal dimension by stream order is applied to the Snyder's synthetic unit hydrograph method. The topographic factors, used in the Snyder's synthetic unit hydrograph method, which have a property of the stream length like $L_{ma}$ (mainstream length) and $L_{ca}$ (length along the mainstream to a point nearest the watershed centroid) were considered. In order to simplify the fractal property of stream length, it is supposed that $L_{ma}$ has not the fractal dimension and the stream length between $L_{ma}$ and ($L_{ma}\;-\;L_{ca}$) has the fractal dimension of 1.027. From the utilization of this supposition, a new Snyder's relation which consider the fractal dimension of the stream length occurred by the map scale used was finally suggested.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.31-40
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• 2012

u-GIS DSMSs have been researched to deal with various sensor data from GeoSensors in ubiquitous environment. Also, they has been more important for high availability. The data from GeoSensors have some characteristics that increase explosively. This characteristic could lead memory overflow and data loss. To solve the problem, various load shedding methods have been researched. Traditional methods drop the overloaded tuples according to a particular criteria in a single server. Tuple deletion sensitive queries such as aggregation is hard to satisfy accuracy. In this paper a dual processing load shedding method is suggested to improve the accuracy of aggregation in clustering environment. In this method two nodes use replicated stream data for high availability. They process a stream in two nodes by using a characteristic they share stream data. Stream data are synchronized between them with a window as a unit. Then, processed results are merged. We gain improved query accuracy without data loss.