• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.119-125
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• 2024

This paper investigates the effect of an artificial intelligence attention redirection compensation strategy system on the data labeling work attention concentration by individuals with developmental disabilities. Task accuracy and task performance for each session were used as measures of attention concentration. As a result of the study, after the intervention was applied, a significant improvement in attention concentration was observed in all study subjects compared to self-serving task. These results mean that artificial intelligence technology can have a positive effect on improving the attention span of people with developmental disabilities during data labeling tasks. This study shows that the application of artificial intelligence technology can improve the quality of learning data by improving the accuracy of data labeling tasks for people with developmental disabilities, and is expected to provide important implications for vocational training programs related to data labeling for people with developmental disabilities.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.300-306
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• 2012

An image attention model and its application to image quality assessment are discussed in this paper. The attention model is based on rarity quantification, which is related to self-information to attract the attention in an image. It is relatively simpler than the others but results in taking more consideration of global contrasts between a pixel and the whole image. The visual attention model is used to develop a local distortion predictor, named color visual differences predictor (CVDP), in color images in order to effectively detect luminance and color distortions.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.21-28
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• 2019

Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism(ADD-Net), recognition of human motion behavior in the video.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.485-487
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• 2023

컴퓨터 비전의 하위 태스크(Task)인 의미론적 분할(Semantic Segmentation)은 자율주행, 해상에서 선박찾기 등 다양한 분야에서 연구되고 있다. 기존 FCN(Fully Conovlutional Networks) 기반 의미론적 분할 모델은 다운샘플링(Dowsnsampling)과정에서 공간정보의 손실이 발생하여 정확도가 하락했다. 본 논문에서는 공간정보 손실을 완화하고자 PSA(Polarized Self-attention)의 공간정보 강조 모듈을 HRNet(High-resolution Networks)의 합성곱 블록 사이에 추가한다. 실험결과 파라미터는 3.1M, GFLOPs는 3.2G 증가했으나 mIoU는 0.26% 증가했다. 공간정보가 의미론적 분할 정확도에 영향이 미치는 것을 확인했다.

• Journal of the Korean Society of Costume
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• v.22
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• pp.23-44
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• 1994

The purpose of this study was to clarify the cultural characteristic of dress and adornments by examining articles on dress adornments and related items in Korean newspaper over periods historically and objectively by means of content analysis. This study attempted to at-tain a macro-cultural view by analysing to at-tain a macro-cultural view by analysing closely the cultural characteristics of dress and adornments as a micro-cultural system through culturally based model suggested by hamilton. The two-hundreds and eighty articles on dress and adornments were selected from newspapers(most by form Chosun Ilbo and partly from Maeil Shinbo) pulished between 1920 and 1990. The results were as follows: 1. The culture of dress and adornment received much attention during the 1930's and 1960's and little during 1950's. 2. Various cultural characteristics of dress and adornments appeared on and after 1960's: reporting more foreign news items showing foreign-oriented and future-oriented features showing cultural relativism. In the 1920's and 1970's the contents of news items on dress and adornments show the most common- mass- oriented character. Foreign-oriented cultural tendencies in cloth-ing were increasing during from 1960's to 1970's but the tendencies were turned to rather tradition-oriented features on and after 1980's compare with 1960's-1970's. Advisory critical articles on dress and adornments were small in number and insignificant but compare with other periods these received much atten-tion during the 1920's and 1980's. 3. Ideological components received much at-tention on and after 1920's to 1990. Techo-nological components received much attention during 1920's and little during 1960's. The social structural components received a little attention on and after 1920's-1940's and 1990. 4. News items on women's dress and adornments received much attention from the 1920's to 1960's and news items on both men's and women's dress and adorments were in-creasing and received much attention on and after the 1970's. 5. The pragmatic cultures were mostly re-lated to techonological components and evaluative-normative culture were mostly re-lated to ideological and social structural components. In the light of these results dress and adorments as a cultural sub-system comprise a dynamic inteacting system that articulated directly with the macro-cultural system.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.95-115
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• 2022

The purpose of this study is to transform the sub-categorization terms of the National Science and Technology Standards Classification System into technical keywords by applying a machine learning algorithm. For this purpose, AttentionMeSH was used as a learning algorithm suitable for topic word recommendation. For source data, four-year research status files from 2017 to 2020, refined by the Korea Institute of Science and Technology Planning and Evaluation, were used. For learning, four attributes that well express the research content were used: task name, research goal, research abstract, and expected effect. As a result, it was confirmed that the result of MiF 0.6377 was derived when the threshold was 0.5. In order to utilize machine learning in actual work in the future and to secure technical keywords, it is expected that it will be necessary to establish a term management system and secure data of various attributes.

• 대한상한금궤의학회지
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• v.15 no.1
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• pp.85-115
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• 2023

Objectives : This study reports on the cases of four ADHD patients who treated with herbal medication based on Disease Pattern Identification Diagnostic System (DPIDS). Methods : Two children and two adults were diagnosed with ADHD according to DSM-V, and Galgeunhwangryeonhwanggeum-tang was administered. The parents of the two children completed the Korean ADHD Rating Scale (K-ARS) before and after treatment, while the other patients used the Korean ADHD Self Rating Scale (K-ASRS). The patients' somatic symptoms and other issues were evaluated through interviews. Results : In the first case, the K-ARS scale exhibited a change from 28 to 3. The patient demonstrated improvements in attention, concentration, and social behavior, along with the elimination of self-injurious behavior. In the second case, the K-ARS scale showed a change from 41 to 9. The patient in the third case demonstrated improved chronic fatigue, increased attention, work efficiency, and enhanced social skills, leading to maintaining employment for over six months for the first time in their life after the treatments. Additionally, the patient in the fourth case demonstrated improved work concentration and relief in nocturia. Conclusions : In this study, four patients exhibited improvements in symptoms of attention deficit, hyperactivity, and impulsivity before and after herbal treatment, leading to enhanced academic and social relationships. As the patients progressed in their health restoration, symptoms related to the genitourinary system also showed improvement.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.85-90
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• 2021

A ADAS(Advanced Driver Assistance System) for the safe driving is an important area in autonumous car. Specially, a ADAS software using an image sensors attached in previous car is low in building cost, and utilizes for various purpose. A algorithm for detecting the break-lamp from the tail-lamp of preceding vehicle is proposed in this paper. This method can perceive the driving condition of preceding vehicle. Proposed method uses the YOLO techinicque that has a excellent performance in object tracing from real scene, and extracts the intensity variable region of break-lamp from HSV image of detected vehicle ROI(Region Of Interest). After detecting the candidate region of break-lamp, each isolated region is labeled. The break-lamp region is detected finally by using the proposed selective-attention model that percieves the shape-similarity of labeled candidate region. In order to evaluate the performance of the preceding vehicle break-lamp detection system implemented in this paper, we applied our system to the various driving images. As a results, implemented system showed successful results.