KJC Korea
Journal Central

In this paper, we design and implement a GPU-based parallel algorithm to effectively solve the traveling salesman problem through an ant color system. The repetition process of generating hundreds or thousands of tours simultaneously in TSP utilizes GPU's task-level parallelism, and the update process of pheromone trails data actively exploits data parallelism by 32x32 thread blocks. In particular, through simultaneous memory access of multiple threads, the coalesced accesses on continuous memory addresses and concurrent accesses on shared memory are supported. This experiment used 127 to 1002 city data provided by TSPLIB, and compared the performance of sequential and parallel algorithms by using Intel Core i9-9900K CPU and Nvidia Titan RTX system. Performance improvement by GPU parallelization shows speedup of about 10.13 to 11.37 times.

In this study, we propose a method for moving a device such as a flexible air sculpture while stably maintaining the user's desired posture. To accomplish this, a robot system with a structure of a mobile robot capable of running according to a given trajectory was studied by applying the PI algorithm and horizontal maintenance posture control using IMU. The air sculptures used in this study often use thin strings in a fixed posture. Another method is to put a load on the center of gravity to maintain the posture, and it is a system with flexibility because it uses air pressure. It is expected that these structures can achieve various results by combining flexible structures and mobile robots through the convergence process of digital sensor technology. In this study, posture control was performed by fusion of the driving technology of AGV(Automatic Guided Vehicle),, a field of robot, and technologies applying various sensors. For verification, the given performance evaluation was performed through an accredited certification test, and its validity was verified through an experiment.

In the field of patents, as NLP(Natural Language Processing) is a challenging task due to the linguistic specificity of patent literature, there is an urgent need to research a language model optimized for Korean patent literature. Recently, in the field of NLP, there have been continuous attempts to establish a pre-trained language model for specific domains to improve performance in various tasks of related fields. Among them, ELECTRA is a pre-trained language model by Google using a new method called RTD(Replaced Token Detection), after BERT, for increasing training efficiency. The purpose of this paper is to propose KorPatELECTRA pre-trained on a large amount of Korean patent literature data. In addition, optimal pre-training was conducted by preprocessing the training corpus according to the characteristics of the patent literature and applying patent vocabulary and tokenizer. In order to confirm the performance, KorPatELECTRA was tested for NER(Named Entity Recognition), MRC(Machine Reading Comprehension), and patent classification tasks using actual patent data, and the most excellent performance was verified in all the three tasks compared to comparative general-purpose language models.

In this paper, we propose a pet monitoring system based on deep learning using an activity recognition device. The system consists of a pet's activity recognition device, a pet owner's smart device, and a server. Accelerometer and gyroscope data were collected from an Arduino-based activity recognition device, and the number of steps was calculated. The collected data is pre-processed and the amount of activity is measured by recognizing the activity in five types (sitting, standing, lying, walking, running) through a deep learning model that hybridizes CNN and LSTM. Finally, monitoring of changes in the activity, such as daily and weekly briefing charts, is provided on the pet owner's smart device. As a result of the performance evaluation, it was confirmed that specific activity recognition and activity measurement of pets were possible. Abnormal behavior detection of pets and expansion of health care services can be expected through data accumulation in the future.

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.

In this paper, we suggest the method to improve the existing method leaning context-free grammar(CFG) using tabular representation(TBL) as a chromosome of genetic algorithm in grammatical inference and show the more efficient experimental result. We have two improvements. The first is to improve the formula to reflect the learning evaluation of positive and negative examples at the same time for the fitness function. The second is to classify partitions corresponding to TBLs generated from positive learning examples according to the size of the learning string, proceed with the evolution process by class, and adjust the composition ratio according to the success rate to apply the learning method linked to survival in the next generation. These improvements provide better efficiency than the existing method by solving the complexity and difficulty in the crossover and generalization steps between several individuals according to the size of the learning examples. We experiment with the languages proposed in the existing method, and the results show a rather fast generation rate that takes fewer generations to complete learning with the same success rate than the existing method. In the future, this method can be tried for extended CYK, and furthermore, it suggests the possibility of being applied to more complex parsing tables.

