• Geomechanics and Engineering
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• v.28 no.4
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• pp.421-435
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• 2022

The necessity of estimating the time and cost required for tunnel construction has led to extensive research in this regard. Since geological conditions are significant factors in terms of time and cost of road tunnels, considering these conditions is crucial. Uncertainties about the geological conditions of a tunnel alignment cause difficulties in planning ahead of the required construction time and costs. In this paper, the continuous-space, discrete-state Markov process has been used to predict geological conditions. The Monte-Carlo (MC) simulation (MCS) method is employed to estimate the construction time and costs of a road tunnel project using the input data obtained from six tunneling expert questionnaires. In the first case, the input data obtained from each expert are individually considered and in the second case, they are simultaneously considered. Finally, a comparison of these two modes based on the technique presented in this article suggests considering views of several experts simultaneously to reduce uncertainties and ensure the results obtained for geological conditions and the construction time and costs.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.24-31
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• 1994

In this paper, we report on the development of a speaker independent continuous speech recognition system using continuous hidden Markov models. The continuous hidden Markov model consists of mean and covariance matrices and directly models speech signal parameters, therefore does not have quantization error. Filter bank coefficients with their 1st and 2nd-order derivatives are used as feature vectors to represent the dynamic features of speech signal. We use the segmental K-means algorithm as a training algorithm and triphone as a recognition unit to alleviate performance degradation due to coarticulation problems critical in continuous speech recognition. Also, we use the one-pass search algorithm that Is advantageous in speeding-up the recognition time. Experimental results show that the system attains the recognition accuracy of $83\%$ without grammar and $94\%$ with finite state networks in speaker-indepdent speech recognition.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.1
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• pp.22-32
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• 2003

Many of multimedia applications in distributed environments generate the packets which have the real-time characteristics for continuous audio/video data and transmit them according to the teal-time task scheduling theories. In this paper, we model the traffic for continuous media in the distributed multimedia applications based on the high-bandwidth networks and introduce the PDMA algorithm which is the hard real-time task scheduling theory for guaranteeing QoS requested by the clients. Furthermore, we propose the admission control to control the new request not to interfere the current services for maintaining the high quality of services of the applications. Since the proposed admission control is sufficient for the PDMA algorithm, the PDMA algorithm is always able to find the feasible schedule for the set of messages which satisfies it. Therefore, if the set of messages including the new request to generate the new traffic. Otherwise, it rejects the new request. In final, we present the simulation results for showing that the scheduling with the proposed admission control is of practical use.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.199-202
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• 2009

Adaptive memory management is a serious issue in data stream management. Data stream differ from the traditional stored relational model in several aspect such as the stream arrives online, high volume in size, skewed data distributions. Data skew is a common property of massive data streams. We propose the predicted allocation strategy, which uses predictive processing to cope with time varying data skew. This processing includes memory usage estimation and indexing with timestamp. Our experimental study shows that the predictive strategy reduces both required memory space and latency time for skewed data over varying time.

• Journal of the Korea Safety Management & Science
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• v.7 no.2
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• pp.73-83
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• 2005

This paper is to propose tolerance intervals for expected time at the given reliability and confidence level for continuous and discrete reliability model. We consider guaranteed - coverage tolerance intervals, that is, reliability - confidence level tolerance intervals. These proposed methodologies can be applied to any industrial application where the customer's operating specification require a high level of reliability.

• Management Science and Financial Engineering
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• v.10 no.2
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• pp.53-71
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• 2004

In this study, a mathematical model that shows the optimal payout policy is developed. The model is new and unique in the sense that not only continuous-time framework is used, but also both partial differential equation (PDE) and real-option approach are utilized in the derivation of optimal payouts for the first time. In the model building, non-linear demand curve for dividend payouts in the competitive capital markets is assumed. From the sensitivity analysis using traditional comparative static analysis, some useful managerial implications which are consistent with famous previous studies are derived under realistic conditions. All results in this study, however, are valid under the assumption that the opportunity costs follow geometric Brownian motion, which is widely used in economic science and finance literature.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.507-510
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• 2003

The development of a basin-wide runoff analysis model is to analysis monthly and daily hydrologic runoff components including surface runoff, subsurface runoff, return flow, etc. at key operation station in the targeted basin. A short-term water demand forecasting technology will be developed taking into account the patterns of municipal, industrial and agricultural water uses. For the development and utilization of runoff analysis model, relevant basin information including historical precipitation and river water stage data, geophysical basin characteristics, and water intake and consumptions needs to be collected and stored into the hydrologic database of Integrated Real-time Water Information System. The well-known SSARR model was selected for the basis of continuous daily runoff model for forecasting short and long-term natural flows.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.17 no.1
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• pp.55-65
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• 2013

The purpose of this paper is to prove existence of solutions of an SIRS epidemic model with time delay of continuous type and the variable incidence rate and to investigate some asymptotic behaviors of the SIRS epidemic model. An example illustrating the stability of the model is given. The results extend the corresponding results in the literature.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.200-204
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• 2012

In engineering hydrology, an estimation of precipitation loss is one of the most important issues for successful modeling to forecast flooding or evaluate water resources for both surface and subsurface flows in a watershed. An accurate estimation of precipitation loss is required for successful implementation of rainfall-runoff models. Precipitation loss or hydrological abstraction may be defined as the portion of the precipitation that does not contribute to the direct runoff. It may consist of several loss elements or abstractions of precipitation such as infiltration, depression storage, evaporation or evapotranspiration, and interception. A composite loss rate model that combines four loss rates over time is derived as a lumped form of a continuous time function for a storm event. The composite loss rate model developed is an exponential model similar to Horton's infiltration model, but its parameters have different meanings. In this model, the initial loss rate is related to antecedent precipitation amounts prior to a storm event, and the decay factor of the loss rate is a composite decay of four losses.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.11-20
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• 2012

Stream data shows a sequence of values changing continuously over time. Due to the nature of stream data, its trend is continuously changing according to various time intervals. Therefore the prediction of stream data must be carried out simultaneously with respect to multiple intervals, i.e. Continuous Multiple Prediction(CMP). In this paper, we propose a Continuous Integrated Hierarchical Temporal Memory (CIHTM) network for CMP based on the Hierarchical Temporal Memory (HTM) model which is a neocortex leraning algorithm. To develop the CIHTM network, we created three kinds of new modules: Shift Vector Senor, Spatio-Temporal Classifier and Multiple Integrator. And also we developed learning and inferencing algorithm of CIHTM network.