Neural Network Approach To Classification Of Infrasound Signals

Thesis (Ph.D.) University of Alaska Fairbanks, 2010As part of the International Monitoring Systems of the Preparatory Commissions for the Comprehensive Nuclear Test-Ban Treaty Organization, the Infrasound Group at the University of Alaska Fairbanks maintains and operates two infrasound stations to monitor global nuclear activity. In addition, the group specializes in detecting and classifying the man-made and naturally produced signals recorded at both stations by computing various characterization parameters (e.g. mean of the cross correlation maxima, trace velocity, direction of arrival, and planarity values) using the in-house developed weighted least-squares algorithm. Classifying commonly observed low-frequency (0.015--0.1 Hz) signals at out stations, namely mountain associated waves and high trace-velocity signals, using traditional approach (e.g. analysis of power spectral density) presents a problem. Such signals can be separated statistically by setting a window to the trace-velocity estimate for each signal types, and the feasibility of such technique is demonstrated by displaying and comparing various summary plots (e.g. universal, seasonal and azimuthal variations) produced by analyzing infrasound data (2004--2007) from the Fairbanks and Antarctic arrays. Such plots with the availability of magnetic activity information (from the College International Geophysical Observatory located at Fairbanks, Alaska) leads to possible physical sources of the two signal types. Throughout this thesis a newly developed robust algorithm (sum of squares of variance ratios) with improved detection quality (under low signal to noise ratios) over two well-known detection algorithms (mean of the cross correlation maxima and Fisher Statistics) are investigated for its efficacy as a new detector. A neural network is examined for its ability to automatically classify the two signals described above against clutter (spurious signals with common characteristics). Four identical perceptron networks are trained and validated (with >92% classification rates) using eight independent datasets; each dataset consists of three-element (each element being a characterization parameter) feature vectors. The validated networks are tested against an expert, Prof. Charles R. Wilson, who has been studying those signals for decades. From the graphical comparisons, we conclude that such networks are excellent candidate for substituting the expert. Advantages to such networks include robustness and resistance to errors and the bias of a human operator

Time-Limited Trials Among Critically Ill Patients With Advanced Medical Illnesses to Reduce Nonbeneficial Intensive Care Unit Treatments: Protocol for a Multicenter Quality Improvement Study.

BackgroundInvasive intensive care unit (ICU) treatments for patients with advanced medical illnesses and poor prognoses may prolong suffering with minimal benefit. Unfortunately, the quality of care planning and communication between clinicians and critically ill patients and their families in these situations are highly variable, frequently leading to overutilization of invasive ICU treatments. Time-limited trials (TLTs) are agreements between the clinicians and the patients and decision makers to use certain medical therapies over defined periods of time and to evaluate whether patients improve or worsen according to predetermined clinical parameters. For patients with advanced medical illnesses receiving aggressive ICU treatments, TLTs can promote effective dialogue, develop consensus in decision making, and set rational boundaries to treatments based on patients' goals of care.ObjectiveThe aim of this study will be to examine whether a multicomponent quality-improvement strategy that uses protocoled TLTs as the default ICU care-planning approach for critically ill patients with advanced medical illnesses will decrease duration and intensity of nonbeneficial ICU care without changing hospital mortality.MethodsThis study will be conducted in medical ICUs of three public teaching hospitals in Los Angeles County. In Aim 1, we will conduct focus groups and semistructured interviews with key stakeholders to identify facilitators and barriers to implementing TLTs among ICU patients with advanced medical illnesses. In Aim 2, we will train clinicians to use protocol-enhanced TLTs as the default communication and care-planning approach in patients with advanced medical illnesses who receive invasive ICU treatments. Eligible patients will be those who the treating ICU physicians consider to be at high risk for nonbeneficial treatments according to guidelines from the Society of Critical Care Medicine. ICU physicians will be trained to use the TLT protocol through a curriculum of didactic lectures, case discussions, and simulations utilizing actors as family members in role-playing scenarios. Family meetings will be scheduled by trained care managers. The improvement strategy will be implemented sequentially in the three participating hospitals, and outcomes will be evaluated using a before-and-after study design. Key process outcomes will include frequency, timing, and content of family meetings. The primary clinical outcome will be ICU length of stay. Secondary outcomes will include hospital length of stay, days receiving life-sustaining treatments (eg, mechanical ventilation, vasopressors, and renal replacement therapy), number of attempts at cardiopulmonary resuscitation, frequency of invasive ICU procedures, and disposition from hospitalization.ResultsThe study began in August 2017. The implementation of interventions and data collection were completed at two of the three hospitals. As of September 2019, the study was at the postintervention stage at the third hospital. We have completed focus groups with physicians at each medical center (N=29) and interviews of family members and surrogate decision makers (N=18). The study is expected to be completed in the first quarter of 2020, and results are expected to be available in mid-2020.ConclusionsThe successful completion of the aims in this proposal may identify a systematic approach to improve communication and shared decision making and to reduce nonbeneficial invasive treatments for ICU patients with advanced medical illnesses.International registered report identifier (irrid)DERR1-10.2196/16301

Attrition rate of iron ore in the gas-solid fluidized beds with the wide size distribution

The effects of superficial gas velocity (Ug = 1.25 – 3.00 m/s) and distributor hole size (8.0 – 12.4 mm) on the attrition rate of iron ore in a gas-solid fluidized bed with 0.076 m ID ´ 3.7 m height with or without circulation were investigated. The particle density and the Sauter mean diameter of fresh iron ore were 3,705 kg/m3 and 357 m, respectively. When the kinetic energy rate from the orifice was equal or greater than 180 J/s, the trend of attrition rate could be determined. The attrition rate was determined by measuring the fractional mass of fine particle formation (- 500 m fraction) during 30 min without circulation. In experiments with circulation, the attrition rate was determined by measuring a different threshold size, 63 m. The attrition rate increases with increasing kinetic energy rate from the orifice (J/s). The kinetic energy rate from the orifice was calculated using the mass flow rate and orifice nozzle velocity. The correlation of attrition rate with the kinetic energy rate from the orifice was. When the bed height rapidly decreased below the jet length under very severe conditions, the attrition rate did not follow the correlation