• Asia Marketing Journal
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• v.17 no.1
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• pp.97-121
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• 2015

In this research, we examine whether and why temporal distance influences evaluations of two different types of brand extensions: concept-based extensions, defined as extensions primarily based on the importance or relevance of brand concepts to extension products; and similarity-based extensions, defined as extensions primarily based on the amount of feature similarity at the product-category level. In Study 1, we test the hypothesis that concept-based extensions are evaluated more favorably when they are framed to launch in the distant rather than in the near future, whereas similaritybased extensions are evaluated more favorably when they are framed to launch in the near rather than in the distant future. In Study 2, we confirm that this time-dependent differential evaluation is driven by the difference in construal level between the bases of the two types of extensions - i.e., brand-concept consistency and product-category feature similarity. As such, we find that conceptbased extensions are evaluated more favorably under the abstract than concrete mindset, whereas similarity-based extensions are evaluated more favorably under the concrete than abstract mindset. In Study 3, we extend to the case for a broad brand (i.e., brands that market products across multiple categories), finding that making accessible a specific product category of a broad parent brand influences evaluations of near-future, but not distant-future, brand extensions. Combined together, our findings suggest that temporal distance influences brand extension evaluation through its effect on the importance placed on brand concepts and feature similarity. That is, consumers rely on different bases to evaluate brand extensions, depending on their perception of when the extensions take place and on under what mindset they are placed. This research makes theoretical contributions to the brand extension research by identifying one important determinant to brand extension evaluation and also uncovering its underlying dynamics. It also contributes to expanding the scope of the construal level theory by putting forth a novel interpretation of two bases of perceived fit in terms of construal level. Marketers who are about to launch and advertise brand extensions may benefit by considering temporal-distance information in determining what content to deliver about extensions in their communication efforts. Conceptual relation of a parent brand to extensions needs to be emphasized in the distant future, whereas feature similarity should be highlighted in the near future.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Journal of Information Display
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• v.11 no.2
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• pp.57-62
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• 2010

A method of characterizing time-sequential stereoscopic 3D displays based on the measurement of the temporal behavior of the systems vs. the grey levels is proposed. An Nvidia 3D vision kit with a 3D-ready SAMSUNG 2233RZ LCD display is characterized in the paper. OPTISCOPE SA especially designed for the precise measurements of the luminance and temporal behavior of LCD displays was used. The transmittance and response time of the shutter glasses was first evaluated. Then the grey-to-grey response times of the display were measured. The 2D and 3D behaviors of the display were then compared. Finally, the temporal behavior of the complete system was modeled, and the grey-level variations on one view were deduced as a function of the synchronization and level of the other eye. The main sources of imperfection were identified and quantified, and a full computation of the system performances was done.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.101-103
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• 2009

Minimizing the residual impurity level on the MgO layer is the key factor for reducing temporal dark image sticking on bright screen. In this paper, to reduce the residual impurity level on the MgO layer of 50-in. full-HD ac-PDP with He (35%) - Xe (11%) contents, RF-plasma treatments on the MgO layer are adopted under various gases for plasma treatment. As a result of monitoring the difference in the display luminance between the before and after 5-min. sustain discharge with a square-type image at peak luminance, the Ar and Ar>$O_2$ plasma treatments can reduce the temporal dark image sticking on the bright screen in an ac-PDP.

• Journal of the Korean Geographical Society
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• v.37 no.2
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• pp.191-202
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• 2002

Characteristics and patterns of geographic features and human activities can be interpreted in terms of spatiality and temporality. The necessity to record the historical changes and the ability to reason in the real world has lead to a new field of research so called Integrated Spatio-Temporal analysis. The objective of this study is to investigate temporal maps for Spatio-temporal analysis, which have the integration functionality for visualizing spatiality and temporality of the geographic appearances and human activities. Land information is composed of spatial, attribute and temporal data and requires spatio-temporal representations. It is possible to visualize spatio-temporal variations with spatio-temporal databases and temporal map produced by integrated data models. This study constructs spatio-temporal model for temporal maps of land price variation analysis. Taking advantage of the spatio-temporal model proposed here, it is possible to visualize spatio-temporal variations with spatio-temporal database and temporal map. On a practical level, this study would be extended and utilized to various geographic features.

