• Journal of Information Processing Systems
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• v.19 no.6
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• pp.713-721
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• 2023

Conventional mining enterprises, particularly coal-related ones, exhibit substantial environmental pollution and high energy consumption, while those involved in new energy resources, such as lithium and cobalt, face severe resource shortages. Consequently, the economic efficiency of China's mining enterprises is significantly constrained. This study examines data from nine representative listed enterprises in China spanning 2016 to 2021. Employing the DEA model-i.e., BCC (VRS) model, we analyze the economic efficiency of mining enterprises with a focus on stakeholders. The paper provides static and dynamic analyses, offering insights and recommendations for enhancing technology, reducing costs, and fortifying social relationships.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.931-934
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• 2019

Research using artificial intelligence to generate captions for an image has been studied extensively. However, these systems are unable to create creative stories that include more than one sentence based on image content. A story is a better way that humans use to foster social cooperation and develop social norms. This paper proposes a framework that can generate a relatively short story to describe based on the context of an image. The main contributions of this paper are (1) An unsupervised framework which uses recurrent neural network structure and encoder-decoder model to construct a short story for an image. (2) A huge English novel dataset, including horror and romantic themes that are manually collected and validated. By investigating the short stories, the proposed model proves that it can generate more creative contents compared to existing intelligent systems which can produce only one concise sentence. Therefore, the framework demonstrated in this work will trigger the research of a more robust AI story writer and encourages the application of the proposed model in helping story writer find a new idea.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.101-111
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• 2015

Establishment of appropriate data in certain formats is essential for agricultural water cycle analysis, which involves complex interactions and uncertainties such as climate change, social & economic change, and watershed environmental change. The main objective of this study was to develop web-based Data processing and Model linkage Techniques for Agricultural Water-Resource analysis (AWR-DMT). The developed techniques consisted of database development, data processing technique, and model linkage technique. The watershed of this study was the upper Cheongmi stream and Geunsam-Ri. The database was constructed using MS SQL with data code, watershed characteristics, reservoir information, weather station information, meteorological data, processed data, hydrological data, and paddy field information. The AWR-DMT was developed using Python. Processing technique generated probable rainfall data using non-stationary frequency analysis and evapotranspiration data. Model linkage technique built input data for agricultural watershed models, such as the TANK and Agricultural Watershed Supply (AWS). This study might be considered to contribute to the development of intelligent watercycle analysis by developing data processing and model linkage techniques for agricultural water-resource analysis.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.223-230
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• 2020

Companies have made an investment of cost and time to optimize processing of a new business model in a cloud environment, applying collaboration technology utilizing business processes in a social network. The collaborative processing method changed from traditional BPM to the cloud and a mobile cloud environment. We proposed a collaborative system for operating processes in social networks using MOXMDR-DAI+ (eXtended Metadata Registry-Data Access & Integration based multimedia ontology). The system operating cloud-based collaborative processes in application of MOXMDR-DAI+, which was suitable for data interoperation. MOXMDR-DAI+ applied to this system was an agent effectively supporting access and integration between multimedia content metadata schema and instance, which were necessary for data interoperation, of individual local system in the cloud environment, operating collaborative processes in the social network. In operating the social network-based collaborative processes, there occurred heterogeneousness such as schema structure and semantic collision due to queries in the processes and unit conversion between instances. It aimed to solve the occurrence of heterogeneousness in the process of metadata mapping using MOXMDR-DAI+ in the system. The system proposed in this study can visualize business processes. And it makes it easier to operate the collaboration process through mobile support. Real-time status monitoring of the operation process is possible through the dashboard, and it is possible to perform a collaborative process through expert search using a community in a social network environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.9_10 s.146
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• pp.1329-1339
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• 2005

The purpose of this study was to investigate the effect of consumer knowledge and involvement of apparel products on information processing style. The subjects of this study were female adults who lived in Seoul, Kyunggi or Incheon areas and Quota sampling using age and residential areas was employed. The data were obtained from 603 questionnaires. Data were statistically analyzed using SPSS 10 and LISREL 7.0. Major statistical methods were factor analysis, Cronbach's a coefficient, multiple regression analysis, and structural equation model analysis. The results were as follows: 1. Consumer knowledge significantly influenced information processing styles. Rational processing style was significantly influenced by objective knowledge, while experiential processing style was significantly influenced by subjective knowledge. 2. Involvement was related to the subjective knowledge more than objective knowledge. Consumers who had higher interest, social importance and followed latest fashion trends tended to process information more experientially.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.

• Korean journal of communication and information
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• v.78
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• pp.116-140
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• 2016

The present study examined the factors influencing individuals' risk information seeking and processing using the case of 2015 MERS outbreak in Korea. Analyses of two-wave online panel data demonstrated that perceived risk, negative affect, subjective norm, and information insufficiency predicted the risk information seeking/avoiding as well as information processing mode, which validates the Risk Information Seeking and Processing(RISP) model. More importantly, this study found new evidence that information seeking and systematic processing promoted MERS preventive behaviors. In addition, active SNS use moderated the link between perceived risk and negative affects about MERS crisis as well as the relationship between social normative pressure and to seek the risk related information.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.335-340
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• 2014

Crowd Sourcing-based Social App Manufacturing environment, a small app development project by a team of anonymous virtual performed without the constraints of time and space, and manage it for the app development process need to be automated method. Virtual teams with anonymity is a feature of the Social App Manufacturing, is an important factor that increases the uncertainty of whether the completion of the project or reduction in visibility of the progress of the project. In this study, as one of how to manage the project of Social App Manufacturing environment, the impact of risk that can be used to quantitatively measure the impact of the risk of delay in development has on the project also proposes a measurement model. Effects of risk and type of the impact of risks associated with delays in the work schedule also define the characteristic function, measurement model that has been proposed, suggest the degree of influence measurement equation of risk of the project in accordance with the progressive. The advantage of this model, the project manager is able to ensure the visibility of the progress of the project. In addition, identify the project risk of work delays, and to take precautions.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.754-771
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• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.