We proposed elemental technique for reducing power supply voltage and circuit scale for CMOS exponentiation conversion IC utilizing weak inversion operation to mount it on microcomputer chip. This IC implements to change the power exponent to any value by amplifying with adjusting gain after converting signal logarithmically, and finally converting it exponentially. We showed that the circuit scale can be reduced to several tenth times in order to generate a weak inversion current by cascading multiple stages of current-dividing current mirror circuits. Due to this current mirror circuit, circuit scale was reduced to 34.4%. We also proposed an exponential converting circuit that can halve the input voltage by using one pair of weak inversion MOSFETs. However, the proposed control method of the exponential value by utilizing the substrate effect was found to have about 8% of narrow control range. Although measured result shows that this IC does exponentiation conversion, power exponent value was 56% smaller than simulation result.

MRI (Magnetic resonance imaging) generates a strong magnetic field. This strong magnetic field may cause adverse effects on other medical equipment and cardiac pacemakers in the hospital. To prevent them, ICNIRP regulates the leakage magnetic field outside the room to within 0.5mT, and the top, bottom and side surfaces of the MRI room must be enclosed with ferromagnetic walls. However, its shape gives the subject a feeling blocked-up. Recently, it has been recommended to develop an open-type magnetic shield room that has an opening on the side and can take in light and scenery into the MSR. However, the open magnetic shield room has a problem that the volume of the ferromagnetic material increases due to the shield shape of the opening.

In this study, in order to propose the open type MSR with a ferromagnetic volume equal to or less than that of the closed type model, we analyzed shield position, number of divisions in the X - Z plane and the width of both ends.

Early detection of fracture risk is important to prevent fractures, which account for 12% of the causes of needing care. The current diagnosis of fracture risk is based on the FRAX index defined by the WHO. However, since this is a numerical assessment only for experts, it is difficult for patients to recognize their own fracture risk, and this does not lead to improved awareness of fracture prevention. Therefore, the authors obtained epidemiological data on 397 fracture-related items and conducted a follow-up survey on the fact of falling one year later in 136 elderly women in Hino-cho, Hino-gun, Tottori Prefecture. In this paper, we used the fact of falling, which is a risk factor for fracture, as a classification criterion, and extracted epidemiological data with high relevance to the fact of falling by principal component analysis of the epidemiological data, and expressed them in the form of a two-dimensional map. In addition, the reliability of the two-dimensional map was demonstrated by referring to the correlation between epidemiological data and the fact of falling.

Driving under the influence of alcohol is prohibited by the Road Traffic Law, and penalties are imposed when the alcohol concentration in the breath is 0.15 mg/l or higher. However, even if the concentration value is below the standard value, traffic accidents occur frequently and may cause a decline in brain function. In the present study, we used event-related potentials to evaluate the decline in brain function caused by low-load drinking. Beer with 5% alcohol content was used as the drinking load, and a go/no go task was applied to evoke event-related potentials. As a result, the alcohol concentration in the breath increased significantly (p < 0.05) 10, 30, and 50 minutes after drinking compared to before drinking. In addition, the event-related potentials at these times changed significantly (p < 0.05). On the other hand, there was no significant difference in the breath alcohol concentration 70 minutes after drinking, whereas there was a significant change in the event-related potential. The present study suggests that low-load alcohol consumption causes a decline in brain function.

AI, Artificial Intelligence, is being used in many products and services while still being developed. Each company needs to give customers more chances to buy its products by constantly evaluating their AI needs. To accomplish this, sales representatives in companies need to determine significant AI needs after they have learned AI technologies and AI case studies. Therefore, they have to improve comprehension of AI. We're studying AI education course and the understanding for people who haven't majored in AI. In this paper, we propose the course using UX (User Experience) design after sales representatives and other business persons who are not familiar with IT have learned AI. Also, we describe the practical evaluation and result of their understanding.

We are developing an information platform to enhance power resilience in the event of a disaster. To reflect it in the platform design, we investigated the use cases of power outage-related information in the event of a disaster. This paper reports the results of a survey of local governments and medical institutions.

We aimed to evaluate the aging and deterioration of Japanese sake by its electrical properties. The time dependence of Japanese sake after opening the package was electrically measured. As a results, Japanese sake preserved in an environment that does not easily deteriorate tended to have little change in electrical properties.

