• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1245-1254
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• 2023

Significant research has been conducted on the W-band synthetic aperture radar (SAR) system that utilizes the 77 GHz frequency modulation continuous wave (FMCW) radar. To reconstruct the high-resolution W-band SAR image, it is necessary to transform the point cloud acquired from the stereo cameras or the LiDAR in the direction of 6 degrees of freedom (DOF) and apply them to the SAR signal processing. However, there are difficulties in matching images due to the different geometric structures of images acquired from different sensors. In this study, we present the method to extract an optimized depth map by obtaining 6 DOF of the point cloud using a gradient descent method based on the entropy of the SAR image. An experiment was conducted to reconstruct a tree, which is a major road environment object, using the constructed W-band SAR system. The SAR image, reconstructed using the entropy-based gradient descent method, showed a decrease of 53.2828 in mean square error and an increase of 0.5529 in the structural similarity index, compared to SAR images reconstructed from radar coordinates.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.363-370
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• 2023

Object detection technology that accurately recognizes the road and surrounding conditions is a key technology in the field of autonomous driving. In the field of autonomous driving, object detection technology requires real-time performance as well as accuracy of inference services. Task offloading technology should be utilized to apply object detection technology for accuracy and real-time on resource-constrained devices rather than high-performance machines. In this paper, experiments such as performance comparison of task offloading, performance comparison according to input image resolution, and performance comparison according to camera object resolution were conducted and the results were analyzed in relation to the application of task offloading for real-time object detection of autonomous driving in resource-constrained devices. In this experiment, the low-resolution image could derive performance improvement through the application of the task offloading structure, which met the real-time requirements of autonomous driving. The high-resolution image did not meet the real-time requirements for autonomous driving due to the increase in communication time, although there was an improvement in performance. Through these experiments, it was confirmed that object recognition in autonomous driving affects various conditions such as input images and communication environments along with the object recognition model used.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.15-30
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• 2023

Recently, various services using artificial intelligence(AI) are emerging in the media field as well However, most of the video editing, which involves finding an editing point and attaching the video, is carried out in a passive manner, requiring a lot of time and human resources. Therefore, this study proposes a methodology that can detect the edit points of video according to whether person in video are spoken by using Video Swin Transformer. First, facial keypoints are detected through face alignment. To this end, the proposed structure first detects facial keypoints through face alignment. Through this process, the temporal and spatial changes of the face are reflected from the input video data. And, through the Video Swin Transformer-based model proposed in this study, the behavior of the person in the video is classified. Specifically, after combining the feature map generated through Video Swin Transformer from video data and the facial keypoints detected through Face Alignment, utterance is classified through convolution layers. In conclusion, the performance of the image editing point detection model using facial keypoints proposed in this paper improved from 87.46% to 89.17% compared to the model without facial keypoints.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.375-393
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• 2023

The demand for Less than Container Load (LCL) has been on the rise due to the growing need for various small-scale production items and the expansion of the e-commerce market. Consequently, more companies in the International Freight Forwarder are now handling LCL. Given the variety in cargo sizes and the diverse interests of stakeholders, there's a growing need for a container loading algorithm that optimizes space efficiency. However, due to the nature of the current situation in which a cargo loading plan is established in advance and delivered to the Container Freight Station (CFS), there is a limitation that variables that can be identified at industrial sites cannot be reflected in the loading plan. Therefore, this study proposes a container loading methodology that makes it easy to modify the loading plan at industrial sites. By allowing the characteristics of cargo and the status of the container to be considered, the requirements of the industrial site were reflected, and the three-dimensional space was manipulated into a two-dimensional planar layer to establish a loading plan to reduce time complexity. Through the methodology presented in this study, it is possible to increase the consistency of the quality of the container loading methodology and contribute to the automation of the loading plan.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.111-127
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• 2023

