• Journal of KIISE:Computer Systems and Theory
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• v.31 no.11
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• pp.658-672
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• 2004

Multimedia applications are required to process the huge amount of data at high speed in real time. The memory reference instructions such as loads and stores are the main factor which limits the high speed execution of processor. To enhance the memory reference speed, cache prefetch schemes are used so as to reduce the cache miss ratio and the total execution time by previously fetching data into cache that is expected to be referenced in the future. In this study, we present an advanced data cache prefetching scheme that improves the conventional RPT (reference prediction table) based scheme. We considers the cache line size in calculation of the address stride referenced by the same instruction, and enhances the prefetching algorithm so that the effect of prefetching could be maintained even if an irregular address stride is inserted into the series of uniform strides. According to experiment results on multimedia benchmark programs, the cache miss ratio has been improved 29% in average compared to the conventional RPT scheme while the bus usage has increased relatively small amount (0.03%).

• Korean Journal of Construction Engineering and Management
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• v.24 no.3
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• pp.3-11
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• 2023

This research examines the need for a quantitative and objective method of calculating baseline productivity in the construction industry, which is known for its high volatility in performance and productivity. The existing literature's baseline productivity calculation methods rely heavily on subjective criteria, limiting their effectiveness. Additionally, data collection methods such as the "Five-minute Rating" are costly and time-consuming, making it challenging to collect detailed data at construction sites. To address these issues, this study proposes an objective baseline calculation method using unimpacted productivity BP, a work check sheet to systematically record detailed data, and a data collection and utilization process that minimizes cost and time requirements. This paper also suggests using unimpacted productivity BP and comparative analysis to address the objectivity and reliability issues of existing baseline productivity calculation methods.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.5
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• pp.243-254
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• 2005

Soft real-time applications such as multimedia feature highly variable processor requirements and probabilistic guarantees on deadline misses, meaning that each task in the application meets its deadline with a given probability. Thus, for such soft real-time applications, a system designer may want to improve the system utilization by allocating to each task a processor time less than its worst-case requirement, as long as the imposed probabilistic timing constraint is met. In this case, however, we have to address how to schedule jobs of a task that require more than (or, overrun) the allocated processor time to the task. In this paper, to address the overrun problem, we propose an overrun control method, which probabilistically controls the execution of overrunning jobs. The proposed overrun control method probabilistically allows overrunning jobs to complete for better system utilization, and also probabilistically prevents the overrunning jobs from completing so that the required probabilistic timing constraint for each task can be met. In the paper, we show that the proposed method outperforms previous methods proposed in the literature in terms of the overall deadline miss ratio, and that it is possible to synthesize the scheduling parameters of our method so that all tasks can meet the given probabilistic timing constraints.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.61-69
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• 2012

Smart Grid is the next-generation intelligent power grid that maximizes energy efficiency with the convergence of IT technologies and the existing power grid. Smart Grid is created solution for standardization and interoperability. Smart Grid industry enables consumers to check power rates in real time for active power consumption. It also enables suppliers to measure their expected power generation load, which stabilizes the operation of the power system. Smart industy was ecolved actively cause Wireless communication is being considered for AMI system and wireless communication using ZigBee sensor has been applied in various industly. In this paper, we proposed efficient addressing scheme for improving the performance of the routing algorithm using ZigBee in Smart Grid environment. A distributed address allocation scheme used an existing algorithm has wasted address space. Therefore proposing x, y coordinate axes from divide address space of 16 bit to solve this problem. Each node was reduced not only bitwise but also multi hop using the coordinate axes while routing than Cskip algorithm. I compared the performance between the standard and the proposed mechanism through the numerical analysis. Simulation verify performance about decrease averaging multi hop count that compare proposing algorithm and another. The numerical analysis results show that proposed algorithm reduce multi hop than ZigBee distributed address assignment and another.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.100-104
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• 1993

