• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.133-139
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• 2013

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle's position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

• Journal of Fashion Business
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• v.14 no.5
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• pp.112-127
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• 2010

Primitivism is a concept that expresses and organizes natural feelings of human beings which is hard to be identified by a rigid definition. It means "staying in the beginning or the initial state, not evolving or developing, and not affected by human beings from the intact natural state". Based on this meaning, the artistic style features inherent natural beauties, as well as plain and inornate design. These features have been reflected in a variety of art pieces. The aesthetic features shown in the primitivism art pieces can be categorized into four different aspects: naturalness, folksiness, sentimentality, and humorousness. These features, influencing modern fashion, have been reinvented by a number of fashion designers. They also adopted ideas from the fancy clothes and ornaments created in carefree life style of the regions retaining their primitive cultures, such as Africa, Oceania, and Pacific coasts, and applied those ideas to various silhouette, colors, patterns, and textiles. Particularly as for textile expressions, they tried printing techniques using the patterns motivated from primitive folk symbols or the nature, applied objet of primitive materials and elaborated ornaments that represent folk and primitive feelings, and employed the primitive techniques such as knotting, crude cutting, or natural draping, to reinvent them as textile expressions in modern fashion.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.814-819
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• 2005

There is growing consciousness of the environmental performance of buildings in Hong Kong. The Buildings Department, the Lands Department and the Planning Department of the Hong Kong Government issued the first of a series of joint practice notes [1] to promote the construction of green and innovative buildings. Green features are architectural features used to mitigate migration of noise and various air-borne pollutants and to moderate the transport of heat, air and transmission of daylight from outside to indoor environment in an advantageous way. This joint practice note sets out the incentives to encourage the industry in Hong Kong to incorporate the use of green features in building development. The use of green features in building design not only improves the environmental quality, but also reduces the consumption of non-renewable energy used in active control of indoor environment. Larger window openings in the walls of a building may provide better natural ventilation. However, it also increases the penetration of direct solar radiation into indoor environment. The use of wing wall, one of the green features, is an alternative to create effective natural ventilation. This paper therefore presents a preliminary numerical study of its ventilation performance using Computational Fluid Dynamics (CFD). The numerical results will be compared with the results of the wind tunnel experiments of Givoni.

• Journal of The Korean Association For Science Education
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• v.28 no.3
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• pp.197-210
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• 2008

The purpose of this study is to explore the cases and features of the abductive inference used by young children when trying to explain natural phenomena in everyday life. From observing a 5-year-old's daily activities with his family, and analyzing the data according to the criterion extracted from the form of abductive inference described by C. S. Peirce, a few cases where the child used abductive inferences to explain natural phenomena were found. The abductive inferences in the cases were conducted: (a) based on figural resemblance and behavioral resemblance (b) under the influence by individual belief and communal belief, then (c) resulted in new categorization accompanied by over generalization. Such features of the abductive inference showed the 'double faces'; sometimes encourages and sometimes discourages children's generating better scientific hypotheses and explanations. These results suggest that even young children use abductive inference to explain doubtful natural phenomena in everyday life, although we need to consider carefully with the double aspects of the features of abductive inference for the practical applications to the fields of science education. Finally, several suggestions and following studies for science education are proposed.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.553-556
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• 2003

In the field of content-based image retrieval, various mathematical low-level features have been proposed to describe the perceptual content of images. Since most of the features are assumed to be independent of each other, one feature is extracted from images without any consideration of the other features. Recently proposed CCE and SCFT taking advantage of the correlation between color and texture have shown relatively good performance. In this paper, the performance of CCE, SCFT, and the traditional regular weighted comparison method are evaluated. Simulation results with natural images have shown that CCE outperforms the other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1583-1600
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• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.1
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• pp.103-111
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• 2010

The study was conducted to examine the possible psychological difference in visual preference for natural landscape depending on the residence characteristic of the visitors, with the aim to use as the reference when creating space for natural landscape. Visitors were classified into three groups, namely, urban, suburban, and rural residents, and their preferred type of landscape was surveyed. The result showed that regardless of their residence the highest preference was to the place with superior natural landscape features, and among such features the common preference was toward the landscapes with horizons and those with animals. Urban and suburban residents showed similar preference pattern, and there was artistically significant difference with rural residents. In this respect, the difference in preference for natural landscapes could be induced between urban and rural residents. Rural residents had similar preference with urban residents in that they both preferred the place with superior natural landscape features, but differed in that the former had significantly higher preference toward the landscapes that have mixed artificial elements. From the above research, the following conclusions can be inferred:for regions that need to create natural landscapes, differentiated visual landscape plans and strategies need to be established taking into account, inter alia, the natural richness of the neighborhood natural environment; for regions that need to conserve and manage natural environment and landscapes, the measures to adjust the natural landscape management plans need to be established taking into account the neighborhood environment of the protected areas and the number of visitors to the region.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1718-1723
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• 2014

We proposes the method to detect the Hangul and English character region from natural image using structural feature of Hangul and English Characters. First, we extract edge features from natural image, Next, if features are not corresponding to the heuristic rule of character features, extracted features filtered out and select candidates of character region. Next, candidates of Hangul character region are merged into one Hangul character using Hangul character merging algorithm. Finally, we detect the final character region by Hangul character class decision algorithm. English character region detected by edge features of English characters. Experimental result, proposed method could detect a character region effectively in images that contains a complex background and various environments. As a result of the performance evaluation, A proposed method showed advanced results about detection of Hangul and English characters region from natural image.

• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.197-209
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• 2011

This paper presents a localization system using ceiling images in a large indoor environment. For a system with low cost and complexity, we propose a single camera based system that utilizes ceiling images acquired from a camera installed to point upwards. For reliable operation, we propose a method using hybrid features which include natural landmarks in a natural scene and artificial landmarks observable in an infrared ray domain. Compared with previous works utilizing only infrared based features, our method reduces the required number of artificial features as we exploit both natural and artificial features. In addition, compared with previous works using only natural scene, our method has an advantage in the convergence speed and robustness as an observation of an artificial feature provides a crucial clue for robot pose estimation. In an experiment with challenging situations in a real environment, our method was performed impressively in terms of the robustness and accuracy. To our knowledge, our method is the first ceiling vision based localization method using features from both visible and infrared rays domains. Our system can be easily utilized with a variety of service robot applications in a large indoor environment.