No description available.

This thesis presents the design and implementation of PMOS-based integrated charge pumps with extended voltage range and their regulation circuits in a standard process. The performance of charge pumps are evaluated by their output resistances and power conversion efficiencies. Formulas which describe the charge pump characteristics are developed and presented. Existing charge pumps are analyzed and studied to understand their limitations in generating high voltages and achieving high performance. The proposed charge pump structures are designed to use PMOS switches to alleviate the high voltage stresses across transistors by biasing their bulk independently. The voltages across transistors and capacitors are kept within the suggested voltage rating (VDD)regardless of how high the output voltage is, thus the maximum voltage range is extended and no longer limited by the breakdown voltages of the devices. The charge pump circuits only need low-voltage devices and standard processes, and can be easily integrated in a digital or mixed-signal design. The proposed charge pump regulation circuits include a voltage divider, a voltage controlled ring oscillator and a feedback operational amplifier. The regulation circuits are able to adjust the clock frequency to regulate the charge pump to a steady output voltage (set by the reference voltage) under a large range of current loads. A test chip including the proposed charge pumps and regulation circuits was fabricated in a 0.18 um digital CMOS process provided by Taiwan Semiconductor Manufacturing Company (TSMC). The proposed charge pumps were tested and demonstrated the reliable generation of output voltages up to 11.47 V using only low-voltage devices. The simulation and measurement results have been presented and compared, demonstrating the functionality and performance of the proposed circuits. / Kapik Integration, Mitacs

Flexible job-shop scheduling problem (FJSP) is a generalization of the classical job-shop scheduling problem (JSP). It takes shape when alternative production routing is allowed in the classical job-shop. However, production scheduling becomes very complex as the number of jobs, operations, parts and machines increases. Until recently, scheduling problems were studied assuming that all of the problem parameters are known beforehand. However, such assumption does not reflect the reality as accidents and unforeseen incidents happen in real manufacturing systems. Thus, an optimal schedule that is produced based on deterministic measures may result in a degraded system performance when released to the job-shop. For this reason more emphasis is put towards producing schedules that can handle uncertainties caused by random disruptions. The current research work addresses solving the deterministic FJSP using evolutionary algorithm and then modifying that method so that robust and/or stable schedules for the FJSP with the presence of disruptions are obtained. Evolutionary computation is used to develop a hybridized genetic algorithm (hGA) specifically designed for the deterministic FJSP. Its performance is evaluated by comparison to performances of previous approaches with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. After that, the previously developed hGA is modified to find schedules that are quality robust and/or stable in face of random machine breakdowns. Consequently, a two-stage hGA is proposed to generate the predictive schedule. Furthermore, the effectiveness of the proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods. Subsequently, the hGA is modified to consider FJSP when processing times of some operations are represented by or subjected to small-to-medium uncertainty. The work compares two genetic approaches to obtain predictive schedule, an approach based on expected processing times and an approach based on sampling technique. To determine the performance of the predictive schedules obtained by both approaches with respect to two types of robustness, an experimental study and Analysis of Variance (ANOVA) are conducted on a number of benchmark problems.

No description available.

No description available.

No description available.

Flexible job-shop scheduling problem (FJSP) is a generalization of the classical job-shop scheduling problem (JSP). It takes shape when alternative production routing is allowed in the classical job-shop. However, production scheduling becomes very complex as the number of jobs, operations, parts and machines increases. Until recently, scheduling problems were studied assuming that all of the problem parameters are known beforehand. However, such assumption does not reflect the reality as accidents and unforeseen incidents happen in real manufacturing systems. Thus, an optimal schedule that is produced based on deterministic measures may result in a degraded system performance when released to the job-shop. For this reason more emphasis is put towards producing schedules that can handle uncertainties caused by random disruptions. The current research work addresses solving the deterministic FJSP using evolutionary algorithm and then modifying that method so that robust and/or stable schedules for the FJSP with the presence of disruptions are obtained. Evolutionary computation is used to develop a hybridized genetic algorithm (hGA) specifically designed for the deterministic FJSP. Its performance is evaluated by comparison to performances of previous approaches with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. After that, the previously developed hGA is modified to find schedules that are quality robust and/or stable in face of random machine breakdowns. Consequently, a two-stage hGA is proposed to generate the predictive schedule. Furthermore, the effectiveness of the proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods. Subsequently, the hGA is modified to consider FJSP when processing times of some operations are represented by or subjected to small-to-medium uncertainty. The work compares two genetic approaches to obtain predictive schedule, an approach based on expected processing times and an approach based on sampling technique. To determine the performance of the predictive schedules obtained by both approaches with respect to two types of robustness, an experimental study and Analysis of Variance (ANOVA) are conducted on a number of benchmark problems.