YouTube realistically shows fake news and biased content based on facts that have not been verified due to low entry barriers and ambiguity in video regulation standards. Therefore, this study aims to analyze the influence of the media and YouTube on individual behavior and their relationship. Data from YouTube and Twitter are randomly imported with selenium, beautiful soup, and Twitter APIs to classify the 31 most frequently mentioned keywords. Based on 31 keywords classified, data were collected from YouTube, Twitter, and Naver News, and positive, negative, and neutral emotions were classified and quantified with NLTK's Natural Language Toolkit (NLTK) Vader model and used as analysis data. As a result of analyzing the correlation of data, it was confirmed that the higher the negative value of news, the more positive content on YouTube, and the positive index of YouTube content is proportional to the positive and negative values on Twitter. As a result of this study, YouTube is not consistent with the emotion index shown in the news due to its secondary processing and affected characteristics. In other words, processed YouTube content intuitively affects Twitter's positive and negative figures, which are channels of communication. The results of this study analyzed that YouTube plays a role in assisting individual discrimination in the current situation where accurate judgment of information has become difficult due to the emergence of yellow media that stimulates people's interests and instincts.

As the delivery industry grew around the restaurant industry along with the COVID-19 situation, the number of delivery workers increased significantly. Along with that, new forms of delivery using personal mobility (PM) also emerged and two-wheeled or PM-related accidents are steadily increasing. This study manufactures a PM’s driving analysis device to establish a safe delivery monitoring environment. This system was constructed to process data collected from the driving analysis device and through a cloud server, which would recognize and record special situations (acceleration/deceleration, speed bump) that could occur during the PM's driving situation. As a result, the angular speed, acceleration, and geomagnetic values collected from the IMU in the device were able to determine whether to drive, drive on the sidewalk, and drive on the speed bump. This technology was able to achieve approximately 1600 times more driving information storage efficiency than conventional image-based recording devices.

Using the TAM, this study investigates the impact of Perceived Usefulness(PU), Perceived Ease of Use(PEOU) of the Animal-Crossing users on game satisfaction(DOS), immersion(DOI), and the relationship between user’s attitude and intention through the google online survey. The decorative elements of the game generate a healing effect and more than half of the survey participants share game activities with the online community in terms of PU. In the case of PEOU, the survey participants positively evaluate the technological advancement such as graphics and sounds of the game. In addition, both high degree of satisfaction and immersion in the game have significant correlations with the intention for replaying the game and intention for recommending the game to others.

This study aims to analyze the images of Instagram posts and to draw implcations regarding the exhibition of <David Hockney>. This study collects and crawl 24,295 images from Instagram posts as a dataset. We use the Google Cloud Vision API for labeling the images and a total of 212,567 clusters of labels are finally classified into 9 categories using Word2Vec. The categories of museum spaces, photo zone, architecture category are dominant along with people category. In conclusion, visitors curate their experiences and memories of physical places and spaces while they are experiencing with the exhibition. This result reproves the results of previous studies which emphasize a sense of social presence and place making. The convergent approach of art management and art technology used in this study help museum professionals have an insight on big data based visitor research on a practical level.

The purpose of this study is to investigate the effect of online basic clinical training on critical thinking disposition, self determination motivation and learning satisfaction for nursing student in the COVID-19 situation. This study is one group pre-test and post-test design. The subjects of this study were collected using an online questionnaire for third-year nursing students located in G city, and 41 students participated in the final. The results of this study showed that critical thinking disposition(p=.013) and self determination motivation(p=.007) increased statistically significantly after the online basic clinical training. This findings indicate that online basic clinical training is effective in improving critical thinking disposition, self determination motivation. As it is difficult to perform direct nursing in the COVID-19 medical field recently, it is expected that the limitations of online clinical practice can be overcome by using various online contents.

In this paper, We propose a method for identifying objects by setting inscriptions and landmarks of dogs. The phenomenon of abandoning dogs is on the rise, and the number of abandoned individuals is also rapidly increasing. These abandoned dogs are becoming wild animals, causing a lot of damage to people's daily life, causing serious problems. As a solution to this problem, the animal registration system is being implemented, but there is a phenomenon that some dog owners avoid the registration method that inserts a chip, so the complete registration system is not settled. When registering a dog, removing the avoidance of dog owners will help establish the companion animal registration system. In this paper, we present a technique to identify objects by setting inscriptions and landmarks of dogs so that dog owners can register their dogs in a friendly way to eliminate this avoidance phenomenon.

