• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.189-190
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• 2023

Environmental pollution problems are occurring in Jeju Island due to negative treatment of basalt waste. Measures for various approaches and utilization measures are needed to solve the problem of waste stones that occur during basalt processing. In this study, the Properties of permeable blocks with basalt were identified and the applicability and functionality as building materials were reviewed. This experiment is basic data for evaluating the functionality of the permeable block by manufacturing permeable blocks using basalt waste stones and analyzing flexural strength and compressive strength. The higher the basalt waste stone replacement rate, the lower the flexural strength and compressive strength, but it was judged that 20% of basalt waste stone replacement rate that satisfies the minimum flexural strength (4.0MPa) stipulated in KS F 4419 was appropriate. In addition, additional permeability coefficient and absorption rate experiments tended to increase as the basalt lung stone replacement rate increased. Therefore, it is judged that the permeable block using basalt waste stone is superior to the existing permeable block.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.300-305
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• 2023

Audience reactions in response to remote virtual performances must be compressed before being transmitted to the server. The server, which aggregates these data for group insights, requires a distribution code for the transfer. Recently, distributed learning algorithms such as federated learning have gained attention as alternatives that satisfy both the information security and efficiency requirements. In distributed learning, no individual user has access to complete information, and the objective is to achieve a learning effect similar to that achieved with the entire information. It is therefore important to distribute interdependent information among users and subsequently aggregate this information following training. In this paper, we present a new extension technique for minimal code that allows a new minimal code with a different length and Hamming weight to be generated through the product of any vector and a given minimal code. Thus, the proposed technique can generate minimal codes with previously unknown parameters. We also present a scenario wherein these combined methods can be applied.

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.601-629
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• 2022

Social networking sites (SNSs) have become an essential part of people's lives. It is thus crucial to understand how individuals use these platforms. Previous literature has divided usage into numerous activities and then grouped them into dimensions to avoid excessive granularity. However, these categories have not been derived from a uniform theoretical background; consequently, these dimensions are dispersed, overlapping, and disconnected from each other. This study argues that "SNS usage" is a complex phenomenon consisting of multiple activities that can be grouped into dimensions under the umbrella of communication theories and these dimensions are related to each other in a particular multi-dimensional architecture. "SNS usage" is conceptualized as a third-order construct formed by "producing," "consuming," and "communicating." "Producing," in turn, is proposed as a second-order construct manifested by "commenting," "general information sharing," and "self-disclosure." The proposed model was assessed with data collected from 414 USA adult users and PLS-SEM technique. The results show empirical support for the theorized model. SNS providers now have this architecture that clarifies the role of each dimension of use, which will allow them to design effective strategies to encourage the use of these networks.

• PNF and Movement
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• v.22 no.2
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• pp.289-303
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• 2024

Purpose: In this study, 100 stroke patients and 205 physical therapists were surveyed to determine the essential functions needed in the rehabilitation process of stroke patients. Methods: This study involved 100 stroke patients and 205 physical therapists. Sixteen functions suggested in the previous study as necessary in the rehabilitation process of stroke patients were selected, and a revised questionnaire was prepared and distributed to several institutions. A frequency analysis of the collected data was conducted to aggregate the functions required in rehabilitation, and a scoring process was used to determine their ranking among the 16 functions. Results: The functions required in the rehabilitation process, as selected by stroke patients, were ranked as follows: walking, toileting, eating, using products and technology for communication, and washing oneself. The functions required in the rehabilitation process, as selected by physical therapists, were ranked as follows: muscle power functions, maintaining body position, muscle tone functions, attention functions, and walking. Conclusion: The results of the study confirm the importance of an agreed goal between the stroke patient and the therapist regarding the functions required for the rehabilitation. This understanding plays a significant role in achieving the patient's expectations and the therapist's predicted performance, thereby providing reassurance and confidence in the impact of the research.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.31-40
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• 2012

