• 제목/요약/키워드: Angle Learning

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Comparison of Pattern Design Functions in YUKA and CLO for CAD Education: Focusing on Skirt Patterns (캐드 교육을 위한 YUKA와 CLO의 패턴 제도 기능 비교: 스커트패턴을 중심으로)

  • Younglim Choi
    • Fashion & Textile Research Journal
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    • 제26권1호
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    • pp.65-77
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    • 2024
  • This study aimed to propose effective ways to integrate CLO into educational settings by conducting a comparative analysis of pattern functions in YUKA and CLO, specifically focusing on skirt prototypes and variations. CLO, being a 3D virtual sample CAD tool, is mainly used in education to facilitate the creation of 3D virtual clothing. In order to explore the applicability of CLO's pattern functions in pattern education, CAD education experts were asked to produce two types of skirt prototypes and two skirt variations. Subsequently, in-depth interviews were conducted. In addition, the skirt pattern creation process was recorded on video and used for comparative analysis of YUKA and CLO pattern functions. The comparison revealed that CLO provides the pattern tools necessary for drafting skirt prototypes. The learning curve for acquiring the skills necessary for drafting and transforming skirt prototypes was found to be relatively shorter for CLO compared to YUKA. In addition, due to CLO's surface-based pattern drawing method, it is difficult to move or copy only specific parts of the outline, and there are some limitations in drawing right angle lines. In the pattern transformation process, CLO's preview function proved to be advantageous, and it was highly rated on user convenience due to the intuitive UI. Thus, CLO shows promise for pattern drafting education and is deemed to have high scalability as it is directly linked to 3D virtual clothing.

Ship s Maneuvering and Winch Control System with Voice Instruction Based Learning (음성지시에 의한 선박 조종 및 윈치 제어 시스템)

  • Seo, Ki-Yeol;Park, Gyei-Kark
    • Journal of the Korean Institute of Intelligent Systems
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    • 제12권6호
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    • pp.517-523
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    • 2002
  • In this paper, we propose system that apply VIBL method to add speech recognition to LIBL method based on human s studying method to use natural language to steering system of ship, MERCS and winch appliances and use VIBL method to alternate process that linguistic instruction such as officer s steering instruction is achieved via ableman and control steering gear, MERCS and winch appliances. By specific method of study, ableman s suitable steering manufacturing model embodies intelligent steering gear controlling system that embody and language direction base studying method to present proper meaning element and evaluation rule to steering system of ship apply and respond more efficiently on voice instruction of commander using fuzzy inference rule. Also we embody system that recognize voice direction of commander and control MERCS and winch appliances. We embodied steering manufacturing model based on ableman s experience and presented rudder angle for intelligent steering system, compass bearing arrival time, evaluation rule to propose meaning element of stationary state and correct steerman manufacturing model rule using technique to recognize voice instruction of commander and change to text and fuzzy inference. Also we apply VIBL method to speech recognition ship control simulator and confirmed the effectiveness.

Invariant Classification and Detection for Cloth Searching (의류 검색용 회전 및 스케일 불변 이미지 분류 및 검색 기술)

  • Hwang, Inseong;Cho, Beobkeun;Jeon, Seungwoo;Choe, Yunsik
    • Journal of Broadcast Engineering
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    • 제19권3호
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    • pp.396-404
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    • 2014
  • The field of searching clothing, which is very difficult due to the nature of the informal sector, has been in an effort to reduce the recognition error and computational complexity. However, there is no concrete examples of the whole progress of learning and recognizing for cloth, and the related technologies are still showing many limitations. In this paper, the whole process including identifying both the person and cloth in an image and analyzing both its color and texture pattern is specifically shown for classification. Especially, deformable search descriptor, LBPROT_35 is proposed for identifying the pattern of clothing. The proposed method is scale and rotation invariant, so we can obtain even higher detection rate even though the scale and angle of the image changes. In addition, the color classifier with the color space quantization is proposed not to loose color similarity. In simulation, we build database by training a total of 810 images from the clothing images on the internet, and test some of them. As a result, the proposed method shows a good performance as it has 94.4% matching rate while the former Dense-SIFT method has 63.9%.

Production Techniques for Mobile Motion Pictures base on Smart Phone (스마트폰 시장 확대에 따른 모바일 동영상 편집 기법 연구)

  • Choi, Eun-Young;Choi, Hun
    • The Journal of the Korea Contents Association
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    • 제10권5호
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    • pp.115-123
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    • 2010
  • Because of development of information technology, moving picture can run various platforms. We should consider and apply users' attitude as well as production technique because convergence between mobile and media technology may be increased full-browsing service using mobile device. Previous research related to production technique in various platforms only focus on video quality and adjustment of screen size. However, besides of technical side, production techniques should be changed such as image production as well as image editing by point of view aesthetic. Mise-en-scene such as camera angle, composition, and lighting is changed due to HD image. Also image production should be changed to a suitable full-browsing service using mobile device. Therefore, we would explore a new suitable production techniques and image editing for smart phone. To propose production techniques for smart phone, we used E-learning production system, which are transition, editing technique for suitable converting system. Such as new attempts are leading to new paradigm and establishing their position by applying characteries such as openness, timeliness to mobile. Also it can be extended individual area and established as expression and play tool.

