3 research outputs found
MODEL LEARNING ORGANIZATION CAPABILITY DAN STRATEGIC FLEXIBILITY DALAM MENINGKATKAN KINERJA PERGURUAN TINGGI SWASTA DI JAWA BARAT
At present, the performance of private higher education in West Java is one
of the issues that has been highlighted related to their performance in rankings at the national and international levels. The decline in the number of private tertiary institutions in Indonesia is due to the fact that these tertiary institutions are no longer able to compete with other tertiary institutions. Several studies on the performance of private tertiary institutions in the world include explaining the extent to which the role of higher education management must prioritize programs that are strategic in nature with a broad and global scope of scientific impact. This
research is designed to develop a Strategic Flexibility capability and Learning Organization Capability Model to improve the performance of private universities in Indonesia. The purpose of this research is to explore data and information and understand strategic flexibility capability and learning organization capability in improving the performance of private universities in Indonesia. This research design uses a quantitative approach with descriptive and verification research types. The object of research involves the performance of private tertiary institutions, external environment, internal environment, learning organization, strategic flexibility and implementation of agility programs. The survey was
conducted at 81 private higher education institutions in West Java, especially those in the form of universities or institutes. The data collection technique was carried out by proportional random sampling. The analysis used to prove the hypothesis in this study is Structural Equation Modeling (SEM) based on component or variance commonly known as Partial Least Square (PLS). The results of the descriptive analysis show that the performance of private tertiary institutions in Indonesia can
be categorized as high, which means that the performance carried out by the leaders of private tertiary institutions in Indonesia can be carried out through empirical evidence. It is found that strategic flexibility has an influence on the performance of private tertiary institutions. indicates that increasing the level of implementation of strategic flexibility will improve the performance of private tertiary institutions. So that this series of models will have implications for improving performance positively and significantly
Optimization of System Identification for Multi-Rail DC-DC Power Converters
Ph. D. Thesis.There have been many recursive algorithms investigated and introduced in real time
parameter estimation of Switch Mode Power Converters (SMPCs) to improve estimation
performance in terms of faster convergence speed, lower computational cost and higher
estimation accuracy. These algorithms, including Dichotomous Coordinate Descent (DCD) -
Recursive Least Square (RLS), Kalman Filter (KF) and Fast Affine Projection (FAP), etc., are
commonly applied for performance comparison of system identification of single-rail power
converters. When they need to be used in multi-rail architectures with a single centralized
controller, the computational burden on the processor becomes significant. Typically, the
computational effort is directly proportional to the number of converters/rails. This thesis
presents an iterative decimation approach to significantly alleviate the computational burden of
centralized controllers applying real-time recursive system identification algorithms in multirail power converters. The proposed approach uses a flexible and adjustable update rate rather
than a fixed rate, as opposed to conventional adaptive filters. In addition, the step size/forgetting
factors are varied, as well, corresponding to different iteration stages. As a result, reduced
computational burden and faster model update can be achieved. Recursive algorithms, such as
Recursive Least Square (RLS), Affine Projection (AP) and Kalman Filter (KF), contain two
important updates per iteration cycle. Covariance Matrix Approximation (CMA) update and
the Gradient Vector (GV) update. Usually, the computational effort of updating Covariance
Matrix Approximation (CMA) requires greater computational effort than that of updating
Gradient Vector (GV). Therefore, in circumstances where the sampled data in the regressor
does not experience significant fluctuations, re-using the Covariance Matrix Approximation
(CMA), calculated from the last iteration cycle for the current update can result in
computational cost savings for real- time system identification. In this thesis, both iteration rate
adjustment and Covariance Matrix Approximation (CMA) re-cycling are combined and applied
to simultaneously identify the power converter model in a three-rail power conversion
architecture.
Besides, in multi-rail architectures, due to the high likelihood of the at-the-same-time need
for real time system identification of more than one rail, it is necessary to prioritize each rail to
guarantee rails with higher priority being identified first and avoid jam. In the thesis, a workflow,
which comprises sequencing rails and allocating system identification task into selected rails,
was proposed. The multi-respect workflow, featured of being dynamic, selectively pre-emptive,
cost saving, is able to flexibly change ranks of each rail based on the application importance of
rails and the severity of abrupt changes that rails are suffering to optimize waiting time and
make-span of rails with higher priorities