• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.63-71
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• 2012

Recently, due to the development of the mobile communication system and communication technology, a communication paradigm changes from human-to-human(H2H) communication to machine-to-human(M2H) and machine-to-machine(M2M) communication. The machine means a device with networking capabilities Also, if M2M services will be activated, we can expect that many M2M devices connect to mobile communication networks, which leads to the lack of M2M identifier to identify a M2M device in mobile communication system. Therefore, this paper proposes the group identifier based M2M identification scheme to identify many M2M devices in mobile communication based M2M service network. In the future, the proposed M2M identification scheme can be utilized as a way to solve the shortage problem of M2M identifier.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1821-1829
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• 2017

A point machine requires lots of time and attention in terms of maintenance, since it causes 40% of failures among the railway signaling devices. The senescence of the point machine critically matters to the overall turnout system. In this paper, we analyzed the aging characteristics of the point machine by using the switching current of the point machine. The analysis is done based on the switching current measurements of the point machines deployed in the Sehwa outdoor test site and the Korail Kyongbu line. It is expected that the analysis result can be utilized for real-time diagnosis in the aspect of the maintenance of the point machines and predicting abnormal operations.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.181-186
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• 2014

In this paper, we propose an overload control scheme to resolve an overload problem in a random access channel of cellular machine-to-machine (M2M) communication networks. The M2M applications are characterized by small-sized data intermittently transmitted by a massive number of machines. Due to this characteristics, an overload situation in random access channel (RACH) can happen when a large number of devices try to send a signal via the RACH. To address this overload problem, we propose a scheme in which a base station estimates the total load in the network and controls the load by using a p-persistent method based on the estimated load.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.755-760
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• 2019

In this paper, we developed the multi-tool carving machine which integrates the existing hot-wire carving machine, hot-wire cutting machine and spindle so that the shape of complex structure can be produced easily and quickly. We have also developed software that solves the problem that G-Code applies only to a single tool, and controls the details of the machine's operations that can not be managed with existing 3D modeling tools.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.235-241
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• 2020

Artificial intelligence and machine learning are used in many parts of our daily lives, but the basic processes and concepts are barely exposed to most people. Understanding these basic concepts is becoming increasingly important as kids don't have the opportunity to explore AI processes and improve their understanding of basic machine learning concepts and their essential components. Machine learning educational tools can help children easily understand artificial intelligence and machine learning. In this paper, we examine machine learning education tools and compare their features.

• Journal of Computing Science and Engineering
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• v.6 no.2
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• pp.119-126
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• 2012

Machine-to-machine communications for healthcare is emerging for the benefit of humans. In addition to novel medium access, we provide a systematic view to look for ways to develop technology to accomplish this goal, and a thorough vision toward effective system and network design.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.1
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• pp.109-113
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• 2016

The necessity of M2M(Machine to Machine) and Multimedia CSCW is described, and error control for multimedia CSCW(Computer Supported Cooperated Works) based on M2M is suggested. The relationship of information collection and utility is relationship of man to man, but is developed object to object by information communication and control system. This paper describes an error control and a white board in multimedia CSCW for M2M environment.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.416-419
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• 2010

IT관련 제품(데스크톱, 노트북, 스마트폰 등)과 자동차, 선박, 자판기 등 기계(Machine)관련 제품과의 융합이 이뤄지고 있으며 각 장비간의 통합 융합으로 변화됨에 따라 네트워크 시스템과 S/W에 대한 보호가 더욱 어려워지고 있는 상황이다. 이러한 문제는 산업계 내에서 다양한 그룹의 사용자들이 다양한 장비를 사용하므로 더 복잡해지며, 결국에는 수많은 기계장치의 사용자 수준에 따라 다른 수준의 보안이 필요하게 된다. 본 논문에서는 차세대 이동통신의 한분야인 M2M(Mahcine to Machine) 통신에 대한 동향 소개와 M2M의 보안 위협요소 분석을 통한 키관리 기법과 Threshold 암호 기법을 소개한다.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.