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The influence of information sensitivity during the COVID-19 pandemic on perceived risk and personal preventive behaviors of consumers in China had been investigated. The participants were Chinese individuals experiencing the pandemic as it happened. Participants voluntarily completed an online questionnaire to provide their COVID-19 information sensitivity, their perceived COVID-19 risk, preventive behavior and their respective age. Our study discovered that COVID-19 information sensitivity positively influence perceived risk and preventive behavior. Moreover, young individuals show higher levels of online information sensitivity, which influenced their personal protective behavior when compared to that of middle-aged and elderly participants. Furthermore, Perceived risk significantly affected preventive behaviors. The results of this study may assist the government and marketeers in comprehending information sensitivity which can affect consumers’ protective behavior toward reducing COVID-19 infections.

As an important part of virtual production, In-camera VFX is the process of shooting actual objects and virtual three-dimensional backgrounds in real-time through computer graphics technology and display technology, and obtaining the final film. In the In-camera VFX process, there are currently only two types of medium used to undertake background imaging, LED wall and chroma key screen. Among them, the In-camera VFX based on LED wall realizes background imaging through LED display technology. Although the imaging quality is guaranteed, the high cost of LED wall increases the cost of virtual production. The In-camera VFX based on chroma key screen, the background imaging is realized by real-time keying technology. Although the price is low, due to the limitation of real-time keying technology and lighting conditions, the usability of the final picture is not high. The short-throw projection technology can compress the projection distance to within 1 meter and get a relatively large picture, which solves the problem of traditional projection technology that must leaving a certain space between screen and the projector, and its price is relatively cheap compared to the LED wall. Therefore, in the In-camera VFX process, short-throw projection technology can be tried to project backgrounds. This paper will analyze the principle of short-throw projection technology and the existing In-camera VFX solutions, and through the comparison experiments, propose a low-cost solution that uses short-throw projectors to project virtual backgrounds and realize the In-camera VFX process.

Due to the recent advances in high-speed communication technologies as well as the easy production of high-quality video contents, IPTV is becoming increasingly popular. Meanwhile, as the number of IPTV channels increases, channel search time to find the desired channel keeps increasing. In this paper, we discuss how to improve the channel search latency in IPTV, and introduce caching and prefetching techniques that are widely used in memory management systems. Specifically, we adopt memory replacement, prefetching, and caching techniques in IPTV channel search interfaces and show the effectiveness of these techniques as the number of channels are varied.

To improve spectrum and energy efficiency in the fifth generation (5G) wireless channels, intelligent reflecting surface (IRS) transmissions have been envisioned, possibly towards the sixth generation (6G) networks. In this paper, we analyze the bit-error rate (BER) performance of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. First, we derive a closed-form expression of the BER in terms of Q functions. Then we analyze the BER improvement of the IRS NOMA system over the conventional NOMA system with respect to the power allocation. Furthermore, we also demonstrate numerically the BER improvement of the IRS NOMA network over the conventional NOMA network in respect of the number of reflecting devices.

Rendering is widely used for visual effects in animations and movies. Although rendering is computing-intensive, we observe that it accompanies heavy I/O because of large input data. This becomes technical hurdles for multi-node rendering performed on public cloud nodes. To reduce the overhead of data transmission in multi-node rendering, this paper analyzes the characteristics of rendering workloads, and presents the cooperative caching scheme for multi-node rendering. Our caching scheme has the function of synchronization between original data in local storage and cached data in rendering nodes, and the cached data are shared between multiple rendering nodes. We perform measurement experiments in real system environments and show that the proposed cooperative caching scheme improves the conventional caching scheme used in the network file system by 27% on average.