ZUgl.: Bremen, Univ., Diss., 2007

Purpose: Despite its many health benefits, moderate exercise can induce joint discomfort when done infrequently or too intensely even in individuals with healthy joints. This study was designed to evaluate whether NEM (R) (natural eggshell membrane) would reduce exercise-induced cartilage turnover or alleviate joint pain or stiffness, either directly following exercise or 12 hours post exercise, versus placebo. Patients and methods: Sixty healthy, postmenopausal women were randomly assigned to receive either oral NEM 500 mg (n=30) or placebo (n=30) once daily for two consecutive weeks while performing an exercise regimen (50-100 steps per leg) on alternating days. The primary endpoint was any statistically significant reduction in exercise-induced cartilage turnover via the biomarker C-terminal cross-linked telopeptide of type-II collagen (CTX-II) versus placebo, evaluated at 1 and 2 weeks of treatment. Secondary endpoints were any reductions in either exercise-induced joint pain or stiffness versus placebo, evaluated daily via participant questionnaire. The clinical assessment was performed on the per protocol population. Results: NEM produced a significant absolute treatment effect (TEabs) versus placebo for CTX-II after both 1 week (TEabs - 17.2%, P=0.002) and 2 weeks of exercise (TEabs - 9.9%, P=0.042). Immediate pain was not significantly different; however, rapid treatment responses were observed for immediate stiffness (Day 7) and recovery pain (Day 8) and recovery stiffness (Day 4). No serious adverse events occurred and the treatment was reported to be well tolerated by study participants. Conclusion: NEM rapidly improved recovery from exercise-induced joint pain (Day 8) and stiffness (Day 4) and reduced discomfort immediately following exercise (stiffness, Day 7). Moreover, a substantial chondroprotective effect was demonstrated via a decrease in the cartilage degradation biomarker CTX-II.

Laser-Induced Breakdown Spectroscopy (LIBS) is a powerful tool for performing chemical analysis measurements of materials, such as slurries, soils, plastics and powder samples. The LIBS technique has proven to be sensitive, selective and robust for rapid, in situ analysis. The focus of this dissertation is the optimization of laser spectroscopic sensing methodologies for material characterization. The applications of the LIBS technique to slurry samples is very challenging due to the water content (~80%). A new sample preparation method called “spin-on-glass” was adopted to reduce the water content in slurry samples and improve the LIBS signal. The feasibility of using the new sampling method with a LIBS system was tested by applying multivariate analysis to the LIBS spectral data. The calibration results demonstrated that the LIBS technique with the new sampling method could successfully predict the elemental concentrations of slurry samples qualitatively and quantitatively. The possibility of developing a LIBS-based sensor system for total carbon quantification in soil samples was studied. The soil samples were studied in pellet form and the calibration models were developed by using simple linear regression (SLR) and multiple linear regression (MLR) analysis. It was found that both SLR- and MLR-based calibrations successfully predicted the carbon concentration in an unknown sample with relative accuracy (RA) within 8%. The LIBS experimental setup was designed, developed and tested for the determination of elemental impurities in plastic calibration standards that are used in dual-energy computed tomography (CT) scanning for petrophysical applications. Univariate calibration (UC) and multiple linear regression (MLR) analysis were used to develop calibration models. From this study, it was concluded that MLR improved the calibration results and data derived from the LIBS analysis enhanced the predictive capabilities of dual-energy CT scanning in general. A comparative study was performed for quantification of strontium (Sr) in an aluminum (Al) batch with both the atomic and molecular LIBS emissions. The calibration models were developed using SLR analysis and the limits of detection (LOD) were obtained. The study confirmed that molecular LIBS could be used for quantification of Sr in a binary mixture.