DESIGN AND IMPLEMENTATION OF HOME USE PORTABLE SMART ELECTRONICS

The widespread of low cost embedded electronics makes it easier to implement the smart devices that can understand either the environment or the user behaviors. The main object of this project is to design and implement home use portable smart electronics, including the portable monitoring device for home and office security and the portable 3D mouse for convenient use. Both devices in this project use the MPU6050 which contains a 3 axis accelerometer and a 3 axis gyroscope to sense the inertial motion of the door or the human hands movement. For the portable monitoring device for home and office security, MPU6050 is used to sense the door (either home front door or cabinet door) movement through the gyroscope, and Raspberry Pi is then used to process the data it receives from MPU6050, if the data value exceeds the preset threshold, Raspberry Pi would control the USB Webcam to take a picture and then send out an alert email with the picture to the user. The advantage of this device is that it is a small size portable stand-alone device with its own power source, it is easy to implement, really cheap for residential use, and energy efficient with instantaneous alert. For the 3D mouse, the MPU6050 would use both the accelerometer and gyroscope to sense user hands movement, the data are processed by MSP430G2553 through a digital smooth filter and a complementary filter, and then the filtered data will pass to the personal computer through the serial COM port. By applying the cursor movement equation in the PC driver, this device can work great as a mouse with acceptable accuracy. Compared to the normal optical mouse we are using, this mouse does not need any working surface, with the use of the smooth and complementary filter, it has certain accuracy for normal use, and it is easy to be extended to a portable mouse as small as a finger ring.

Dynamics and kinematics of eruptive activity at Fuego volcano, Guatemala 2005-2009

Volcanoes are the surficial expressions of complex pathways that vent magma and gasses generated deep in the Earth. Geophysical data record at least the partial history of magma and gas movement in the conduit and venting to the atmosphere. This work focuses on developing a more comprehensive understanding of explosive degassing at Fuego volcano, Guatemala through observations and analysis of geophysical data collected in 2005 – 2009. A pattern of eruptive activity was observed during 2005 – 2007 and quantified with seismic and infrasound, satellite thermal and gas measurements, and lava flow lengths. Eruptive styles are related to variable magma flux and accumulation of gas. Explosive degassing was recorded on broadband seismic and infrasound sensors in 2008 and 2009. Explosion energy partitioning between the ground and the atmosphere shows an increase in acoustic energy from 2008 to 2009, indicating a shift toward increased gas pressure in the conduit. Very-long-period (VLP) seismic signals are associated with the strongest explosions recorded in 2009 and waveform modeling in the 10 – 30 s band produces a best-fit source location 300 m west and 300 m below the summit crater. The calculated moment tensor indicates a volumetric source, which is modeled as a dike feeding a SW-dipping (35°) sill. The sill is the dominant component and its projection to the surface nearly intersects the summit crater. The deformation history of the sill is interpreted as: 1) an initial inflation due to pressurization, followed by 2) a rapid deflation as overpressure is explosively release, and finally 3) a reinflation as fresh magma flows into the sill and degasses. Tilt signals are derived from the horizontal components of the seismometer and show repetitive inflation deflation cycles with a 20 minute period coincident with strong explosions. These cycles represent the pressurization of the shallow conduit and explosive venting of overpressure that develops beneath a partially crystallized plug of magma. The energy released during the strong explosions has allowed for imaging of Fuego’s shallow conduit, which appears to have migrated west of the summit crater. In summary, Fuego is becoming more gas charged and its summit centered vent is shifting to the west - serious hazard consequences are likely.

Triggering of volcanic activity by large earthquakes

Statistical analyses of temporal relationships between large earthquakes and volcanic eruptions suggest seismic waves may trigger eruptions even over great (>1000 km) distances, although the causative mechanism is not well constrained. In this study the relationship between large earthquakes and subtle changes in volcanic activity was investigated in order to gain greater insight into the relationship between dynamic stresses propagated by surface waves and volcanic response. Daily measurements from the Ozone Monitoring Instrument (OMI), onboard the Aura satellite, provide constraints on volcanic sulfur-dioxide (SO2) emission rates as a measure of subtle changes in activity. Time series of SO2 emission rates were produced from OMI data for thirteen persistently active volcanoes from 1 October 2004 to 30 September 2010. In order to quantify the affect of earthquakes at teleseismic distances, we modeled surface-wave amplitudes from the source mechanisms of moment magnitude (Mw) ≥7 earthquakes, and calculated the Peak Dynamic Stress (PDS). We assessed the influence of earthquakes on volcanic activity in two ways: 1) by identifying increases in the SO2 time series data and looking for causative earthquakes and 2) by examining the average emission rate before and after each earthquake. In the first, the SO2 time series for each volcano was used to calculate a baseline threshold for comparison with post-earthquake emission. Next, we generated a catalog of responses based on sustained SO2 emission increases above this baseline. Delay times between each SO2 response and each prior earthquake were analyzed using both the actual earthquake catalog, and a randomly generated catalog of earthquakes. This process was repeated for each volcano. Despite varying multiple parameters, this analysis did not demonstrate a clear relationship between earthquake-generated PDS and SO2 emission. However, the second analysis, which was based on the occurrence of large earthquakes indicated a response at most volcanoes. Using the PDS calculations as a filtering criterion for the earthquake catalog, the SO2 mass for each volcano was analyzed in 28-day windows centered on the earthquake origin time. If the average SO2 mass after the earthquake was greater than an arbitrary percentage of pre-earthquake mass, we identified the volcano as having a response to the event. This window analysis provided insight on what type of volcanic activity is more susceptible to triggering by dynamic stress. The volcanoes with very open systems included in this study, Ambrym, Gaua, Villarrica, Erta Ale and, Turrialba, showed a clear response to dynamic stress while the volcanoes with more closed systems, Merapi, Semeru, Fuego, Pacaya, and Bagana, showed no response.

OUT-OF-STEP DETECTION BASED ON ZUBOV’S APPROXIMATION BOUNDARY METHOD

Disturbances in power systems may lead to electromagnetic transient oscillations due to mismatch of mechanical input power and electrical output power. Out-of-step conditions in power system are common after the disturbances where the continuous oscillations do not damp out and the system becomes unstable. Existing out-of-step detection methods are system specific as extensive off-line studies are required for setting of relays. Most of the existing algorithms also require network reduction techniques to apply in multi-machine power systems. To overcome these issues, this research applies Phasor Measurement Unit (PMU) data and Zubov’s approximation stability boundary method, which is a modification of Lyapunov’s direct method, to develop a novel out-of-step detection algorithm. The proposed out-of-step detection algorithm is tested in a Single Machine Infinite Bus system, IEEE 3-machine 9-bus, and IEEE 10-machine 39-bus systems. Simulation results show that the proposed algorithm is capable of detecting out-of-step conditions in multi-machine power systems without using network reduction techniques and a comparative study with an existing blinder method demonstrate that the decision times are faster. The simulation case studies also demonstrate that the proposed algorithm does not depend on power system parameters, hence it avoids the need of extensive off-line system studies as needed in other algorithms.