• PNF and Movement
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• v.11 no.1
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• pp.55-62
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• 2013

Purpose : This study was assessed muscle activity and onset time in trunk and upper extremity on functional reaching. Methods : The participant was 18 female(young 10, old 8). As functional reaching, we collection data by using EMG(MP150) on transverse abdominis, external oblique, erector spinae, deltoid middle and serratus anterior. Results : 1) In functional reaching, transverse abdominis, external oblique, erector spinae and deltoid middle muscle activity was augmented on old female(p>.05). Serratus anterior was augmented on young female(p>.05). 2) In functional reaching, transverse abdominis and erector spinae muscle onset time is significantly faster old female than young female(p<.05). External oblique and serratus anterior muscle onset time is faster old female than young female(p>.05). 3) As increase of age muscle activity of external oblique was more increased that we found .511 a coefficient correlation and onset time more faster on transverse abdominis and erector spinae were each -.492 and -.554 coefficient correlation. Conclusion : The muscle activity and onset time was difference in functional reaching according to ageing and task context. It is necessary concentration and attention to old female than young female. Therefore, these results suggest that importance of anticipatory postural control and selective strategy of postural control.

• Journal of English Language & Literature
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• v.64 no.3
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• pp.483-530
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• 2018

Unpacking and dispersing rights of various kinds formerly enjoyed by a selected few has been the constant motivation behind the democratization and modernization of human society. Human rights and later civil rights have continuously been constituted and reconstituted in response to the demands of the laboring class, slaves, women, subalterns, animals, and things, expanding beyond the boundaries of class, race, nation, sexuality, gender, species and organism. Calling attention to the ways in which literary and cultural texts have narrated rights so as to inscribe these human, nonhuman, and inhuman demands. Narrating rights offer opportunities to interrogate the lasting contributions of English language and literature to questioning, reforming, and practicing rights. The interrogation is particularly pertinent in this age in which revised and dispersed rights are creating new conflicts, requiring them to be narrated differently and imaginatively so as to allow all the parties in conflict to participate in working out the conflicts. With the 2017 theme of "Literature and Human Rights," JELL editorial collective hope to explore the relationship between literature and human rights in its multiple simultaneous, and plural manifestations in an open platform. "Narrating Rights" is a double-edged task that, on one hand, reflects the singular life conditions or contexts of a human, inhuman or nonhuman being and, on the other hand, aspires to the perpetual process of rights' universal application. Eleven out of all the keynote speakers at the 2017 ELLAK Convention were invited to this roundtable on Literature and Human Rights. The following transcription includes the dialogues of the eleven discussants.

• Journal of the Korean Institute of Educational Facilities
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• v.28 no.2
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• pp.3-10
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• 2021

Community-based design is also becoming important in Korea recently. However, the existing design methods of the "conformity" method had the problem of excluding the participation of residents. Therefore, the "decision-making" method, in which residents participate in the design themselves, is drawing attention. Development of specific methods is important for residents to actively participate in "decision making." The theory of "Design Games" has long been studied as a method of community-based design in many countries. The old downtown areas of Jeju Special Self-Governing Province are increasing in number of buildings abandoned due to aging and declining. Abandoned buildings are causing many social problems. A decision-making method has been developed in Jeju for the regeneration design of abandoned buildings. This study conducted a design workshop involving residents on abandoned buildings in the old city center of Jeju City. The possibility and task of decision-making method were analyzed. As a result, participating residents were actively involved in decision-making. It also helped residents understand and learn about the attractions of the neighborhood. Meanwhile, there were also difficulties in communicating among some participants. This is a structural problem with this method. Studies have also shown that it is important for residents themselves to try to understand the neighborhood.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Smart Structures and Systems
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• v.30 no.3
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• pp.273-286
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• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3355-3372
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• 2022

Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment. Algorithmic composition aims to develop algorithms for music composition. Recently, algorithmic composition using artificial intelligence technologies received considerable attention. In particular, computational intelligence is widely used and achieves promising results in the creation of music. This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm. First, transform the MIDI music format files to digital data. Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern. Except for time series, the converted data also includes duration, pitch, and velocity. Second, using MC simulation to deal with them summed up their distribution law respectively. The two main control parameters are the value of discrete sampling and standard deviation. Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• Asia-Pacific Journal of Business
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• v.12 no.2
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• pp.25-37
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• 2021

Purpose - This study analyzes the situational relationship between the components of job crafting and innovation performance, and based on this, suggests practical alternatives to the effect of the control variables of organizational support. Design/methodology/approach - For this survey, 350 questionnaires were distributed to Korean SME workers from October 5, 2020 to March 20, 2021, and 230 questionnaires were collected. In order to check the validity of the questionnaire, the questionnaire judged to be inappropriate in response was excluded. The recovery rate was 65.7%, and the effectiveness of the questionnaire was 82%. Structural equation model and hierarchical regression analysis are used to analyze those data. Findings - First, job enhancement through job redesign as well as organizational support is a key task in order to expect innovative results from field members. Innovative performance is not created by individual jobs, but is created between jobs and jobs, tasks and tasks, teams and teams, and departments and departments. This is why it is worth paying attention not to the functional approach, but to the interconnection structure of the process. Research implications or Originality - In this study, it was analyzed that structural job resource increase and social job resource increase, which are components of job crafting, had a positive effect on innovation performance, and that challenging job will had no significant effect. Challenging work will itself does not negatively affect innovation performance. Combining the survey and interview, field members who make up the majority of respondents say that they do not lack the will to work. They claim that there is no channel or opportunity to express or practice a challenging will.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.312-318
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• 2022

From the past two eras, artificial intelligence has gained the attention of researchers of all research areas. Video editing is a task in the list that starts leveraging the blessing of Artificial Intelligence (AI). Since AI promises to make technology better use of human life although video editing technology is not new yet it is adopting new technologies like AI to become more powerful and sophisticated for video editors as well as users. Like other technologies, video editing will also be facilitated by the majestic power of AI in near future. There has been a lot of research that uses AI in video editing, yet there is no comprehensive literature review that systematically finds all of this work on one page so that new researchers can find research gaps in that area. In this research we conducted a statically approach called, systematic mapping study, to find answers to pre-proposed research questions. The aim and objective of this research are to find research gaps in our topic under discussion.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.1-9
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• 2022

Recently, interest in living safety and crime prevention is increasing. The reality is that most women have anxiety about social safety and ultimately want a safe return to home. As a result of these issues, the Seoul Metropolitan Government and the National Police Agency are implementing various services to alleviate them. However, there are limitations such as that the service can be used only during a limited time or the process of checking whether the patrol is really completed is complicated. Therefore, in this paper, we propose a service platform that overcomes these limitations and suggests the best and shortest patrol route to the police based on the desired patrol location applied by citizens. It is designed based on the MVC pattern, and the functions are divided for each user. It is hoped that the platform will reduce crime rates and allow citizens to ultimately return home with peace of mind. Also we expect that the police will ablet to find places where they did not know about or need to patrol with more attention through the recommended route of the platform, which will be helpful in their task.