Earticle

IoT technology application has been part of modern people’s everyday life. This technology makes people to depend on smart devices. ‘Smartphone’ is the most typical example. Despite the advantages given by this device, the bad side effects are also notable. This paper presents interactive content on the dependence of smartphone device. The research objective is to make individuals to reduce the use of smartphones by realizing their own problems of smartphone dependency. Research was progressed by analyzing aspects obtained from users participating in the self-developed interactive game contents. As a result of the analysis, approximately 68% of people were aware of the problem through this content. This indicates the potential of using convergence research in interactive contents with other fields furthermore.

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

The global warming by greenhouse gases causes climate change and disasters such as earthquakes and tsunamis frequently, leading to great damage. It is important to build efficient and scalable disaster response services to minimize the damage. Existing disaster warning service by the mobile text is limited by the scalability and the data size to be delivered. In this paper, we propose a framework for disaster response services that is efficient and flexible by allowing to adopt various indexing schemes and scalable by supporting any number of clients in disaster situations anytime and anywhere. Also, the framework by wireless data broadcast can be free from the limitation of the size of data to be delivered. We design and implement the proposed framework and evaluate the framework. For the evaluation, we simulate the implemented framework by adopting various indexing schemes like HCI, DSI and TTSI, and by comparing the access times of the clients. Through the evaluation, we show that the proposed framework can provide efficient and scalable and flexible disaster response services.

The purpose of this study is to measure and compare the balance of motion between the left and right using a wearable sensor during upper limb exercise using an exercise equipment. Eight participants were asked to perform upper limb exercise using exercise equipment, and exercise data were measured through IMU sensors attached to both wrists. As a result of the PCA test, Euler Yaw(Left: 0.65, Right: 0.75), Roll(Left: 0.72, Right: 0.58), and Gyro X(Left: 0.64, Right: 0.63) were identified as the main components in the Butterfly exercise, and Euler Pitch(Left: 0.70, Right 0.70) and Gyro Z(Left: 0.70, Right: 0.71) were identified as the main components in the Lat pull down exercise. As a result of the Paired-T test of the Euler value, Yaw's Peak to Peak at Butterfly exercise and Roll’s Mean, Yaw's Mean and Period at Lat pull down exercise were smaller than the significance level of 0.05, proving meaningful difference was found. In the Symmetry Index and Symmetry Ratio analysis, 89% of the subjects showed a tendency of dominant limb maintaining relatively higher angular movement performance then non-dominant limb as the Butterfly exercise proceeds. 62.5% of the subjects showed the same tendency during the Lat pull down exercise. These experimental results indicate that meaningful difference at balance of motion was found according to an increase in number of exercise trials.

As digital advertising continues to dominate the advertising industry and experience substantial growth, associated criminal activities such as advertising fraud and brand safety risks have become increasingly prevalent. Despite the severity and extent of these crimes, the response in Korea has been lackluster, often perceiving them merely as losses in advertising expenses incurred by corporations. However, it is important to note that these issues have direct repercussions on the end-consumer in the form of increased prices for goods and services. Furthermore, illegal and intrusive advertisements not only cause inconvenience to the viewer, but may also indiscriminately target cognitively vulnerable groups, such as children and the elderly, with the intention of manipulating advertising metrics and artificially inflating performance indicators. In this study, we aim to explore the concept and significance of advertising fraud and brand safety, and to evaluate the current measures taken in the Korean market. Additionally, we will delve into the implications of related policies and emphasize the necessity of digital advertising literacy in addressing these issues.

This study aims to enhance the engineering education for non-majors by incorporating the concept of Global Citizenship Education and addressing the need for education that responds to climate and ecological changes. The study uses robot programming as a tool to foster the development of global citizens. Non-majors often struggle with producing more than just motionless forms or solid productions, due to a lack of understanding of mechanisms and coding. The study proposes the use of the Convergence D-SteamRobot (DSR) to address this issue by blending humanities and engineering. This is achieved by presenting problems through books to increase empathy, integrating simple machine mechanisms, and creating prototypes to solve self-defined problems. Through this process, learners determine the SDGs topic they want to solve and learn about the simple mechanical mechanism involved in producing the prototype. The educational model provides a constructivist learning environment that emphasizes empathy and exploration, encourages peer-learning, and improves divergent thinking and problem-solving skills.

