In this paper, we implemented a hierarchical encryption system using two-step phase-shifting digital holography(PSDH) technology based on XOR and scramble operations. The proposed encryption system is a system that authenticates access through the issuance of an encryption key for access to individual laboratories, department offices, and universities. In the encryption process, we proposed a double encryption method using XOR and scramble operation with digital technology and two-step phase-shifting digital holography with optical technology. In the two-step PSDH process, an new method of determining the reference wave intensity without measuring it by using random common object image gererated from digital encryption process was also proposed. In the decryption process, the process is performed in the reverse order of encryption process. And only when the various key information used in the encryption process is correct, the encrypted information can be decrypted, so that the user can access the desired place. That is, there is a feature that can hierarchically control the space that can be accessed according to the type of key issued in the proposed encryption system. Through the computer simulation, the feasibility of the proposed hierarchical encryption system was confirmed.

Recently, object recognition using image/video signals is rapidly spreading on autonomous driving and mobile phones. However, the actual input image/video signals are easily exposed to a poor illuminance environment. A recent researches for improving illumination enable to estimate and compensate the illumination parameters. In this study, we propose VE-DCE (video enhancement zero-reference deep curve estimation) to improve the illumination of low-light images. The proposed VE-DCE uses unsupervised learning-based zero-reference deep curve, which is one of the latest among learning based estimation techniques. Experimental results show that the proposed method can achieve the quality of low-light video as well as images compared to the previous method. In addition, it can reduce the computational complexity with respect to the existing method.

The use of appropriate fonts is not only an aesthetic point of view, but also a factor influencing the reinforcement of meaning. However, it is a difficult process and wastes a lot of time for general users to choose a font that suits their needs and emotions. Therefore, in this study, keywords and fonts to be used in the experiment were selected for emotion-based font recommendation, and keyword values for each font were calculated through an experiment to check the correlation between keywords and fonts. Using the experimental results, a prototype of a keyword-based font recommendation system was designed and the possibility of the system was tested. As a result of the usability evaluation of the font recommendation system prototype, it received a positive evaluation compared to the existing font search system, but the number of fonts was limited and users had difficulties in the process of associating keywords suitable for their desired situation. Therefore, we plan to expand the number of fonts and conduct follow-up research to automatically recommend fonts suitable for the user's situation without selecting keywords.

Autonomous driving of unmanned aerial vehicles may have to pay expensive cost to create and switch new routes if unexpected obstacles exist or local map updates occured by the control system due to incorrect route information. Integrating digital twins into the path-following process requires more computing resources to quickly switch the wrong path to an alternative path, but it can quickly update the path during flight. In this study, we design a DEVS-based simulation environment which can modify optimized paths through short-term simulation of multi-virtual UAVs for applying digital twin concepts to path follow. Through simulation, we confirmed the possibility of increasing the mission stability of UAV.

It is important to develop a transducer that generates uniform output power through frequency control of the HIFU at 4 MHz frequency for the high intensity focused ultrasound (HIFU) skin diseases treatment. In this paper, a 4 MHz frequency band HIFU system for skin disease treatment was designed, manufactured and developed. In HIFU, even for the ultrasonic vibrator in the 4 MHz frequency band, the characteristics of the output power of the HIFU are different depending on the difference in the thickness of the PZT material. Through the development of a system amplifier, the sound output of the HIFU transducer was improved to more than 48 W and uniform output power control was possible. And, it is possible to control the output power even in a frequency band of 4.0 to 4.7 MHz, which is wider than 4.0 MHz, and shows the resonance frequency of the transducer. The maximum output power for each frequency was 49.969 W and the minimum value was 48.018 W. The maximum output power compared to the minimum output power is 49.969 W, which is uniform within 4.1%. It was confirmed that the output power of the HIFU through the amplifier can be uniformly controlled in the 4 MHz frequency band.

Ultrasound is one of the effective methods for skin treatment. The skin penetration depth of the ultrasound depends on the ultrasonic frequency, that is, when the ultrasonic frequency is high, the depth is shallow. We have developed a transducer which can generate effectively 3 different ultrasonic frequencies removing interference between 3 types of frequencies according to impedance matching technology. The generated powers of transducer are 40.67W at 3.MHz, 17.46W at 11.7 MHz, and 14.79W at 21.5 MHz. The signal interference between the three frequencies is designed so that they do not interfere with each other by separating the signals using the SPDT (Single Pole, Double Throw) switch. The developed hybrid ultrasound transducer can be applied in skin care or skin treatment and beauty therapy.

