• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.197-205
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• 2022

The Cyber Physical System(CPS) is an important concept in achieving SMSs(Smart Manufacturing Systems). Generally, CPS consists of physical and virtual elements. The former involves manufacturing devices in the field space, whereas the latter includes the technologies such as network, data collection and analysis, security, and monitoring and control technologies in the cyber space. Currently, all these elements are being integrated for achieving SMSs in which we can control and analyze various kinds of producing and diagnostic issues in the cyber space without the need for human intervention. In this study, we focus on implementing a production equipment monitoring system related to building a SMS. First, we describe the development of a fog-based gateway system that links physical manufacturing devices with virtual elements. This system also interacts with the cloud server in a multimedia network environment. Second, we explain the proposed network infrastructure to implement a monitoring system operating on a cloud server. Then, we discuss our monitoring applications, and explain the experience of how to apply the ML(Machine Learning) method for predictive diagnostics.

• Annual Conference of KIPS
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• 2020.11a
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• pp.613-616
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• 2020

본 연구는 소상공인에게 쉽고 간단한 사용자 인터페이스를 통한 효과적인 창고 운용 최적화 솔루션을 제시하며, 장기적으로 소상공인의 종합적인 온라인 판로 개척 체계 확립을 목표로 한다. 세부적으로 최신 물류 트렌드인 RFID 기술을 접목한 Smart 입출고 Machine 의 개발과 Machine Learning 기술을 이용한 창고 보안 Smart 개폐 장치, 안정적인 제품/주문 Data 관리를 위한 클라우드 서버(AWS) 서비스를 제공함과 더불어 Data 분석을 통한 트렌드 분석으로 소상공인이 온라인 생태계에 수익을 높이며 안정적으로 정착할 수 있는 방안을 제시한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.263-266
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• 2019

In this paper, we suggest an activity and health monitoring system to observe the status of the dogs in real time. We also propose a k-days algorithm which helps monitoring pet health status using classified activity data from a machine learning approach. One of the best machine learning algorithm is used for the classification activity of dogs. Dog health status is acquired by comparing current activity calculation with passed k-days activities average. It is considered as a good, warning and bad health status for differences between current and k-days summarized moving average (SMA) > 30, SMA between 30 and 50, and SMA < 50, respectively.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.8-13
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• 2019

Smart Farming has been regarded as an important application in information and communications technology (ICT) fields. Selecting crops for cultivation at the pre-production stage is critical for agricultural producers' final profits because over-production and under-production may result in uncountable losses, and it is necessary to predict crop production to prevent these losses. The ITU-T Recommendation for Smart Farming (Y.4450/Y.2238) defines plan/production consultation service at the pre-production stage; this type of service must trace crop production in a predictive way. Several research papers present that machine learning technology can be applied to predict crop production after related data are learned, but these technologies have little to do with standardized ICT services. This paper clarifies the relationship between agricultural consultation services and predicting crop production. A prediction scheme is proposed, and the results confirm the usability and superiority of machine learning for predicting crop production.

• Smart Media Journal
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• v.10 no.3
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• pp.9-22
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• 2021

Basically, machine learning models use input data to produce results. Sometimes, the input data is too complicated for the models to learn useful patterns. Therefore, feature engineering is a crucial data preprocessing step for constructing a proper feature set to improve the performance of such models. One of the most efficient methods for automating feature engineering is the autoencoder, which transforms the data from its original space into a latent space. However certain factors, including the datasets, the machine learning models, and the number of dimensions of the latent space (denoted by k), should be carefully considered when using the autoencoder. In this study, we design a framework to compare two data preprocessing approaches: with and without autoencoder and to observe the impact of these factors on autoencoder. We then conduct experiments using autoencoders with classifiers on popular datasets. The empirical results provide a perspective regarding the best suited autoencoder for these factors.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.295-302
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• 2022

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.77-82
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• 2024

The Smart home energy consumption represents much of the total energy consumed in advanced countries. For this reason, the main objectif of this paper is to study the energy consumption profile by day for each home appliances: controllable appliances for example Washing machine, Tumble dryer and Air conditioning and uncontrollable appliances for example TV, PC, Lighting, Refrigerator and Electric heater. In this paper, we start with presentation of a smart home energy management systems. Next, we present the modeling and simulation of controllable appliances and uncontrollable appliances. Finally, concludes this paper with some prospects. The modeling and the simulation of a Smart home appliances is based on MATLAB/Simulink software.

• Smart Media Journal
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• v.10 no.1
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• pp.25-31
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• 2021

As the machine learning becomes more common, development of application using machine learning is actively increasing. In addition, research on machine learning platform to support development of application is also increasing. However, despite the increasing of research on machine learning platform, research on suitable load balancing for machine learning platform is insufficient. Therefore, in this paper, we propose a load balancing scheme that can be applied to machine learning distributed environment. The proposed scheme composes distributed servers in a level hash table structure and assigns machine learning task to the server in consideration of the performance of each server. We implemented distributed servers and experimented, and compared the performance with the existing hashing scheme. Compared with the existing hashing scheme, the proposed scheme showed an average 26% speed improvement, and more than 38% reduced the number of waiting tasks to assign to the server.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.397-405
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• 2013

The development of the technology of the 21st century, wireless Internet technology development in smart environments, was rapidly settled. In such an environment, the user is faced with many smart devices and smart content. This study is the analysis of the smart environment and smart devices, and user-to-user cognitive out about the effects reported. Cognitive effects observed behavior, technology, and user-centered system design, and plays a very important role to play in educating the users. And theoretical consideration about the UX (User eXperience) and UXD (User eXperience Design), by case analysis on the technical aspects of 'effective' visual aspect of interoperation aspects (interaction), and the cognitive effects of UXD (User eXperience Design) examined. As a result, on the visual aspects of the user experience based on the design that can be used to know, and be sound or through interaction with the user of the machine-to-machine interaction (and interaction) that must be provided, such as location-based or speech recognition technology will help you through the convenience of the user. Through this research, the smart environment and helping act of understanding, effective UXD (User eXperience Design) to take advantage of to help.