• Journal of KIISE:Computer Systems and Theory
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• v.30 no.5_6
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• pp.299-306
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• 2003

This paper formulates a problem of embedded real-time system re-engineering, and presents its solution approach. Embedded system re-engineering is defined as a development task of meeting performance requirements newly imposed on a system after its hardware and software have been fully implemented. The performance requirements nay include a real-time throughput and an input-to-output latency. The proposed solution approach is based on a bottleneck analysis and nonlinear optimization. The inputs to the approach include a system design specified with a process network and a set of task graphs, task allocation and scheduling, and a new real-time throughput requirement specified as a system's period constraint. The solution approach works in two steps. In the first step, it determines bottleneck precesses in the process network via estimation of process latencies. In the second step, it derives a system of constraints with performance scaling factors of processing elements being variables. It then solves the constraints for the performance staling factors with an objective of minimizing the total hardware cost of the resultant system. These scaling factors suggest the minimal cost hardware upgrade to meet the new performance requirement. Since this approach does not modify carefully designed software structures, it helps reduce the re-engineering cycle.

• The Journal of KAIRB
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• v.3 no.1
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• pp.11-18
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• 2021

Purpose: The purpose of this study is to investigate a satisfaction survey of untact education and platforms that can be used for untact education to provide recommendations on future development of Education of Persons Conducting Clinical Trials. Methods: Online survey was distributed among students who have taken Untact Education of Persons Conducting Clinical Trials. The result was separated according to topic and descriptive statistics was used for analysis. The satisfaction survey used 10-point scale. Results: Of the 1,720 students who received the survey, 1,347 (78.3%) responded to the lecture satisfaction survey. The satisfaction level for broadcasting program (Kakao TV), an untact educational platform for the education of clinical trial workers at Kyung Hee University Medical Center, was relatively high with 8.09±1.99 points. Average score respondents recommending Kyung Hee University Untact Education of Persons Conducting Clinical Trials was 8.03±1.83 and customer recommendation score (Net Promotor Score) was 27.1%. Satisfaction level of the preferred training time was divided into weekday-morning (8-11 AM) (8.16±1.75), weekday-afternoon (12-4 PM) (7.73±2.07), weekday-evening (5-9 PM) (7.78±2.22), and weekend-morning (9-11 AM) real-time untact education (8.48±1.76) and analyzed. There was a noticeable difference between weekend-morning and weekday-afternoon (p<0.0001) and weekend-morning and weekday-evening (p=0.0001) real-time untact education. When asked about conducting education after COVID-19 pandemic ends, 79.2% (1,012 of 1,279) of the respondents answered that they prefer real-time untact education while 20.8 % (266 of 1,279) preferred face-to-face education. Conclusion: Online education, without time and space constraint, is expected to be the mainstream market in Korea for Education of Persons Conducting Clinical. Kyung Hee University Untact Education of Persons Conducting Clinical has achieved above average satisfaction using Kakao TV. Kyung Hee University Real-time Untact Education of Persons Conducting Clinical Net Promotor Score is 27.1%, which is above industry average, communication with trainees should be considered to improve Net Promotor Score.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.31-39
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• 2010

The goal of automatic velocity analysis is to extract accurate velocity from voluminous seismic data with efficiency. In this study, we developed an efficient automatic velocity analysis algorithm by using bootstrapped differential semblance (BDS) and Monte Carlo inversion. To estimate more accurate results from automatic velocity analysis, the algorithm we have developed uses BDS, which provides a higher velocity resolution than conventional semblance, as a coherency estimator. In addition, our proposed automatic velocity analysis module is performed with a conditional initial velocity determination step that leads to enhanced efficiency in running time of the module. A new optional root mean square (RMS) velocity constraint, which prevents picking false peaks, is used. The developed automatic velocity analysis module was tested on a synthetic dataset and a marine field dataset from the East Sea, Korea. The stacked sections made using velocity results from our algorithm showed coherent events and improved the quality of the normal moveout-correction result. Moreover, since our algorithm finds interval velocity ($\nu_{int}$) first with interval velocity constraints and then calculates a RMS velocity function from the interval velocity, we can estimate geologically reasonable interval velocities. Boundaries of interval velocities also match well with reflection events in the common midpoint stacked sections.

