Earticle

Development of ICT technologies leads exponential growth of various sharing economy over the last couple of years. The intuitive advantage of the sharing economy is efficient utilization of idle goods and services, but there are safety and security concerns. In this paper, we propose a onetime password based access control system to support secure accommodation sharing service and show the implementation results. To provide a secure service to both the provider and the user, the proposed system issues a onetime access password that is valid only during the sharing period reserved by the user, thereafter access returns to the accommodation owner. Especially, our system provides secure user access by merging the two elements of speaker recognition using voice and a one-time password to open and close the door lock. In this paper, we propose a secure system for accommodation sharing services as a use-case, but the proposed system can be applicable to various sharing services utilizing security-sensitive facilities.

Soccer is one of the popular sports globally, and South Korea is no exception; the professional domestic soccer tournament has been held in South Korea since 1983. The national soccer team in South Korea has shown outstanding performances, hosting the 2002 World Cup and achieving a bronze medal at the London 2012 Olympic Games and second place at the 2019 U-20 World Cup. In addition, one of the South Korean soccer players, Heung-min Son, has played a remarkable role in the English Premier League. Like other global soccer clubs, South Korean domestic soccer clubs create and manage their own YouTube channel to interact with fans and keep them updated. In this study, we explore how and why Seoul E-Land Football Club (SEFC) utilizes its YouTube channel. SEFC is distinctive in that they are playing at the second division, their home ground is based in Seoul, and they are sponsored by a company run on Christian values. We analyze one of the soccer club’s YouTube channels multi-dimensionally and discover the roles of YouTube.

Recently, a lossless non-orthogonal multiple access (NOMA) implementation without successive interference cancellation (SIC) has been proposed in the literature of NOMA. This lossless non-SIC NOMA was achieved via correlated superposition coding (CSC), in contrast to conventional independent superposition coding (ISC). However, only the achievable data rates for CSC NOMA were investigated. Thus, this paper proposes a practical CSC NOMA scheme under Rayleigh fading channel environments. First, we design the practical CSC NOMA scheme, namely quadrature correlated superposition modulation (CSM) NOMA, without channel coding, i.e., uncoded systems. In addition, we calculate the symbol error rates (SERs) for this quadrature CSM NOMA scheme. Then, simulations demonstrate that for the weak channel gain’s user, the SER performance of the proposed quadrature CSM NOMA is shown to be improved greatly, compared to that of the conventional quadrature amplitude modulation (QAM) NOMA, whereas for the strong channel gain’s user, the SER performance of the proposed quadrature CSM NOMA degrades a little, compared to that of the conventional QAM NOMA. As a result, the proposed quadrature CSM NOMA scheme could be considered as a practical NOMA scheme for CSC NOMA schemes toward the fifth-generation (5G) and next generation communications.

Energy-harvesting wireless sensor networks(EH-WSNs) can collect energy from the environment and overcome the technical limitations of existing power. Since the transmission distance in a wireless sensor network is limited, the data are delivered to the destination node through multi-hop routing. In EH-WSNs, the routing protocol should consider the power situations of nodes, which is determined by the remaining power and energy-harvesting rate. In addition, in applications such as environmental monitoring, when there are urgent data, the routing protocol should be able to transmit it stably and quickly. This paper proposes an adaptive routing protocol that satisfies different requirements of normal and urgent data. To extend network lifetime, the proposed routing protocol reduces power imbalance for normal data and also minimizes transmission latency by controlling the transmission power for urgent data. Simulation results show that the proposed adaptive routing can improve network lifetime by mitigating the power imbalance and greatly reduce the transmission delay of urgent data.

Recently, a lossless non-successive interference cancellation (SIC) non-orthogonal multiple access (NOMA) implementation has been proposed. Such lossless NOMA without SIC is achieved via correlated superposition coding (SC), in comparison with conventional independent SC. However, only high-correlated SC was investigated in the lossless non-SIC NOMA implementation. Thus, this paper investigates lowcorrelated SC, especially a lossless interval, owing to low-correlation between signals. First, for the low-correlated SC scheme, we derive the closed-form expressions for the two roots with which the lossless interval is defined. Then, simulations demonstrate that the lossless interval of low-correlated SC NOMA is enlarged, with a degraded middle interval, compared to that of high-correlated SC NOMA. Moreover, we also show that such tendency becomes stronger as the value of the correlation coefficient varies. As a result, the proposed low-correlated SC scheme could be considered as a promising correlated SC scheme, with the enlarged lossless interval in NOMA toward the future sixth-generation (6G) ultra-reliable low-latency communications (URLLC).