The Development and the Effects of Educational Program applied on STEAM for the Mathematical Prodigy (융합인재교육(STEAM)을 적용한 초등 수학영재 교육 프로그램의 개발과 적용 효과)

  • Lee, Seungwoo;Baek, Jongil;Lee, Jeonggon
    • Education of Primary School Mathematics
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    • 제16권1호
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    • pp.35-55
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    • 2013
  • The purposes of this study were to development and verify the effect of educational program apply on STEAM for the mathematical prodigy. To accomplish these purposes literature review on development of the program and qualitative study were conducted. The mixed-model design was applied for this qualitative experimental study. The conclusions of this study were as follows. First, the program for mathematical prodigy education applied on the conceptual model of STEAM integration approach was developed. Second, a learning satisfaction about constitution of the workbook was lowly. Third, principal of STEAM was the best interest and difficult of the program applied on STEAM. Fourth, the creativity and problem solving ability was founded about angle and velocity of mathematical domain and making the Angrybirds Game on GeoGebra environment. In spite of difficulty about principal of the Angrybirds Game, confidence and satisfaction were founded about a result product.

Development of a method for urban flooding detection using unstructured data and deep learing (비정형 데이터와 딥러닝을 활용한 내수침수 탐지기술 개발)

  • Lee, Haneul;Kim, Hung Soo;Kim, Soojun;Kim, Donghyun;Kim, Jongsung
    • Journal of Korea Water Resources Association
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    • 제54권12호
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    • pp.1233-1242
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    • 2021
  • In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.

Effect of All Sky Image Correction on Observations in Automatic Cloud Observation (자동 운량 관측에서 전천 영상 보정이 관측치에 미치는 효과)

  • Yun, Han-Kyung
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • 제15권2호
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    • pp.103-108
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    • 2022
  • Various studies have been conducted on cloud observation using all-sky images acquired with a wide-angle camera system since the early 21st century, but it is judged that an automatic observation system that can completely replace the eye observation has not been obtained. In this study, to verify the quantification of cloud observation, which is the final step of the algorithm proposed to automate the observation, the cloud distribution of the all-sky image and the corrected image were compared and analyzed. The reason is that clouds are formed at a certain height depending on the type, but like the retina image, the center of the lens is enlarged and the edges are reduced, but the effect of human learning ability and spatial awareness on cloud observation is unknown. As a result of this study, the average cloud observation error of the all-sky image and the corrected image was 1.23%. Therefore, when compared with the eye observation in the decile, the error due to correction is 1.23% of the observed amount, which is very less than the allowable error of the eye observation, and it does not include human error, so it is possible to collect accurately quantified data. Since the change in cloudiness due to the correction is insignificant, it was confirmed that accurate observations can be obtained even by omitting the unnecessary correction step and observing the cloudiness in the pre-correction image.

Development of Stability Evaluation Algorithm for C.I.P. Retaining Walls During Excavation (가시설 벽체(C.I.P.)의 굴착중 안정성 평가 알고리즘 개발)

  • Lee, Dong-Gun;Yu, Jeong-Yeon;Choi, Ji-Yeol;Song, Ki-Il
    • Journal of the Korean Geotechnical Society
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    • 제39권9호
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    • pp.13-24
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    • 2023
  • To investigate the stability of temporary retaining walls during excavation, it is essential to develop reverse analysis technologies capable of precisely evaluating the properties of the ground and a learning model that can assess stability by analyzing real-time data. In this study, we targeted excavation sites where the C.I.P method was applied. We developed a Deep Neural Network (DNN) model capable of evaluating the stability of the retaining wall, and estimated the physical properties of the ground being excavated using a Differential Evolution Algorithm. We performed reverse analysis on a model composed of a two-layer ground for the applicability analysis of the Differential Evolution Algorithm. The results from this analysis allowed us to predict the properties of the ground, such as the elastic modulus, cohesion, and internal friction angle, with an accuracy of 97%. We analyzed 30,000 cases to construct the training data for the DNN model. We proposed stability evaluation grades for each assessment factor, including anchor axial force, uneven subsidence, wall displacement, and structural stability of the wall, and trained the data based on these factors. The application analysis of the trained DNN model showed that the model could predict the stability of the retaining wall with an average accuracy of over 94%, considering factors such as the axial force of the anchor, uneven subsidence, displacement of the wall, and structural stability of the wall.