Medical tourism is recognized as a high value-added industry due to its longer stay and more useful costs than general tourism, and is growing rapidly around the world, especially in Asia. In addition, medical tourism is a new form of tourism that combines medical services with tourism activities such as recreation, leisure, and cultural activities, nowadays, medical tourism is emphasizing the combination of medical and tourism. Although Korea medical system and clinic have excellent medical equipment and infrastructure, it is not consistent with policy support and legislation. That have make it difficult for consumers to choose clinics for medical tourism. Therefore, it is necessary to provide customized information to the clinics that consumers want to match each other's services. In particular, it is aimed at consumers with simple procedures and cosmetic purposes, especially at small hospitals. In this paper, we propose a customized service platform that matches users seeking clinics and also medical tourism. Also we can easy to reservation clinic for medical tour.

Recently, as the need for data access by integrating information in a distributed cloud environment increases in enterprise-wide enterprises, interoperability for collaboration between existing legacy systems is emphasized. In order to interconnect independent legacy systems, it is necessary to overcome platform heterogeneity and semantic heterogeneity. To solve this problem, middleware was built using EMRA (Extended MetaData Registry Access) based on ISO/IEC 11179. However, the designed middleware cannot guarantee the efficiency of information utilization because it does not have an adjustment function for each node's resource status and work status. Therefore, it is necessary to manage and adjust the legacy system. In this paper, we coordinate the correct data access between the information requesting agent and the information providing agent, and integrate it by designing a cooperative agent responsible for information monitoring and task distribution of each legacy system and resource management of local nodes. to make efficient use of the available information.

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.

A smart tourism chatbot is needed as a user interface to efficiently provide smart tourism services such as recommended travel products, tourist information, my travel itinerary, and tour guide service to tourists. We have been developed a smart tourism app and a smart tourism information system that provide smart tourism services to tourists. We also developed a smart tourism chatbot service consisting of khaiii morpheme analyzer, rule-based intention classification, and tourism information knowledge base using Neo4j graph database. In this paper, we develop the Korean and English smart tourism Name Entity (NE) datasets required for the development of the NER model using the pre-trained language models (PLMs) for the smart tourism chatbot system. We create the tourism information NER datasets by collecting source data through smart tourism app, visitJeju web of Jeju Tourism Organization (JTO), and web search, and preprocessing it using Korean and English tourism information Name Entity dictionaries. We perform training on the KoBERT-CRF NER model using the developed Korean and English tourism information NER datasets. The weight-averaged precision, recall, and f1 scores are 0.94, 0.92 and 0.94 on Korean and English tourism information NER datasets.

We develop a smart tourism information system that provides smart tourism services to free independent tourists (FITs) through various content distribution channels such as mobile apps, Instagram, YouTube, and chatbots. The smart tourism information system provides location and storytelling-based tourism information, accommodation, restaurant information, and recommended travel products so that tourists can create a travel itinerary based on personalized situation awareness. The smart tourism information system also provides smart tourism services using commercial maps, navigation, and weather forecast APIs from the Korea Meteorological Administration to provide smart tour guide services to tourists who travel according to the travel itinerary. In this paper, we develop the React Native app that provides smart tourism services provided by the smart tourism information system. The smart tourism React Native app has implemented two methods: a method that directly connects to the smart tourism information system, and a method that provides services by interworking through the GraphQL Query Language developed by META (Facebook). The smart tourism React Native app implements OSMU (One Source Multi-use) by providing tourism information from mobile apps, photos from Instagram, and drone videos from YouTube as an integrated UI.

The trenchless entire repair technology (TERT) have many different names depending on the companies, and they have the same or similar methods as well as materials. In addition, there was quiet difficulty in application of field working and even confuse to classify for new TERT, while application criteria of TERT is not listed systematically. This study proposed standardization and method for TERT affecting by pipe condition, working materials, working method, specification/quantity per unit according to field condition, strange affairs and others. Determination criteria of defective sewer pipe has proposed to effective method which modify a criteria between Ministry of Environment and Seoul Metropolitan City. A Cured-in-place pipe (CIPP) is suitable for TERT, and the design criteria for the entire broken pipe are proposed when the pipe itself deforms more than 10%. A check lists are specimen preparing, hardening site made by clamped mold, numbers, flexural and tensile property, water leak and desquamation test, and have to use material property of minimum CIPP value depend on ISO 11296-4, ISO 11297-4, KS M3550-7 and KS M3550-9.

