• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.63-68
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• 2018

Research on smart farms centering on greenhouse cultivation is actively under way due to the decrease in agriculture population and aging, but in the case of vegetables such as vegetables, outdoor cultivation is 70%. Therefore, there is a need to improve productivity and prevent soil contamination by automating, cultivating, and intelligentizing the outdoor cultivation of agriculture crops. In this paper, we show the case of establishing a outdoor production system using the Internet of things and define the environmental variables in the outdoor production system. By measuring soil temperature, water content, electrical conductivity and acidity through sensors, LoRa communication module transmits the information to the outdoor production system. The outdoor production system controls the amount of fertilizer and the volume of water based on this sensor data. We have developed a system that manages a wide range of crops using LoRa technology, which is a suitable communication method for cultivating crops, and manages production volume and sales performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.737-740
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• 2013

The widespread use of smart home appliances with smart home networking is leading to a next generation of a new technology that exploit user contextual information to provide a richer experience. However, in the most part of home networking systems the intelligent personalization services between user and home appliances have not been adequately supplied in respect of the long lifespan of the legacy home appliances. In addition, without installing smart home appliances when constructing new building the users can not use this service. To use this service, the legacy home appliance need to attach enabler equipped with the various sensors just as it is smart home appliances. The goal is to create a service that enables appliances to exchange information and control their actions through the IoT gateway to achieve nonintrusive behavior and customized services without human intervention. In this paper, we propose a ontology model to represent the user context to be able to induce right service automatically and use it to discover, utilize and enhance different services in real time that the user may be interested in.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.866-872
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• 2020

Indoor location-based services provide a service based on the distance between an object and a person. Recently, indoor location-based services are often implemented using inexpensive depth sensors such as Kinect. The depth sensor provides a function to measure the position of a person, but the position of an object must be acquired manually using a tool. To acquire a 3D position of an object, it requires 3D interaction, which is difficult to a general user. GUI(Graphical User Interface) is relatively easy to a general user but it is hard to gather a 3D position. This study proposes the Interactive LOcation Context Authoring Toolkit(ILOCAT), which enables a general user to easily acquire a 3D position of an object in real space using GUI. This paper describes the interaction design and implementation of ILOCAT.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1039-1045
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• 2021

In modern society, due to the influence of the 4th industrial revolution, camera sensors and image-based automation systems are being used in various fields, and interest in image and signal processing is increasing. In this paper, we propose a digital filter algorithm for image reconstruction in an impulse noise environment. The proposed algorithm divides the image into eight masks in vertical, horizontal, and diagonal directions based on the local mask set in the image, and compares the standard deviation of each segmentation mask to obtain a reference value. The final output is calculated by applying the weight according to the spatial distance and the weight using the reference value to the local mask. To evaluate the performance of the proposed algorithm, it was simulated with the existing algorithm, and the performance was compared using enlarged images and PSNR.

• Journal of Platform Technology
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• v.8 no.4
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• pp.59-69
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• 2020

Recently, the seriousness of ultrafine dust and fine dust has emerged as a national disaster, but small and medium-sized cities in provincial areas lack fine dust monitoring stations compared to their area, making it difficult to manage fine dust. Although the computing resources for collecting and processing fine dust data are not large, it is necessary to utilize cloud and private and public data to share data. In this paper, we proposed a small edge computing system that can measure fine dust, ultrafine dust and temperature and humidity and process it to provide real-time control of fine dust and service to the public. Collecting fine dust data and using public and private data to service fine dust ratings is efficient to handle with edge computing using raspberry pie because the amount of data is not large and the processing load is not large. For the experiment, the experiment system was constructed using three sensors, raspberry pie and Thinkspeak, and the fine dust measurement was conducted in northern part of kyongbuk region. The results of the experiment confirmed the measured fine dust measurement results over time based on the GIS data of the private sector.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.186-197
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• 2023

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.125-130
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• 2024

With the rapid growth of mobility technology, the industrial sector is demanding storage devices that can reliably process data from various equipment and sensors in vehicles. NAND flash memory is being utilized as a storage device in mobility environments because it has the advantages of low power and fast data processing speed as well as strong external shock resistance. However, flash memory is characterized by data corruption due to long-term exposure to high temperatures. Therefore, a dedicated system for temperature management is required in mobility environments where high temperature exposure due to weather or external heat sources such as solar radiation is frequent. This paper designs a dedicated temperature management system for managing storage device temperature in a mobility environment. The designed temperature management system is a hybrid of traditional air cooling and water cooling technologies. The cooling method is designed to operate adaptively according to the temperature of the storage device, and it is designed not to operate when the temperature step is low to improve energy efficiency. Finally, experiments were conducted to analyze the temperature difference between each cooling method and different heat dissipation materials, proving that the temperature management policy is effective in maintaining performance.

• Information Systems Review
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• v.24 no.3
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• pp.23-41
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• 2022

This case research investigates on how hearing aids, which is one of disability aids, is becoming a smart device, focusing on the case of Starkey Hearing Technologies(Starkey Inc.). Starkey, founded in 1967, has been a leader in innovating forms and functions of hearing aids, and has recently introduced the world's first hearing aid implemented with AI and biological sensors. In this study, history of disability aids, hearing aids(especially Starkey Inc.'s), smart wearable devices and smart earphones are compared. It has been found that recently, there has been a breakdown of boundaries between hearing aids and smart wearable devices in terms of their functions, since entertainment and life assistant functions are added to hearing aids. Based on this trend, the development model of disability aids and smart wearable devices are derived, and according social changes are discussed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.553-568
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• 2024

Due to the rapid development of IoT technology and the increase in home activities after the COVID-19 pandemic, users' demand for smart homes has increased significantly. As the size of the smart home market increases every year and the number of users increases, the importance of personal information protection and various security issues is also growing. It often grants temporary users smart home owner rights and gives them access to the system. However, this can easily allow access to third parties because the authorities granted are not properly managed. In addition, it is necessary to prevent the possibility of secondary damage using personal information collected through smart home devices and sensors. Therefore, in this paper, to prevent indiscriminate access to smart home systems without reducing user convenience, access rights are subdivided and designed according to the functions and types of smart home devices, and a token-based user access control technique using personal devices is proposed.