• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.216-221
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• 2022

The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.478-480
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• 2022

Smart Railway as a next generation railway technology is expected to have rapid evolution with developments of information and communications tehchology. Especially, smart railway will be progressed more evolved transportation means for railway operation and costomer service based with spread of commercial 5G communication. So, it is very important to investigate and analyze trends of smart railway related tehcnology of 5G mobile communication for samrt railway infra structure, server technolgy for AI, big data, deep learning, information security technology, sensor and IoT. In this paper, 5G based communicaion technology and application techology related smart railway is described and trends of new techlogy on this communication tehnology is investigated. The results of this study can be used for smart railway study and implementation, research and development for smart railway communicaion technology, etc.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.57-62
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• 2021

As the parallel computing capability for artificial intelligence improves, the field of artificial intelligence technology is expanding in various industries. In particular, artificial intelligence is being introduced to process data generated from IoT sensors that have enoumous data. However, the limitation exists when applying the AI techniques on IoT network because IoT has time series data, where the importance of data changes over time. In this paper, we propose time-weighted and user-state based artificial intelligence processing techniques to effectively process IoT sensor data. This technique aims to effectively classify IoT sensor data through a data pre-processing process that personalizes time series data and places a weight on the time series data before artificial intelligence learning and use status of personal data. Based on the research, it is possible to propose a method of applying artificial intelligence learning in various fields.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.117-127
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• 2024

Purpose: The purpose is to conduct basic research to save energy and improve the safety of manufacturing plant infrastructure by comprehensively monitoring energy management, temperature, humidity, dust and gas, air quality, and machine operation status in small and medium-sized manufacturing plants. Method: To this end, energy-related data and environmental information were collected in real time through digital power meters and IoT sensors, and research was conducted to disseminate and respond to situations for energy saving through monitoring and analysis based on the collected information. Result: We presented an application plan that takes into account energy management, cost reduction, and safety improvement, which are key indicators of ESG management activities. Conclusion: This study utilized various sensor devices and related devices in a smart factory as a practical case study in a company. Based on the information collected through research, a basic system for energy saving and safety improvement was presented.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.53-58
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• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.75-81
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• 2023

This study propose the traffic shaping scheme on IoT Network. The proposed scheme can be operated on the gateway which called sink node and control the IoT traffic with considering the traffic type(real-time based or non real-time based). It is proved that the proposed scheme shows a efficient and compatible result by the numerical analysis and the simulation on the proposed model. And the efficient of the proposed scheme by the numerical analysis has a approximate result of the simulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.877-884
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• 2023

In this study, when another vehicle is parked in a designated space where a personal vehicle can park and a defined personal use time, an ultrasonic object recognition sensor is used to determine vehicle entry, and a camera sensor recognizes a license plate. If the vehicle is not recognized by the individual vehicle owner, the "private parking lot operation block" of the application server receives the individual phone number based on the National Police Agency's Vehicle Number Information Inquiry Open API. Afterwards, when parking is processed, the non-right holder receives the approval of the parking right holder, parks for the recognized time, and deposits the parking fee into the public account of the city hall. Through this study, it was possible to find an operation processing method that can most effectively manage parking in the city center in a private parking space recognized by the city hall.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.225-236
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• 2022

Urbanization due to population growth and regional development can cause various environmental problems, such as the urban heat island phenomenon. A planned city is considered an appropriate study site to analyze changes in urban climate caused by rapid urbanization in a short-term period. In this study, changes in land cover and surface heat island phenomenon were analyzed according to the development plan in Sejong City from 2013 to 2020 using Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite imagery. The surface temperature was calculated in consideration of the thermal infrared band value provided by the satellite image and the emissivity, and based on this the surface heat island effect intensity and Urban Thermal Field Variance Index (UTFVI) change analysis were performed. The level-2 land cover map provided by the Ministry of Environment was used to confirm the change in land cover as the development progressed and the difference in the surface heat island intensity by each land cover. As a result of the analysis, it was confirmed that the urbanized area increased by 15% and the vegetation decreased by more than 28%. Expansion and intensification of the heat island phenomenon due to urban development were observed, and it was confirmed that the ecological level of the area where the heat island phenomenon occurred was very low. Therefore, It can suggest the need for a policy to improve the residential environment according to the quantitative change of the thermal environment due to rapid urbanization.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.165-167
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• 2022

The proportion of cat cats among companion animals has been increasing at an average annual rate of 25.4% since 2012. Cats have strong wildness compared to dogs, so they have a characteristic of hiding diseases well. Therefore, when the guardian finds out that the cat has a disease, the disease may have already worsened. Symptoms such as anorexia (eating avoidance), vomiting, diarrhea, polydipsia, and polyuria in cats are some of the symptoms that appear in cat diseases such as diabetes, hyperthyroidism, renal failure, and panleukopenia. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia (drinking a lot of water), polyuria (a large amount of urine), and frequent urination (urinating frequently) more quickly. In this paper, 1) Efficient version of DeepLabCut for posture prediction running on an artificial intelligence server, 2) yolov4 for object detection, and 3) LSTM are used for behavior prediction. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the main server system.