• International journal of advanced smart convergence
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• v.12 no.1
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• Proceedings of the Korean Geotechical Society Conference
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• 2001.10c
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• pp.79-88
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• 2001

기계, 전기, 전자, 재료, 전산공학 등은 근래에 이르러 혁명적인 발전을 거듭하고 있으며, 이에 따라 새로운 개념의 기기들이 등장하고, 토목계측분야에서도 새로운 방식 및 기기들이 등장하고 있다. 특히 최근의 Smart Material, MEM (Micro-Electro-Machine), Nano- Technology 및 통신기술들은 과거의 공상과학소설에서나 가능하였던 내용들을 실제로 가능케 하였으며, 일부 기술들은 경제성까지 갖춰 상용화되고 있다. 본 고에서는 지반공학적 관점에서 본 이러한 신기술과, 이를 이용한 지반구조물의 정보화에 대하여 살펴보았다.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.233-238
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• 1998

SMART 인간기계연계(MMI : Man Machine Interface)는 발달된 컴퓨터 기술과 디스플레이 기법을 기반으로 설계되고 있으며, 이는 안전성과 생산성을 향상시키려는 의도를 갖고 있다. 현재, 최신 기술을 적용한 제어실 설계는 인간기계연계 설계자, 발전소 운영회사, 규제기관 등 모두에게 관심의 대상이다. 최신 기술을 제어실 설계에 적용할 경우에는 객관성 있는 설계 평가를 통하여 설계 결과가 안전성 향상에 기여함은 물론 운전작업자에게 유용함을 가시적으로 입증할 필요가 있다. 본 논문에서는 SMART 인간기계연계설계 평가를 위해 개발된 절차의 구성과 평가작업 내용을 기술하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.603-604
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• 2022

The configuration and function of an AIoT-based leak detection system are proposed. In addition, we propose a smart metering gateway with machine learning function and broadband communication function in the leak detection system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.2
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• pp.99-106
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• 2006

In this paper, the new method that estimates a sensing ability of smart sensor will be proposed. A study is estimation method that evaluates sensing ability about smart sensor respectively. According to acceleration(g) and displacement changing, we estimated sensing ability of smart sensor using SAI(Sensing Ability Index) method respectively. Smart sensors was made fer experiment. The types of smart sensor are two types(hard and soft smart sensor). Smart sensors developed for recognition of material. Experiment and analysis are executed for estimate the SAI method. In develop a smart sensor, the SAI method will be useful for finding optical design condition of smart sensor that can sense a material. And then dynamic characteristics of smart sensors(frequency changing, acceleration changing, critical point, etc.) are evaluated respectively through new method(SAI) that use the power spectrum density. Dynamic characteristic of sensor is evaluated with SAI method relatively. We can use the SAI for finding critical point of smart sensor, too.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2348-2354
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• 2016

In recent days, IoT (Internet of Things) technology has been widely used in the field of agriculture, which enables the collection of environmental data and biometric data into the database. The availability of big data on agriculture results in the increase of the machine learning based analysis. Through the analysis, it is possible to forecast agricultural production and the diseases of livestock, thus helping the efficient decision making in the management of smart farm. Herein, we use the environmental and biometric data of Smart Pig farm to derive the accurate relationship model between the environmental information and the daily weight increase of swine and verify the accuracy of the derived model. To this end, we applied the M5P tree algorithm of machine learning which reveals that the wind speed is the major factor which affects the daily weight increase of swine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1421-1427
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• 2018

Due to the rapid development of Internet-of-Things technology, different types of smart sensors are now devised and deployed widely. These smart sensors are now used in animal husbandry which was traditionally managed by the experience of farmers, such that wearable sensors for livestock, and the smart farm which is equipped with multiple sensors are utilized to increase the efficiency of livestock management. Herein, we consider a scheme in which the body temperature and the level of activity are measured by smart sensor which is attached to the neck of dairy cattle and the health condition is monitored based on collected data. Especially, we find that the estrous of dairy cattle which is one of most important metric in milk production, can be predicted with high precision using various machine learning techniques. By utilizing the proposed prediction scheme, estrous of cattle can be detected immediately and this can improve the efficiency of cattle management.

• Smart Media Journal
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• v.6 no.4
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• pp.9-16
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• 2017

The railway point machine is an especially important component that changes the traveling direction of a train. Failure of the point machine may cause a serious railway accident. Therefore, early detection of failures is important for the management of railway condition monitoring systems. In this paper, we propose a noise-robust anomaly detection method in railway condition monitoring systems using sound data. First, we extract feature vectors from the spectrogram image of sound signals and convert it into modulation feature to ensure robust performance, and lastly, use the support vector machine (SVM) as an early anomaly detector of railway point machines. By the experimental results, we confirmed that the proposed method could detect the anomaly conditions of railway point machines with acceptable accuracy even under noisy conditions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.2
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• pp.69-80
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• 2017

In or To use, manage, and preserve sustainable water resources for the current and future generations amid the threat of abnormal climate, it is necessary to establish a smart water grid system, the next-generation intelligent water management system. In this study, sensors, which make use of the five senses to watch, listen, and detect machine vibration, bearing temperature, machine operation sounds, current, voltage, and other symptoms that cannot be verified when the irrigation facilities are running, are used to establish various decision-making criteria appropriate to on-site situations. Based on such criteria, the unmanned conditions in the facilities were verified and analyzed. Existing technologies require on-site workers to check any defects caused by overloading of machines, which is the biggest constraining factor in the application of an unmanned control system for irrigation facilities. The new technology proposed in this study, on the other hand, allows for the unmanned analysis of the existence of machine vibration. This controls the decision-making process of any defect based on the analysis results, and necessary measures are taken automatically, resulting in improved reliability of the unmanned automation.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.