u-GIS DSMSs have been researched to deal with various sensor data from GeoSensors in ubiquitous environment. Also, they has been more important for high availability. The data from GeoSensors have some characteristics that increase explosively. This characteristic could lead memory overflow and data loss. To solve the problem, various load shedding methods have been researched. Traditional methods drop the overloaded tuples according to a particular criteria in a single server. Tuple deletion sensitive queries such as aggregation is hard to satisfy accuracy. In this paper a dual processing load shedding method is suggested to improve the accuracy of aggregation in clustering environment. In this method two nodes use replicated stream data for high availability. They process a stream in two nodes by using a characteristic they share stream data. Stream data are synchronized between them with a window as a unit. Then, processed results are merged. We gain improved query accuracy without data loss.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Korean Journal of Agricultural Science
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• v.18 no.1
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• pp.33-40
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• 1991

This study was performed to obtain the basic data applied to use of synthetic lightweight aggregate concrete affected by alkali silica reaction. The results obtained were summarized as follows; 1. The compressive strength of type A concrete was increased with increase of curing age. At the curing age 28 days, the highest compressive strength was showed at type Band C concrete, respectively. But, it was gradually decreased with increase of curing age at those concrete. 2. The flexural strength of type A concrete was increased with increase of curing age. At the curing age 14 days, the highest flexural strength was showed at type Band C concrete, respectively. But, it was gradually decreased with increase of curing age at those concrete. 3. The correlation between compressive and flexural strength of the sample was shown highly significant only at type A concrete. 4. It was shown that the water absorptions of the type Band C were 7.0-7.8 times higher than the type A concrete. It was significantly higher at the early stage of immersed time at all sample. 5. The correlation between compressive strength and water absorption of the sample was significant only at the type A concrete.

• Journal of the Korea Concrete Institute
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• v.27 no.4
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• pp.427-434
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• 2015

The objective of this study is to establish an rational mixture-proportioning procedure for low-$CO_2$ concrete using supplementary cementitious materials (SCMs) achieving the targeted $CO_2$ reduction ratio as well as the conventional requirements such as initial slump, air content, and 28-day compressive strength of concrete. To evaluate the effect of SCM level on the $CO_2$ emission and compressive strength of concrete, a total of 12537 data sets were compiled from the available literature and ready-mixed concrete plants. The amount of $CO_2$ emission of concrete was assessed under the system boundary from cradle to concrete production stage at a ready-mixed concrete plant. Based on regression analysis using the established database, simple equations were proposed to determine the mixture proportions of concrete such as the type and level of SCMs, water-to-binder ratio, and fine aggregate-to-total aggregate ratio. Furthermore, the $CO_2$ emissions for a given concrete mixture can be straightforwardly calculated using the proposed equations. Overall, the developed mixture-proportioning procedure is practically useful for determining the initial mixture proportions of low-$CO_2$ concrete in the ready-mixed concrete field.

• KDI Journal of Economic Policy
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• v.25 no.2
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• pp.3-53
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• 2003

Using the plant level panel data on Korean manufacturing during 1990-98 period, this study tries to assess the role of entry and exit in enhancing aggregate productivity, both qualitatively and quantitatively. Main findings of this study are summarized as follows. First, plant entry and exit rates in Korean manufacturing seem quite high: they are higher than in the U.S. or several developing countries for which comparable studies exist. Second, in line with existing studies on other countries, plant turnovers reflect underlying productivity differential in Korean manufacturing, with the "shadow of death" effect as well as selection and learning effects all present. Third, plant entry and exit account for as much as 45 and 65 percent in manufacturing productivity growth during cyclical upturn and downturn, respectively. The findings of this study show that the entry and exit of plants has been an important source of productivity growth in Korean manufacturing. Plant birth and death are mainly a process of resource reallocation from plants with relatively low and declining productivity to a group of heterogeneous plants, some of which have the potential to become highly efficient in future. The most obvious lesson from this study is that it is important to establish policy or institutional environment where efficient businesses can succeed and inefficient businesses fail.