• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.153-167
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• 2022

Purpose: The purpose of this study is to guide the operation managers who plan daily production of large mass-processing facility that services multi-customers with multi-product, small-batch item characteristics by providing the practical best production quantity and the inventory allowed to build. Methods: Close observation of a subcontract paint-shop operator captured the daily decision process which was reflected in the subcontractor-unique mathematical model and the system dynamics simulation model. Multiple simulations were run to find the practical best production quantity and the maximum allowable stock level of inventory that did not undermine the profit from practical best daily production. Actual data and a few constant values were obtained from the firm under study. Results: While the inventory holding cost for the customer-owned material harms the total profit of the subcontractor, the running cost of the processing facility hinders production in small batches. This balances the maximum possible productions and results in practical best daily production which can be found through simulation runs with actual data. The maximum level of stocked inventory is deduced from the practical best daily production. Conclusion: To build a large volume that enables economy-of-scale production, operators should deal with multi-product small-batch items from multiple customers. When the planned schedule of the time and amount of material in-flow tend not to be reliable, operators can find it practical to execute level production across the planning horizon instead of adjusting to day-to-day in-flow fluctuations.

• Advances in concrete construction
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• v.13 no.2
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• pp.123-132
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• 2022

This paper aims to adapt Multilinear regression (MLR) to predict the strength and toughness of SIFCON containing various pozzolanic materials. Slurry Infiltrated Fibrous Concrete (SIFCON) is one of the most common terms used in concrete manufacturing, known for its benefits such as high ductility, toughness and high ultimate strength. Assessment of compressive strength (CS.), flexural strength (F.S.), splitting tensile strength (STS), dynamic elasticity modulus (DME) and impact energy (I.E.) using the experimental approach is too costly. It is time-consuming, and a slight error can lead to a repeat of the test and, to solve this, alternative methods are used to predict the strength and toughness properties of SIFCON. In the present study, the experimentally investigated SIFCON data about various mix proportions are used to predict the strength and toughness properties using regression analysis-multilinear regression (MLR) models. The input parameters used in regression models are cement, fibre, fly ash, Metakaolin, fine aggregate, blast furnace slag, bottom ash, water-cement ratio, and the strength and toughness properties of SIFCON at 28 days is the output parameter. The models are developed and validated using data obtained from the experimental investigation. The investigations were done on 36 SIFCON mixes, and specimens were cast and tested after 28 days of curing. The MLR model yields correlation between predicted and actual values of the compressive strength (C.S.), flexural strength, splitting tensile strength, dynamic modulus of elasticity and impact energy. R-squared values for the relationship between observed and predicted compressive strength are 0.9548, flexural strength 0.9058, split tensile strength 0.9047, dynamic modulus of elasticity 0.8611 for impact energy 0.8366. This examination shows that the MLR model can predict the strength and toughness properties of SIFCON.

• Journal of Digital Forensics
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• v.13 no.2
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• pp.125-134
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• 2019

The process of identifying system behavior or user behavior from data collected during the digital forensics investigation is essential. In the case of PCs, there are many different types of system behavior or remaining logs depending on the operating system, and the analysis results of the de facto forensics tools that analyze the data are different. Because the reliability of a tool in the digital forensics field is an important factor, cross-analysis is usually performed with multiple tools for one digital evidence, and if the analysis results differ from one tool to the other, it is difficult to aggregate the analysis results. Therefore, a standard for processing information centered on the land-to-ground principle is proposed to facilitate sharing and intuitively identifying the analysis results of digital evidence collected. It also proposes a way to use it as an indicator to verify the reliability of an analysis tool by comparing the performance of a digital forensics analysis tool.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.71-79
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• 2005

Existing arterial link travel time estimation methods relying on either aggregate point-based or individual section-based traffic data have their inherent limitations. This paper demonstrates the utility of data fusion for improving arterial link travel time estimation. If the data describe traffic conditions, an operator wants to know whether the situations are going better or worse. In addition, some traffic information providing strategies require predictions of what would be the values of traffic variables during the next time period. In such situations, it is necessary to use a prediction algorithm in order to extract the average trends in traffic data or make short-term predictions of the control variables. In this research. a multi-step ahead prediction algorithm using Data fusion was developed to predict a link travel time. The algorithm performance were tested in terms of performance measures such as MAE (Mean Absolute Error), MARE(mean absolute relative error), RMSE (Root Mean Square Error), EC(equality coefficient). The performance of the proposed algorithm was superior to the current one-step ahead prediction algorithm.

