• Korean Journal of Acupuncture
• /
• v.39 no.3
• /
• pp.71-83
• /
• 2022

Objectives : Instructional design is the systematic approach to the Analysis, Design, Development, Implementation, and Evaluation of learning materials and activities. We aimed to apply the rapid prototyping to instructional systems design (RPISD) in meridianology laboratory, a subject in which students train acupuncture to develop lesson plan. Methods : The needs of the stakeholders including client, subject matter expert and students were analyzed using the performance needs analysis model. Task analysis was implemented by observation and interview. First prototype was drafted and implemented in meridianology laboratory class once. The second prototype was modified from the first, by usability evaluation of the stakeholders. Results : The client requested an electronically documented manual to improve the quality of acupuncture training. The learner requested an extension of practice time and detailed practice guidelines. The main problems of students' performance were some cases of violation of clean needle technique, the lack of communication between the operator and recipient in direct, and lack of confidence in their own performance. Stakeholders were generally satisfied with the proposed first prototype. Second prototype of lesson plan was produced by modifying some contents. Conclusions : A lesson plan was developed by applying the systematic RPISD model. It is expected that the developed instructional design may contribute to the quality improvement of meridianology laboratory education.

• Steel and Composite Structures
• /
• v.47 no.2
• /
• pp.167-184
• /
• 2023

In this study, a new recentering friction device (RFD) to retrofit steel moment frame structures is introduced. The device provides both self-centering and energy dissipation capabilities for the retrofitted structure. A hybrid performance-based seismic design procedure considering multiple limit states is proposed for designing the device and the retrofitted structure. The design of the RFD is achieved by modifying the conventional performance-based seismic design (PBSD) procedure using computational intelligence techniques, namely, genetic algorithm (GA) and artificial neural network (ANN). Numerous nonlinear time-history response analyses (NLTHAs) are conducted on multi-degree of freedom (MDOF) and single-degree of freedom (SDOF) systems to train and validate the ANN to achieve high prediction accuracy. The proposed procedure and the new RFD are assessed using 2D and 3D models globally and locally. Globally, the effectiveness of the proposed device is assessed by conducting NLTHAs to check the maximum inter-story drift ratio (MIDR). Seismic fragilities of the retrofitted models are investigated by constructing fragility curves of the models for different limit states. After that, seismic life cycle cost (LCC) is estimated for the models with and without the retrofit. Locally, the stress concentration at the contact point of the RFD and the existing steel frame is checked being within acceptable limits using finite element modeling (FEM). The RFD showed its effectiveness in minimizing MIDR and eliminating residual drift for low to mid-rise steel frames models tested. GA and ANN proved to be crucial integrated parts in the modified PBSD to achieve the required seismic performance at different limit states with reasonable computational cost. ANN showed a very high prediction accuracy for transformation between MDOF and SDOF systems. Also, the proposed retrofit showed its efficiency in enhancing the seismic fragility and reducing the LCC significantly compared to the un-retrofitted models.

• Smart Media Journal
• /
• v.12 no.7
• /
• pp.59-67
• /
• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• Journal of Veterinary Science
• /
• v.23 no.1
• /
• pp.17.1-17.10
• /
• 2022

Background: Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives: This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods: Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results: Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions: Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

• Korean Journal of Radiology
• /
• v.22 no.6
• /
• pp.912-921
• /
• 2021

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

• Structural Engineering and Mechanics
• /
• v.91 no.1
• /
• pp.75-86
• /
• 2024

In this paper, the porous metal PM-35 is proposed as the filler material of filled thin-walled tubes (FTTs), and a series of experimental study is conducted to investigate the dynamic behavior and energy absorption performance of PM-35 filled thin-walled tubes under impact loading. Firstly, cylinder solid specimens of PM-35 steel are tested to investigate the impact mechanical behavior by using the Split Hopkinson pressure bar set (SHP); Secondly, the filled thin-walled tube specimens with different geometric parameters are designed and tested to investigate the feasibility of PM-35 steel applied in FTTs by the orthogonal test. According to the results of this research, it is concluded that PM-35 steel is with the excellent characteristics of high energy absorption capacity and low yield strength, which make it a potential filler material for FTTs. The micron-sizes pore structure of PM-35 is the main reason for the macroscopic mechanical behavior of PM-35 steel under impact loading, which makes the material to exhibit greater deformation when subjected to external forces and obviously improve the toughness of the material. In addition, PM-35 steel core-filled thin-wall tube has excellent energy absorption ability under high-speed impact, which shows great application potential in the anti-collision structure facilities of high-speed railway and maglev train. The parameter V0 is most sensitive to the energy absorption of FTT specimens under impact loading, and the sensitivity order of different variations to the energy absorption is loading speed V0>D/t>D/L. The loading efficiency of the FTT is affected by its different geometry, which is mainly determined by the sleeve material and the filling material, which are not sensitive to changes in loading speed V0, D/t and D/L parameters.