This study is a study on user perception of ChatGPT use. The goal of this study is to analyze the relationship between user policy expectations and user innovativeness on ChatGPT technology acceptance and intention to use using variables of TRA (Theory of Reasoned Action). The impact of policy expectations and user innovativeness on the intention to use by mediating usefulness and hedonic motivation, and the impact of subjective norms on the usefulness and intention to use were analyzed by dividing them into the MZ generation and the non-MZ generation. It was verified whether there was a moderating effect on the effect of age differences on usefulness by interacting with policy expectations. An online survey was conducted on 300 ChatGPT users using PLS (Partial Least Square) structural equations and SPSS Package, and statistical analysis was performed using PLS and SPSS. According to the analysis results, it was confirmed that the higher the initial user's innovativeness, the higher the intention to use ChatGPT. In addition, the moderating effect analysis comparing the differences between the MZ generation and the non-MZ generation showed that policy expectations had a negative effect on the usefulness of ChatGPT use.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.158-177
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• 2024

This paper proposes and tests a theoretical model of the relational link between a novel form of customer-perceived fairness for a reward design (distributive peer justice climate) and C2C helping intention via community identification and online C2C interaction (friend-, neighboring customer-, audience-interaction) qualities in a collective consumption context (MMORPG). To test hypotheses, we amassed survey data within a collective consumption context (massively multiplayer online role-playing games, MMORPGs). We used structural equation modeling in analyzing the survey data. The results reveal that user-perceived distributive peer justice climate for a reward design enhances their C2C helping intention via community identification and C2C interactions in MMORPG contexts. Collective consumption-type service managers should focus on promoting the user-perceived distributive peer justice climate for their reward system to enhance users' present C2C co-creation experience (community identification, C2C interaction) and future C2C co-creation behavior (helping intention). By adopting an intra-unit level distributive justice concept (customer-perceived distributive peer justice climate) to a reward design in a collective consumption context (MMORPGs), this study informed collective consumption-type service managers of the importance of its management.

• Membrane Journal
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• v.34 no.2
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• pp.154-162
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• 2024

Anion exchange membranes (AEMs) are essential components in water electrolysis systems, serving to physically separate the generated hydrogen and oxygen gases while enabling the selective transport of hydroxide ions between electrodes. Key characteristics sought in AEMs include high ion conductivity and robust chemical and mechanical stability in alkaline. In this study, quaternized Poly(terphenyl piperidinium)/cellulose nanocrystals (qPTP/CNC) mixed matrix membrane was fabricated. The polymer matrix, PTP, was synthesized via super-acid polymerization, known for its excellent ion conductivity and alkaline durability. The qPTP/CNC membrane showed a dense and uniform morphology without significant voids or large aggregates at the polymer-nanoparticle interface. The qPTP/CNC membrane containing 2 wt% CNC demonstrated a high ion exchange capacity of 1.90 mmol/g, coupled with low water uptake (9.09%) and swelling ratio (5.56%). Additionally, the qPTP/CNC membrane showed significantly lower resistance and superior alkaline stability (384 hours at 50℃ in 1 M KOH) compared to the commercial FAA-3-50 membrane. These results highlight the potential of hydrophilic additive CNC in enhancing ion conductivity and alkaline durability of ion exchange membranes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.49-62
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• 2024

Reckless development of the underground by rapid urbanization causes inspection delay on replacement of existing structure and installation new facilities. However, frequent accidents occur due to deviation in construction design planned by inaccurate location information of underground structure. Meanwhile, the electrical resistivity survey, knowns as non-destructive method, is based on the difference in the electric potential of electrodes to measure the electrical resistance of ground. This method is significantly advanced with multi-electrode and deep learning for analyzing strata. However, there is no study to quantitatively assess change in electrical resistance according to geometric conditions of structures. This study evaluates changes in electrical resistance through geometric parameters of electrodes and structure. Firstly, electrical resistance numerical module is developed using generalized mesh occurring minimal errors between theoretical and numerical resistance values. Then, changes in resistances are quantitatively compared on geometric parameters including burial depth, diameter of structure, and distance electrode and structure under steady current condition. The results show that higher electrical resistance is measured for shallow depth, larger size, and proximity to the electrode. Additionally, electric potential and current density distributions are analyzed to discuss the measured electrical resistance around the terminal electrode and structure.