• The Korean Journal of Physiology
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• v.24 no.1
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• pp.123-129
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• 1990

A comparative study of acid-base balance has been made between acidemia-induced hyperkalemia and hyperkalemia-induced acidemia. A group of rabbits was infused 0.1 N hydrochloric acid solution and metabolic acidosis was induced. Another group was administered 20 mM potassium chloride solution and hyperkalemia was induced. The third group was infused 0.1 N hydrochloric acid and 20 mM potassium chloride solution, simultaneously. Acid-base data and plasma potassium ion concentration were monitored every thirty minutes in these three groups of rabbits. Following results were obtained: 1 ) Along with the infusion of hydrochloric acid, acute metabolic acidosis was induced in the rabbits. Plasma bicarbonate ion concentration decreased primarily in this group. As a respiratory compensation, there was a tendency of reduction of arterial $Pco_{2}$. The alteration of data became larger along with the amount of administration and the time elapsed. However, hyperkalemia was not so severe compared with the second group. 2) In potassium chloride infused group, plasma potassium ion concentration increased along with the time elapsed and the amount of infusion. And the alteration of acid-base data was parrallel to the level of potassium ion concentration, above all depression of pH was prominent. 3) Above data suggest that when acute metabolic acidosis was induced, exchange of intracellular potassium ion with extracellular hydrogen ion seems significant for the regulation of extracellular acid-base balance. And when hyperkalemia was induced with the infusion of potassium chloride solution, the exchange of intracellular hydrogen ion with extracellular potassium ion also seems significant for the regulation of extracellular potassium balance. 4) In the group of rabbits infused hydrochloric acid and potassium simultaneously, disturbances of acid-base balance and potassium balance were much more severe than two other groups. In these mixed disturbances, the process of compensatory mechanism might be inhibited and one disturbance might aggregate each other. 5) Through above data it has been postulated that in acid-base disturbance potassium balance can be sacrificed as a compensatory mechanism, and vice versa in disturbance of potassium balance. And our data also suggest that hydrogen ion and potassium ion are compensatory pair, one another.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.1-11
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• 2020

As an important basic building block of the smart grid environment, smart meter provides real-time electricity consumption information to the utility. However, ensuring information security and privacy in the smart meter data aggregation process is a non-trivial task. Even though the secure data aggregation for the smart meter has been a lot of attention from both academic and industry researchers in recent years, most of these studies are not secure against internal attackers or cannot provide data integrity. Besides, their computation costs are not satisfactory because the bilinear pairing operation or the hash-to-point operation is performed at the smart meter system. Recently, blockchains or distributed ledgers are an emerging technology that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. In particular, blockchains are identified as having the potential to bring significant benefits and innovation for the electricity consumption network. This study suggests a distributed, privacy-preserving, and simple secure smart meter data aggregation system, backed up by Blockchain technology. Smart meter data are aggregated and verified by a hierarchical Merkle tree, in which the consensus protocol is supported by the practical Byzantine fault tolerance algorithm.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.101-116
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• 1995

The fact that the demands on traffic data collection are imposed by economic and safety considerations raisese the question of the potential for complementing existing traffic data collection programs with satellite data. Evaluating and monitoring traffic characteristics is becoming increasingly important as worsening congestion, declining economic situations, and increasing environmental sensitivies are forcing the government and municipalities to make better use of existing roadway capacities. The present system of using automatic counters at selected points on highways works well from a temporal point of view (i.e., during a specific period of time at one location). However, the present system does not cover the spatial aspects of the entire road system (i.e., for every location during specific periods of time); the counters are employed only at points and only on selected highways. This lack of spatial coverage is due, in part, to the cost of the automatic counters systems (fixed procurement and maintenance costs) and of the personal required to deploy them. The current procedure is believed to work fairly well in the aggregate mode, at the macro level. However, at micro level, the numbers are more suspect. In addition, the statistics only work when assuming a certain homogenity among characteristics of highways in the same class, an assumption that is impossible to test whn little or no data is gathered on many of the highways for a given class. In this paper, a remote sensing system as complement of the existing system is considered and implemented. Since satellite imagery with high resolution is not available, digitized panchromatic imagery acquired from an aircraft platform is utilized for initial test of the feasibility and performance capability of remote sensing data. Different levels of imagery resolutions are evaluated in an attempt to determine what vehicle types could be classified and counted against a background of pavement types, which might be expected in panchromatic satellite imagery. The results of a systematic study with three different levels of resolutions (1m, 2m and 4m) show that the panchromat ic reflectances of vehicles and pavements would be distributed so similarly that it would be difficult to classify systematically and analytically remotely sensing vehicles on pavement within panchromatic range. Anaysis of the aerial photographs show that the shadows of the vehicles could be a cue for vehicle detection.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5A
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• pp.433-442
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• 2010

