• Language and Information
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• v.4 no.2
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• pp.27-40
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• 2000

The purpose of this paper is to propose a way of analyzing English embedded tense in terms of temporal per- spective time. To this end, the notion of temporal perspective time and Cooper and Ginzburg's(1996) attitude account are employed. Temporal perspective time is used to define the tense and to capture the anaphoric property of embedded tense,: the embedded temporal perspective time draws the embedding event time by anaphora. The ambiguity in the sequence of tense construction is described in terms of the attitude tense constraint reflecting the anaphoric property and two definitions of the past tense. The double access property in the present-under-past construction is described in terms of the constraint, the notion of eventuality, and the situation theoretic existential quantifier.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.185-190
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• 2009

The event sequence of the same type from a set of events having time property can be summarized in one event. But if the event sequence having an interval, It is reasonable to be summarized more than one in independent sub event sequence of each other. In this paper, we suggest a method of temporal data mining that summarizes the interval events based on Allen's interval algebra and finds out interval event association rule from interval events. It provides better knowledge than others by using concept of an independent sub sequence and finding interval event association rules.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.149-157
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• 2001

In the video retrieval system, visual clues of still images and motion information of video are employed as feature vectors. We generate the temporal textures to express the motion information whose properties are simple expression, easy to compute. We make those temporal textures of wavelet coefficients to express motion information, M components. Then, temporal texture feature vectors are extracted using spatial texture feature vectors, i.e. spatial gray-level dependence. Also, motion amount and motion centroid are computed from temporal textures. Motion trajectories provide the most important information for expressing the motion property. In our modeling system, we can extract the main motion trajectory from the temporal textures.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.509-513
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• 2008

In this paper, we investigate how generators' ramp-rate constraints may influence their equilibrium strategy formulation. In the market model proposed in this study, the generators' ramp-rate constraints are explicitly represented. In order to fully characterize the inter-temporal nature of the ramp-rate constraints, a dynamic game model is presented. The subgame perfect Nash equilibrium is adopted as the solution of the game and the backward induction procedure for the solution of the game is designed in this paper. The inter-temporal nature of the ramp-rate constraints results in the Markov property of the game, and we have found that the Markov property of the game significantly simplifies the subgame perfect Nash equilibrium characterization. Finally, a simple electricity market numerical illustration is presented for the successful application of the approach proposed.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.28-36
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• 2010

Some context is characterized by a single event in computing environment, but many other contexts are determined by a lot of things which occur with a space and a time. The Realtime Sensor Network context-awareness service that interacts with the physical space can have property such as time. A methodology that is specified the relationship between the contexts and the service needs to be developed to Realtime context-awareness deal with spatio-temporal. In this paper, we propose an approach which should include spatio-temporal property in the context model, and verify its effectiveness using enhanced Petri-Net. The context-awareness service modeling of Realtime Sensor Network is discussed the properties of model such as basic Petri-Net, patterned Petri-Net, or Spatio-temporal Petri-Net. The proposed methodology demonstrated using an example that is SAEMANGUEM warming watching system. The use of Spatio-temporal Petri-Net will contribute not only to develop the application but also to model the spatio-temporal context awareness.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.101-108
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• 2019

Workflow management systems (WfMSs) automate and manage workflows, which are implementations of organizational processes operated in process-centric organizations. In this paper, wepropose an algorithm to discover temporal work transference networks from workflow execution logs. The temporal work transference network is a special type of enterprise social networks that consists of workflow performers, and relationships among them that are formed by work transferences between performers who are responsible in performing precedent and succeeding activities in a workflow process. In terms of analysis, the temporal work transference network is an analytical property that has significant value to be analyzed to discover organizational knowledge for human resource management and related decision-making steps for process-centric organizations. Also, the beginning point of implementinga human-centered workflow intelligence framework dealing with work transference networks is to develop an algorithm for discovering temporal work transference cases on workflow execution logs. To this end, we first formalize a concept of temporal work transference network, and next, we present a discovery algorithm which is for the construction of temporal work transference network from workflow execution logs. Then, as a verification of the proposed algorithm, we apply the algorithm to an XES-formatted log dataset that was released by the process mining research group and finally summarize the discovery result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.347-356
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• 1996

In this paper, we present the circular array structure for estimating the temporal and spatial specturm of multiple narrowband incoherent signals which have different frequencies. The conventional linear array is computationally demanding for simultaneously estimating the spatial and temporal spectrum since it requires the tapped delay line filer. The statistical performance of the circular array is never deteriorated eve though it requires much less computational load than the uniform linear array. Especially, it is shown that the circular array resolves the direction-of-arrivals of the multiple signals without the spatial and temporal aliasing the fundamental nonuniform-sampling property prossessed by it. Computer simulation results are shown to demonstrate the better performance achieved with the circular array geometry relative to that obtained with a uniform linear array with taps.

• ETRI Journal
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• v.32 no.3
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• pp.490-492
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• 2010

This letter proposes a robust feature extraction method using a spatially normalized mean for temporal ordinal measurement. Before computing a rank matrix from the mean values of non-overlapped blocks, each block mean is normalized so that it obeys the invariance property against linear additive and subtractive noise effects and is insensitive against multiplied and divided noise effects. Then, the temporal ordinal measures of spatially normalized mean values are computed for the feature matching. The performance of the proposed method showed about 95% accuracy in both precision and recall rates on various distortion environments, which represents the 2.7% higher performance on average compared to the temporal ordinal measurement.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.