Recommended practice for emergency power systems in health care facilities
Chapter V of the White Book deals with the requirements and equipment which are recommended for health care facility emergency power systems and their operation. Suggested methods for system design, installation, testing, and maintenance are included. The main components of the emergency power system are the generator sets, automatic transfer switches, engine generator controls, engine cranking batteries, and battery charger. Additional equipment may include synchronizing and paralleling equipment for multigenerator set installations, utility peak demand reduction controls, elevator emergency power selector systems, and bypass-isolation switches for automatic transfer switches.
An analytical look at standby diesel engine/generator loading
Diesel engine/generators are complex systems which utilize different governors, voltage regulators, excitation systems, excitation support systems, and turbochargers. Even the engine and the generator have widely varying characteristics and ratings and are often matched differently for different applications. In sizing and specifying these units, the engineer should use `rules of thumb' cautiously. As many known factors as possible should be applied to the sizing and specifying process. In the future, the computer will probably be used routinely to predict diesel engine/generator response to dynamic loads.
Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG Signals
Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.