As cyber attacks become more intelligent, there is difficulty in detecting advanced attacks in various fields such as industry, defense, and medical care. IPS (Intrusion Prevention System), etc., but the need for centralized integrated management of each security system is increasing. In this paper, we collect big data for intrusion detection and build an intrusion detection platform using deep learning and CNN (Convolutional Neural Networks). In this paper, we design an intelligent big data platform that collects data by observing and analyzing user visit logs and linking with big data. We want to collect big data for intrusion detection and build an intrusion detection platform based on CNN model. In this study, we evaluated the performance of the Intrusion Detection System (IDS) using the KDD99 dataset developed by DARPA in 1998, and the actual attack categories were tested with KDD99's DoS, U2R, and R2L using four probing methods.

In this paper, we proposed a technique for hiding the double-encrypted confidential data in the image using the pixel value of the image and the spatial encryption technique. The proposed technique inserts encrypted confidential data into the LSB of an image pixel in order to maintain high image quality. The stego-image generated by hiding the encrypted confidential data has very good quality and is visually indistinguishable from the original cover image, so that it is impossible to recognize whether the confidential data is hidden in the stego-image. It is possible to extract the original confidential data from the stego-image without loss. By conducting an experiment on the proposed technique, it was confirmed that the proposed technique is an effective technique for the practical application of data hiding. The proposed technique can be used in applications such as military and intellectual property protection that require high security.

MSRDS(Microsoft Robotics Developer Studio) provides the ability to simulate these technologies. SPL(Simple Programming Language) of MSRDS provides many functions for sensor programming to control autonomous robots. Sensor programming in SPL can be implemented in two types of schemes: procedure sensor notification and while–loop schemes. We considered the three programming schemes to control the robot movement after studying the advantages and disadvantages of the sensor notification procedure and the while-loop scheme. We also created simulation environments to evaluate the performance of the three considered schemes when applied to four different mazes. The simulation environment consisted of a maze and a robot with the most powerful sensor, i.e., the LRF(Laser Range Finder) sensor. We measured the required travel time and robot actions (number of turns and number of collisions) needed to escape the maze and compared the performance outcomes of the three considered schemes in the four different mazes.

Mobile crowd-sensing (MCS) is a promising sensing paradigm that leverages mobile users with smart devices to perform large-scale sensing tasks in order to provide services to specific applications in various domains. However, MCS sensing tasks may not always be successfully completed or timely completed for various reasons, such as accidentally leaving the tasks incomplete by the users, asynchronous transmission, or connection errors. This results in missing sensing data at specific locations and times, which can degrade the performance of the applications and lead to serious casualties. Therefore, in this paper, we propose a missing data inference approach, called missing data approximation with probabilistic tensor factorization (MDI-PTF), to approximate the missing values as closely as possible to the actual values while taking asynchronous data transmission time and different sensing locations of the mobile users into account. The proposed method first normalizes the data to limit the range of the possible values. Next, a probabilistic model of tensor factorization is formulated, and finally, the data are approximated using the gradient descent method. The performance of the proposed algorithm is verified by conducting simulations under various situations using different datasets.

With the development of science and technology, virtual reality (VR) has become increasingly popular, being widely used in various fields such as aviation, education, medical science, culture, art, and entertainment. This technology with great potential has changed the way of human-computer interaction and the way people live and entertain. In the field of animation, virtual reality also brings a new viewing form and immersive experience. The paper demonstrates the production of VR animation <Help Us!> and then discusses camera’s position in VR animation. Where to place the VR camera to bring a comfortable viewing experience. The paper, with the proxemics as its theoretical framework, proposes the hypothesis about the camera position. Then the hypothesis is verified by a series of experiments in animation <Help Us!> to discuss the correlation between camera position and proxemics theory.

The purpose of this study is to investigate the structural relationship between resort selection attributes, emotional responses, and empirical values of scuba diving participants. The general population who enjoys scuba diving in Korea was selected as the population. Using the convenience sampling method, 553 of the 600 questionnaire samples were extracted as the final valid sample. For data processing, frequency analysis, exploratory factor analysis, and Cronbach's α test were performed using SPSS 23, and confirmatory factor analysis and structural equation model analysis were performed with AMOS 18. The results are as follows: First, among the sub-factors of selection attributes, equipment, facility environment, and diving point showed a positive effect on emotional response, but staff service did not have any significant effect. Second, the emotional response positively affected by the selection attribute showed a positive effect on all factors of service excellence, consumer utility, fun value, and aesthetic value of empirical value. Therefore, scuba diving resort managers must recognize the importance of equipment, facility environment, and diving point among these selection attributes of customers. And to satisfy the customer needs the resort must accurately identify the needs for diving equipment, facility environment and diving point. Various methods for this should be explored through the needs of the identified customers, and efforts should be made to provide safe equipment, comfortable facilities, and various diving points.

