I guess so.
You may think that in life, a lot of things happen to you along the way. The truth is, in life, you happen to a lot of things along the way.
For the proud touch of blue, we will be with you until the end of the world.
为了那骄傲的一抹蓝，我愿陪你到世界终结
M.S. in Electrical Engineering, Circuits and Embedded Systems Track
PhD PRELIMINARY exam is not that easy :( but I'll endeavor to prepare for it
Excited by designing, programming, traveling, innovation, conversation, cooking and opera.
Email: mingao at ee dot ucla dot edu
In Hybrid Electricle Vehicles (HEV), performing online energy management is an important task to be achieved to reduce emissions, fuel consumption and increase vehicle performance. For this task, estimating the State of Charge (SOC) is needed since it serves as a measure of energy that is left inside an electrochemical battery. We use behavioral framework to avoid postulation of a specific model for a battery and develop a new and simple SOC estimation algorithm. Once the problem is formulated as the computation of a specific free response of the battery, algorithm computes this response using only terminal current and terminal voltage measurements.
The SRAM cell is an important memory component that is widely used in integrated circuit design. Failure probability of an SRAM cell must be kept extremely small. These extremely small probability events are considered to be “rare events”. Monte Carlo is impractical in the case of rare events because of its drastically long run time. A fast method hereby is proposed. It increases the convergence rate by finding the closest approximation of the optimal distribution used in Importance Sampling. Experimental results show that the proposed method can have an average of 5x speed up compared to Probability Collective based Importance Sampling and 3000x speed up comparing with Monte Carlo simulation.
Usually, Charging process for EV would be done in a charge station just like gas station for normal vehicles. Since electricity generated by single distributed generator might not satisfy the need of electricity quality for the conventional power grid, a feasible solution is to build a Distributed Power System specially for EV station. By given system topology, load current over time, capacity, SOC and other parameters, we obtain an optimal battery and super capacitor discharging schedule from distributed power source so as to prolong battery life and minimize the energy loss in a distributed generation based EV charging station.
We are developing a cloud based ANSI-C source code testing tool which can help software testers with bug finding in relatively short time instead of intolerable long time random generating test inputs and verifying them manually. For the front end, we have built a user-freindly web interface which can be easily familiar with in less than 5 minutes. We also have a bunch of functionalities to make sure web security of this tool. In terms of back end, we are trying to write an efficient with high source code coverage bug finder to become backbone of this tool.
To be released