In Part 1, we derived robust stability conditions for an LTI interconnected to time-varying nonlinear perturbations belonging to several classes of nonlinearities. These conditions were presented in terms of positive definite solutions to LMI. In this paper we address a problem of synthesizing feedback controllers for linear time-invariant systems under structured time-varying uncertainties, combined with a worst-case H$_{2}$ performance. This problem is introduced in [7, 8, 15, 35] in case of time-invariant uncertainties, where the necessary conditions involve highly coupled linear and nonlinear matrix equations. Such coupled equations are in general difficult to solve. A convex optimization approach will be employed in this synthesis problem in order to avoid solving highly coupled nonlinear matrix equations that commonly arises in multiobjective synthesis problem. Using LMI formulation, this convex optimization problem can in turn be cast as generalized eigenvalue minimization problem, where an attractive algorithm based on the method of centers has been recently introduced to find its solution [30, 361. In the present paper we will restrict our discussion to state feedback case with Popov multipliers. A more general case of output feedback and other types of multipliers will be addressed in a future paper.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.514-526
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• 2013

With a long sensing period, the inter-frame spectrum sensing in IEEE 802.22 standard is vulnerable to the effect of the traffic of the primary user (PU). In this article, we address the two degrading factors that affect the inter-frame sensing performance with respect to the random arrival/leaving of the PU traffic. They are the noise-only samples under the random arrival traffic, and the PU-signal-contained samples under the random leaving traffic. We propose the model in which the intra-frame sensing cooperates with the inter-frame one, and the inter-frame sensing uses the time-of-arrival (ToA), and time-of-leave (ToL) detectors to reduce the two degrading factors in the inter-frame sensing time. These ToA and ToL detectors are used to search for the sample which contains either the ToA or ToL of the PU traffic, respectively, which allows the partial cancelation of the unnecessary samples. At the final stage, the remaining samples are input into a primary user detector, which is based on the energy detection scheme, to determine the status of PU traffic in the inter-frame sensing time. The analysis and the simulation results show that the proposed scheme enhances the spectrum-sensing performance compared to the conventional counter-part.

• Journal of Korean Society on Water Environment
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• v.22 no.2
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• pp.193-202
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• 2006

About 99% of microorganisms in the environment are unculturable. However, advances in molecular biology techniques allow for the analysis of living microorganisms in the environment without any cultivation. With the advent of new technologies and the optimization of previous methods, various approaches to studying the analysis of living microorganisms in the environment are expanding the field of microbiology and molecular biology. In particular, real-time PCR provides methods for detecting and quantifying microorganisms in the environment. Through the use of the methods, researchers can study the influence of environmental factors such as nutrients, oxygen status, pH, pollutants, agro-chemicals, moisture and temperature on the performances of environmental processes and some of the mechanisms involved in the responses of cells to their environment. This review will also address information gaps in the analysis of the microorganisms using real-time PCR in the environmental process and possible future research to develop an understanding of microbial activities in the environment.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.243-251
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• 2013

Cash management is a major concern for all contractors in the construction industry. It is arguable that cash is the most critical resource of all. A contractor needs to secure sufficient funds to navigate the project to the end, while keeping an eye on maximizing profits along the way. Past research attempted to address such topic via developing models to tackle the time-cost tradeoff problem, cash flow forecasting, and cash flow management. Yet, little was done to integrate the three aspects of cash management together. This paper, as such, presents a comprehensive model that integrates the time-cost tradeoff problem, cash flow management, and cash flow forecasting. First, the model determines the project optimal completion time by considering the different alternative construction methods available for executing project activities. Second, it investigates different funding alternatives and proposes a project-level cash management plan. Two funding alternatives are considered; they are borrowing and company own financing. The model was built as a combinatorial optimization model that utilizes ant colony search capabilities. The model also utilizes Microsoft Project software and spreadsheets to maintain an environment that incorporates activities, their durations, and other project data, in order to estimate project completion time and cost. Ant Colony Optimization algorithm was coded as a Macro program using VBA. Finally, an example project was used to test the developed model, where it acted reliably in maximizing the contractor's profit in the test project.

• Smart Structures and Systems
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• v.33 no.5
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.