With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.

In order to improve performance in IoT, edge computing system, a memory is usually configured in a hierarchical structure. Based on the distance from CPU, the access speed slows down in the order of registers, cache memory, main memory, and storage. Similar to the change in performance, energy consumption also increases as the distance from the CPU increases. Therefore, it is important to develop a technique that places frequently used data to the upper memory as much as possible to improve performance and energy consumption. However, the technique should solve the problem of cache performance degradation caused by lack of spatial locality that occurs when the data access stride is large. This study proposes a technique to selectively place data with large data access stride to a software-controlled cache. By using the proposed technique, data spatial locality can be improved by reducing the data access interval, and consequently, the cache performance can be improved.

Although previous studies on social commerce have provided much insight, more studies in the perspective of social network are needed because social commerce happens within online communities or virtual groups, where buyers connect and interact with each other by sharing information. So, the purpose of this study is to investigate how transactive memory as network characteristic can affect social commerce behavior through social support and relationship quality. We verified the relational effect among social support, relationship quality, and social commerce intention in the Korean market context. Moreover, we found transactive memory capability also played an essential role in the field of social commerce. Specifically, we found consumer’s transactive memory capability plays a significant moderating role in the relation between social support and relationship quality.

Recently, digital crime is becoming more intelligent, and efficient digital forensic techniques are required to collect evidence for this. In the case of important files related to crime, a specific person may intentionally delete the file. In such a situation, data recovery is a very important procedure that can prove criminal charges. Although there are various methods to recover deleted files, we focuses on the recovery technique using HxD editor. When recovering a deleted file using the HxD editor, check the file structure and access the file data area through calculation. However, there is a possibility that errors such as arithmetic errors may occur when a file approach through calculation is used. Therefore, in this paper, we propose an algorithm that automatically calculates the header and footer of a file after checking the file signature in the root directory for efficient file recovery. If the algorithm proposed in this paper is used, it is expected that the error rate of arithmetic errors in the file recovery process can be reduced.

As the 4th industrial era is in full swing, the public's interest in related technologies such as artificial intelligence, big data, and block chain is increasing. As artificial intelligence technology is used in various industrial fields, the need for research methods incorporating artificial intelligence technology in related fields is also increasing. Evidence collection among digital forensic investigation techniques is a very important procedure in the investigation process that needs to prove a specific person's suspicions. However, there may be cases in which evidence is damaged due to intentional damage to evidence or other physical reasons, and there is a limit to the collection of evidence in this situation. Therefore, this paper we intends to propose an artificial intelligence-based evidence collection system that analyzes numerous image files reported by citizens in real time to visually check the location, user information, and shooting time of the image files. When this system is applied, it is expected that the evidence expected data collected in real time can be actually used as evidence, and it is also expected that the risk area analysis will be possible through big data analysis.

The purpose of this study was to investigate the effect of the intensity of warm-up exercise on the physiological response and exercise performance during resistance exercise. For this purpose, 8 male college students with at least 1 year of experience in resistance movement were selected. The warm-up condition was set to NON condition (preparatory exercise not performed), 3 set condition, 6 set condition, and 9 set condition, and the intensity was different for each set. After warm-up, 75% 1RM of main exercise was performed, and blood pressure and heart rate were measured immediately after exercise to measure RPP. The number of repetitions and total work for each condition were measured as the number of repetitions and momentum during the main exercise. The measured data were analyzed by repeated measures two-way ANOVA. As a result, although there was no difference in Rate Pressure Product according to the warm-up conditions, the number of repetitions and total amount of exercise showed the highest in the 3-set condition. Therefore, it is thought that the number and intensity of warm-up exercises should be set appropriately during resistance exercise, and warm-up of 3 sets or more during resistance exercise is thought to decrease exercise performance.