Delegation is a powerful mechanism that allocates access rights to users to provide flexible and dynamic access control decisions. It is also particularly useful in a distributed environment. Among the representative delegation models, the RBDM0 and RDM2000 models are role delegation as the user to user delegation. However, In RBAC, the concept of inheritance of the role class is not well harmonized with the management rules of the actual corporate organization. In this paper, we propose an Adding Attributes on Permission-Based Delegation Model (ABDM) that guarantees the permanence of delegated permissions. It does not violate the separation of duty and security principle of least privilege. ABDM based on RBAC model, supports both the role to role and user to user delegation with an attribute. whenever the delegator wants the permission can be withdrawn, and A delegator can give permission to a delegatee.

In this paper, we present an NFT auction service for the next generation, named NextAuction, which can reliably trade ownership of individual content using DID technology. Recently, as the types and sizes of tradable digital assets have expanded, the number of NFT transactions has also increased, and a significant number of marketplaces are being operated. But, the current user authentication methods of NFT marketplaces are done only through the associated blockchain wallets. It is desirable that ownership transfer through NFT transactions be transparently managed based on a more reliable identity authentication service. NextAuction increases the reliability of auction service participants by transparently and consistently providing identity authentication for users of auction services based on the DID technique using the Klaytn blockchain. In addition, in preparation for server failure that may occur during the auction of individual content, it provides users with a robust auction service using the BR2K technique that continuously provides consistent service through the replication of a target service. The NextAuction service is developed by extending BCON, a blockchain-based content management service.

In this paper, we propose an extension method of the BR2K technique for enhancing the robustness of blockchain application services. The BR2K (Blockchain application, Replication & Recovery technique using Kubernetes) technique was recently developed to support the robustness of blockchain services through service replication and rapid restart. The proposed technique extends the existing BR2K technique to provide a state version, which is meta-information about the service state, and a method for safely managing it, and use the state version to determine the timing for service state recovery. Also, the technique provides systematic service state backup for service recovery and joining of new service nodes by utilizing the version information and the service registry which acts as a service recovery center in the BR2K technique. Based on this, it is possible to support new service nodes to join the replication service with consistency. As a result, new service nodes can be quickly added to the BR2K service in operation, enhancing the robustness of the BR2K service. In addition, the extended method is applied to the pilot blockchain application service and tested in a Kubernetes environment composed of virtual machines to confirm the validity of service replication consistency and rapid service recovery in the event of node failures.

Electromagnetic topology (EMT) technique is a method to analyze each component of the electromagnetic propagation environment and combine them in the form of a network in order to effectively model the complex propagation environment. In a typical commercial communication channel model, since the propagation environment is complex and difficult to predict, a probabilistic propagation channel model that utilizes an average solution, although with low accuracy, is used. However, modeling techniques using EMT technique are considered for application of propagation and coupling analysis of threat electromagnetic waves such as electromagnetic pulses, radio wave models used in electronic warfare, local communication channel models used in 5G and 6G communications that require relatively high accuracy electromagnetic wave propagation characteristics. This paper describes the effective implementation method, algorithm, and program implementation of the electromagnetic topology (EMT) method analyzed in the frequency domain. Also, a method of deriving a response in the time domain to an arbitrary applied signal source with respect to the EMT analysis result in the frequency domain will be discussed.

In the study, the author aims to investigate the social media platform that helps promotion of stress relieve and social abilities that exist in lifestyle of persons living in the modern times and perform literature studies of characteristics of stress in each life cycle and social media thereof. In results, it is concluded that persons living in the modern times are under various stress during adolescence, middle age and elderly periods of the entire life cycle and especially, in Korea, stress index is increasing rapidly. To resolve stress, as an alternative, internet based social medial platform can be used to achieve various information supply and access. It is suggested that the development and accessibility of platforms for each inclination should be made easy in line with each inclination and desire for complex and diverse personal inclinations and individualistic activities, and related research should be continued.