• (The) Research of the performance art and culture
• /
• no.23
• /
• pp.37-61
• /
• 2011

The Noh(能) performance is a traditional drama that represents Japan. The Noh performance was approved in the background of religious thought such as Shintoism(神道), Buddhisms(佛敎), and Syugendo(修驗道). Especially, the influence from Shugendo is large. Shugendo was active in the Middle Ages. Especially, the influence from Shugendo is large. Shugendo was active in the Middle Ages. The Noh was approved while receiving a large influence from Shugendo. It can know the feature of the Shugen(修驗) culture in the Middle Ages through the consideration of . Moreover, the appearance of the training of 'Yamabusi(山伏)' can be seen. "Yamabusi" has not been paid to attention up to now in the research of . And, the focus was appropriated to Yamabusi and it researched in this text. Moreover, the problem of "Chigo(稚子)" is thought through . "Chigo culture" was general in the Middle Ages. It is thought that "Chigo culture" is reflected in . is an Noh performance for the boy named 'Wakamatsu' to enter the mountain and to train. It is because mother's sickness was cured. However, the boy gets sick while it is training. It was dropped to the valley according to the law of Shugendo, and it died. However, it revives by the Yamabusi's prayers. 'Taniko' is to drop to the valley and to bury it when the Yamabusi gets sick while lived. The title of the Noh originated in here. has elements of history, content of training of Shugendo, "Filial piety", and the Chigo culture, etc. These are features of the culture in the Middle Ages. It is not only a sad content though this is a content of the cruel remainder. It is because of the revival though waited rapidly at the end. As for the difficulty of training is drawn in the round, and the appearance of the training at that time is understood well. The essence of Shugendo is to train in the mountain. Supernatural power can be obtained through training. Moreover, it was thought that it was able to be newly reborn through training. The leading part of Shugendo is an Yamabusi. The Yamabusi took an active part in not only the mountain but also the village. The Yamabusi is ordinary people's lives and because the relation is deep, an important factor it knows the folk customs of Japan. The word 'Chigo' is not written in . However, a spectator at that time is 'Chigo' Wakamatsu and is already sure to have understood 'Chigo'. Because everyone knew the Chigo culture in the Middle Ages. A religion at that time and knowledge of the society are necessary to understand the play of Nho well.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.309-328
• /
• 2022

This study empirically analyzed a Korean pre-trained language models (PLMs) designed for natural language generation. The performance of two PLMs - BART and GPT - at the task of abstractive text summarization was compared. To investigate how performance depends on the characteristics of the inference data, ten different document types, containing six types of informational content and creation content, were considered. It was found that BART (which can both generate and understand natural language) performed better than GPT (which can only generate). Upon more detailed examination of the effect of inference data characteristics, the performance of GPT was found to be proportional to the length of the input text. However, even for the longest documents (with optimal GPT performance), BART still out-performed GPT, suggesting that the greatest influence on downstream performance is not the size of the training data or PLMs parameters but the structural suitability of the PLMs for the applied downstream task. The performance of different PLMs was also compared through analyzing parts of speech (POS) shares. BART's performance was inversely related to the proportion of prefixes, adjectives, adverbs and verbs but positively related to that of nouns. This result emphasizes the importance of taking the inference data's characteristics into account when fine-tuning a PLMs for its intended downstream task.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.4
• /
• pp.199-207
• /
• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.17 no.2
• /
• pp.57-66
• /
• 2009

In this paper, the steering characteristics of tracked vehicles are studied for the improvement of steering performance. The important design factor of military vehicles is high mobility. It is influenced by weight of a vehicle, engine capacity, power-train, and steering system. The military vehicle, which is equipped with caterpillar, has unique steering characteristics and is quite different from that of a wheeled vehicle. The steering of tracked vehicles is operated in the power pack due to different speeds of both sprockets. Under cornering conditions, power split and power regeneration are happened in the power pack. In case of power regeneration, power is transferred outside track after adding engine power and power inputted inside track from the ground. However, excessive power regeneration is transferred in the power pack. It damages mechanical elements. Therefore, it is necessary to analyze the steering system and check mentioned problem above. In this study, the detailed dynamic model of steering system is presented, which includes slippage between track and roadwheel, inertia force, and inertia moment. Finally, our model is compared with the Kitano model and we verified the validity of the model.