• 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.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Asia Marketing Journal
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• v.16 no.1
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• pp.1-12
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• 2014

The goal-gradient hypothesis states that the tendency to approach a goal increases with the increasing proximity of the goal (Hull 1932). It was initially supported with an evidence of animal experiments and since then, several papers have investigated the goal-gradient hypothesis in humans. Although there are some evidences related to the goal-gradient hypothesis in human behaviors, none of previous studies can properly explain its underlying mechanism, and what's more, they were not able to suggest useful managerial applications in human behaviors. From these perspectives, this work points out that there are some theoretical weaknesses to apply the goal-gradient hypothesis into the complicated human decision-making behaviors and proposes an alternative theoretical mechanismthat underlies the goal-gradient hypothesis in human. Finally, it offers insights into managerial implications of the goal-gradient hypothesis in the marketing field. This study focuses on the changes in motivations for achieving goals, in terms of how approaches to goals vary according to temporal distance from those goals. Specifically, the temporal construal theory (Liberman and Trope 1998) is considered as the underlying mechanism of the goal-gradient in that the temporal construal theory argues how the temporal distance from a goal makes people change their associated values regarding to that goal. According to the temporal construal theory, the value of distant future outcomes (near future outcomes) is construed on the basis of abstract and central features (concrete and peripheral features), and it argues that distant future situations are construed on a higher level than near future situations. This means that the value associated with the high-level construal is enhanced over delay, whereas the value associated with the low-level construal is discounted over delay. Our propositions suggest that the goal-gradient behavior in human can be motivated by the different aspects or characteristics of the goal as time changes based on the temporal construal theory. Thus, the following propositions are proposed. P 1-1: If the goal is far away, consumers put more value on the central features that are more associated with the desirability of the goal. P 1-2: If the goal is far away, consumers put more effort into accomplishing the goal that has more central features, regardless of its peripheral features. P 2-1: If a goal is near, consumers put more value on the peripheral features that are more associated with the feasibility of the goal. P 2-2: If a goal is near, consumers put more effort into accomplishing the goal that has more peripheral features, regardless of its central features. We hope to provide sufficient managerial implications for the companies as our research aims to show how consumers react differently as they progress toward the goal. Proposed propositions may provide guidance for companies developing a loyalty program, enabling them to understand what kinds of benefits or services they should provide or emphasize to consumers in loyalty programs on the basis of the time-dependent changes in outcome values (such as gifts, reward coupons). The effects of temporal distance from a goal should inform companies' marketing activities and help themto determine where emphasis should be placed in designing the benefits of their loyalty program.

• Journal of Communications and Networks
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• v.5 no.2
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• pp.104-115
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• 2003

It is well known that Multiple Input Multiple Output (MIMO) systems offer the promise of achieving very high spectrum efficiencies (many tens of bit/s/Hz) in a mobile environment. The gains in MIMO capacity are sensitive to the presence of spatial and temporal correlation introduced by the radio environment. In this paper, we examine how MIMO capacity is influenced by a number of factors e.g., a) temporal correlation b) various combinations of low/high spatial correlations at either end, c) combined spatial and temporal correlations. In all cases, we compare the channel capacity that would be achievable under independent fading. We investigate the behaviour of "capacity fades," examine how often the capacity experiences the fades, develop a method to determine level crossing rates and average fade durations and relate these to antenna numbers. We also evaluate the influence of channel correlation on the capacity autocorrelation and assess the fit of a Gaussian random process to the temporal capacity sequence. Finally we note that the particular spatial correlation structure of the MIMO channel is influenced by a large number of factors. For simplicity, it is desirable to use a single overall correlation measure which parameterizes the effect of correlation on capacity. We verify this single parameter concept by simulating a large number of different spatially correlated channels.