• Science of Emotion and Sensibility
• /
• v.12 no.1
• /
• pp.77-86
• /
• 2009

A comfort evaluation of car seat clothing has been proposed for high comforts interior seat clothing. Car seat covers have received wide spread attention due to their man-machine interface working. And then, it will be necessary for measurements on delicate basic mechanical-properties, which closely relate with human touch feeling of its materials. In this research, we have utilized $KES-FB^{(R)}$(Kawabata Evaluation System) series, $^ST300{(R)}$ analogue softness tester and friction tester for measurement a physical properties. In order to consider both kansei and physical properties on interior seat covers, we firstly have established subjective words of judgement for the seat covers. Secondly, related them to the objective measurement of physical properties. Each kansei-language has clearly defined as 'Softness', 'Elasticity', 'Volume' and 'Stickiness' for the adjectives of leather car seat covers. These technical terms have correlated to physical properties in other words, h (mm), bending moment ($gf^*$cm/cm), To-Tm (mm) and ${\mu}$. At this time, fuzzy logic has utilized to predict the value of kansei language through physical values. On the basis of this result, finally it is possible to predict quality index of car seat covers using neural networks technique. In short, we develop a quality evaluation system of car seat clothing combining four physical quantities with kansei engineering.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.19 no.1
• /
• pp.95-107
• /
• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of the Korean Society for information Management
• /
• v.30 no.3
• /
• pp.273-296
• /
• 2013

The purpose of this study is to examine the data coverage and citation threshold for analyzing SPLC(Search Path Link Count) as a main path of a historiograph of a certain topic in order to provide 'core' papers of global research trends to a researcher affiliated with a local R&D institution. 5 datasets were constructed by retrieving and collecting 2,318 articles on RGB LED on Web of Science published from 1990-2013 and 20,109 articles which cited these original 2,318. The SPLC analysis was performed on each dataset by increasing the threshold of citation counts, and the changes and resilience of the 28 extraced networks were compared. The results of user feedback on 198 unique core papers from 28 SPLC networks received from LED researchers affiliated with a Korean government-sponsored research institution were also analyzed. As a result, it is found that the nodes in each SPLC network in each dataset were differentiated by the citation counts, while the changes in the structure of SPLC networks were slight after the networks' citation counts were set at 40. Additionally, the user feedback showed that personalized research interest generally matched to the global research trends identified by the SPLC analysis.

• Journal of Forest and Environmental Science
• /
• v.30 no.2
• /
• pp.189-200
• /
• 2014

The tropical wet evergreen, tropical semi evergreen and moist deciduous forest types are projected to be impacted by climate change. In the Western Ghats region, a biodiversity hotspot, evergreen forests including semi evergreen account for 30% of the forest area and according to climate change impact model projections, nearly a third of these forest types are likely to undergo vegetation type change. Similarly, tropical moist deciduous forests which account for about 28% of the forest area are likely to experience change in about 20% of the area. Thus climate change could adversely impact forest biodiversity and product flow to the forest dependent households and communities in Uttara Kannada district of the Western Ghats. This study analyses the distribution of non-timber forest product yielding tree species through a network of twelve 1-ha permanent plots established in the district. Further, the extent of dependence of communities on forests is ascertained through questionnaire surveys. On an average 21% and 28% of the tree species in evergreen and deciduous forest types, respectively are, non-timber forest product yielding tree species, indicating potential high levels of supply of products to communities. Community dependence on non-timber forest products is significant, and it contributes to Rs. 1199 and Rs. 3561/household in the evergreen and deciduous zones, respectively. Given that the bulk of the forest grids in Uttara Kannada district are projected to undergo change, bulk of the species which provide multiple forest products are projected to experience die back and even mortality. Incorporation of climate change projections and impacts in forest planning and management is necessary to enable forest ecosystems to enhance resilience.