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

Recently, research using image-based deep learning is being conducted to determine similar fonts or recommend fonts. In order to increase the accuracy in judging the similarity of Hangul fonts, a previous study was conducted to calculate the similarity according to the combination of stroke elements. In this study, we tried to solve this problem by designing an integrated model that reflects the weights for each stroke element. By comparing the results of the user's font similarity calculation conducted in the previous study and the weighted model, it was confirmed that there was no difference in the ranking of the influence of the stroke elements. However, as a result of comparison by letter sizes, it was confirmed that there was a difference in the ranking of the influence of stroke elements. Accordingly, we proposed a weighted model set separately for each font size.

IP mobility can be handled in different layers of the protocol. Mobile IP has been proposed to handle the mobility of Internet hosts in the network layer. Recently, a new method based on the SIGMA mobility architecture has been proposed to support mobility management with reduced packet loss and latency. The location management structure is not suitable for frequent mobile handover due to the high mobility of the user with this transport layer solution. In this paper, we propose a location management optimization method in a mobile communication network by applying hierarchical location management using MMPs(Mobility Management Points) for transport layer mobility management. Therefore, we propose an efficient hierarchical mobility management structure even between heterogeneous wireless networks using MMPs for the probability that a mobile terminal can change multiple location areas between two messages and calls. The proposed method shows reduction in location update cost and data retrieval cost using MMPs, and as opposed to mobility appearing in time intervals with the minimum cost required to reach 90% of the stabilized cost, the mobility location update search, location It was found that the message processing cost per area was reduced.

Internet of Things(IoT) has become a key available technology for efficiently implementing device to device(D2D) services in various domains such as smart home, healthcare, smart city, agriculture, energy, logistics, and transportation. A lightweight publish/subscribe(Pub/Sub) messaging protocol not only establishes data dissemination pattern but also supports connectivity between IoT devices and their applications. Also, a Pub/Sub broker is deployed to facilitate data exchange among IoT devices. A scalable edge-based publish/subscribe (Pub/Sub) broker overlay networks support latency-sensitive IoT applications. In this paper, we design a hypercube grid quorum(HGQ) for distributed Pub/Sub systems based IoT applications. In designing HGQ, the network of hypercube structures suitable for the publish/subscribe model is built in the edge layer, and the proposed HGQ is designed by embedding a mesh overlay network in the hypercube. As their applications, we propose an HGQ-based mechansim for dissemination of the data of sensors or the message/event of IoT devices in IoT environments. The performance of HGQ is evaluated by analytical models. As the results, the latency and load balancing of applications based on the distributed Pub/Sub system using HGQ are improved.

The manufacturing industry is continuously pursuing advanced technology and smartization as it converges with innovative technology. Improvement of manufacturing productivity is achieved by monitoring, analyzing, and controlling the facilities and processes of the manufacturing site in real time through a network. In this paper, we proposed a new OPC-UA based digital twin model for smart factory facilities. A testbed system for USB flash drive packaging facility was implemented based on the proposed digital twin model and OPC-UA data communication scheme. Through OPC-UA based digital twin model, equipment and process status information is transmitted and received from PLC to monitoring and control 3D digital models and physical models in real time. The usefulness of the developed digital twin testbed system was evaluated through usability test.

This paper aimed to design and implement an EMS for real-time power generation control based on wake effect optimization of wind farm, and then to test it in commercial operating wind farm. For real-time control, we proposed the wake band-based optimization and setting the wake effect distance limit, and when the wake effect distance limit was set to 7D in the actual wind farm layout, the calculation time was improved by about 93.94%. In addition, we designed and implemented the script-based EMS for flexible operation logic management in preparation for unexpected issues during testing, and it was installed and tested on a wind farm in commercial operation. However, three issues arose during the testing process. These are the communication interface problem of meteorological tower, the problem of an abnormal wake effect, and the problem of wind turbine yaw control. These issues were solved by modifying the operation logic using EMS's script editor, and the test was successfully completed in the wind farm in commercial operation.

