• Geomechanics and Engineering
• /
• v.30 no.1
• /
• pp.67-73
• /
• 2022

In this study, the fiber orientation distribution (FOD) is investigated using both micro-CT (computerized tomography) and image analysis of physically cut specimens prepared from Polyvinyl Alcohol (PVA) fiber reinforced cemented Toyoura sand. The micro-CT images of the fiber reinforced cemented sand specimens were visualized in horizontal and vertical sections. Scans were obtained using a frame rate of two frames and an exposure time of 500 milliseconds. The number of images was set to optimize and typically resulted in approximately 3000 images. Then, the angles of the fibers for horizontal sections and in vertical section were calculated using the VGStudio MAX software. The number of fibers intersecting horizontal and vertical sections are counted using these images. A similar approach was used for physically cut specimens. The variation of results of fiber orientation between micro-CT scans and visual count were approximately 4-8%. The micro-CT scans were able to precisely investigate the fiber orientation distribution of fibers in these samples. The results show that 85-90% of the PVA fibers are oriented between ±30° of horizontal, and approximately 95% of fibers have an orientation that lies within ±45° of the horizontal plane. Finally, a comparison of experimental results with the generalized fiber orientation distribution function 𝜌(θ) is presented for isotropic and anisotropic distribution in fiber reinforced cemented Toyoura sand specimens. Experimentally, it can be seen that the average ratio of the number of fibers intersecting the finite area on a vertical plane to number of fibers intersecting the finite area on a horizontal plane (N^V_tot/N^H_tot) cut through a sample varies from 2.08 to 2.12 (an average ratio of 2.10 is obtained in this study). Based up on the analytical predictions, it can be seen that the average N^V_tot/N^H_tot ratio varies from 2.13 to 2.17 for varying n values (an average ratio of 2.15).

• Clinics in Shoulder and Elbow
• /
• v.25 no.3
• /
• pp.202-209
• /
• 2022

Background: The purpose of our study was to investigate short-term outcomes of two-stage reverse total shoulder arthroplasty (RTSA) with an antibiotic-loaded cement spacer for shoulder infection. Methods: Eleven patients with shoulder infection were treated by two-stage RTSA following temporary antibiotic-loaded cement spacer. Of the 11 shoulders, nine had pyogenic arthritis combined with complex conditions such as recurrent infection, extensive osteomyelitis, osteoarthritis, or massive rotator cuff tear and two had periprosthetic joint infection (PJI). The mean follow-up period was 29.9 months (range, 12-48 months) after RTSA. Clinical and radiographic outcomes were evaluated using the visual analog scale (VAS) score for pain, American Shoulder and Elbow Surgeons (ASES) score, subjective shoulder value (SSV), and serial plain radiographs. Results: The mean time from antibiotic-loaded cement spacer to RTSA was 9.2 months (range, 1-35 months). All patients had no clinical and radiographic signs of recurrent infection at final follow-up. The mean final VAS score, ASES score, and SSV were significantly improved from 4.5, 38.6, and 29.1% before RTSA to 1.7, 75.1, and 75.9% at final follow-up, respectively. The mean forward flexion, abduction, external rotation, and internal rotation were improved from 50.0°, 50.9°, 17.7°, and sacrum level before RTSA to 127.3°, 110.0°, 51.8°, and L2 level at final follow-up, respectively. Conclusions: Two-stage RTSA with antibiotic-loaded cement spacer yields satisfactory short-term clinical and radiographic outcomes. In patients with pyogenic arthritis combined with complex conditions or PJI, two-stage RTSA with an antibiotic-loaded cement spacer would be a successful approach to eradicate infection and to improve function with pain relief.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.195-207
• /
• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.251-266
• /
• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Journal of the Society of Disaster Information
• /
• v.19 no.1
• /
• pp.76-83
• /
• 2023