Recently, securing stable supply of fresh food is deemed as one of the important tasks. Accordingly, now the presence of cold chain along with the needs of a comfortable and healthy life is growing as the online market expands and the contactless industry grows, however, cold chain is being studied only in the aspect of ground and sea transportation. And, due to global warming and strengthening global environmental regulations, we believe that it is necessary to convert the existing road-centered logistics system into a railway-centered logistics system, a low-carbon transportation means. Therefore, in this paper we calculated the maximum energy required by the reefer container as a basic research necessary for constructing the low temperature distribution and cold chain based on the reefer container railway, and conducted a study on methods of supplying the reefer container power utilizing 1. tramline, 2. battery, 3. generator. The results of this paper can be utilized as a foundational study for building a cold chain based on a reefer container dedicated to freight trains in the future.

The growth of online market is accelerating due to the development of technology and the era of pandemic. In order to deliver the ordered product to customers courier service should be used. In addition, the courier logistics market is growing with the growth of the online market. With the growth of the logistics market, traffic and environmental problems are emerging as social issues. Logistics technology of urban community logistics courier terminal system utilizing vacant space in the city has been developed as a new alternative to environment and traffic problems by increasing logistics efficiency in the city area. In this study, we propose a concept of a system that performs operational concept definition and scenario analysis by applying model-based perspective analysis to urban community logistics courier terminal system under development. Through this study, we defined the operating concept of the urban community logistics courier terminal, which is the target of development, and defined the scenario for system operation by grasping the structure and function of the system and applied it to system development.

In this study, social network analysis was performed to compare and analyze changes in domestic tourism trends before and after the outbreak of COVID-19 in a situation where the damage to the tourism industry due to COVID-19 is increasing. Using Textom, a big data analysis service, data were collected using the keywords “travel destination” and “travel trend” based on the collection period of 2019 and 2020, when the epidemic spread to the world and became chaotic. After extracting a total of 80 key words through text mining, centrality was analyzed using NetDraw of Ucinet6, and clustered into 4 groups through CONCOR analysis. Through this, we compared and analyzed changes in domestic tourism trends before and after the outbreak of COVID-19, and it is judged to provide basic data for tourism marketing strategies and tourism product development in the post-COVID-19.

Workers’ anomalous trajectories allow us to detect emergency situations in the workplace, such as accidents of workers, security threats, and fire. In this work, we develop a scheme to detect abnormal trajectories of workers using the edit distance on real sequence (EDR) and density method. Our anomaly detection scheme consists of two phases: offline phase and online phase. In the offline phase, we design a method to determine the algorithm parameters: distance threshold and density threshold using accumulated trajectories. In the online phase, an input trajectory is detected as normal or abnormal. To achieve this objective, neighbor density of the input trajectory is calculated using the distance threshold. Then, the input trajectory is marked as an anomaly if its density is less than the density threshold. We also evaluate performance of the proposed scheme based on the MIT Badge dataset in this work. The experimental results show that over 80 % of anomalous trajectories are detected with a precision of about 70 %, and F1-score achieves 74.68 %.

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

Currently, the rapid growth of electric vehicles and the collection and disposal of waste batteries are becoming a social problem. The purpose of this paper is to propose a fast and efficient battery screening method through a safe inspection and storage method according to the collection and storage of waste batteries of electric vehicles. In addition, as the resistance inside the waste battery increases, an instantaneous voltage drop occurs, and there is a risk of overcharging and overdischarging compared to the initial state of the battery. Accordingly, there are great difficulties in operation, so the final goal of this study is to develop a device for diagnosing aging through real-time battery internal resistance measurement. Final result As a result of simulation of the internal resistance measurement test circuit through external impedance (AC), the actual simulation value was 0.05Ω, RS = Vrms / Irms => Vrms = 8.0036mV, Irms = 162.83Ma. Substitute the suggested method. The result was calculated as Rs = 0.0495Ω. It is possible to measure up to 64 impedances inside the aging diagnostic equipment that enables real-time monitoring of the developed battery cells, and the range can be changed according to the application method.