A control of chloride diffusion coefficient is very essential for service life of reinforced concrete (RC) structures exposed to chloride attack so that much studies have been focused on this work. The purpose of this study is to derive the intended diffusion coefficient which satisfies intended service life and propose a technique for optimum concrete mixture through genetic algorithm(GA). For this study, 30 data with mixture proportions and related diffusion coefficients are analyzed. Utilizing 27 data, fitness function for diffusion coefficient is obtained with variables of water to binder ratio(W/B), weight of cement, mineral admixture(slag, flay ash, and silica fume), sand, and coarse aggregate. 3 data are used for verification of the results from GA. Average error from fitness function is observed to 18.7% for 27 data for diffusion coefficient with 16.0% of coefficient of variance. For the verification using 3 data, a range of error for mixture proportions through GA is evaluated to 0.3~9.3% in 3 given diffusion coefficients. Assuming the durability design parameters like intended service life, cover depth, surface chloride content, and replacement ratio of mineral admixture, target diffusion coefficient, where exterior conditions like relative humidity(R.H.) and temperature, is derived and optimum design mixtures for concrete are proposed. In this paper, applicability of GA is attempted for durability mixture design and the proposed technique would be improved with enhancement of comprehensive data set including wider range of diffusion coefficients.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.5
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• pp.20-29
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• 2024

The chloride diffusion coefficient is a critical indicator for assessing the durability of concrete marine substructures. This study develops a prediction model for the chloride diffusion coefficient using data from concrete bridges located in marine exposure zones (atmospheric, splash, tidal), an aspect that has not been considered in previous studies. Chloride profile data obtained from these bridge substructures were utilized. After data preprocessing, machine learning models, including Random Forest (RF), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN), were optimized through hyperparameter tuning. The performance of these models was developed and compared under three different variable sets. The first model uses six variables: water-to-binder (W/B) ratio, cement type, coarse aggregate volume ratio, service life, strength, and exposure environment. The second model excludes the exposure environment, using only the remaining five variables. The third model relies on just three variables: service life, strength, and exposure environment factors that can be obtained from precision safety diagnostics. The results indicate that including the exposure environment significantly enhances model performance for predicting the chloride diffusion coefficient in concrete bridges in marine environments. Additionally, the three variable model demonstrates that effective predictions can be made using only data from precision safety diagnostics.

• Analytical Science and Technology
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• v.28 no.5
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• pp.331-340
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• 2015

Mutations in Fused in Sarcoma (FUS) have been identified in patients with amyotrophic lateral sclerosis (ALS) or Frontotemporal Dementia (FTD). Pathological FUS is mis-localized to cytosol and forms aggregates associated with stress granules (SG), while FUS is normally localized to nucleus. However, it is largely unknown how pathological FUS forms SG-aggregates and which domains are responsible for this process. In this study, we examined cellular localization and aggregation of ALS-linked FUS missense mutants (P525L, R521C, R521H, R521G), analyzed the domains responsible for cytosolic FUS aggregation in HEK293T cells, and confirmed this in cultured mouse neurons. To do this, we firstly generated missense mutants of FUS and then examined their cellular localization. We found that P525L was mostly mis-localized to cytosol and formed FUS-positive SG aggregates while R521C, R521H, or R521G was localized to both nucleus and cytosol. To further characterize the domains required for aggregate formation of cytosolic FUS, we generated different domain-deletion mutants using FUS-∆17 which has a deletion of nuclear localization signal. Interestingly, cytosolic FUS without SYGQ and RGG1 domain or cytosolic FUS without RGG2-ZnF-RGG3 domain did not form FUS-positive SG aggregates, while cytosolic FUS without RRM domain generated more aggregates compared to FUS-∆17. Taken together, these data suggest that SYGQ-RGG1 or RGG2-ZnF-RGG3 domain contributes to formation of cytosolic aggregate, while RRM domain might interfere with FUS aggregation. Therefore, our studies will provide important insight for understanding cellular pathogenesis of neurodegeneration associated with FUS aggregate as well as finding therapeutic targets for ALS or FTD.