Electronics and Communications in Japan最新文献

A polyclonal antibody against the analog of piperine, which is a typical pungent taste substance, was produced, and its binding ability to piperine was evaluated using a surface plasmon resonance sensor. The antibody was labeled with glucose oxidase (GOx), and the hydrogen ions produced by the enzymatic reaction were measured at a lipid polymer membrane electrode for sourness in a taste sensor. The results showed that piperine could be detected by that polyclonal antibody; the response of the sourness sensor increased in dependence on the GOx-labeled antibody concentration when the GOx of GOx-labeled antibody was reacted with glucose. Thus, it was shown that uncharged pungent compounds can be converted into hydrogen ions. Pungent taste can be measured by the taste sensor using GOx-labeled antibodies, and GOx-labeled antibodies can be used as a pretreatment.

This paper presents a method for forecasting the change direction of nonstationary time series using the improved leading indicator. The leading indicator is a method developed by Ehlers that translates a time series mathematically in the future direction with respect to the time axis and calculates the leading value of the time series. However, this method has the problem that the leading value can be calculated only in the low frequency region with a normalized frequency of 0.06 or more (normalized period of 17 or more) at the maximum. In order to solve this problem, by the gentle slope the amplitude characteristics in the low frequency region of the leading indicator, it is possible to calculate the leading value in the frequency domain with a normalized frequency of 0.25 or more (normalized period of four or more) at the maximum. By applying the preceding value to the instantaneous periodic time series by the improved sine wave indicator developed by Ehlers, it is possible to forecast the change direction of the non-stationary time series in the short term.

Aiming at the control of a powered prosthetic hand, this paper compares methods for the classification of intended hand motions using muscle bulging patterns caused by muscle contraction. Two sheets of Polyvinylidene Difluoride (PVDF) film were used as sensors to detect the muscle bulging on the forearm caused by intended hand motions. A neural network had been successfully trained for the classification of six types of hand motions using the muscle bulging pattern detected by the two PVDF sensors. In this paper, we further studied the motion classification methods of back propagation neural network (BPNN), k-nearest neighbor algorithm (k-NN), and support vector machine (SVM) to compare their classification performance. We found that all three methods had a similar classification rate of about 95% for six types of hand motions. Moreover, a regressive analysis comparison of the time for each classification method to converge to 95% of the total classification rate showed that SVM converged significantly earlier than BPNN and k-NN. The time it takes for SVM to converge the classification rate to 95% is less than 0.1 s, suggesting that real-time motion classification is possible by using SVM. In a similar manner, we found that SVM requires the least training data of the three methods at only nine trials for a type of motion. Furthermore, SVM had the highest classification rate at about 90% in practical experimental conditions. In conclusion, SVM was found to be the most practical of the three methods.

A social issue that leads to the safety and security of people is the network that monitors the safety of people living alone in remote areas. Various research and developments of indoor human activity recognition using IoT sensor nodes have been conducted in recent years. In this research, we focus on turning on and off indoor lighting and propose an activity recognition system at home using an illuminance sensor. An IoT sensor node with a built-in illuminance sensor is attached to the wall near the indoor lighting equipment. The living activities of people in the room are estimated from changes in the illuminance. We assume a significant change in the illuminance of the indoor lighting is observed at least once a day at the sampling interval of the IoT sensor node. In that case, it is possible to estimate the operation of the indoor lighting due to daily activities and to confirm the safety of people living alone in remote areas. The activity recognition system was evaluated for a total of 380 days, excluding the missing period. The estimation of indoor lighting manipulation by daily activities was accurate for 376 days. Precision, Recall, and F-measure score values, which are evaluation indices for activity estimation, were 94.9%, 98.2%, and 96.5%, respectively.

In recent years, resilience-enhanced building buildings have been attracting increasing attention, in which photovoltaic (PV) power generation and storage batteries are installed in office buildings to continue operations even during a grid outage. PV power generation and storage batteries can be introduced to office buildings to continue commercial operations in the case of grid outages. When introducing PV power generation and storage batteries into a building, the total cost, which is the sum of the equipment installation cost and the damage cost in the event of a disaster, is crucial. When installing PV power generation and storage batteries in a building and calculating the total cost of using these facilities in the event of a disaster, it is necessary to consider the uncertainty of power demand and the amount of power generated by PV power generation. Monte Carlo Simulation is a method to consider uncertainty. However, it has a problem that the combination of conditions becomes enormous, and the calculation load is high when the total cost is calculated. Therefore, in this study, we propose convolution as a method to reduce the computational load generated when calculating the probability distribution of the total cost generated by strengthening the resilience of the building.