As urbanization accelerates and facilities that use energy increase, human life and property damage due to fire is increasing. Therefore, a fire monitoring system capable of quickly detecting a fire is required to reduce economic loss and human damage caused by a fire. In this study, we aim to develop an improved artificial intelligence model that can increase the accuracy of low fire alarms by mixing DeepSORT, which has strengths in object tracking, with the YOLOv5s model. In order to develop a fire detection model that is faster and more accurate than the existing artificial intelligence model, DeepSORT, a technology that complements and extends SORT as one of the most widely used frameworks for object tracking and YOLOv5s model, was selected and a mixed model was used and compared with the YOLOv5s model. As the final research result of this paper, the accuracy of YOLOv5s model was 96.3% and the number of frames per second was 30, and the YOLOv5s_DeepSORT mixed model was 0.9% higher in accuracy than YOLOv5s with an accuracy of 97.2% and number of frames per second: 30.

In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.

In this paper, we design and implement an ADHD diagnostic rating scale algorithm and virtual reality-based digital treatment by combining virtual reality technology that enables multiple access based on specialized medical data. The VR ADHD digital treatment to be implemented is intended to be applied directly to diagnosis and treatment of child/adolescent ADHD by applying various psychiatric treatment technologies. In order to evaluate the usability of the digital therapeutic agent developed in this paper, an expert group consisting of 2 males and 12 females "targeted counseling psychology majors at the Asian Dementia Center once a day for 3 weeks, ADHD VR-based digital therapeutics for children Experienced and usability evaluation conducted. As a final result of this thesis, it was possible to develop attentional concentration ability, working memory improvement ability, and impulsive control ability in four games of digital therapy, and it was possible to confirm significant results that can be expected to have ADHD therapeutic effects.

Since the electric vehicle uses an electric motor, problems have arisen as the driver hears the inherent noise of the motor or external noise, which was not a problem in the past, due to the overall lower noise environment than when using an internal combustion engine. Therefore, the purpose of this paper is to reduce the noise and vibration of electric motors for electric vehicles, and recently, to increase the speed of high-power, high-efficiency electric motors in a small size, and to develop low-noise motors, IPM motors are applied to produce 35KW electric motors for electric vehicles. A motor for low noise was designed and implemented. N-T Curve and efficiency map were confirmed as the final result of developing a 35KW low-noise motor for electric vehicles by applying the IPM motor applied in this paper. Based on 3500 rpm, Max Torque [Nm]: 121.15, Max Power [KW]: 44.04, and Max Efficiency [%]: 97.65, showing high efficiency.

As the IoT market continues to grow, security threats to IoT devices with limited resources are also increasing. However, the application of security technology to the existing system to IoT devices with limited resources is impossible due to the inherent characteristics of IoT devices. Various methods for solving related problems have been studied in existing studies to solve this problem. Therefore, this study analyzes the characteristics of domestic IoT authentication standards and existing research to propose an algorithm that applies blockchain-based authentication and lightweight encryption algorithms to IoT equipment with limited resources. In this study, a key generation method was applied using a Lamport hash-chain and data integrity between IoT devices were provided using a Merkle Tree, and an LEA encryption algorithm was applied using confidentiality in data communication. In the experiment, it was verified that the efficiency is high when the LEA encryption algorithm, which is a lightweight encryption algorithm, is applied to IoT devices with limited resources.

This study surveyed the island located in Sinan-gun and Wando-gun four times, once per season from September 2020 to November 2021 in order to understand the distribution of waterbirds on the island of the southwest coast. As a result of the investigation, a total of waterbirds of 7 orders, 12 families of 50 species, and 3,680 individuals were identified. By taxon, the largest number of species were identified as 11 species of waders, and the number of ducks was the largest with 2,035 individuals. In Bigeum-do Island, where forests are distributed less, the proportion of farmland is large, and tidal flats are widely distributed, the largest number of species and individuals of herons and waders were identified. Ducks and gulls have also been identified in large numbers of individuals in Bigeum-do Island. Most of them are forests and farmland, and the largest number of species and individuals of ducks have been identified in Pyeongil-do Island, where the coast is deep. In addition, Nodae-do Island, Sobyeongpung-do, and Daebyeongpung-do Island with monotonous habitats have been identified with fewer species and individuals. As a result, We learned that various types of waterbirds lived in islands with various habitats, and that the preferred habitat was different depending on the characteristics of the taxon group. By season, the largest number of species was identified as 35 in autumn, and the largest number of individuals was identified as 1,681 in winter. Although most of the seasonally identified waterbirds were winter birds, the largest number of species were identified in autumn, and most of the confirmed waterbird migration were identified in the autumn, indicating that waterbirds used the west-south coast islands as an intermediate stopover while traveling.

