• Korean Journal of Remote Sensing
• /
• v.35 no.5_1
• /
• pp.681-692
• /
• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.3
• /
• pp.269-276
• /
• 2010

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

• The Korean Journal of Applied Statistics
• /
• v.36 no.1
• /
• pp.1-19
• /
• 2023

The purpose of this study was to find out what attributes are valid for the edge between students through longitudinal network analysis, and the results of TERGM (temporal exponential random graph model) and SAOM (stochastic actor-oriented model) statistical models were compared. The TERGM model interprets the research results based on the edge formation of the entire network, and the SAOM model interprets the research results on the surrounding networks formed by specific actors. The TERGM model expressed the influence of a previous time through a time term, and the SAOM model considered temporal dependence by implementing a network that evolves by an actor's opportunity as a ratio function.

• Journal of Korean Society for Geospatial Information Science
• /
• v.23 no.2
• /
• pp.105-111
• /
• 2015

Domestic 2D maps includes only most up-to-date information at the time of production without historical information. Therefore, it is hard to identify the change history of real world objects. In this research, Spatio-temporal model for 2D map were developed and it's compatibility was verified through the pilot project conducted on the Gwanggyo area of Gyeonggi province. Also, the procedure to generate 2D spatio-temporal database using maps made periodically on the same target area was introduced for showing the possibility of realizing nation wide spatio-temporal 2D map using the national base map updated periodically.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.10
• /
• pp.2632-2649
• /
• 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 KIISE:Databases
• /
• v.31 no.3
• /
• pp.252-260
• /
• 2004

Recently, the TPR-tree has been proposed to support spatio-temporal queries for moving objects. Subsequently, various methods using the TPR-tree have been intensively studied. However, although the TPR-tree is one of the most popular access methods in spatio-temporal databases, any cost model for the TPR-tree has not yet been proposed. Existing cost models for the spatial index such as the R-tree do not accurately ostinato the number of disk accesses for spatio-temporal queries using the TPR-tree, because they do not consider the future locations of moving objects. In this paper, we propose a cost model of the TPR-tree for moving objects for the first time. Extensive experimental results show that our proposed method accurately estimates the number of disk accesses over various spatio-temporal queries.

• Journal of applied mathematics & informatics
• /
• v.27 no.1_2
• /
• pp.385-400
• /
• 2009

The adverse effect of harmful plankton on the marine ecosystem is a topic of deep concern. To investigate the role of such phytoplankton, a mathematical model containing distinct dynamical equations for toxic and non-toxic phytoplankton is analyzed. Stability analysis of the resulting three equation model is carried out. A continuous time variation in toxin liberation process is incorporated into the model and a stability analysis of the resulting delay model is performed. The distributed delay model is then extended to include the spatial distribution of plankton and the delay-diffusion model is analyzed with spatial and spatiotemporal kernels. Conditions for diffusion-driven instability in both the cases are derived and compared to explore the significance of these kernels. Numerical studies are performed to justify analytical findings.

• Journal of KIISE:Software and Applications
• /
• v.31 no.11
• /
• pp.1560-1567
• /
• 2004

In temporal logic model checking, the number of states is exponentially increased by the size of a model. This is called the state explosion problem. Abstraction, partial order, symmetric, etc. are widely used to avoid the problem. They reduce a number of states by exploiting structural information in a model. Instead, this paper proposes the relay model checking that decomposes a temporal formula to be verified into several sub-formulas and then model checking them one by one. As a result, we solve complex games that can't handle with previous techniques.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.24 no.6
• /
• pp.645-654
• /
• 2011

Temporal supports is proposed for the large block construction of a double-deck truss girder of a bundle type cable-stayed bridge. The design force of the temporal bents cannot be evaluated by a conventional design procedure with gust factored static wind loads. The uplift forces in BS5400 also can not estimate the design forces of the temporal bents properly for the turbulent wind loads. A frequency-domain buffeting analysis is performed to evaluate the design forces of the temporal bents considering the interactions between the girder and temporal supports. Two cases of modeling are compared to estimate the stiffness contribution of temporal supports in determining design forces, i.e., an analysis model including temporal bents in the structural analysis modeling and an analysis model with fixed supports at the bent tops neglecting the stiffness of temporal bents. The consideration of bent stiffness usually generates smaller reaction forces than rigid support modeling. Consequently, the effectiveness and usefulness of the buffeting analysis procedure with full modeling of temporal supports are demonstrated for the design of a temporal bents of the construction of a bundle type cable-stayed bridge.

• The KIPS Transactions:PartD
• /
• v.9D no.3
• /
• pp.365-380
• /
• 2002

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.