13197 research outputs found
Sort by
LocatAR: An AR Object Search Assistance System for a Shared Space
Get PDF奈良先端科学技術大学院大学修士(工学)master thesi
3D Body Tracking for Gait Tracking with Foot Mounted IMU and Target-Tracking Camera
Get PDF奈良先端科学技術大学院大学修士(工学)master thesi
Challenge in the Interaction between Pedestrians and Autonomous Vehicles
No full textvideo/mp4Compared to a manual driving vehicle (MV), an automated driving vehicle lacks a way to communicate with the pedestrian through the driver when it interacts with the pedestrian because the driver usually does not participate in driving tasks. Thus, an external human machine interface (eHMI) can be viewed as a novel explicit communication method for providing driving intentions of an automated driving vehicle (AV) to pedestrians when they need to negotiate in an interaction, e.g., an encountering scene. However, the eHMI may not guarantee that the pedestrians will fully recognize the intention of the AV. In this paper, we propose that the instruction of the eHMI's rationale can help pedestrians correctly understand the driving intentions and predict the behavior of the AV, and thus their subjective feelings (i.e., dangerous feeling, trust in the AV, and feeling of relief) and decision-making are also improved. The results of an interaction experiment in a road-crossing scene indicate that the participants were more difficult to be aware of the situation when they encountered an AV w/o eHMI compared to when they encountered an MV; further, the participants' subjective feelings and hesitation in decision-making also deteriorated significantly. When the eHMI was used in the AV, the situational awareness, subjective feelings and decision-making of the participants regarding the AV w/ eHMI were improved. After the instruction, it was easier for the participants to understand the driving intention and predict driving behavior of the AV w/ eHMI. Further, the subjective feelings and the hesitation related to decision-making were improved and reached the same standards as that for the MV.講演日:2023年6月15日 3限講演場所: 情報科学棟 エーアイ大講義室(L1)vide
デジタル ギジュツ ニ ヨル ブッシツ タンサク
No full textvideo/mp4近年、デジタル技術の急速な発達によって様々な分野において人工知能の有用性が示され、物質創成や材料探索においても盛んに活用されています。この講座では、これまでの科学の歴史の概観から始め、現代の物質探索におけるシミュレーションや人工知能などデジタル技術の活用事例の説明と実験自動化について紹介します。講座を通して、人工知能による新物質設計、そしてロボットによる実証実験という未来を感じて貰えればと思います。講演場所: ミレニアムホール講演日: 2023年10月14日講演者所属: 本学物質創成科学領域vide
FD SD ケンシュウカイ ゲンザイ カラ ショウライ モトメラレル ダイガクゾウ
No full textvideo/mp4講演日: 2023年12月8日講演場所: Rethink バイオサイエンス大講義室vide
Prominin-1 deletion results in spermatogenic impairment, sperm morphological defects, and infertility in mice
No full textPurpose: Spermatogenesis is a complex process orchestrated by several essential genes. Prominin-1 (Prom1/PROM1) is a gene that is expressed in the testis but with a poorly understood role in spermatogenesis.
Methods: We used Prom1 knockout (Prom1 KO) mice to assess the role of Prom1 in spermatogenesis. To this end, we performed immunohistochemistry, immunofluorescence, western blotting, β-galactosidase staining, and apoptosis assay. Additionally, we analyzed the morphology of sperm and assessed litter sizes.
Results: We observed that PROM1 is localized to the dividing spermatocytes in seminiferous epithelial cells, sperm, and columnar epithelium in the epididymis. In the Prom1 KO testis, an aberrant increase in apoptotic cells and a decrease in proliferating seminiferous epithelial cells were observed. Cellular FLICE-like inhibitory protein (c-FLIP) and extracellular signal-regulated kinase 1/2 (ERK1/2) expression were also significantly decreased in Prom1 KO testis. In addition, a significantly increased number of epididymal spermatozoa with abnormal morphology and less motility was found in Prom1 KO mice.
Conclusions: PROM1 maintains spermatogenic cell proliferation and survival via c-FLIP expression in the testis. It is also involved in sperm motility and fertilization potential. The mechanism underlying the effect of Prom1 on sperm morphology and motility remains to be identified.journal articl
Bus Ridership Prediction with Time Section, Weather, and Ridership Trend Aware Multiple LSTM
Get PDFPublic transportation has been essential in people's lives in recent years. Bus ridership is a factor in people's choice to board the bus. Therefore, from the perspective of improving service quality, it is important to inform passengers who have not boarded the bus yet about future bus ridership. However, there is a concern that providing inaccurate information may cause a negative experience. Against this backdrop, there is a need to provide bus passengers who have not boarded yet with highly accurate predictions. Many researchers are working on studies on this. However, two issues summarize related studies. The first is that the correlation of bus ridership between consecutive bus stops should be considered for the prediction. The second is that the prediction has yet to be made using all of the features shown to be useful in each related study. This study proposes a prediction method that addresses both of these issues. We solve the first issue by designing an LSTM-based architecture for each bus stop and a single model for the entire bus stop. We solve the second issue by inputting all useful data, the past bus ridership, day of the week, time section, weather, and precipitation, as features. Bus ridership at each bus stop collected from buses operated by Minato Kanko Bus Inc, in Kobe city, Hyogo, Japan, from October 1, 2021, to September 30, 2022, were used to compare accuracy. The proposed method improved RMSE by 23% on average and up to 27% compared to existing methods.conference pape