We aims to examine the determinants of visitors’ positive word-of-mouth (WOM) in a web-based virtual reality contemporary art exhibition. We also examines the effects of 3 realms of experience (entertainment, esthetic, escapist) on emotional arousal, how this emotional arousal affects memory and positive word-of-mouth, how memory affects positive word-of-mouth and how age moderate all of the paths. We examined a total of 297 visitors and was conducted through an online survey focusing on Chinese users of ages 20-49. The analysis results showed that entertainment and esthetic have an effect on emotional arousal, but escapist did not. The results also showed emotional arousal has an effect on memory and positive WOM. Memory did not affect positive word-of-mouth. Finally, age has a moderate effect on all the paths, except for the path form escape towards emotional arousal and memory to positive word-of-mouth. The theoretical implications of this study are meaningful exhibition research. While, it also will be helpful to segment the web-based virtual reality art exhibition visitors by dividing into 3 groups (20s, 30s, 40s) and to provide marketing and operation strategies.

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, onsite PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.

With the global COVID-19 pandemic, the tourism sector and all consumption have contracted with the untact era. Wine will also be sold and developed in various ways non-face-to-face in the future. Therefore, it is necessary to develop apps and web servers that focus on health in the era of single-person households and non-face-to-face. This study used facial recognition data based on photos of adult men and women in their 40s and 50s to analyze the Sasang constitution through a mobile app and web server, and suggested wine recommendations suitable for their constitution. First, the user's body information is entered. And through the facial recognition mobile app, recommend the right wine after analyzing the body type. if it's not like the first recommended wine, it is configured to receive another wine recommendation. In the future, the number of single-person households will increase further, and in the age of well-being, wine recommendations that fit my body will be useful. Wine recommendation suitable for Sasang constitution will be a useful mobile application to manage personal healt

Unlike in the past, profit-making companies use design thinking to solve their customers' essential needs rather than just solving problems in a conventional way. Design syncing is a creative strategy to solve problems by using designers' senses and methods in the process of design. Design thinking is a new way of thinking for designers to design their own ways and solve problems. In other words, design thinking can solve complex problems in a new and creative way. In order to realize the necessary functions, an aggregation of relevant elements in accordance with the new law can create an innovative design. Additionally, if a convergence system is applied that is organized and regularly functional to accomplish the functions of different kinds, design-thinking outcomes can make the necessary functions more specific. Through our research, we would like to explore the differential features of design and discuss the direction of design for consumer needs through a case analysis of design thinking with creative convergence system. We would like to propose competitive design product development and creative design thinking through case analysis such as products and systems with design thinking applied. We hope that this research will help businesses and individuals who make design thinking a problem.

Recently, the use of electric and electronic control systems is increasing in the automobile industry. This increase in the electric and electronic control system greatly increases the complexity of designing a vehicle, which leads to an increase in the malfunction of the system, and a safety problem due to the malfunction is becoming an issue. Based on IEC 61508 relating to the functional safety of electrical/electronic/programmable electronics, the ISO 26262 standard specific to the automotive sector was first established in 2011, and a revision was published in 2018. Malfunctions due to system failure are covered by ISO 26262, but ISO/PAS 21448 is proposed to deal with unintended malfunctions caused by changes in the surrounding environment. ISO 26262 sets out safety-related requirements for the entire life cycle. Functional safety analysis includes FTA (Fault Tree Analysis), FMEA (Failure Mode and Effect Analysis), and HAZOP (Hazard and Operability). These analysis have limitations in dealing with failures or errors caused by complex interrelationships because it is assumed that a failure or error affecting the risk occurs by a specific component. In order to overcome this limitation, it is necessary to apply the STPA (System Theoretic Process Analysis) technique.

File system forensics typically focus on the contents or timestamps of a file, and it is common to work around file/directory centers. But to recover a deleted file on the disk or use a carving technique to find and connect partial missing content, the evidence must be analyzed using cluster-centered analysis. Forensics tools such as EnCase, TSK, and X-ways, provide a basic ability to get information about disk clusters, but these are not the core functions of the tools. Alternatively, Sysinternals' DiskView tool provides a more intuitive visualization function, which makes it easier to obtain information around disk clusters. In addition, most current tools are for Windows. There are very few forensic analysis tools for MacOS, and furthermore, cluster analysis tools are very rare. In this paper, we developed a tool named FACT (Forensic Analyzer based Cluster Information Tool) for analyzing the state of clusters in a HFS+ file system, for digital forensics. The FACT consists of three features, a Cluster based analysis, B-tree based analysis, and Directory based analysis. The Cluster based analysis is the main feature, and was basically developed for cluster analysis. The FACT tool’s cluster visualization feature plays a central role. The FACT tool was programmed in two programming languages, C/C++ and Python. The core part for analyzing the HFS+ filesystem was programmed in C/C++ and the visualization part is implemented using the Python Tkinter library. The features in this study will evolve into key forensics tools for use in MacOS, and by providing additional GUI capabilities can be very important for cluster-centric forensics analysis.