• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.3
• /
• pp.469-478
• /
• 2022

This paper contains the results of developing smart devices and applications to monitor the load power of the industrial manufacturing machine and evaluate its performance. The smart devices in this paper are divided into two functionalities, which are collecting load power along with operating environment data of industrial manufacturing machines and transmitting the data to servers. Load power data collected from the smart devices are uploaded to MariaDB inside the Amazon Web Service (AWS) server. Using the RESTFul API, the uploaded power data can be retrieved and shown on the web and mobile application in the form of a graph to provide monitoring capability. To evaluate the performance of the developed system, the response time from MariaDB to web and mobile applications was measured. The results is ranging from 0.0256 to 0.0545 seconds in a 4G (LTE) network environment and from 0.6126 to 1.2978 seconds in a 3G network environment, which is considered a satisfactory result.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.95-102
• /
• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.381-382
• /
• 2013

본 논문에서는 iOS 플랫폼을 위한 스마트 가상기계(Smart Virtual Machine)를 설계하고 구현하였다. iOS 플랫폼을 위한 스마트 가상기계는 가상기계 기반의 컴파일러가 C/C++나 Java 언어로 작성된 프로그램과 의미적으로 동등한 중간 코드를 생성하면 이를 입력으로 받아 실행시켜주는 프로그램이다. 스마트 가상기계는 실행 파일인 SEF(Smart Executable Format) 파일을 입력으로 받아 실행하며, SEF 로더와 인터프리터, 내장 라이브러리, 실행 환경으로 구성되어 있다. 스마트 가상기계는 스마트 기기 등에 탑재되어 하드웨어 독립적으로 실행되며 어플리케이션 형태로 실행이 가능하므로 플랫폼에 독립적인 장점을 가진다. 또한, 프로그래머는 개발 언어의 제약 없이 프로그램을 작성할 수 있고, 프로그램을 플랫폼 독립적으로 실행할 수 있다.

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

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.613-616
• /
• 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
• /
• 2023.05a
• /
• pp.179-179
• /
• 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
• /
• 2019.07a
• /
• pp.263-266
• /
• 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
• /
• v.17 no.1
• /
• pp.8-13
• /
• 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
• /
• v.10 no.3
• /
• pp.9-22
• /
• 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
• /
• v.20 no.4
• /
• pp.295-302
• /
• 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.