Development of High-Resolution Fog Detection Algorithm for Daytime by Fusing GK2A/AMI and GK2B/GOCI-II Data (GK2A/AMI와 GK2B/GOCI-II 자료를 융합 활용한 주간 고해상도 안개 탐지 알고리즘 개발)

  • Ha-Yeong Yu;Myoung-Seok Suh
    • Korean Journal of Remote Sensing
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    • 제39권6_3호
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    • pp.1779-1790
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    • 2023
  • Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

Manganese and Iron Interaction: a Mechanism of Manganese-Induced Parkinsonism

  • Zheng, Wei
    • Proceedings of the Korea Environmental Mutagen Society Conference
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    • 한국환경성돌연변이발암원학회 2003년도 추계학술대회
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    • pp.34-63
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    • 2003
  • Occupational and environmental exposure to manganese continue to represent a realistic public health problem in both developed and developing countries. Increased utility of MMT as a replacement for lead in gasoline creates a new source of environmental exposure to manganese. It is, therefore, imperative that further attention be directed at molecular neurotoxicology of manganese. A Need for a more complete understanding of manganese functions both in health and disease, and for a better defined role of manganese in iron metabolism is well substantiated. The in-depth studies in this area should provide novel information on the potential public health risk associated with manganese exposure. It will also explore novel mechanism(s) of manganese-induced neurotoxicity from the angle of Mn-Fe interaction at both systemic and cellular levels. More importantly, the result of these studies will offer clues to the etiology of IPD and its associated abnormal iron and energy metabolism. To achieve these goals, however, a number of outstanding questions remain to be resolved. First, one must understand what species of manganese in the biological matrices plays critical role in the induction of neurotoxicity, Mn(II) or Mn(III)? In our own studies with aconitase, Cpx-I, and Cpx-II, manganese was added to the buffers as the divalent salt, i.e., $MnCl_2$. While it is quite reasonable to suggest that the effect on aconitase and/or Cpx-I activites was associated with the divalent species of manganese, the experimental design does not preclude the possibility that a manganese species of higher oxidation state, such as Mn(III), is required for the induction of these effects. The ionic radius of Mn(III) is 65 ppm, which is similar to the ionic size to Fe(III) (65 ppm at the high spin state) in aconitase (Nieboer and Fletcher, 1996; Sneed et al., 1953). Thus it is plausible that the higher oxidation state of manganese optimally fits into the geometric space of aconitase, serving as the active species in this enzymatic reaction. In the current literature, most of the studies on manganese toxicity have used Mn(II) as $MnCl_2$ rather than Mn(III). The obvious advantage of Mn(II) is its good water solubility, which allows effortless preparation in either in vivo or in vitro investigation, whereas almost all of the Mn(III) salt products on the comparison between two valent manganese species nearly infeasible. Thus a more intimate collaboration with physiochemists to develop a better way to study Mn(III) species in biological matrices is pressingly needed. Second, In spite of the special affinity of manganese for mitochondria and its similar chemical properties to iron, there is a sound reason to postulate that manganese may act as an iron surrogate in certain iron-requiring enzymes. It is, therefore, imperative to design the physiochemical studies to determine whether manganese can indeed exchange with iron in proteins, and to understand how manganese interacts with tertiary structure of proteins. The studies on binding properties (such as affinity constant, dissociation parameter, etc.) of manganese and iron to key enzymes associated with iron and energy regulation would add additional information to our knowledge of Mn-Fe neurotoxicity. Third, manganese exposure, either in vivo or in vitro, promotes cellular overload of iron. It is still unclear, however, how exactly manganese interacts with cellular iron regulatory processes and what is the mechanism underlying this cellular iron overload. As discussed above, the binding of IRP-I to TfR mRNA leads to the expression of TfR, thereby increasing cellular iron uptake. The sequence encoding TfR mRNA, in particular IRE fragments, has been well-documented in literature. It is therefore possible to use molecular technique to elaborate whether manganese cytotoxicity influences the mRNA expression of iron regulatory proteins and how manganese exposure alters the binding activity of IPRs to TfR mRNA. Finally, the current manganese investigation has largely focused on the issues ranging from disposition/toxicity study to the characterization of clinical symptoms. Much less has been done regarding the risk assessment of environmenta/occupational exposure. One of the unsolved, pressing puzzles is the lack of reliable biomarker(s) for manganese-induced neurologic lesions in long-term, low-level exposure situation. Lack of such a diagnostic means renders it impossible to assess the human health risk and long-term social impact associated with potentially elevated manganese in environment. The biochemical interaction between manganese and iron, particularly the ensuing subtle changes of certain relevant proteins, provides the opportunity to identify and develop such a specific biomarker for manganese-induced neuronal damage. By learning the molecular mechanism of cytotoxicity, one will be able to find a better way for prediction and treatment of manganese-initiated neurodegenerative diseases.

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