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Analysis of Cs Removal Process from Phytoliths by FIB-TOF-SIMS
Get PDFWe have been conducting research on the development and analysis of FIB-TOF-SIMS (Focused Ion Beam Time-of-Flight Secondary Ion Mass Spectrometry) instrumentation. As a part of this research, we are also working on the content of research for decontamination activities in the Fukushima Daiichi Nuclear Power Plant accident. In our previous study, we confirmed that Cs is adsorbed on phytolith, which is a silicate material contained in plants. In this study, we analyzed the desorption process of Cs from plant opal using FIB-TOF-SIMS from the viewpoint of heat treatment
Smooth sectioning of biological samples by FIB-TOF-SIMS
Get PDFSpheroids, which are three-dimensionally cultured cells that resemble actual living organisms, have been attracting attention. FIB-TOF-SIMS (Focused Ion Beam Time-of-Flight Mass Spectrometry) is capable of simultaneous mass imaging of multiple elements without the need for labeling. FIB-TOF-SIMS is expected to process the spheroid and image the cross-sectional components. However, FIB processing of spheroids larger than 100μm often results in uneven cross sections due to the so-called curtain effect. The unevenness of the cross-section affects the sputtering and hinders component imaging. In this experiment, we considered the processing of spheroids by FIB from multiple directions to suppress the curtain effect. The curtain effect was evaluated by comparing the processing from one direction and from multiple directions
Efficient Convolutional Neural Networks for Brain Machine Interface Systems : A transfer learning approach
Get PDFThe goal of Brain Machine Interface (BMI) systems is to enable humans to interact with computers or machines by using their brain activity. BMI systems capture the user’s brain activity and translate it to a message or a command for certain interactive applications. Systems that make use of BMIs paradigms permit the disabled and elderly people to control wheelchairs, home appliances and robots. Another application is to write sentences and move the cursor on the screen by using brain signals, playing video games, creating arts, etc. Brain Computer Interface (BCI) systems are also investigated on stroke rehabilitation and real time health monitoring.Motor imagery and motor execution are two of the methods that are used to map the EEG signals into external robotic or computer applications. Motor imagery is a dynamic state during which the subjects imagine the performing of an action. In motor execution applications, the subject performs actual movements (for example, moving arms and legs), or other daily life activities. The motor cortex is the part of the brain where the brain signals for limb motions originates. Several studies have compared motor imagery with motor execution. In both paradigms, the brain signals have patterns which can be used for classification.In the recent years, the research literature on BMI systems shifted to implementing Deep Learning (DL) models to map the brain signals into the desired command. In addition, DL has shown good performance in robotics applications. Eliminating one or more of intermediate processing steps, such as preprocessing, feature extraction and classification is one of the advantages of applying DL in BMI/BCI systems. There are two ways to implement CNNs on BMI/BCI systems. In the first method, the raw EEG (electroencephalography) data are directly fed into the CNN. The second implementation is to use the CNN only as a classifier, and employ other algorithms for the feature extraction, such as Short-time Fourier Transform (STFT), or Common Spatial Patterns (CSP) and more advanced versions of it, like Filter Bank Common Spatial Pattern (FBCSP).The deep neural network can do these two functions simultaneously: 1) feature extraction and 2) classification. However, transfer learning has not been yet fully utilized to improve the performance of the deep learning architectures for the BMI systems. Therefore, in this thesis the main motivation is to improve the performance of CNNs by transfer learning. Especially, the impact of transfer learning according to window size and hop size is deeply investigated. Window size is the number of data points taken from the brain signal for classification, while the hop size is the number of data points this window jumps each time. The reason why these parameters are so important is because window size is strongly related to the time it takes to map the user’s brain signals to the robot motion. Therefore, the window size impacts the latency between the command and the robot response. Hop size also impacts the latency due to classification time correlation with the number of augmented samples.We implemented the proposed algorithm in motor and imaginary task using the brain signals. In addition to implementing transfer learning for similar tasks using the same subject, we implemented transfer learning among different subjects. The experimental results show that transfer learning can successfully be utilized to increase the performance of deep learning architectures for brain signal classification applications. A larger window size corresponds to a larger accuracy, but a shorter window size can be utilized by increasing its accuracy through transfer learning. Accuracy can also be increased through a small hop size.The trained CNNs using transfer learning are also implemented to map in real time the brain signals to the robot motion. In the first implementation, the humanoid robot with 18 degrees of freedom developed in our laboratory is controlled using brain signals. We also implemented the imaginary motor task to control the robot action. As stated above there is a tradeoff between window size and robot command accuracy. A small window size leads to faster response of the robotic arm, but the accuracy is not optimal. As the window size gets bigger, the robotic arm responses with higher accuracy, but the motion takes longer to start.This thesis shows that transfer learning can successfully utilized to increase the performance of deep learning architectures on brain signal classification. The results show a satisfying improvement in the CNNs performance.博士（工学）法政大学 (Hosei University
Consideration of the International Standard (ISO) 22458, "Consumer Vulnerability : Requirements and Guidelines for the Design and Delivery of Inclusive Service"
Get PDFWe consumers all entail the possibility of having consumer vulnerabilities or falling into vulnerable situations during our lifetime. However, even if we are potentially “vulnerable consumers,” the manifestation of such vulnerabilities can be suppressed depending on how business operators conduct solicitation, how customer service are provided, and how products and services are designed. It was for this purpose that the ISO22458 was published in April 2022.Therefore, in this article, by changing the conventional way of thinking and attempting to take proactive actions on the part of business operators and the market, according to the ISO22458, we will actively control and eventually eliminate consumer vulnerability, and, as a result, include everyone within the market. ISO22458 aims to build a trading environment that is “fair for all consumers” by establishing a social and legal system that includes all consumers without allowing exploitation. In doing so, we will consider concepts such as “supported decision-making” and “social disability” as prescribed by Article 12 of the United Nations Convention on the Rights of Persons with Disabilities, obligations of “reasonable accommodation,” and “reduce inequality within and among countries” in the SDG Goal 10. In this paper the research will be conducted in a way that takes into consideration the integration of consumer law and welfare law from the perspective of the rights of self-decision.In conclusion, we will see that ISO 224258, as an actor that can control "consumer vulnerability," has the potential to support, supplement, or prepare the ground for future amendments to Japanese consumer law
<Lecture> The Question of the Living Standards of Coal Miners during Japan’s Period of Rapid Economic Growth
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A Study on Aging in Place through the Japanese Long-term-care Insurance System
Get PDFThis study is about aging in place under the revision of the Long-term Care Insurance Law and the data of long-term care insurance. The Long-term Care Insurance Law is focused on aging in place as the preferred option. Many revisions have been made to the law since its enactment. In the 2005 revision, the concept of community-based comprehensive care was introduced. Community-based comprehensive care is defined in various ways according to the actual conditions of the community, but since the establishment of the community based comprehensive care system, the theme has been to “continue to live in a familiar community.”In order to objectively observe the degree to which aging in place has been realized, the ratio of persons receiving long-term care and living at home at level 4 and level 5 was considered by prefecture. As a result, looking at the data, Osaka Prefecture had the largest number of people living at home with care. By way of contrast, in Kochi prefecture, the percentage of people who receive long-term care services at home was low. Further analysis of Osaka Prefecture revealed that the amount of service supplied at home and in the hospital was the lowest in Japan