Search results for: Ryosuke Yokochi
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4

Search results for: Ryosuke Yokochi

4 Enhancing the Oxidation Resistance of Copper at High Temperature by Surface Fluorination

Authors: Jae-Ho Kim, Ryosuke Yokochi, Miho Fuzihashi, Susumu Yonezawa

Abstract:

The use of silver nanoparticles in conductive inks and their printing by injecting technology has been known for years. However, the very high cost of silver limits wide industrial applications. Since copper is much cheaper but possesses a very high conductivity (only 6% less than that of Ag), Cu nanoparticles can be considered as a replacement for silver nanoparticles. However, a major problem in utilizing their copper nanoparticles is their inherent tendency to oxidize in ambient conditions. In conductive printing applications, the presence of copper oxide on the surface of nanoparticles has two negative consequences: it increases the required sintering temperature and reduces the electrical conductivity. Only a limited number of reports have attempted to address the oxidation problem, which in general is based on minimizing the exposure of the copper nanoparticles to oxygen by a protective layer composed of a second material at the surface of the particles. To form the protective layer on the surface, carbon-based materials, surfactants, metals, and so on. In this study, we tried to modify the oxide on Cu particles using fluorine gas. And the creation effects of oxyfluorides or fluorides on the oxidation resistance of Cu particles were investigated. Compared with untreated sample (a), the fluorinated samples can restrain the weight increase even at 200℃ from the TG-DTA results. It might be considered that the substantial oxyfluorides on the surface play a role in protecting metal oxidation.

Keywords: copper metal, electrical conductivity, oxidation resistance, surface fluorination

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3 Various Advanced Statistical Analyses of Index Values Extracted from Outdoor Agricultural Workers Motion Data

Authors: Shinji Kawakura, Ryosuke Shibasaki

Abstract:

We have been grouping and developing various kinds of practical, promising sensing applied systems concerning agricultural advancement and technical tradition (guidance). These include advanced devices to secure real-time data related to worker motion, and we analyze by methods of various advanced statistics and human dynamics (e.g. primary component analysis, Ward system based cluster analysis, and mapping). What is more, we have been considering worker daily health and safety issues. Targeted fields are mainly common farms, meadows, and gardens. After then, we observed and discussed time-line style, changing data. And, we made some suggestions. The entire plan makes it possible to improve both the aforementioned applied systems and farms.

Keywords: advanced statistical analysis, wearable sensing system, tradition of skill, supporting for workers, detecting crisis

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2 A Quantification Method of Attractiveness of Stations and an Estimation Method of Number of Passengers Taking into Consideration the Attractiveness of the Station

Authors: Naoya Ozaki, Takuya Watanabe, Ryosuke Matsumoto, Noriko Fukasawa

Abstract:

In the metropolitan areas in Japan, in many stations, shopping areas are set up, and escalators and elevators are installed to make the stations be barrier-free. Further, many areas around the stations are being redeveloped. Railway business operators want to know how much effect these circumstances have on attractiveness of the station or number of passengers using the station. So, we performed a questionnaire survey of the station users in the metropolitan areas for finding factors to affect the attractiveness of stations. Then, based on the analysis of the survey, we developed a method to quantitatively evaluate attractiveness of the stations. We also developed an estimation method for number of passengers based on combination of attractiveness of the station quantitatively evaluated and the residential and labor population around the station. Then, we derived precise linear regression models estimating the attractiveness of the station and number of passengers of the station.

Keywords: attractiveness of the station, estimation method, number of passengers of the station, redevelopment around the station, renovation of the station

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1 Deep-Learning Coupled with Pragmatic Categorization Method to Classify the Urban Environment of the Developing World

Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

Abstract:

Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

Procedia PDF Downloads 141