Electrical contacts are separated at a constant opening speed in a 48VDC/50A-600A resistive circuit. Break arcs are observed using two high-speed cameras from the top and side directions. Lengths of the break arcs are analyzed from images taken by the cameras. Arc voltages and currents corresponding to the analyzed arc lengths are investigated to obtain voltage-current characteristics of the break arcs. Relationships between the arc length versus gap voltage and the arc length versus circuit current are obtained. These results are slightly scattered. Therefore, to obtain one-to-one relationships between the arc length and the gap voltage, approximate curves should be determined for these results. Using these approximate curves, eventually, the voltage-current characteristics for each arc length are indicated.

In a 100VDC/5A resistive circuit, silver electrical contacts with airflow ejection structure are separated at a constant speed. Break arcs are generated between the contacts and blown by the airflow between the contact gap. Airflow rate is varied by changing shapes of the contacts. The break arcs are observed by two high-speed cameras. Following results are shown. Arc duration is shortened by the airflow. When the airflow rate is increased, the arc duration becomes shorter, and the break arcs are driven farther outward from the center axis of the contacts and are extinguished in a shorter length.

The Ground-Air Frequency Domain Electromagnetic Method (GAFDEM) is a fast and effective semi-airborne electromagnetic exploration method for subsurface anomaly targets. Based on the depth-focused transmission waveform, this method can realize the high-resolution detection of underground targets at specific depths. However, due to the high inductance and resistance parameters of the transmitting load in GAFDEM exploration, the transmission current of the depth-focused waveform decays rapidly in the middle and high-frequency bands, which restricts the detection signal intensity. To solve this problem, a broadband resonant circuit and its parameter design method are proposed. According to the typical transmission frequency range and load, the parameters are designed, and the circuit model is simulated and tested. The results show that the designed broadband resonant circuit can increase the transmission active power of the depth-focused waveform by more than 490%, reduce the reactive power by more than 37%, and increase the transmission current intensity of the target frequency by 2.64 times. Moreover, this circuit has good robustness. It can achieve a good resonance effect within the error range of ±10% of capacitor. This design provides an effective way for GAFDEM to enhance the intensity of high-frequency detection signals and improve the shallow exploration effect.

Transcatheter renal denervation (RDN) is a treatment for resistant hypertension, which is performed by ablating the renal nerves located outside the artery using a catheter from inside the artery. Our previous studies simulated the temperature during RDN by using constant physical properties of biological tissue to validate the various catheter RDN devices. Some other studies report temperature dependence of physical properties of biological tissues. However, there are no studies that have measured the electrical properties of low water content tissues. Adipose tissue, a type of low water content tissue, is related to RDN closely. Therefore, it is important to know the temperature dependence of the electrical constants of adipose tissue. In this study, we measured the relationship between the electrical constants and the temperature of bovine adipose tissue. Next, the obtained equation of the relationship between relative permittivity of adipose tissue and temperature was introduced. In addition, the temperature dependence of the electrical constants of high water content tissues and the temperature dependence of the thermal constants of biological tissues were also introduced into the temperature analysis. After 180 seconds of heating, the temperature of the model with the temperature dependence of the physical properties was 7.25°C lower than the model without the temperature dependence of the physical properties at a certain position. From the results, it can be said that the temperature dependence of physical properties will be significant when an accurate temperature analysis is required.

A binarized neural network (BNN) inference accelerator is designed in which weights are stores in loadless four-transistor static random access memory (4T SRAM) cells. A time-multiplexed exclusive NOR (XNOR) multiplier with switched capacitors is proposed which prevents the loadless 4T SRAM cell from being destroyed in the operation. An accumulator with current sensing scheme is also proposed to make the multiply-accumulate operation (MAC) completely linear and read-disturb free. The BNN inference accelerator is applied to the MNIST dataset recognition problem with accuracy of 96.2% for 500 data and the throughput, the energy efficiency and the area efficiency are confirmed to be 15.50TOPS, 72.17TOPS/W and 50.13TOPS/mm², respectively, by HSPICE simulation in 32nm technology. Compared with the conventional SRAM cell based BNN inference accelerators which are scaled to 32nm technology, the synapse cell size is reduced to less than 16% (0.235 μm²) and the cell efficiency (synapse array area/synapse array plus peripheral circuits) is 73.27% which is equivalent to the state-of-the-art of the SRAM cell based BNN accelerators.