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.

Video on demand (VOD) platforms provide immersive, inspiring, and commercial-free binge watching experiences. Recently, the number of these platform users increased dramatically as users can enjoy various contents without physical and time constraints during COVID-19. However, such platforms do not provide sufficient video censorship services while there is a strong need. In this study, we investigated the users' desire for video censorship when choosing and watching movies on VOD platforms, and how video censorship can be applied to different types of scenes to increase the censoring effect without diminishing the enjoyment. We first conducted an online survey with 98 respondents to identify the types of discomfort while watching sexual, violent, or drug-related scenes. We then conducted an in-depth online interview with 18 participants to identify the effective video filtering types and regions for each of the three scenes. Based on the findings, we suggest implications for designing a censor application for videos that contain uncomfortable scenes.

Today, automobiles have become an indispensable element in people's lives, and the distribution of vehicles with various autonomous driving functions is expanding. Sensors such as cameras are used to recognize various situations on the road as an essential element for autonomous driving functions, but camera sensors have disadvantages that are vulnerable to bad weather. In this paper, we present a derivation process that defines external weather environment factors that negatively affect the performance of a camera for an autonomous vehicle. Through the proposed process, it is expected that it will contribute to securing the reliability of the camera and further improving the safety of autonomous vehicles.

In this study, by classifying latent groups for disaster safety awareness focusing on the four sub-factors of the developed disaster awareness scale of high school students, the characteristics of each group were examined, and the differences between latent classes according to inter-individual differences were investigated. As a result of analysis based on the data of a total of 1054 high school students, the disaster safety awareness of high school students was classified into three latent groups. Each latent group was named 'High Safety Awareness Type(SAT)', 'Normal SAT', and 'Low SAT' according to its characteristics. In all four fire safety awareness sub-factors, 'High SAT', which had a high score, accounted for 56.5% of the total, and 'Normal SAT', which had a moderate score in the sub-factors, had the lowest ratio at 20.3%. There were no significant differences by gender, grade, and academic achievement of the latent group. These results are not only meaningful as the first study of the latent profile analysis of high school students on disaster safety awareness, but also help to identify the characteristics of individuals in each latent group with more subdivisions and provide useful data for disaster safety awareness education according to individual differences. The implications of this study and suggestions for follow-up studies were discussed.

The purpose of this study, the wearing conditions of functional pressure clothing applied with the thermotherapy device were determined by three types (NW, CW, TCW) and the difference in isokinetic strength, muscle activity around the forearm was investigated and the effects of products mixed with thermotherapy and pressure treatment were verified. Ten men in their 20s were selected as subjects, and all subjects were randomly assigned three wearing conditions, and wrist flexion/extension exercise was performed at 30° and 90° angular velocity in isokinetic equipment. Peak torque, average power, and EMG were measured during exercise in all conditions. For peak torque, CW was significantly highest at velocity of 30°/sec flexion. Average power showed no significant difference by condition. In the angular velocity of 90°/sec, flexion was significantly higher in CW and TCW than in NW. As a result, wearing clothes with pressure effect and heat effect can show high efficiency in high muscle strength development and fast contraction activity during low speed exercise, and it is thought that it can show improvement of exercise ability through efficient recruitment of motor unit.