Peripheral sensitization, decrease in pain threshold in sensory neurons, can cause chronic pain. Little is known about how peripheral sensitization led to chronic pain. Here, we aimed to develop a method for evaluating peripheral sensitization in cultured sensory neurons with electrical recording. Sensory neurons from rat dorsal root ganglion were cultured on high-density microelectrode arrays (HD-MEA), and their activity was evoked by capsaicin stimulation with or without substance P. Fluorescent imaging-based electrode selection effectively selected five times as many capsaicin-sensitive sensory neurons as the existing method. Peripheral sensitization by substance P was detected from 31.9% of selected sensory neurons, and majority of these neurons co-expressed capsaicin and substance P receptors. These results indicate that our method is suitable for evaluating peripheral sensitization by substance P in cultured sensory neurons.

In agricultural field, compact sensors that can measure both water content and electric conductivity are needed. Focusing on the fact that water content and conductivity depend on soil impedance, our group has been developing a new compact and noise-resistant driving circuit that measured soil impedance by detecting transient characteristics of the voltage obtained by injecting current through the soil and converting it to frequency information. In order to realize agricultural field measurements with sensors using this new driving circuit, it had been necessary to miniaturize the part that detected the output from the driving circuit, but it hadn't been achieved in our previous work. In this research, a microcomputer-based frequency counter was developed to downsize the system for on-site measurement. In general, there are two methods of frequency measurement: direct counting and reciprocal. The direct-counting method is suited for high-frequency measurements, while the reciprocal method is suited for low-frequency measurements. The developed frequency counter could measure with an error of 0.005% over the assumed frequency range by switching between those two measurement methods according to the measured frequency. Using this driving circuit and frequency counter, we measured the model soil and succeeded in capturing changes in water content as changes in impedance.

We have developed tactile sensor systems for next-generation robots. To install a large number of tactile sensors, we have proposed micro electro mechanical systems-large-scale integration (MEMS-LSI) integrated tactile sensors. The integrated device has the following features: capacitive type three-axis force sensing, embedded diode-based temperature sensing, signal processing for sensing data digitalization, and event-driven response for efficient serial bus communication. This paper demonstrates a sensor array system as up-to 40 integrated tactile sensors which are connected on one bus line. After acquiring the sensing data from the sensor array system, we applied a machine learning technique for target object judgment. The objective of the judgment is to classify the targets into normal object and abnormal object. With the sensor array system, data preprocessing and tuned Recurrent Neural network (RNN)/Long Short-Term Memory (LSTM) neural network models, we achieved high-accuracy, high-precision, and high-recall scores for the experiment of the judgment.

This paper addresses a pressure sensor for body pressure measurement. Many pressure sensors have been developed for disease treatment in the medical field. However, these sensors require inserting electronic components and other sensor elements via surgical operations, so the measurement physically burdens patients. Hence, we proposed a needle-type pressure sensor using MEMS (Micro Electro Mechanical Systems) cantilever. The sensor has a needle part with a thin membrane on the tip and is filled with incompressible fluid inside. The fractional resistance of the built-in piezoresistive cantilever varies according to the pressure change applied to the needle tip. We can measure body fluid pressure with minimal surgery by inserting a simple needle into the target organ. Because the fabricated sensor responded to pseudo-body pressure changes, it was suggested that the fabricated needle-type pressure sensor could detect pressure inside the body.

This paper presents a capacitor-voltage-balancing control scheme for an isolated medium-voltage AC-DC converter using level-shifted pulse width modulation (PWM) control of a modular matrix converter (MMxC). The proposed isolated AC-DC converter is composed of the primary MMxC with level-shifted PWMcontrol, high-frequency transformer, and secondary H-bridge circuits. The high-frequency transformer of the proposed circuit can be downsized because the level-shifted PWM control scheme makes the rated frequency of the high-frequency transformer equal to the switching-frequency. The proposed capacitor-voltage-balancing control can compensate for the capacitor voltage imbalance of the MMxC using the positive-sequence and negative-sequence circulating-currents with a power supply frequency and high-frequency voltage. In the proposed control system, the three feedback control systems with DC control variables can be developed to compensate for the capacitor voltage imbalance. The effectiveness of the proposed capacitor-voltage-balancing control scheme and the control systems is verified by experiments using a 200 V, 5-kW laboratory prototype.