• Journal of Korean Society of Transportation
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• v.19 no.3
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• pp.101-113
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• 2001

Since late 1970s. one of the principal research areas in transportation problem is dynamic traffic assignment (DTA). Although many models have been developed regarding DTA, yet they have some limits of describing real traffic patterns. This reason comes from the fact that DTA model has the time varying constraints such as state equation, flow propagation constraint, first in first out(FIFO) rule and queuing evolution. Thus, DTA model should be designed to satisfy these constraints as well as dynamic route choice condition, dynamic user equilibrium. In this respect, link-based DTA models have difficulty in satisfying such constraints because they have to satisfy the constraints for each link, while path-based DTA models may easily satisfy them. In this paper we develop a path-based DTA model. The model includes point queue theory to describe the queue evolution and simulation loading method for depicting traffic patterns in more detail. From a numerical test, the model shows promising results.

• Journal of KIISE:Databases
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• v.30 no.1
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• pp.64-79
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• 2003

In client-server computing environments, update transactions of spatial data have the following characteristics. First, a transaction to update maps needs interactive work, and therefore it nay take a long time to finish. Second, a long transaction should be allowed to read the dirty data to enhance parallelism of executing concurrent transactions. when %he transaction is rolled back, it should guarantee the cascading rollback of all of the dependent transactions. Finally, two spatial objects may have a weak dependency constraint, called the spatial relationship, based on geometric topology. The existing recovery approaches cannot be directly applied to this environment, due to the high rollback cost and the overhead of cascading rollbacks. Furthermore, the previous approaches cannot guarantee the data integrity because the spatial relationship, which is a new consistency constraint of spatial data, is not considered. This paper presents new recovery schemes for update transactions of spatial data. To guarantee the data integrity, this paper defines recovery dependency as a rendition of cascading rollbacks. The partial-rollback is alto suggested to solve the problem of high rollback cost. The recovery schemes proposed in this paper can remove the unnecessary cascading rollbacks by using undo-delta, partial -redo and partial-undo. Finally, the schemes are performed to ensure the correctness.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.231-242
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• 2007

The existing pattern mining methods[1,2,3,4,5,6,11,12,13] do not use location generalization method on the set of location history data of moving object, but even so they simply do extract only frequent patterns which have no spatio-temporal constraint in moving patterns on specific space. Therefore, it is difficult for those methods to apply to frequent pattern mining which has spatio-temporal constraint such as optimal moving or scheduling paths among the specific points. And also, those methods are required more large memory space due to using pattern tree on memory for reducing repeated scan database. Therefore, more effective pattern mining technique is required for solving these problems. In this paper, in order to develop more effective pattern mining technique, we propose new location generalization method that converts data of detailed level into meaningful spatial information for reducing the processing time for pattern mining of a massive history data set of moving object and space saving. The proposed method can lead the efficient spatial moving pattern mining of moving object using by creating moving sequences through generalizing the location attributes of moving object into 2D spatial area based on $R^*$-Tree and Area Grid Hash Table(AGHT) in preprocessing stage of pattern mining.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.11-22
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• 2004

A distributed constraint satisfaction problem (distributed CSP) is a constraint satisfaction problem(CSP) in which variables and constraints are distributed among multiple automated agents. ACSP is a problem to find a consistent assignment of values to variables. Even though the definition of a CSP is very simple, a surprisingly wide variety of AI problems can be formalized as CSPs. Similarly, various application problems in DAI (Distributed AI) that are concerned with finding a consistent combination of agent actions can be formalized as distributed CAPs. In recent years, many new backtracking algorithms for solving distributed CSPs have been proposed. But most of all, they have common drawbacks that the algorithm assumes the priority of agents is static. In this thesis, we establish a basic algorithm for solving distributed CSPs called dynamic priority search algorithm that is more efficient than common backtracking algorithms in which the priority order is static. In this algorithm, agents act asynchronously and concurrently based on their local knowledge without any global control, and have a flexible organization, in which the hierarchical order is changed dynamically, while the completeness of the algorithm is guaranteed. And we showed that the dynamic priority search algorithm can solve various problems, such as the distributed 200-queens problem, the distributed graph-coloring problem that common backtracking algorithm fails to solve within a reasonable amount of time. The experimental results on example problems show that this algorithm is by far more efficient than the backtracking algorithm, in which the priority order is static. The priority order represents a hierarchy of agent authority, i.e., the priority of decision-making. Therefore, these results imply that a flexible agent organization, in which the hierarchical order is changed dynamically, actually performs better than an organization in which the hierarchical order is static and rigid. Furthermore, we describe that the agent can be available to hold multiple variables in the searching scheme.

