Equipment fault diagnosis system of sequencing batch reactors using rule-based fuzzy inference and on-line sensing data.

The importance of a detection technique to prevent process deterioration is increasing. For the fast detection of this disturbance, a diagnostic algorithm was developed to determine types of equipment faults by using on-line ORP and DO profile in sequencing batch reactors (SBRs). To develop the rule base for fault diagnosis, the sensor profiles were obtained from a pilot-scale SBR when blower, influent pump and mixer were broken. The rules were generated based on the calculated error between an abnormal profile and a normal profile, e(ORP)(t) and e(DO)(t). To provide intermediate diagnostic results between "normal" and "fault", a fuzzy inference algorithm was incorporated to the rules. Fuzzified rules could present the diagnosis result "need to be checked". The diagnosis showed good performance in detecting and diagnosing various faults. The developed algorithm showed its applicability to detect faults and make possible fast action to correct them.     

Heart rate variability characterization in daily physical activities using wavelet analysis and multilayer fuzzy activity clustering

A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed to classify the physical activity based on body accelerations. Discrete wavelet transform was incorporated to retrieve time-varying characteristics of heart rate variability under different physical activities. Nine healthy subjects were included to investigate activity-related heart rate variability during 24 h. The results showed that the heartbeat fluctuations in high frequencies were the greatest during lying and the smallest during standing. Moreover, very-low-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying).     

Optimized structured treatment interruption for HIV therapy and its performance analysis on controllability

This paper presents dynamic programming therapy to reduce medication and establish long-term immune response against HIV-infection. Understanding HIV-related immune system control enables better HIV therapy without using full-treatments. Discrete regimen and continuous regimen characteristics are compared. Controllability of HIV-related immune system is analyzed for better understanding of optimal control in HIV therapy. Using optimal control provides more effective therapy than the full treatment without interruption in terms of controllability analysis. Case studies indicated the proposed therapy induces long-term nonprogression while preserving high CD4 T-helper cell count and low virus load in HIV-infected patients.