• Human Ecology Research
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• v.61 no.2
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• pp.247-261
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• 2023

The purpose of this study was to examine the effect of consumer consumption behavior on sustainable consumption through social media and to verify the mediating effects of social connectedness on sustainable consumption. A survey comprising 222 men and women in their 20s and 30s was conducted in which their consumption expression behavior, sustainable consumption and social connectedness were measured. The results were analyzed using SPSS Process Macro Model 4 and it was found that consumers who shared a considerable amount of personal information on their lifestyle on social media as a means of self-expression exhibited a higher level of sustainable consumption. It also became apparent that the degree of consumption expression behavior had a positive effect on social connectedness and that social connectedness had a positive impact on sustainable consumption by mediating the degree of consumption-focused self-expression on social media. Based on these results, it was confirmed that social media can serve as a mechanism to lead consumers' consumption behavior beyond providing a basis for forming a human network. Implications for the impact of social media on consumption behavior were presented, and proposals for exploring the environment in which sustainable consumption can be activated were suggested, together with ways in which new media can be utilized. The findings from this study indicate that sharing consumption behavior and having a broad range of connections on social media can create an environment in which sustainable consumption is promoted.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.321-327
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• 2023

With the development of artificial intelligence, the prediction system has become one of the essential technologies in our lives. Despite the growth of these technologies, traffic congestion at intersections in the 21st century has continued to be a problem. This paper proposes a system that predicts intersection traffic jams using a Convolutional LSTM (Conv-LSTM) algorithm. The proposed system models data obtained by learning traffic information by time zone at the intersection where traffic congestion occurs. Traffic congestion is predicted with traffic volume data recorded over time. Based on the predicted result, the intersection traffic signal is controlled and maintained at a constant traffic volume. Road congestion data was defined using VDS sensors, and each intersection was configured with a Conv-LSTM algorithm-based network system to facilitate traffic.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.483-486
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• 2021

With the explosive increase in data, the 'big data era' has arrived, focusing on deriving new values and insights through data. With the development of data analysis technology, the importance of data analysis and utilization in the field of diagnosis and treatment as well as prevention is expanding, while the use of big data is emerging in the healthcare field. Moreover, as the three data-related laws (Personal Information Protection Act, Information and Communication Network Act, and Credit Information Act) were passed in January 2020, it became possible to use a wide range of big data through pseudonym information. However, the use of healthcare big data is still struggling due to various policies and regulations, inconsistent data quality, and the absence of specialized personnel. Therefore, in this study, examines the current state of use of big data in the healthcare field, and analyzes the challenges, overseas cases, plans, and expected effects for activation of healthcare big data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.194-198
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• 2021

LoRa is a physical layer technology that is designed to provide a reliable long-range communication with introducing CSS and with introducing a loco parentis tree network. Since a leaf can utilize multiple parents at the same time with a single transmission, PDR increases logarithmically as the number of gateways increases. Because of the ALOHA-like MAC of LoRa, however, the PDR degrades even under the loco parentis tree topology similarly to the single-gateway environment. Our proposed method is aimed to achieve SDMA approach to reuse the same frequency in different areas. For that purpose, it elaborately controls each TxPower of the senders for each message in concurrent transmission to survive the collision at each different gateway. The gain from this so-called capture effect increases the capacity of resource-hungry LPWAN. Compared to a typical collision-free controlled-access scheme, our method outperforms by 10-35% from the perspective of the total count of the consumed time slots. Also, due to the power control mechanism in our method, the energy consumption reduced by 20-40%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.7-14
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• 2006

The radar relationship was estimated for the selected rainfall event at Yeongchun station within Chungjudam basin where the discharge record was the range of from 1,000 CMS to 9,000 CMS. By calibrating the rainfall coefficient parameter estimated by radar relationship in small hydrology basin, rainfall with the topography properties was calculated. Three different rainfall estimation methods were compared:(1) radar relationship method (2) Thiessen method (3) Isohyetal method (4) Inverse distance method. Basin model was built by applying HEC-GeoHMS which uses digital elevation model to extract hydrological characteristic and generate river network. The proposed basin model was used as an input to HEC-HMS to build a runoff model. The runoff estimation model applying radar data showed the good result. It is proposed that the radar data would produce more rapid and accurate runoff forecasting especially in the case of the partially concentrated rainfall due to the atmospheric change. The proposed radar relationship could efficiently estimate the rainfall on the study area(Chungjudam basin).

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.259-277
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• 2023

This study aims to identify the academic landscape of the field of Library and Information Science by analyzing the research areas of the four major domestic journals using structural topic modeling and network analysis. The results show that each journal focuses on different research areas. The Journal of the Korean Society for Library and Information Science covers the most comprehensive range of research areas in the field, while the Journal of the Korean Biblia Society for Library and Information Science shows a similar research trend but with a higher preference for research areas related to library management and library programs. The Journal of Korean Library and Information Science Society deals more with topics related to school libraries and reading education and the Journal of the Korean Society for Information Management focuses more on information technology and information science. This study is able to provide valuable foundational data for researchers in submitting their papers and for the topical specialization and diversification of the journals in the field of Library and Information Science.

• Journal of Information Science Theory and Practice
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• v.11 no.4
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• pp.51-81
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• 2023

This study aimed to study its required performance requirements and proposes a competency framework necessary for the digital workforce of the Provincial Offices in Thailand. The specific primary informants were determined as 17 people. The collecting process was performed using the Delphi technique and the electronic Delphi technique in two phases, totaling four rounds. In the first time, a structured interview was used to conduct online interviews for 15 people. Content validation was performed to determine issues of the competency framework essential for the digital workforce with 7-level scaled questionnaires, and then online reviews were collected between 10-15 people (2nd to 4th times). A consensus was found and confirmed four times with descriptive statistics, namely frequency, mean, standard deviation, mode, median, and the absolute value of the difference between mode and median, interquartile range, and application of the conceptual framework. The research findings revealed that the essential competency requirements for the digital workforce were covered in digital literacy (six aspects), digital skills (four aspects), and digital characteristics (four aspects). Consensus was confirmed for 84 issues. Therefore, it was concluded that 61 points for building an essential competency framework for the digital workforce made them effective in using digital technology as a labor-saving instrument, as well as for expanding the breadth of development of digital expertise to include members of the organization's digital practitioner network. This development will benefit government agencies and the private sector, both national and international, in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.