• International journal of advanced smart convergence
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• v.12 no.4
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.253-261
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• 2024

The impact of temperature on electrode reactions in 100 cm² molten carbonate cells operating as Fuel Cells (FC) and Electrolysis Cells (EC) was examined using the Reactant Gas Addition (RA) method across a temperature range of 823 to 973 K. The RA findings revealed that introduction of H₂ and CO₂, reduced the overpotential at Hydrogen Electrode (HE) in both the modes. However, no explicit temperature dependencies were observed. Conversely, adding O₂ and CO₂ to the Oxygen Electrode (OE) displayed considerable temperature dependencies in FC mode which can be attributed to increased gas solubility due to the electrolyte melting at higher temperatures. In EC mode, there was no observed temperature dependence for overpotential. Furthermore, the addition of O₂ led to a decrease in overpotential, while CO₂ addition resulted in an increased overpotential, primarily due to changes in the concentration of O₂ species.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.28-35
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• 2015

In this paper, we propose to identify redundant power states of high-level power model based on clock gating enable signals(CGENs) using dependencies of Boolean functions and structural dependencies of clock gating cells. Three functional dependencies between two CGENs, namely equvalence, inversion, and inclusion, are used. Functions of CGENs in a circuit are represented by binary decision diagrams (BDDs) and the functional relations are used to reduce the number of power states. The structural dependency appears when a clock gating cell drives another clock gating cells in a circuit. Automatic dependency checking algorithm has been proposed. The experimental results show the average number of power state is reduced by 59%.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.55-56
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• 2007

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.49-61
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• 2010

The time series dependencies of Financial volatility are frequently measured by the autocorrelation function of power-transformed absolute returns. It is known as the Taylor property that the autocorrelations of the absolute returns are larger than those of the squared returns. Hass (2009) developed a simple method for detecting the Taylor property in absolute-value-GAROH(1,1) (AVGAROH(1,1)) model. In this article, we fitted AVGAROH(1,1) model for various Korean financial time series and observed the Taylor property.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2632-2649
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• 2012

Most approaches to human action recognition is limited due to the use of simple action datasets under controlled environments or focus on excessively localized features without sufficiently exploring the spatio-temporal information. This paper proposed a framework for recognizing realistic human actions. Specifically, a new action representation is proposed based on computing a rich set of descriptors from keypoint trajectories. To obtain efficient and compact representations for actions, we develop a feature fusion method to combine spatial-temporal local motion descriptors by the movement of the camera which is detected by the distribution of spatio-temporal interest points in the clips. A new topic model called Markov Semantic Model is proposed for semantic feature selection which relies on the different kinds of dependencies between words produced by "syntactic " and "semantic" constraints. The informative features are selected collaboratively based on the different types of dependencies between words produced by short range and long range constraints. Building on the nonlinear SVMs, we validate this proposed hierarchical framework on several realistic action datasets.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.41-47
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• 2000

Ideograph is a truly unifies data and procedural dependencies. Ideograph can be used to assist various program optimization, such as common expression elimination, code motion, constant folding etc. In this paper, we propose an improved representation of the data and control flow dependencies information for the efficient program execution. In pursuing this goal, we propose a model and in particularly implement a dependency information extractor and information table, which contains data and control flow information per a basic block And then we design and implementation of the optimized abstract syntax tree using Ideograph which has a control flow information and data flow information for source program.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.379-387
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• 2003

A lot of microbial genome projects have been completed to pour the enormous amount of genomic sequence data. In this context. the problem of identifying promoters in genomic DNA sequences by computational methods has attracted considerable research attention in recent years. In this paper, we propose a new model of prokaryotic core promoter region including the -10 region and transcription initiation site, that is Dependency-Reflecting Decomposition Model (DRDM), which captures the most significant biological dependencies between positions (allowing for non-adjacent as well as adjacent dependencies). DRDM showed a good result of performance test and it will be employed effectively in predicting promoters in long microbial genomic Contigs.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.385-402
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• 2010

Most of the data warehouse (DW) requirements engineering approaches have not distinguished the early requirements engineering phase from the late requirements engineering phase. There are very few approaches seen in the literature that explicitly model the early & late requirements for a DW. In this paper, we propose an AGDI (Agent-Goal-Decision-Information) model to support the early and late requirements for the development of DWs. Here, the notion of agent refers to the stakeholders of the organization and the dependency among agents refers to the dependencies among stakeholders for fulfilling their organizational goals. The proposed AGDI model also supports three interrelated modeling activities namely, organization modeling, decision modeling and information modeling. Here, early requirements are modeled by performing organization modeling and decision modeling activities, whereas late requirements are modeled by performing information modeling activities. The proposed approach has been illustrated to capture the early and late requirements for the development of a university data warehouse exemplifying our model's ability of supporting its decisional goals by providing decisional information.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.9
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• pp.43-52
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• 1997

Processors using the superscalar rchitecture can achieve high performance by executing multipel instructions in a clock cycle. It is made possible by having multiple functional units and issuing multiple instructions to functional units simultaneously. But instructions can be dependent on one another and these dependencies prevent some instructions form being issued at the same cycle. In this paper, we designed an issue unit of a superscalar RISC microprocessor that can issue four instructions per cycle. The issue unit receives instructions form a prefetch unit, and issues them in order at a rate of as high as four instructions in one cycle for maximum utilization of functional units. By using an instruction buffer, the unit decouples instruction fetch and issue to improve instruction ussue rate. The issue unit is composed of an instruction buffer and an instruction decoder. The instruction buffer aligns and stores instructions from the prefetch unit, and sends the earliest four available isstructions to the instruction decoder. The instruction decoder decodes instructions, and issues them if they are free form data dependencies and necessary functional units and rgister file prots are available. The issue unit is described with behavioral level HDL (lhardware description language). The result of simulation using C programs shows that instruction issue rate is improved as the instruction buffer size increases, and 12-entry instruction buffer is found to be optimum considering performance and hardware cost of the instruction buffer.