• Journal of Aerospace System Engineering
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• v.15 no.5
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• pp.8-15
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• 2021

This paper describes a design method using an optimization technique to satisfy the longitudinal handling quality of high maneuverable jet aircraft. The dynamic inversion technique was applied to the target aircraft, and the control gain optimization satisfied the longitudinal short-period handling quality, however, the stability margin was not considered. If the stability margin is not satisfied, it is necessary to directly readjust the gains through trial and error methods for improvement. To improve this, an additional compensator and an optimization constraint were added to the control gain optimization procedure. In addition, the degree of handling quality satisfaction with the optimization result was reevaluated, and additional control evaluation criteria for the convergence of the time response and the steady state error that the flight performance requirement set as the optimization constraint cannot be reflected, and the results are described.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.30-30
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• 2023

This study aims to establish the multi-reservoir operation system model in the Upper Mun River Basin which includes 5 main dams namely, Mun Bon (MB), Lamchae (LC), Lam Takhong (LTK), Lam Phraphoeng (LPP), and Lower Lam Chiengkrai (LLCK) Dams. The knowledge and AI technology were applied aiming to develop innovative prototype for SMART dam-reservoir operation in future. Two different sorts of reservoir operation system model namely, Fuzzy Logic (FL) and Constraint Programming (CP) as well as the development of rainfall and reservoir inflow prediction models using Machine Learning (ML) technique were made to help specify the right amount of daily reservoir releases for the Royal Irrigation Department (RID). The model could also provide the essential information particularly for the Office of National Water Resource of Thailand (ONWR) to determine the short-term and long-term water resource management plan and strengthen water security against flood and drought in this region. The simulated results of base case scenario for reservoir operation in the Upper Mun from 2008 to 2021 indicated that in the same circumstances, FL and CP models could specify the new release schemes to increase the reservoir water storages at the beginning of dry season of approximately 125.25 and 142.20 MCM per year. This means that supplying the agricultural water to farmers in dry season could be well managed. In other words, water scarcity problem could substantially be moderated at some extent in case of incapability to control the expansion of cultivated area size properly. Moreover, using AI technology to determine the new reservoir release schemes plays important role in reducing the actual volume of water shortfall in the basin although the drought situation at LTK and LLCK Dams were still existed in some periods of time. Meanwhile, considering the predicted inflow and hydrologic factors downstream of 5 main dams by FL model and minimizing the flood volume by CP model could ensure that flood risk was considerably minimized as a result of new release schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3551-3567
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• 2016

The Low Earth Orbit satellite network has the unique characteristics of the non-uniform and time-variant traffic load distribution, which often causes severe link congestion and thus results in poor performance for delay-sensitive flows, especially when the network is heavily loaded. To solve this problem, a novel adaptive routing algorithm, referred to as the delay-oriented adaptive routing algorithm (DOAR), is proposed. Different from current reactive schemes, DOAR employs Destination-Sequenced Distance-Vector (DSDV) routing algorithm, which is a proactive scheme. DSDV is extended to a multipath QoS version to generate alternative routes in active with real-time delay metric, which leads to two significant advantages. First, the flows can be timely and accurately detected for route adjustment. Second, it enables fast, flexible, and optimized QoS matching between the alternative routes and adjustment requiring flows and meanwhile avoids delay growth caused by increased hop number and diffused congestion range. In addition, a retrospective route adjustment requesting scheme is designed in DOAR to enlarge the alternative routes set in the severe congestion state in a large area. Simulation result suggests that DOAR performs better than typical adaptive routing algorithms in terms of the throughput and the delay in a variety of traffic intensity.