• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.540-546
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• 2023

The scope of IoT use has expanded due to the development of related technologies, and various sensors have been developed and distributed to meet the demand for implementing various services. Measuring alcohol concentration using a sensor can be used to prevent drunk driving, and to make this possible, accurate alcohol concentration must be measured and safe transmission from the smartphone to the server must be guaranteed. Additionally, a process of converting the measured alcohol concentration value into a standard value for determining the level of drinking is necessary. In this paper, we propose and implement a system. Security with remote servers applies SSL at the network layer to ensure data integrity and confidentiality, and the server encrypts the received information and stores it in the database to provide additional security. As a result of analyzing the accuracy of alcohol concentration measurement and communication efficiency, it was confirmed that the measurement and transmission were within the error tolerance.

• Convergence Security Journal
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• v.21 no.4
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• pp.49-57
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• 2021

The 4th industrial revolution will develop the network environment of future warfare through the increase of IoT devices, individual warrior platforms, the operation of manned and unmanned weapon systems, intelligent command post. They are leading to the weapon system combined with hundreds or thousands of sensors will be used for surveillance and reconnaissance, electronic warfare, and deception operations on the battlefield. This change to the environment brings superiority in operational performance on the battlefield, but if the weapon system is exposed to the outside, it will lead to fatal results. In this paper, we analyze the network environment that is changing in the future warfare environment, focusing on the currently used network. In addition, it considers information security issues that must correspond to the evolving network technology and suggests various security measures to suggest the direction our military should take in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.51-57
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• 2023

Smart mirror is an IoT device that attaches a display and an embedded computer to the mirror and provides various information to the user along with the mirror function. This paper presents performance evaluation of the CoMirror system as an extension of the previous research in which proposed and implemented the CoMirror system that connects Smart Mirrors using a network. First, the login performance utilizing face recognition was evaluated. As result of the performance evaluation, it was concluded that the 40 face images are most suitable for face learning and only one face image is most suitable for face recognition for login. Second, as a result of evaluating the message transmission time, the average time was 0.5 seconds for text, 0.63 seconds for audio, and 2.9 seconds for images. Third, as a result of measuring a video communication performance, the average setup time for video communication was 1.8 seconds and the average video reception time was 1.9 seconds. Finally, according to the performance evaluation results, we conclude that the CoMirror system has high practicality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.85-89
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• 2017

This paper proposes an antenna incorporating a bandstop filter to miniaturize the rectenna used for wireless power transmission with the emerging interest these days. To suppress the harmonics that can be re-radiated, this paper proposes a microstrip patch antenna that can suppress the harmonics while maintaining the size of the antenna by inserting a U-slot, which acts as a bandstop filter, on the ground plane of the antenna. As a result, S11 of the second harmonic(4.6GHz) was reduced from -5.61dB to -0.338dB and the efficiency was suppressed significantly from 29.76% to 1.5%. In addition, the maximum gain was reduced to -12dBi from 2.89dBi. On the other hand, at the fundamental frequency (2.45GHz), the S11 value was reduced from -18 dB to -15 dB, and the efficiency was reduced slightly from 68.2% to 60%. In the case of applying a microstrip antenna combined with the proposed bandstop filter to a rectenna, it is believed that the harmonics that degrade the performance of the rectenna can be removed effectively while reducing the large area occupied by harmonic suppression.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.46-52
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• 2017

Along with the expansion of areas in which ICT and Internet of Things (IoT) devices are utilized, open source software has recently expanded its scope of applications to include computers, smart phones, and IoT devices. Hence, as the scope of open source software applications has varied, there have been increasing malicious attempts to attack the weaknesses of open source software. In order to address this issue, various secure coding programs have been developed. Nevertheless, numerous vulnerabilities are still left unhandled. This paper provides some methods to handle newly raised weaknesses based on the analysis of histories and patterns of previous open source vulnerabilities. Through this study, we have designed a weaknesses analysis system that utilizes weakness histories and pattern learning, and we tested the performance of the system by implementing a prototype model. For five vulnerability categories, the average vulnerability detection time was shortened by about 1.61 sec, and the average detection accuracy was improved by 44%. This paper can provide help for researchers studying the areas of weaknesses analysis and for developers utilizing secure coding for weaknesses analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.629-635
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• 2015

In this paper, we propose the reefer container monitoring system that not only monitors internal temperature, humidity of reefer container but also tracks the real-time location using GPS. It consists of a tag of information of situation using 433MHz RF transmitter(communication), GPS to track the real-time location and a device using WCDMA/GSM communication to transmit information to the server. We tested by applying the proposed system in reefer containers with yellow melons, melons transported from Korea to Singapore to track the location and check the temperature and the humidity. The result of this test is that there is a temperature difference around 1.7 degree depending on the position of inside of container and maintains the humidity stably about 97%. If we apply this proposed system to agricultural marketing, it is possible to get the time that fruits start to decay and minimize the loss of fruits by decaying during shipping.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.17-24
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• 2019

Since the information of $4^{th}$ Industrial Revolution is introduced in WEF (World Economic Forum) in 2016, IoT, AI, Big Data, 5G, Cloud Computing, 3D/4DPrinting, Robotics, Nano Technology, and Bio Engineering have been rapidly developed as business applications as well as technologies themselves. Among the diverse business applications for IoT, wearable devices are recognized as the leading application devices for final customers. This longitudinal study is compared to the results of the 1st study conducted to identify customer needs of activity trackers, and links the identified users' needs with the well-known marketing frame of marketing mix. For this longitudinal study, a survey was applied to university students in June, 2018, and ANOVA were applied for major variables on usable features. Further, potential customer needs were identified and visualized by Word Cloud Technique. According to the analysis results, different from other high tech IT devices, activity trackers have diverse and unique potential needs. The results of this longitudinal study contribute primarily to understand usable features and their changes according to product maturity. It would provide some valuable implications in dynamic manner to activity tracker designers as well as researchers in this arena.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.366-375
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• 2022

The monitoring of electricity consumption using Internet of Things (IoT) technology is attracting attention as a technology to reduce operation costs of smart farms. In this study, we propose a method to apply a real-time electrical consumption monitoring system (the e-Gauge system) and utilization of the collected data real-time while a melon-producing smart farm is in operation. For this purpose, the electrical consumption data for the individual smart-farm facilities such as boilers, nutrient distribution systems, automatic controllers, circulation fans, boiler controllers, and other IoT-related utilities were collected during three months of melon cultivation period. By using the monitoring results, the electrical energy consumption pattern was analyzed as an example, and necessary considerations needed to optimally utilize the measurement data were suggested. This paper will be useful in lowering the technological implementation barriers for new researchers to build a electrical consumption monitoring system and reducing trial and errors in the usage of the generated data.