Identifying the distribution of species and specific factors related to it is very important because it is habitat conservation that is greatly linked not only to ecological research but also to human health. After all, protecting each one's habitat will protect wildlife and further maintain a healthy ecosystem. Therefore, in this study, data were collected, compared, and analyzed through existing studies and field-oriented surveys. The results of the survey confirmed high species diversity in agricultural areas and forest areas that can reduce moisture supply and moisture loss, and the non-segmental environment, that is, ecological connectivity, was largely influenced by amphibian species diversity. It was confirmed that the non-fragmented environment, that is, ecological connectivity, was greatly affected by amphibian species diversity. In addition, the three factors that affect amphibian survival are: It was identified as the effect of not establishing a buffer space, the effect on the ecological transformation around the habitat during the summer rainy season, and the effect on the disconnected ecological environment.

In this study, to check the function of managing the severity of ultraviolet rays with a smart watch, a popular health care IT device, It was tested whether measuring heart rate using a PPG(Photoplethysmography) sensor representatively used in a smart watch could tell skin changes caused by ultraviolet rays. Through this experiment, we examined the possibility that the skin color tanned by ultraviolet rays can be determined only by the heart rate measurement function of the PPG sensor. In addition, the possibility of expanding the heart rate measurement function of the PPG sensor to the use of skin condition management was considered. we used an Arduino-based reflective PPG sensor to measure changes in heart rate by selecting body sites with high and low UV rays exposure. A significant value was derived through tests considering factors such as gender, UV exposure, and age. As a result, the study identified the possibility of adding ultraviolet rays and skincare items to future smart watch healthcare items and the possibility of expanding skin measurement methods. It is also possible to suggest the direction of future research.

As we enter a super-aged society, studies are being conducted to reduce complications and deaths caused by falls in elderly adults. Research is being conducted on interventions for preventing falls in the elderly, wearable devices for detecting falls, and methods for improving the performance of fall detection algorithms. Wearable devices for detecting falls of the elderly generally use gyro sensors. In addition, to improve the performance of the fall detection algorithm, an artificial intelligence algorithm is applied to the x, y, z coordinate data collected from the gyro sensor. In this paper, we develop a wearable device that uses a gyro sensor, body temperature, and heart rate sensor for health management as well as fall detection for the elderly. In addition, we develop a fall detection and health management system that works with wearable devices and a guardian's mobile app to improve the performance of the fall detection algorithm and provide health information to guardians.

After a longitudinal analysis of the data on the college entrance examination of students enrolled in the Department of Chemistry Education at Gyeongnam from 2014 to 2021, the effect on the academic achievement of general chemistry according to the type of college entrance examination was studied. And the impact on the academic achievement of general chemistry according to the type of admission screening in face-to-face and non-face-to-face teaching-learning was also studied. As a result of analyzing the academic achievement of general chemistry by admission process, students admitted through occasional screening showed relatively high grades of A and B at 88.7%, and the ratio of grades of 1~3 of chemistry I in high school was high. On the other hand, in the case of students admitted through regular admission, the ratio of grades of A and B in general chemistry was very high at 94.3%, and the ratio of grades of 3~4 in chemistry I of the College Scholastic Ability Test was high. As a result of analyzing the academic achievement of general chemistry by class type and admission process, it was found that the grades of chemistry I by face-to-face classes had an effect on the academic achievement of general chemistry in non-face-to-face classes. In both admissions, the academic achievement of general chemistry by face-to-face classes was relatively higher than that of non-face-to-face-to-face classes.

Based on intelligent visual information and 5G, this paper studies the intelligent visual communication of landscape bamboo buildings, and provides a new method of intelligent perception and interactive computing for the real world, which can represent, model, Perception and cognition; through the integration of virtual and real, the situational understanding of the human-machine-material fusion environment and the interaction with nature. The 5G network can well meet the combination of high-bandwidth uplink transmission and low-latency downlink control. At the same time, 5G-based AR intelligent inspection, remote operation and maintenance guidance, and machine vision inspection. Taking the bamboo building as an example, through field inspections to analyze tourism Bamboo buildings before and after development, and the intelligentization of bamboo buildings based on 5G and visual modeling.

The implementation of a hybrid network utilizing Power Line Communication (PLC) and Wi-Fi technologies has been demonstrated to improve signal strength and coverage in areas with poor connectivity due to internet shadow areas. In this study we strategically positioned Wi-Fi relays and utilized the capabilities of PLC technology to significantly improve signal strength and coverage in areas with poor connectivity. We also analyzed the effects of metallic obstacles on Wi-Fi signal propagation and proposed a solution to strengthen the signal enough to pass through them. Our experiment demonstrated the feasibility and potential of using this hybrid network in industrial scenarios for real-time data transmission. Overall, the results suggest that the use of PLC and Wi-Fi hybrid networks can be a cost-effective and efficient solution for overcoming internet connectivity challenges and has the potential to provide high-speed internet access to areas with unreliable signals.