As the demand for quality sleep increases in modern society, the importance of sleep technology has increased. Recently, development of sleep environment improvement products and research on the user's sleep improvement have been activated. Representatively, user sleep pattern analysis research is being conducted through the existing polysomnography, but it is difficult to use it in the sleep environment of daily life. Therefore, in this paper, we propose a system design that can provide a customized deep sleep service to users by detecting sleep disturbance factors in a motion bed environment. In order to improve the user's sleep satisfaction, a logistic regression-based sleep pattern analysis model is proposed and accuracy and significance are verified through experiments. And to improve user's sleep satisfaction, we propose a logistic regression-based sleep pattern analysis model and verify accuracy and significance through experiments. The proposed system is expected to improve the user's sleep quality and effectively prevent and manage sleep disorders.

With the development of digital technology and the influence of the global pandemic, the metaverse, a three-dimensional virtual world, is receiving attention in society, economy and overall industry, and the manufacturing industry is also accepting it as a major strategic agenda for digital transformation. Therefore, in this study, the concept of the industry metaverse from the perspective of the manufacturing industry was defined, and the types of the industry metaverse were classified into four types by reflecting the characteristics of the manufacturing industry based on the general metaverse scenario presented in previous studies. These are Virtual behavior simulation, Augmented operation of business objects and Virtual experience simulation, Augmented decision of business subjects. In addition, through case analysis of solutions used in the manufacturing industry, it was confirmed that the central technology of the Industry Metaverse is the digital twin, and that it is being implemented by convergence with major digital technologies such as virtual reality, augmented reality, digital human, and AI. This study will be able to provide guidelines for future research on the metaverse from the perspective of the manufacturing industry and establishment of a digital transformation strategy for the industry.

This paper is a method for efficient traffic prediction in mobile edge computing, where many studies have recently been conducted. For distributed processing in mobile edge computing, tasks offloading from each mobile edge must be processed within the limited computing power of the edge. As a result, in the mobile nodes, it is necessary to efficiently select the surrounding edge server in consideration of performance dynamically. This paper aims to suggest the efficient clustering method by selecting edges in a cloud environment and predicting mobile traffic. Then, our dynamic clustering method is to reduce offloading overload to the edge server when offloading required by mobile terminals affects the performance of the edge server compared with the existing offloading schemes.

Multicultural education is not about learning about a specific ethnic group, but rather developing the ability to cross the border of one's own culture and have conversations with people of other cultures. I think the purpose is to promote empathy and consideration. This study emphasizes the importance of developing multi-dimensional educational programs for all members of society for multicultural social integration, and it is necessary to lead personal, social and civic action movements to create a fair society through media-based multicultural literacy education. said that In order to achieve harmony and integration in a multicultural society, it is the most important to acknowledge cultural diversity and to discard cultural prejudices and inequalities for symbiosis between the mainstream culture and the minority culture. In particular, the United States and Germany, which have successfully led multicultural social integration, are comprehensive in all areas, including interculturalism based on peaceful coexistence and respect, labor market issues, vocational education issues, housing and health issues, and communication issues through media literacy. He led a multicultural national integration system with approaches and methods. Therefore, our multicultural education policy should also pursue a new paradigm that presupposes a change in the public's awareness of a multicultural society.

In this study, we examined voice shopping interaction in which consumers can be involved in the decision-making process. Sixteen kinds of voice shopping interaction were designed with differences in the existence of screen/product involvement/conversation style. Their effects on trust, cognitive load, satisfaction, and continuous intention to use were evaluated through a survey experiment. The main effect of conversation style was significant, and it was found that the more deeply involved users have higher trust. The interaction effect between conversation style and product involvement was also significant. Low involvement product buyers had the most positive user experience from the conversation style that included 'Ask for preference,' while high involvement product buyers had the most positive user experience from the conversation style that included both 'Ask for preference' and 'Question and Answer.' The main effect and interaction effect of the existence of screen was not significant. The results indicate that a positive user experience can be obtained when users are deeply involved in consumer decision-making, especially in purchasing high-involvement products.

With the sustainable development and transformation of the city, public art as a business card of the famous city of culture has become a hot topic of research. The intervention of public art in public space not only brings users a sense of space experience, but also becomes a unique carrier of urban and rural image making. Although there is much research on the classification, aesthetics and function of public art, there is few quantitative research on user satisfaction. This paper takes the basic features of airport public art as a research object and the basic features of airport public art as the theoretical basis to study the impact of the basic characteristics of airport public art on user satisfaction. Research methods were based on questionnaire data of 247 people, in which models and hypotheses were tested using SPSS 21.0 software, based on the induction and extraction of nine influential factors in the basic characteristics of public art. The study found that public interpretation, media patterns, color perception, modeling form, place perception, city image and memory have significant positive effects on user satisfaction. The sharedness of public art, cognition and communication in public culture and spatial relations do not affect satisfaction. Conclusion, inspiration and prospect provide suggestions for designers and reference data and theoretical support for public art evaluation.