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

The purpose of this study is to investigate the causal relationship between the variables related to media sports violence viewing experience, negative emotion, aggression and violent imitation behavior of middle school students and high school students. The subjects of 240 middle school students and 240 high school students were surveyed for 2 months. Among the collected data, 64 samples judged to have low reliability were excluded from the analysis, and the final 416 data were used. The questionnaire used in this study consisted of four potential variables including experience of watching media sports violence, negative emotion, aggression, and violent imitation behavior. Among the sub-variables of experience of watching media sports violence, exogenous potential variables except frequency, period and time and questions that are endogenous potential variables were composed of a questionnaire of 5-point Likert scale. results of this study were analyzed using the AMOS statistical program, which was used to identify the causal relationship between watching media sports violence and negative emotion, aggression, and violent imitation behavior. There was no significant difference in violent imitation behavior according to school grade. The causal models for media sports violence viewing experience, negative emotions, aggression and violent imitation behavior were found to be appropriate. It was found that there was a significant causal relationship between the viewing experience of sports violence and the violent imitation behavior. Therefore, in order to reduce the imitation behavior of adolescents, it is necessary to be more careful about the production and viewing of sports violence programs at the institutions and homes that produce media.

As the level of consumption is improved and cultural life is pursued, the consumer's consciousness structure is rapidly changing, and the demand for product selection level, variety, and quality is becoming more diverse. The restaurant economy is falling due to the prolonged COVID-19, the economic recession, income decline, and changes in population structure and lifestyle, but the Meal- kit market is growing rapidly. This study aims to identify the consumer perception of Meal-kit, which is rapidly growing as an alternative to existing meals in the fields of dining out, food, and distribution due to the development of technology and social environment using big data. As a result of the analysis, the keywords with the highest frequency of appearance were in the order of Meal-kit, Cooking, Product, Launching, and Market and were divided into 8 groups through the CONCOR analysis. We want to identify consumer trends related to the key keywords of Meal-kit, present effective data related to Meal-kit demand for Meal-kit specialized companies, and provide implications for establishing marketing strategies for differentiated competitive advantage.

In this paper, the necessity of AI video interview arises when conducting an interview for acquisition of excellent talent in a non-face-to-face situation due to similar situations such as Covid-19. As a matter to be supplemented in general AI interviews, it is difficult to evaluate the reliability and qualitative factors. In addition, the AI interview is conducted not in a two-way Q&A, rather in a one-sided Q&A process. This paper intends to fuse the advantages of existing AI interviews and video interviews. When conducting an interview using AI image analysis technology, it supplements subjective information that evaluates interview management and provides quantitative analysis data and HR expert data. In this paper, image-based multi-modal AI image analysis technology, bioanalysis-based HR analysis technology, and web RTC-based P2P image communication technology are applied. The goal of applying this technology is to propose a method in which biological analysis results (gaze, posture, voice, gesture, landmark) and HR information (opinions or features based on user propensity) can be processed on a single screen to select the right person for the hire.

We identified the correlation between museums, gamification, and viewers based on the results of keyword correlation analysis, designing the museum gamification system. By applying gamification to the museum through big data CONCOR analysis, it was found that viewers had fun watching it through direct experiences and experiences. In this study, we propose a system that directly selects the story desired by the viewer and serves the game suitable for the theme of the museum they visited. In addition, the system proposed in this paper organized a story algorithm so that viewers can directly select the difficulty level based on the story and play personalized games. We collected existing museum information and designed to enable games of various stories suitable for the museum's theme, and based on this, it was applied to the museum gamification app. Moreover, in order to further enhance the immersion of museum viewing. We have been designed and applied based on Virtual Reality (VR).

The intelligent home network system integrates various devices in the home into one communication network to provide information sharing, control, and operation environment between devices. This intelligent home network system operates around a home server. Home appliances in the era of the 4th industrial revolution will have numerous home servers in logical areas as the intelligent home network in the home accelerates. Therefore, the need for systematic management of home servers is emerging. We propose an agent system for efficient intelligent smart home server management. The agent system monitors the home server and operating environment for home server management of the intelligent smart home network. By referring to this monitored information, the service module of the home server is managed, and the home server is dealt with whether it is normal or not. In addition, by referring to the information collected by the service agent created in the group management server while migrating the home server, it is possible to deal with integrated meter reading, crime prevention, and topics. And when a new service is applied to the home server, it is registered in the management server and distributed to the home server through the agent, so that the intelligent smart home network can be efficiently managed.