Physical activities are decreasing and sitting time is increasing due to the automation, smartization, and intelligence of necessary household items throughout daily life. Recent healthcare studies have reported that the likelihood of obesity, diabetes, cardiovascular disease, and early death increases in proportion to sitting time. In this paper, we develop a sitting posture correction cushion in real time using capacitive pressure sensor based on conductive textile. It develops a pressure sensor using conductive textile, a key component of the posture correction cushion, and develops a low power-based pressure measurement circuit. It provides a function to transmit sensor values measured in real time to smartphones using BLE short-range wireless communication on the posture correction cushion, and develops a mobile application to check the condition of the sitting posture through these sensor values. In the mobile app, you can visualize your sitting posture and check it in real time, and if you keep it in the wrong posture for a certain period of time, you can notify it through an alarm. In addition, it is possible to visualize the sitting time and posture accuracy in a graph. Through the correction cushion in this paper, we experiment with how effective it is to correct the user's posture by recognizing the user's sitting posture, and present differentiation and excellence compared to other product.

Currently, as the number of families living with companion animals increases, the demand for information about the health status of companion animals has increased. As the demand for this increases, there is a need for a method to measure respiration and pulse in companion animals. Considering the characteristics of hairy companion animals, we want to measure respiration and pulse signals using BCG, which is different from adsorption ECG. Since this BCG method is made by mixing respiration and pulse signals into one signal, it is necessary to separate the respiration signal waveform and the pulse signal waveform from one signal waveform. In this paper, a wearable device for BCG measurement was implemented to measure the signal, and a method of separating the signal input from the BCG wearable device into a respiration signal and a pulse signal was proposed.

In this paper, we propose a service method that can provide insight into multi-source agricultural data, way to cluster environmental factor which supports data analysis according to time flow, and curate crop environmental factors. The proposed curation service consists of four steps: collection, preprocessing, storage, and analysis. First, in the collection step, the service system collects and organizes multi-source agricultural data by using an OpenAPI-based web crawler. Second, in the preprocessing step, the system performs data smoothing to reduce the data measurement errors. Here, we adopt the smoothing method for each type of facility in consideration of the error rate according to facility characteristics such as greenhouses and open fields. Third, in the storage step, an agricultural data integration schema and Hadoop HDFS-based storage structure are proposed for large-scale agricultural data. Finally, in the analysis step, the service system performs DTW-based time series classification in consideration of the characteristics of agricultural digital data. Through the DTW-based classification, the accuracy of prediction results is improved by reflecting the characteristics of time series data without any loss. As a future work, we plan to implement the proposed service method and apply it to the smart farm greenhouse for testing and verification.

The purpose of this study is to provide basic data on the negative factors of oral health in masks and the importance of oral health management according to the use of masks to prevent novel coronavirus infection (COVID-19). From May 3 to 31, 2021, 232 adults aged 20 to 59 across the country were surveyed and statistically analyzed. As for the mask selection, 63.9% of men and 61.3% of women chose the KF_94 mask for both men and women, and it was found that the older they were, the more they chose the KF_94 mask. Self-recognition of dry mouth and bad breath due to wearing a mask showed that the group wearing a cotton mask felt high dry mouth, and there was a statistically significant difference. There was a significant positive correlation between mask type, dry mouth(r=.142, p<.05), and age(r=.234, p<.01). There was a significant positive correlation between mask wearing time and age(r=.158, p<.05), and it was found to be negatively correlated according to occupation, and was statistically significant(r=-.472, p< .01). Dry mouth had a statistically significant positive correlation with bad breath(r=3.04, p<.01) and age(r=.224, p<.01).

This study is to encourage physical activity and exercise in people with spinal cord disabilities so that they can have a positive effect on health outcomes. Current evidence shows that IT-based muscle strength and muscle endurance, cardiopulmonary exercise, electrical stimulation exercise, and robot exercise can all improve physical components, reduce the risk of secondary health complications, and have a positive impact on the overall health of people with chronic physical disabilities. To improve muscle strength and muscle endurance, exercise frequency should be conducted twice and three sets a week, <5 Reps to improve muscle strength, general strength should be repeated 6 to 15 times, and 15 to 30 times to improve muscle endurance. In order to improve cardiopulmonary ability, it should be conducted 3-5 times a week, 20-60 minutes, and 50-80% of the maximum heart rate. Therefore, higher resource investment is needed to realize various IT-based exercise benefits and access professional equipment, facilities and trainers.