Purpose: In emergency situations such as earthquakes, heavy rains, typhoons, or fires, when quick delivery of emergency alerts is crucial, the hearing impaired are the ones who are the most disadvantaged and vulnerable when alerts are only delivered through auditory or text alerts. They can't perceive auditory information, and many have difficulties in fast understanding text-based alerts. Method: An alert system that can deliver pictograms for specific disaster situations has been devised. Then, a novel approach based on artificial intelligence has been studied so that the pictograms for specific disaster situations can be chosen instantly once a disaster alert is issued in text. Result: A disaster alert system that delivers pictograms for specific disaster situations was developed and a novel method has been suggested for automatic delivery. Conclusion: A system to instantaneously deliver disaster alert information in pictograms has been developed to improve alert delivery to the populations vulnerable to disaster due to hearing impairment by the instantaneous understanding of disaster situations through visual information.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.157-166
• /
• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• International Journal of Advanced Culture Technology
• /
• v.11 no.3
• /
• pp.65-77
• /
• 2023

Chinese martial arts culture is a kind of Chinese kung fu culture, a cultural category that uses martial arts kung fu for chivalry and justice. Chinese martial arts MMORPG online game is the embodiment of Chinese martial arts culture in online games, which is a unique Chinese online game. The image of beggar chivalry is a special chivalrous image in Chinese martial arts culture, and in the top 3 martial arts MMORPG online games, all of them have the image of beggar chivalry, which shows that this image has a wide player base. The Q methodology is an approach that endeavors to discover complex issues in human subjectivity, unlike existing empirical studies. In order to determine the type of beggar chivalry image preference of the game players, 32 beggar chivalry images were selected in the study and three types of beggar chivalry images were found through the Q method: Type 1 is the type of gorgeous and noble beggar chivalry; Type 2 is a competent type and is good at fighting the beggar's chivalry; and Type 3 is comparable relatively refined type. The result of this study is that the image of beggar chivalry preferred by game players is the opposite of the traditional Chinese image of beggar chivalry. The traditional image of beggar is the image of wearing plain and begging in the street, but the image of beggar chivalry that is liked in online games is luxurious, noble, exquisite and about the image of good at fighting. This research result has some value and significance in the development and design of beggar chivalrous image in future martial arts MMORPG online games.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1339-1355
• /
• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• The Research Journal of the Costume Culture
• /
• v.31 no.5
• /
• pp.573-585
• /
• 2023

Comprehending the prevailing ideals of the body within a specific era requires grasping the intricate interplay between social phenomena and the evolution of clothing. Accordingly, this study investigates the distinctive facets of the perception of the Neo-Confucian body as reflected in men's dress during the Joseon Dynasty. We examine a comprehensive body of scholarship, literature, and historical records concerning the body and dress. Additionally, we also employ a framework developed by M. Y. Kim, which categorizes the Neo-Confucian body in three ways: as the natural body, the cultural body, and the body as a fully-realized moral subject. Our findings unveil three crucial insights: firstly, guided by Neo-Confucian discourse positing appearance as a manifestation of innate energy (氣), men's dress was deliberately designed to demarcate stylistic distinctions in women's dress; secondly, the Chinese gwan (冠) was employed as a tool of self-cultivation (修身) to symbolize the legitimacy of Joseon's Neo-Confucian governance; and thirdly, sim-ui (深衣), a philosophical emblem of Confucianism extensively represented across through an intensified exploration of historical sources, served as a means to consolidate the political standing of the Neo-Confucian faction. As a consequence of these factors, the attire of noble men conferred upon them both sexual and moral ascendancy as political entities; men's dress became a visual manifestation of the legitimacy of their power, thus embodying Neo-Confucian ideals. This study carries significance by applying a discourse analysis approach to Korean dress research and elucidating the factors underlying the development of men's dress during the Joseon Dynasty.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.501-508
• /
• 2023

This research endeavors to categorize a diverse range of hairstyles for both men and women in South Korea using hair images and subsequently generate 3D hair models based on this classification. The classification process relies on prominent visual features, resulting in the division of hairstyles into 14 distinct categories, including six styles for men and eight styles for women. By accurately matching the most appropriate hairstyle to the given hair image, the study aims to provide recommendations for the necessary hair models required for metaverse authoring tools, thus enabling realistic hair styling. This capability can be effectively utilized on platforms like metaverse, allowing users to seamlessly find and apply the 3D hair model that closely resembles their remotely captured or pre-existing hair images. Through this innovative approach, users can be presented with the most similar 3D hair model, enhancing their virtual hairstyling experience.