• Journal of Institute of Convergence Technology
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• v.5 no.2
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• pp.37-42
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• 2015

This paper presents the effects of the computational time-varying delay on the stability of the haptic system that includes a virtual wall and a first-order-hold method. The model of a haptic system includes a haptic device model with a mass and a damper, a virtual wall model, a first-order-hold model and a computational time-varying delay model. In this paper, the maximum of the computational time-varying delay is assumed to be as much as the sampling time. Using the simulation, it is analyzed how the sample-hold methods and the computational time-varying delay affect the maximum available stiffness. As the maximum of computational time-varying delay increases, the maximal available stiffness of a virtual wall model is reduced.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.153-164
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• 2021

The increasing importance of such fields as embedded systems, pervasive computing, and hybrid systems control is increasing attention to the time-dependent aspects of system modeling. In this paper, we focus on modeling conceptual time. Conceptual time is time represented in conceptual modeling, where the notion of time does not always play a major role. Time modeling in computing is far from exhibiting a unified and comprehensive framework, and is often handled in an ad hoc manner. This paper contributes to the establishment of a broader understanding of time in conceptual modeling based on a software and system engineering model denoted thinging machine (TM). TM modeling is founded on a one-category ontology called a thimac (thing/machine) that is used to elaborate the design and analysis of ontological presumptions. The issue under study is a sample of abstract modeling domains as exemplified by time. The goal is to provide better understanding of the TM model by supplementing it with a conceptualization of time aspects. The results reveal new characteristics of time and related notions such as space, events, and system behavior.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.1-11
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• 2023

This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to compare the groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series data by the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain's two representative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain and mountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to one model due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore, it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwater forecasting results through time series analysis can be used for sustainable groundwater resource management.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.165-170
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• 1997

In this paper, we consider the aortic sinus baroreceptor, which is the most representative baroreceptors sensing the variance of pressure in the cardiovascular system(CVS), and propose heart activity control model to observe the effect of delay time in heart period and stroke volume under the regulation of baroreflex in arotic sinus. The proposed heart activity baroreflex regulation model contains CVS electric circuit sub-model, baroreflex regulation sub-model and time delay sub-model. In these models, applied electric circuit sub-model is researched by B.C.Choi and the baroreflex regulation sub-model transforms the input, the arotic pressure of CVS electric circuit sub-model, to outputs, heart period and stroke volume by mathematical nonlinear feedback. We constituted the time delay sub-model to observe sensitivity of heart activity baroreflex regulation model by using the variable value to represent the control signal transmission time from the output of baroreflex regulation model to efferent nerve through central nervous system. The simulation object of this model is to observe variability of the CVS by variable value in time delay sub-model. As simulation results, we observe three patterns of CVS variability by the time delay. First, if the time delay is over 2.5 sec, arotic pressure, stroke volume and heart rate is observed nonperiodically and irregularly. Second, if the time delay is from between 0.1 sec and 0.25 sec, the regular oscillation is observed. Finally, if time delay is under 0.1 sec, then heart rate and arotic pressure-heart rate trajectory is maintained in stable state.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.6
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• pp.377-385
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• 2016

The previous Adaptive Control of Thought-Rational (ACT-R) cognitive architecture model has a limitation in that it cannot accurately predict human visual search strategy, because time effect, one of important human cognitive features, is not considered. Thus, the present study proposes ACT-R cognitive modeling that contains the impact of time using a revised utility system in the ACT-R model. Then, the validation of the model is performed by comparing results of the model with eye-tracking experimental data and SEEV-T (SEEV-Time; SEEV model which considers time effect) model in "Where's Wally" game. The results demonstrate that the model data fit fairly well with the eye-tracking data ($R^2=0.91$) and SEEV-T model ($R^2=0.93$). Therefore, the modeling method which considers time effect using a revised utility system should be used in predicting the human visual search paradigm when the available time is limited.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.1-14
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• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.345-359
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• 2024

A regression model represents the relationship between explanatory and response variables. In real life, explanatory variables often affect a response variable with a certain time lag, rather than immediately. For example, the marriage rate affects the birth rate with a time lag of 1 to 2 years. Although deep learning models have been successfully used to model various relationships, most of them do not consider the time lags between explanatory and response variables. Therefore, in this paper, we propose an extension of deep learning models, which automatically finds the time lags between explanatory and response variables. The proposed method finds out which of the past values of the explanatory variables minimize the error of the model, and uses the found values to determine the time lag between each explanatory variable and response variables. After determining the time lags between explanatory and response variables, the proposed method trains the deep learning model again by reflecting these time lags. Through various experiments applying the proposed method to a few deep learning models, we confirm that the proposed method can find a more accurate model whose error is reduced by more than 60% compared to the original model.

• Journal of Industrial Convergence
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• v.2 no.2
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• pp.127-147
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• 2004

This paper examines the time-series relations among expected return, risk, and book-to-market(B/M) at the portfolio level. The time-series analysis is a natural alternative to cross-sectional regressions. An alternative feature of the time-series regressions is that they focus on changes in expected returns, not on average returns. Using the time-series analysis, we can directly test whether the three-factor model explains time-varying expected returns better than the characteristic-based model. These results should help distinguish between the risk and mispricing stories. We find that B/M is strongly associated with changes in risk, as measured by the Fama and French(1993) three-factor model. After controlling for changes in risk, B/M contains little additional information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than the characteristic-based model.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.265-274
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• 2003

Time series data are influenced by the external events such as holiday, strike, oil shock, and political change, so the external events cause a sudden change to the time series data. We regard the observation as outlier that occurred as a result of external events. In general, it is called intervention if we know the period and the reason of external events, and it makes an analyst difficult to establish a time series model. Therefore, it is important that we analyze the styles and effects of intervention. In this paper, we considered the linear time series model with invention and compared with nonlinear time series models such as ARCH, GARCH model and also we compared with the combination prediction method that Tong(1990) introduced. In the practical case study, we compared prediction power with RMSE among linear, nonlinear time series model with intervention and combination prediction method.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1437-1440
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• 2007

Many hydroclimatic time series are marked by interannual and longer quasi-period features that are associated with narrow band oscillatory climate modes. A time series modeling approach that directly considers such structures is developed and presented. The essence of the approach is to first develop a wavelet decomposition of the time series that retains only the statistically significant wavelet components, and to then model each such component and the residual time series as univariate autoregressive processes. The efficacy of this approach is demonstrated through the simulation of observed and paleo reconstructions of climate indices related to ENSO and AMO, tree ring and rainfall time series. Long ensemble simulations that preserve the spectral attributes of the time series in each ensemble member can be generated. The usual low order statistics are preserved by the proposed model, and its long memory performance is superior to the direction application of an autoregressive model.