• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.971-973
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• 2022

본 논문은 수액 치료 부작용 방지와 환자의 안전한 병원 생활환경을 조성하는 "장애인의 안전한 병원 생활을 위한 스마트 링거 폴대"를 제안한다. 본 논문이 제안하는 주요 특징은 다음과 같다. 첫째, 무게 센서로 수액의 잔량이나 주입 속도를 측정하고 조절한다. 사용자는 앱을 통해 수액에 대한 정보를 확인할 수 있다. 둘째, 자이로 센서로 측정한 팔의 높이와 적당하게 자동으로 폴대의 높낮이를 조절한다. 셋째, 앱과 조이스틱으로 링거 폴대를 상하좌우 움직이며 이동 경로에 장애물 존재 시 알람을 울리게 한다. 넷째, 심박수 센서를 통해 환자의 평균 심박수를 측정하고 심박수 값이 정상 범위를 벗어나는 경우 아로마, 백색소음, 수면유도, 심리상담을 통해 심리안정 서비스를 제공한다. 제안된 시스템은 기존 병원에서 수액을 투입하는 방식에 IoT 기술을 적용하여 수액 치료 부작용을 예방하며, 시각장애인 환자의 거동 도움 및 불안감 해소를 위한 편의 서비스를 제공하는 것을 목표로 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.767-769
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• 2022

본 논문에서는 현대인들의 일상생활 속 누적된 스트레스를 완화하고 사용자의 편의를 고려한 "아로마테라피를 지원하는 지능형 샤워부스" 시스템을 제안한다. 제안하는 시스템의 주요 기능은 다음과 같다. 첫째, 적외선 온도 센서와 초음파 센서, 카메라를 통해 사용자의 신체 정보와 기분을 측정한다. 둘째, 측정된 사용자의 신체 정보를 반영하여 Linear actuator를 이용해 샤워기의 높낮이 및 수온을 자동으로 조절한다. 셋째, OpenCV와 앱 내에 만족도 평가를 통해 사용자의 기분에 따라 알맞은 아로마오일을 추천하고 이를 샤워기 필터에 주입한다. IoT기술과 연동된 샤워부스 시스템을 통해 사용자 컨디션에 맞춘 아로마테라피를 지원하여 현대인의 지친 심신 회복과 사용자 편의성이 증대될 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.827-829
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• 2022

본 논문은 코로나19로 인해 발생하는 우울증을 개선하고 태양광 패널을 이용하여 친환경적으로 재배할 수 있는 "태양광 에너지를 활용한 지능형 자율이동 채소재배기"를 제안한다. 본 논문이 제안하는 주요한 특징은 다음과 같다. 첫째, 태양광 패널을 이용하여 재배기에 전원을 공급한다. 둘째, 카메라와 OpenCV를 이용하여 채소의 상태를 매일 확인 후 LED 색상을 조절하여 최적의 채소 성장 환경을 만든다. 셋째, 수위 센서와 모터 펌프를 이용하여 자동으로 물이 공급될 수 있도록 하고, 수온과 수질을 주기적으로 체크하는 등 Human task를 감소시킨다. 넷째, DC모터를 이용하여 실내·외로 자율이동을 하고, 액추에이터를 이용하여 채소가 햇빛을 최대한 많이 받아 성장할 수 있도록 한다. 제안하는 시스템은 가정에서 채소를 재배하는 방식에 IoT기술을 활용하여 사용자의 편의성을 증가시키고, 녹색식물을 통해 '코로나 블루'를 해소하고자 하는 사람에게 필요한 "태양광 에너지를 활용한 지능형 자율이동 채소재배기"의 개발을 목표로 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2732-2749
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• 2023

Device-to-Device (D2D) communication has received increasing attention and been studied extensively thanks to its advantages in improving spectral efficiency and energy efficiency of cellular networks. This paper proposes a novel relay selection algorithm for multi-hop full-duplex D2D communications underlaying cellular networks. By selecting the relay of each hop in a reverse manner, the proposed algorithm reduces the heavy signaling overhead that traditional relay selection algorithms introduce. In addition, the social domain information of mobile terminals is taken into consideration and its influence on the performance of D2D communications studied, which is found significant enough not to be overlooked. Moreover, simulations show that the proposed algorithm, in absence of social relationship information, improves data throughput by around 70% and 7% and energy efficiency by 64% and 6%, compared with two benchmark algorithms, when D2D distance is 260 meters. In a more practical implementation considering social relationship information, although the proposed algorithm naturally achieves less throughput, it substantially increases the energy efficiency than the benchmarks.

• Korea Trade Review
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• v.46 no.2
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• pp.263-280
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• 2021

With the advent of the era of the 4th Industrial Revolution, various support policies and programs are being introduced as the promotion of startups related to the 4th industry is promoted as a core policy of the government. Based on major technologies such as Artificial Intelligence(AI), Big Data, Internet of Things(IoT), Blockchain, and Automation leading the 4th industrial revolution, logistics and distribution companies are expanding the range of markets and services provided. The purpose of this study is to examine the current status of startups in the logistics field based on major technologies of the 4th Industrial Revolution, which are rapidly growing at home and abroad, and suggest implications for revitalizing logistics startups through a policy demand survey. As a result of the study, in order to foster domestic logistics startups, we propose policy support for integration of logistics startups, integrated management of information, provision of physical space, network platform, and practical education and mentoring.

• Design & Manufacturing
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• v.17 no.3
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• pp.34-40
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• 2023

In this study, real-time collection of mold vibration signals during injection molding processes was achieved through IoT devices installed on the mold surface. To analyze changes in the collected vibration signals, injection molding was performed under six different process conditions. Analysis of the mold vibration signals according to process conditions revealed distinct trends and patterns. Based on this result, cosine similarity was applied to compare pattern changes in the mold vibration signals. The similarity in time and acceleration vector space between the collected data was analyzed. The results showed that under identical conditions for all six process settings, the cosine similarity remained around 0.92±0.07. However, when different process conditions were applied, the cosine similarity decreased to the range of 0.47±0.07. Based on these results, a cosine similarity threshold of 0.60~0.70 was established. When applied to the analysis of mold vibration signals, it was possible to determine whether the molding process was stable or whether variations had occurred due to changes in process conditions. This establishes the potential use of cosine similarity based on mold vibration signals in future applications for real-time monitoring of molding process changes and anomaly detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.123-128
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• 2024

Major countries are trying to expand the construction of smart factories by introducing ICT such as the Internet of Things, cloud, and big data into the manufacturing sector to secure national-level manufacturing competitiveness in the era of the 4th industrial revolution. In addition, Germany is pushing for Industry 4.0 to build a fully automatic production system through the Internet of Things, and China is pushing for the expansion of smart factories to enhance the country's industrial competitiveness through Made in China 2025, Japan's intelligent manufacturing system, and the Korean government's manufacturing innovation 3.0. In this study, considering the increasing security connectivity of smart factories, we would like to identify security threats in the external connection part of smart factories and suggest security enhancement measures based on domestic and international standard security models to respond to the identified security threats. Eventually the proposed method can be applied by accurately identifying the smart factory security status, diagnosing vulnerabilities, establishing appropriate improvement plans, and expanding security strategies to respond to security threats.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.57-84
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• 2023

With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified 'recommender system', 'wireless & IoT', 'computer vision', and 'text mining' as increasingly important research fields related to HRS research, and confirmed that 'personalization' and 'privacy' are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.