There was a report that the prevalence of gallbladder polyps (GBPs) and some clinical variables were different between the urban and rural area, which was performed in a mainland. Thus the previous report could have some confounding factors. Therefore, the purpose of this study was to compare the prevalence of GBPs and clinical variables on the isolated island to exclude extrinsic confounding factors. We compared the prevalence of GBPs and clinical variables among 21,734 residents who visited a single medical check-up center in Jeju National University Hospital from January 2010 to December 2019 according to their residences on Jeju Island. The prevalence of GBPs were 10.1% in the urban area 8.7%, respectively (P = 0.006). Among clinical variables, the rate of central obesity, the mean levels of fasting blood glucose, low density lipoprotein-cholesterol, alkaline phosphate and gamma-glutamyltransferase and the mean age among residents in the rural area was statically higher in the urban area; however, the mean total cholesterol level among residents in the urban area significantly higher than in the rural area. This study showed that the prevalence of GBPs and some clinical variables among residents were statistically different between the urban area on Jeju Island.

In this paper, we propose to identify the factors and satisfaction of the selection of University and departments of One university freshmen. Research subjects were 499 freshmen in 2021 at K University located in G city. The data were analyzed by descriptive statistics, t-test, ANOVA, Scheffe’ test and pearson’s correlation coefficient using SPSS WIN 18.0. The results of this study showed that the internet/SNS was the highest in university information media, the school teacher was the highest in information provider, and the employment rate had the most impact on university registration decision. The University satisfaction score was 3.43, and there was a significant difference in gender(t=5.527, p=.019) and admission type(F=5.527, p<.001). The department satisfaction was 3.86 and there was a significant difference in the admissions type(F=3.004, p=.018). Univdrsity satisfaction and Department satisfaction showed a significant positive correlation(r=5.527, p<.001). Universities should improve their competitiveness through systematic admission information system.

The purpose of this study was to apply the Havruta learning method and conduct a study on Shir and learner activity results, which was used as a method to improve learning competency. As the contents of the study, first, the ability to suggest ways to solve problems and the expansion of various thinking can be expected while listening to the questions of other students. Second, you can evaluate your own strengths and weaknesses by strengthening your communication skills and comparing and evaluating teaching methods at the same time. Third, through the experience of debate, one can solidify the establishment of one's own values and ethics, and based on this, one can develop the capacity for logical persuasion and argument. Fourth, it is judged that it is possible to analyze the practical problems suitable for the characteristics of the learner, and to develop a clear understanding of the situation and the ability to apply it.

This study is to structurally analyze the effect of authoritative, authoritarian, and permissive three-dimensional parenting behavior on pragmatic language ability in the field of child language development by using data from the Panel Study on Korean Children, which was completed and published in 2018. As a result of the study, looking at the effect of three-dimensional parenting behavior on children's language ability, first, the authoritative parenting behavior, which provides a clear direction for parents in raising their children, and takes a rational and consistent way, is effective in improving pragmatic language ability appeared to have a positive effect. Second, it was found that authoritarian parenting behavior that strictly controls children's behavior in the direction desired by parents and demands absolute obedience from children did not have a significant effect on the improvement of children's pragmatic language ability. Lastly, it was found that the permissive parenting behavior of the neglect type, which allows children to do whatever they want, has a negative effect on the children's pragmatic language ability. Based on these results, in order to equip children who will live in an era where creativity and problem-solving ability are important to have the ability to express themselves accurately, parenting behavior, which is a very important ecosystem for children's language development, needs to be done correctly.

In this study, we proposed a hierarchical model of a mobile fashion shopping apps to find out factors that consumers consider when choosing a mobile fashion shopping apps, and also derived priorities by calculating the weights of the selection factors. To estimate the priorities of selection factors, we analyzed survey data collected from consumers who are currently using a mobile fashion shopping apps. As a result of this study, the first-step selection factors of the mobile fashion shopping apps consisted of economics, informativity, convenience, reliability, and additional service, and economics factor revealed to be the most important. Additionally, in the result of synthesizing the weights of the first-step and second-step selection factors, the coupons and discount event factor was found to be the most important. In the fierce competition of various mobile fashion shopping apps, the findings of this study not only proposed the evaluation model of mobile fashion shopping apps using the AHP technique but provided the priority of consumers’ selection factors for mobile fashion shopping apps.