In the manufacturing industry, operations such as polishing and welding, wherein sensation and experience account for much of the work, rely on the skills of skilled technicians. However, automation has been sought in such processes. We proposed a method for the evaluation of the polishing conditions of an Oscar-type polishing system, which is used for high-mix low-volume production of lenses with various curvatures, by determining the surface pressure distribution of the lens through the preliminary examination of the lens polishing conditions. In particular, (1) a judgment index focusing on the number of Newtonian rings, which decreases as polishing progresses, is adopted. (2) Extraction of important types of polishing conditions is conducted through standard analysis. (3) By using the numerical values of the polishing conditions, the number of Newton rings is predicted with high accuracy. Through the evaluation based on the above-mentioned factors, the prediction based on the newly introduced surface-pressure-distribution-based method contributed to the improvement of accuracy, and the predicted values were sufficient to cover the ability of the operator’s evaluation.

In this paper, the effect of grouping vehicles on traffic at a signalized intersection is studied. A group of vehicles is formed by sending commands to multiple vehicles to follow the vehicle in front. The application of coordinated traffic signal control to the formed vehicle groups is expected to improve traffic efficiency. The effectiveness of the proposed method is verified through simulations by examining how to form groups of vehicles in the traffic flow simulator and how to apply traffic signal control. As a result of the simulation, it is confirmed that the application of vehicle grouping reduces the waiting time at intersections by 89% and improves the average fuel economy by 8.9%.

The acoustic distance measurement (ADM) method based on phase interference between transmitted and reflected signal has been proposed as a method to accurately measure the distance. However, since its performance is deteriorated by ambient noise. In the previous study, complex ICA(Independent Component Analysis) was applied using adjacent 3ch microphone to reduce the noise. Although ICA achieves high performance for the noise reduction, it is not too practical method because of its high computational cost. This paper describes a practical method to reduce ambient noise in the ADM using adjacent 3ch observations. Concretely, the noise component is reduced by generating two difference signals among three signals of 3ch observations in the noisy environment. Then, the ADM using the crossspectrum method is applied to them. The difference signals can be obtained with a small computational cost. The validity of the proposed method is confirmed in the actual room under the condition where the SNR ratio is -10dB. As a result, the SNR is improved to 10.5 dB, demonstrating the validity of the proposed method.

For tuning controllers, various methods had been proposed. In this paper, input-type virtual internal model tuning (VIMT) for a smith compensator is proposed. Proposed methods can update smith compensator and a nominal model. The validity of the proposed method is verified via numerical simulations. Compare to the conventional method, the proposed method can realize good controllers and models in the case where the cut-off frequency of the initial sensitivity/ complementary sensitivity function is higher than that of the desired transfer function.

In recent years, making computers understand the emotions of users is necessary because emotions are an important factor in human communication. Among many methods of recognizing emotions, EEG is widely used because it has high temporal resolution and it is impossible to disguise intentionally. However, it is necessary to acquire new user data and construct the personal Emotion recognition model since EEG varies widely among individuals. The conventional methods lack practicality because it builds a different model for every new user data. Our method builds a model in a single training using new user data. To reduce the number of new user data for training and to relieve the burden of EEG measurement, we adapt existing user data. We absorb the individual differences in EEG between a new user and existing users by domain adaptation using a small number of new user data and construct a model by deep neural networks. From the experiments, we confirmed that the proposed method performs as well as the conventional methods, even though it is built with single training using new user data.

A generative adversarial network (GAN) is one of the popular deep generative models. It generates new data similar to the data of a dataset but is not intended to generate different data from them. In this paper, we propose a GAN that generates such different types of data, which a user desires to obtain. In the proposed method, some data of the dataset are iteratively exchanged for ones generated by the generator if the generated data are more helpful in generating the user's desirable ones. The performance of the proposed method is evaluated by comparing it with some other GANs.

There is a lot of previous research on maintenance problems. In some previous research, an expected profit is maximized considering sales amount of product. In the previous research, product selection is not considered. The expected profit depends on product selection when sales amount and equipment state transition probabilities for operation (probabilities of equipment deterioration) of each product are different. In this research, maintenance problem with product selection is studied. Sales amount and equipment state transition probabilities for operation of each product are different. The maintenance problem with product selection is modeled by Markov decision processes. A new maintenance method which maximizes the expected profit based on statistical decision theory is proposed. In the proposed method, dynamic programming method is used. The effectiveness of the proposed method is shown by some computational examples. The expected profit of the proposed method is greater than that of a comparison target. In this research, the expected profit is maximized under the condition that all probabilities are known. But the probabilities are unknown in real cases. An expansion of this research with unknown probabilities is one of further works.

Discussion how to select a problem fixing action plan automatically in open systems. First, we define the Simple Problem that has only one root failure point. Our research identified that there are three classes of Simple Problem in open systems. We also introduce the function chain, because we use it to classify a problems. Secondary, we propose the method how to link a class of Simple Problem to dedicated action plan. This approach provides a way how to avoid inappropriate action plan determination and miss detection of DOA. The inappropriate action plan loses a long time for problem fixing. Finally, we show our method efficiency by the analysis of real open system case records.