• Ecology and Resilient Infrastructure
• /
• v.3 no.2
• /
• pp.117-125
• /
• 2016

Current cities encounter various types of water problems due to rapid urbanization and climate change. The increasing significance of urban water problems calls for the establishment of resilient alternatives to prevent and minimize social loss that results from these phenomena. As a background research for establishing resilient infrastructures for the mitigation of urban water problems, we evaluated the robustness of structural alternatives for urban flood as a representative case. Combining the robustness index (RI) and the cost index (CI), we suggested the robustness-cost index (RCI) as an indicator of the robustness of structural alternatives, and applied the index to assess the existing infrastructures and structural alternatives (i.e., sewer network expansion, additional storage tank construction, and green roof construction) at a site prone to floods located around Gangnam-station, Seoul, Korea. At a rainfall intensity frequency range of 2 to 20 years, the usage of a storage tank and a green roof showed relatively high RCI value, with a variation of an alternative showing greater RCI between the two depending on the size of design rainfall. For a rainfall intensity frequency of 30 years, installing a storage tank with some green roofing was the most resilient alternative based on the RCI value. We proposed strategies for establishing resilient infrastructures for the mitigation of urban floods by evaluating the robustness of existing infrastructures and selecting optimal structural alternatives with the consideration of scales of design disaster.

• Journal of KIISE:Computing Practices and Letters
• /
• v.8 no.6
• /
• pp.736-745
• /
• 2002

In network delivery of compressed video, packets may be lost if the channel is unreliable like Internet. Such losses tend to of cur in burst like continuous bit-stream error. In this paper, we propose an effective error-concealment approach to which an error resilient video encoding approach is applied against burst errors and which reduces a complexity of error concealment at the decoder using data hiding. To improve the performance of error concealment, a temporal and spatial error resilient video encoding approach at encoder is developed to be robust against burst errors. For spatial area of error concealment, block shuffling scheme is introduced to isolate erroneous blocks caused by packet losses. For temporal area of error concealment, we embed parity bits in content data for motion vectors between intra frames or continuous inter frames and recovery loss packet with it at decoder after transmission While error concealment is performed on error blocks of video data at decoder, it is computationally costly to interpolate error video block using neighboring information. So, in this paper, a set of feature are extracted at the encoder and embedded imperceptibly into the original media. If some part of the media data is damaged during transmission, the embedded features can be extracted and used for recovery of lost data with bi-direction interpolation. The use of data hiding leads to reduced complexity at the decoder. Experimental results suggest that our approach can achieve a reasonable quality for packet loss up to 30% over a wide range of video materials.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.3
• /
• pp.153-165
• /
• 2023

The purpose of this study is to propose effective scenarios for green areas in apartment complexes that can improve the connection between green spaces considering wind flow, thermal comfort, and mitigation of the urban heat island effect. The study site was an apartment complex in Godeok-dong, Gangdong-gu, Seoul, Korea. The site selection was based on comparing temperatures and discomfort index data collected from June to August 2020. Initially, the thermal and wind environment of the current site was analyzed. Based on the findings, three scenarios were proposed, taking into account both green patches and corridor elements: Scenario 1 (green patch), Scenario 2 (green corridor), and Scenario 3 (green patch & corridor). Subsequently, each scenario's wind speed, wind flow, and thermal comfort were analyzed using ENVI-met to compare their effectiveness in mitigating the urban heat island effect. The study results demonstrated that green patches contributed to increased wind speed and improved wind flow, leading to a reduction of 31..20% in the predicted mean vote (PMV) and 68.59% in the predicted percentage of dissatisfied (PET). On the other hand, green corridors facilitated the connection of wind paths and further increased wind speed compared to green patches. They proved to be more effective than green patches in mitigating the urban heat island, resulting in a reduction of 92.47% in PMV and 90.14% in PET. The combination of green patches and green corridors demonstrated the greatest increase in wind speed and strong connectivity within the apartment complex, resulting in a reduction of 95.75% in PMV and 95.35% in PET. However, patches in narrow areas were found to be more effective in improving thermal comfort than green corridors. Therefore, to effectively mitigate the urban heat island effect, enhancing green areas by incorporating green corridors in conjunction with green patches is recommended. This study can serve as fundamental data for planning green areas to mitigate future urban heat island effects in apartment complexes. Additionally, it can be considered a method to improve urban resilience in response to the challenges posed by the urban heat island effect.