• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.377-389
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• 2021

This study analyzed whether depression and social support had a mediating effect between income status and life satisfaction of middle-aged and elderly people with disabilities using data from the 15th year of the Korea Welfare Panel data. As a result of the analysis, first, the income status of the middle-aged and disabled was depressed(B=.241, p<.001), social support(B=-.167, p<.001), and life satisfaction(B=-.277, p<.001) was confirmed to have a direct effect. Second, the mediating effect of depression and social support was verified on the influence between the income status of middle-aged and disabled people between life satisfaction. Third, it was confirmed that depression and social support had a multi-mediating effect between the income status and life satisfaction of middle-aged and disabled people. Therefore, an income support system that supports middle-aged and disabled people to live at an appropriate level should be prepared. In addition, in order to increase the life satisfaction of middle-aged and disabled people, it is necessary to intervene in mental health support services that can actively cope with depression and to expand the social support network.

• Journal of the Korean Applied Science and Technology
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• v.39 no.3
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• pp.433-441
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• 2022

The purpose of this study is to investigate the relationship between media engagement, media empathy, and media value of MCN sports broadcasting. To achieve this purpose, a survey was conducted on 324 MCN sports broadcast viewers. Exploratory factor analysis was performed to confirm validity, and Cronbach's α test was performed to investigate reliability. In addition, correlation analysis was performed to verify discriminant validity, and linear regression analysis was performed to verify the research hypothesis, and the following conclusions were drawn. Media engagement had a positive effect on media value. Media engagement had a positive effect on media empathy. Media empathy has a positive effect on media value.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.29-38
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• 2023

Although the biosignal is the best way to represent the human condition, it is difficult to acquire the biosignal of a driver driving for detecting driver's condition. As one of the methods to overcome this limitation, this paper proposes a driving stress monitoring system based on information provided by OBD-II(on-board diagnostics version II). The driving information and EDA(Electrodermal activity) data are obtained through the OBD-II scanner and E4 wristband, respectively. EDA data is used as ground truth to distinguish whether driver is stressed or not. MLP(multi-layer perceptron) neural network is used as a model to detect driving stress and is trained using driving data for about a month. To evaluate the proposed system, we used about 1 hour of driving data and the accuracy is 92%.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.185-193
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• 2023

As many different over-the-top (OTT) services become ubiquitous, demands for high-quality content are increasing. However, high dynamic range (HDR) contents, which can provide more realistic scenes, are still insufficient. In this regard, we propose a new HDR video reconstruction technique using multi-exposure low dynamic range (LDR) videos. First, we align a reference and its neighboring frames to compensate for motions between them. In the alignment stage, we perform content-based alignment to improve accuracy, and we also present a high-resolution (HR) module to enhance details. Then, we merge the aligned features to generate a final HDR frame. Experimental results demonstrate that our method outperforms existing methods.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.59-67
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• 2010

Rice varietal information exchange is vital for agricultural experiments and trials. With the growing size of rice data gathered around the world, and numerous research and development achievements, the effective collection and convenient ways of data dissemination is an important aspect to be dealt with. The collection of this data is continuously worked out through various international cooperation and network programs. The problem in acquiring this information anytime anywhere is the new challenge faced by rice breeders, scientist and crop information specialists, in order to perform rapid analysis and obtain significant results in rice research, thus alleviating rice production. To address these constraints, we propose an Online Analytical Query System, a web query application to provide breeders and rice scientist around the world a fast web search engine for rice varieties, giving the users the freedom to choose from which trial it has been used, trait observation parameters as well as geographical or weather conditions, and location specifications. The application uses data warehouse techniques and OLAP for summarization of agricultural trials conducted, and statistical analysis in deriving outstanding varieties used in these trials, consolidated in an Model-View-Controller Web framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1147-1161
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• 2023

The Internet of Things (IoT) requires a new processing model that will allow scalability in cloud computing while reducing time delay caused by data transmission within a network. Such a model can be achieved by using resources that are closer to the user, i.e., by relying on edge computing (EC). The amount of IoT data also grows with an increase in the number of IoT devices. However, building such a flexible model within a heterogeneous environment is difficult in terms of resources. Moreover, the increasing demand for IoT services necessitates shortening time delay and response time by achieving effective load balancing. IoT devices are expected to generate huge amounts of data within a short amount of time. They will be dynamically deployed, and IoT services will be provided to EC devices or cloud servers to minimize resource costs while meeting the latency and quality of service (QoS) constraints of IoT applications when IoT devices are at the endpoint. EC is an emerging solution to the data processing problem in IoT. In this study, we improve the load balancing process and distribute resources fairly to tasks, which, in turn, will improve QoS in cloud and reduce processing time, and consequently, response time.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.871-874
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• 2011

The fast emerging of network technology and the high demand of computing resources have prompted many organizations to outsource their storage and computing needs. Cloud based storage services such as Microsoft's Azure and Amazon's S3 allow customers to store and retrieve any amount of data, at anytime from anywhere via internet. The scalable and dynamic of the cloud storage services help their customer to reduce IT administration and maintenance costs. No doubt, cloud based storage services brought a lot of benefits to its customer by significantly reducing cost through optimization increased operating and economic efficiencies. However without appropriate security and privacy solution in place, it could become major issues to the organization. As data get produced, transferred and stored at off premise and multi tenant cloud based storage, it becomes vulnerable to unauthorized disclosure and unauthorized modification. An attacker able to change or modify data while data inflight or when data is stored on disk, so it is very important to secure data during its entire life-cycle. The traditional cryptography primitives for the purpose of data security protection cannot be directly adopted due to user's lose control of data under off premises cloud server. Secondly cloud based storage is not just a third party data warehouse, the data stored in cloud are frequently update by the users and lastly cloud computing is running in a simultaneous, cooperated and distributed manner. In our proposed mechanism we protect the integrity, authentication and confidentiality of cloud based data with the encrypt- then-upload concept. We modified and applied proxy re-encryption protocol in our proposed scheme. The whole process does not reveal the clear data to any third party including the cloud provider at any stage, this helps to make sure only the authorized user who own corresponding token able to access the data as well as preventing data from being shared without any permission from data owner. Besides, preventing the cloud storage providers from unauthorized access and making illegal authorization to access the data, our scheme also protect the data integrity by using hash function.