3D interaction between humans and computers has been possible with the popularization of virtual reality, and it is important to study natural and efficient virtual reality user interfaces. In user interface development, it is essential to analyze and evaluate user factors. In order to analyze the influence of factors on users who use the virtual reality color picker, this paper divides the user groups based on whether they major in art or design, whether they have experience in virtual reality, and whether they have prior knowledge about 3D color space. The color selection error and color selection time of all user groups were compared and analyzed. Although there were statistically significant differences according to the user groups, all user groups used the virtual reality color picker accurately and effectively without any difficulties.

This study aims to investigate what communication phenomenon social viewing is associated with, and how satisfaction with using live commerce affects, taking into account that live commerce is a form of content consumption based on social viewing. In particular, social viewing is classified in this study as message production and sharing activities that influence users' satisfaction with live commerce viewing, as well as how two-dimensional social viewing behaviors are associated with users' perception of social presence. Then, the impact of these factors on improving viewing satisfaction is determined. It is also designed to investigate the moderating effect of social presence and viewing satisfaction as a function of the size of users' social networks by taking into account how social viewing affects the degree of social exchange among users. The findings of the analysis are as follows. First, social viewing behaviors. i.e., sharing and production behavior were identified as strong factors that have a direct impact on social presence and viewing satisfaction. Second, social presence plays a moderating role in the relationship between social viewing behavior and viewing satisfaction. Third, the impact of sharing behavior on social presence and viewing satisfaction was greater than the that of production behavior. Fourth, network size (number of followers and followings) had no direct effect on social reality or viewing satisfaction, but it had a moderating effect on the relationship between production behavior and social presence. Based on the findings, the implications and recommendations were discussed.

Melanoma is a skin cancer that starts in pigment-producing cells (melanocytes). The death rates of skin cancer like melanoma can be reduced by early detection and diagnosis of diseases. It is common for doctors to spend a lot of time trying to distinguish between skin lesions and healthy cells because of their striking similarities. The detection of melanoma lesions can be made easier for doctors with the help of an automated classification system that uses deep learning. This study presents a new approach for melanoma classification based on an ensemble of deep convolution neural networks and a Log-Gabor filter. First, we create the Log-Gabor representation of the original image. Then, we input the Log-Gabor representation into a new ensemble of deep convolution neural networks. We evaluated the proposed method on the melanoma dataset collected at Yonsei University and Dongsan Clinic. Based on our numerical results, the proposed framework achieves more accuracy than other approaches.

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

Whole body bone scan is the most frequently performed nuclear medicine imaging to evaluate bone metastasis in cancer patients. We evaluated the performance of a VGG16-based transfer learning classifier for bone scan images in which metastatic bone lesion was present. A total of 1,000 bone scans in 1,000 cancer patients (500 patients with bone metastasis, 500 patients without bone metastasis) were evaluated. Bone scans were labeled with abnormal/normal for bone metastasis using medical reports and image review. Subsequently, gradient-weighted class activation maps (Grad-CAMs) were generated for explainable AI. The proposed model showed AUROC 0.96 and F1-Score 0.90, indicating that it outperforms to VGG16, ResNet50, Xception, DenseNet121 and InceptionV3. Grad-CAM visualized that the proposed model focuses on hot uptakes, which are indicating active bone lesions, for classification of whole body bone scan images with bone metastases.

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

When recommending cold items that do not have user-item interactions to users, even we adopt state-of-the-arts algorithms, the predicted information of cold items tends to have lower accuracy compared to warm items which have enough user-item interactions. The lack of information makes for recommender systems to recommend monotonic items which have a few top popular contents matched to user preferences. As a result, under-diversified items have a negative impact on not only recommendation diversity but also on recommendation accuracy when recommending cold items. To address the problem, we adopt a diversification algorithm which tries to make distributions of accumulated contents embedding of the two items groups, recommended items and the items in the target user's already interacted items, similar. Evaluation on a real world data set CiteULike shows that the proposed method improves not only the diversity but also the accuracy of cold item recommendation.