• Smart Media Journal
• /
• v.6 no.3
• /
• pp.41-48
• /
• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• KIISE Transactions on Computing Practices
• /
• v.21 no.4
• /
• pp.290-298
• /
• 2015

This paper suggests a multi-agent message transport architecture based on the message-oriented middleware ZeroMQ. Compared with the other middlewares such as CORBA, Ice, and Thrift, ZeroMQ receives a good score in the evaluation of performance, QoS (Quality of Service), patterns, user friendliness, and resources. The suggested message transfer architecture borrowed many basic concepts like agent platform, AMS (Agent Management System), and MTS (Message Transfer System) from FIPA (Foundation for Intelligent Physical Agents) standard multi-agent specifications, and the architecture inherited the strength of the architecture from the multi-agent framework SMAF (Smart Multi-Agent Framework). The architecture suggested in this paper is a novel peer-to-peer architecture which is not known to the ZeroMQ community. In the suggested architecture, every MTS agent uses only one ZeroMQ router socket to support peer-to-peer communication among MTS agents. The suggested architecture can support closely collaborating software areas such as intelligent robots as well as the traditional application areas of multi-agent architecture. The suggested architecture has interoperability and scalability with the ZeroMQ devices and patterns.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.681-684
• /
• 2010

Immunization information recorded on the Immunization rate in the community to identify the effects of immunization can be monitored. Immunization during an accident can be used as a basis for investigating the cause. Thus, immunization records, and more efficient management in the private and public institutions have conducted immunization information systems to manage the development of integrated system has to be. So, the government projects that promote immunization records were computerized registration. And, in 2009 the development of immunization registration system was completed. In this paper, we use the information to a variety of immunization using smart phone design and implement mobile service.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.118-121
• /
• 2014

In modern society, to be achieved status as a Science edutainment experience, not just a science museum is established. It requires strategy and differentiation of a variety of entertainment, including theme parks, games, movies which are youth like. When the understanding of the audience for the principles of science to improve the use of ICT Convergence contents display, you need to take measures to increase the effectiveness of the Science Museum by giving the function of interesting leisure space in addition to the popular area of Science Education, Scientific and Cultural aspects reached. This role requires a science museum as a cultural space with the community as a chapter in science education. Science Museum is not a need to worry about what will convey to the public as efficiently as the Mecca of the educational content for the National Science Education. The analysis of the external environment, the composition will take on science education as a mecca of science education and how to combining ICT convergence technology in modern society beyond mere science museum experience and education in this study.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.6
• /
• pp.7-14
• /
• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

• Korean Journal of Agricultural Science
• /
• v.46 no.2
• /
• pp.381-393
• /
• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.30-37
• /
• 2022

Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this issue and observed comparable results. However, current transfer learning strategies can be regarded as a supervised method as it hypothesizes that there are many labeled images in a source domain. In contrast, unsupervised transfer learning, using only images in a source domain, gives more convenience as collecting images is much easier than annotating. In this paper, we leverage unsupervised transfer learning to perform plant disease recognition, by which we achieve a better performance than supervised transfer learning in many cases. Besides, a vision transformer with a bigger model capacity than convolution is utilized to have a better-pretrained feature space. With the vision transformer-based unsupervised transfer learning, we achieve better results than current works in two datasets. Especially, we obtain 97.3% accuracy with only 30 training images for each class in the Plant Village dataset. We hope that our work can encourage the community to pay attention to vision transformer-based unsupervised transfer learning in the agricultural field when with few labeled images.

• The Journal of the Korea Contents Association
• /
• v.21 no.12
• /
• pp.463-476
• /
• 2021

This study examined significant differences in the impact of workplace bullying on emotional depletion and organizational commitment, and used 400 questionnaires to examine whether there would be significant differences depending on social support. As an analysis method, we used SPSS 24 for sample characteristics, SmartPLS 3 statistical programs for verifiable factor analysis, reliability and validity, path analysis, and structural equation models. Studies have shown that bullying in the workplace affects significant positive (+) for emotional depletion, and that emotional depletion affects significant negative (-) for tissue commitment. The influence relationship between workplace bullying and tissue commitment was partially mediated in the direction of wealth (-), and the effect of emotional depletion on tissue immersion was shown to be significant differences in the control of social support. Implications are that bullying in the workplace affects emotional depletion, so corporate efforts to prevent bullying are urgently needed, suggesting that employees can improve their organizational commitment, by using a system or community that can promote communication with their bosses.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.2
• /
• pp.47-55
• /
• 2022

The objective of this study was to analyze the effect of the duration and time distribution of probability rainfall on farmland inundation for the paddy fields in the drainage improvement project site. In this study, eight drainage improvement project sites were selected for inundation modeling. Hourly rainfall data were collected, and 20- and 30-year frequency probability rainfalls were estimated for 14 different durations. Probability rainfalls were distributed using Intensity-Duration-Frequency (IDF) and Huff time distribution methods. Design floods were calculated for 48 hr and critical duration, and IDF time distribution and Huff time distribution were used for 48 hr duration and critical duration, respectively. Inundation modeling was carried out for each study district using 48 hr and critical duration rainfalls. The result showed that six of the eight districts had a larger flood discharge using the method of applying critical duration and Huff distribution. The results of inundation depth analysis showed similar trends to those of design flood calculations. However, the inundation durations showed different tendencies from the inundation depth. The IDF time distribution is a distribution in which most of the rainfall is concentrated at the beginning of rainfall, and the theoretical background is unclear. It is considered desirable to apply critical duration and Huff time distribution to agricultural production infrastructure design standards in consideration of uniformity with other design standards such as flood calculation standard guidelines.

• Smart Media Journal
• /
• v.11 no.11
• /
• pp.75-83
• /
• 2022

NIST in the United States has developed SCAP, a protocol that enables automated inspection and management of security vulnerability using existing standards such as CVE and CPE. SCAP operates by creating a checklist using the XCCDF and OVAL languages and running the prepared checklist with the SCAP tool such as the SCAP Workbench made by OpenSCAP to return the check result. SCAP checklist files for various operating systems are shared through the NCP community, and the checklist files include ID, title, description, and inspection method for each item. However, since the inspection items are simply listed in the order in which they are written, so it is necessary to classify and manage the items by type so that the security manager can systematically manage them using the SCAP checklist file. In this study, we propose a method of extracting the description of each inspection item from the SCAP checklist file written in OVAL language, classifying the categories through a machine learning model, and outputting the SCAP check results for each classified item.