31 research outputs found
Effect of three-dimensional cylindrical hole array on energy conversion efficiency of radioisotope battery
No full textConference Name:6th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2011. Conference Address: Kaohsiung, Taiwan. Time:February 20, 2011 - February 23, 2011.Institute of Electrical and Electronics Engineers (IEEE); IEEE Nanotechnology Council (NTC); National Cheng Kung University; National Tsing Hua University; Chinese International NEMS Socity (CINS)This paper presents the energy conversion theory of radioisotope battery and analyzes the effects of a three-dimensional (3-D) silicon p-n diode with cylindrical hole array on the electrical-output parameters. An equivalent circuit model for direct radioisotope-energy conversion battery has been established. We derive the mathematical equations of short-circuit current, open-circuit voltage and maximum output power of silicon radioisotope battery in the case that a plane solid radioactive source is put on the surface of 3-D silicon p-n diode with cylindrical hole array structure. The simulated results show that for a 3-D silicon p-n diode with cylindrical hole array, the short-circuit current rise but open-circuit voltage decrease with the hole radius increasing. Therefore, the maximum output power is not significantly improved in comparison with the 2-D plane p-n diode without cylindrical hole array. ? 2011 IEEE
Demonstration of a GaN betavoltaic microbattery
No full textConference Name:6th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2011. Conference Address: Kaohsiung, Taiwan. Time:February 20, 2011 - February 23, 2011.Institute of Electrical and Electronics Engineers (IEEE); IEEE Nanotechnology Council (NTC); National Cheng Kung University; National Tsing Hua University; Chinese International NEMS Socity (CINS)A GaN-based betavoltaic microbattery was demonstrated. The wide-band gap semiconductor of GaN and pure beta radioisotope source of 63Ni were used as the converter and the energy source. The GaN-based p-i-n junction wafers were epitaxially grown by metal-organic, chemical-vapor deposition (MOCVD) on the 2-inch c-plane sapphire substrates. Under the irradiation of an activity of 2mCi (about 0.13mCi/mm2) 63Ni source, an open-circuit voltage of 474mV and a short-circuit current density of 17nA/cm2 were measured in a fabricated single 11 mm2 cell, and the calculated conversion efficiency of 1.8% lower bound can be obtained. The results suggest that the wide-band gap semiconductor of GaN is a high potential candidate of betavoltaics for big open-circuit voltage and high efficiency. ? 2011 IEEE
Design and simulation of MEMS based radioisotope converter with electrostatic capacitive energy conversion mechanism
No full textConference Name:6th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2011. Conference Address: Kaohsiung, Taiwan. Time:February 20, 2011 - February 23, 2011.Institute of Electrical and Electronics Engineers (IEEE); IEEE Nanotechnology Council (NTC); National Cheng Kung University; National Tsing Hua University; Chinese International NEMS Socity (CINS)This paper presents the design and simulation of electrostatic capacitive vibration-to-electricity energy conversion system based on radioisotope Ni 63 that produces low energy beta particles. The electrostatic capacitive energy conversion utilizes a variable capacitor to convert radioisotope energy into electrical energy by mechanical vibration as transformed intermediate. The MEMS capacitor is designed as a radioisotope actuated parallel-plate spring-mass-damping structure fabricated through the mature silicon-based micromachining processes. Numerical simulations are performed in order to optimize design parameters targeting a maximum output power. Such a MEMS based energy converter is able to provide an average output power of 0.0812 μWcm-2 and energy conversion efficiency of about 4% according to the radioisotope activity of 10 mCicm-2. ? 2011 IEEE
Analysis and improvement of the “red/blue spot” of TFT-LCD
No full text“Red/blue spot” is a common undesirable phenomenon in small and medium-sized TFT-LCD products, which directly affects the display quality of the product. It has always been a difficult problem in the display industry, which greatly reduces the market competitiveness of corresponding products. In this work, pressure tests are carried out on liquid crystal panels with different sizes and resolutions. The effects of different factors such as the flatness of the photo space, the distribution density of the spacers, and the glass thickness on the “red/blue spot” are compared. By increasing the flatness of the PS station, the septum distribution density, and the thickness of the glass, the sample’s anti-extrusion ability can be increased by 46.1%, 30%, and 23.1%, respectively. The experimental results can provide the basis for industry to further improve quality of products
A clinical observation of the therapy for malignant hydrothorax by intra-thoracic injection using IL-2 combining with cisplatin
No full text
Low Frequency Residential Load Disaggregation via Improved Variational Auto-Encoder and Siamese Network
No full textNon-intrusive load monitoring (NILM) can infer load profiles for each individual appliance from aggregated power consumption signals without installing extra sub-meters. However, performance of traditional energy disaggregation methods deteriorates in complex environments, especially susceptible to the presence of other high power consumption appliances. Practicalities are also limited by diversity of household load patterns and measurement errors. In order to address these problems, a hybrid deep learning model consisting of two steps is proposed in this paper. First, an improved variational autoencoder (VAE) structure is introduced for preliminary energy disaggregation, where the encoder and decoder layers are long short-term networks (LSTM) to extract temporal characteristics of active power signals. Afterward, a post-processing method based on Siamese one-dimensional convolutional neural network (S-1D-CNN) is adopted to remove incorrectly predicted activation segments of target appliances. Experiments are conducted on two public datasets, and results show remarkable improvements on prediction accuracy over other deep learning methods. Both transferability and stability of the proposed model are verified under different working conditions
