711 research outputs found
Microbial growth in continuous cultures subject to single and multiple limitations involving carbon and/or nitrogen
No full textMathematical models which utilize standard reaction kinetics to describe microbial growth are generally successful under carbon-limiting conditions in batch and steady-state continuous cultures. However, when a class of substrate other than the available carbon source is limiting even these models fail in predicting microbial behavior under conditions in which they were previously successful. Regardless of the substrate limitation standard kinetic models also poorly represent microbial dynamics exhibited under transient conditions in continuous cultures since they largely ignore the significance of cellular regulatory processes. Therefore, such models are incapable of describing the transitions between different metabolic pathways when limiting substrates are complementary as well as during transient conditions. The need for a versatile and robust modeling framework to describe microbial behavior when limited by complementary as well as substitutable substrates is clearly present. By combining the ideals of the cybernetic framework with a topological perspective of metabolic pathways this work develops an expanded framework which overcomes the previous difficulties in describing the utilization of complementary substrates while retaining the original results proposed by Ramkrishna and co-workers to describe the utilization of substitutable substrates. The growth characteristics of the bacterium Escherichia coli W are experimentally studied in steady-state and transient continuous cultures. Three limiting conditions are investigated: carbon-limiting, nitrogen-limiting, and dual-limiting. Glucose serves as the required carbon source while NH\sbsp{4}{+} supplies the necessary nitrogen. The proposed model quantitatively describes the results under singly-limiting conditions and accurately identifies the limiting substrate as well as the substrate present in excess. All of the model parameters are estimated from results under singly-limiting conditions or results available in the literature. Without any additional modifications the proposed model is also applied to cultures which are dual-limited by both carbon and nitrogen. The model accurately predicts the occurrence of a transition between the carbon- and nitrogen-limiting conditions. Overall, the agreement between the model simulations and the experimental results under dual-limiting conditions is quantitative as well as qualitative. Operating a fermenter under any one of the three limiting conditions offers distinct advantages which are due primarily to the presence of metabolic regulation. (Abstract shortened with permission of author.
Modular Data-flow Analysis of Statically Typed Object-oriented Programming Languages
No full textThe solution of data-flow analysis of object-oriented programming languages such as C++/Java is needed for many important applications: aggressive code optimization, side-effect analysis, program specialization, program slicing and data-flow-based testing. However, data-flow analysis of object-oriented programming languages is difficult due to a large number of heap-allocated objects whose fields point to other heap-allocated objects (recursive structures), dynamic dispatch, frequent method invocations, a large number of methods, many invocation contexts per method and exceptions. In this thesis we present a new data-flow analysis technique called Relevant Context Inference (RCI) for modular, flow- and context-sensitive data-flow analysis of statically typed object-oriented programming languages such as C++ and Java. This technique has been designed to overcome the above difficulties. RCI has several long sought-after characteristics: 1. It can analyze programs by keeping only a part of the programs in memory at a time, with a constant bound on the number of times a procedure needs to be in memory. 2. It can analyze incomplete programs such as libraries. 3. It can analyze programs that have exceptions. We have built a prototype of RCI for points-to analysis of C++ programs. The empirical results obtained using this prototype and presented in this thesis show that RCI is effective in practice. We present several new complexity characterizations of points-to analysis in the presence of object-oriented language constructs: exceptions and dynamic dispatch. Our results clearly identify the difficult features and indicate approximations that any efficient algorithm has to make. We also present a new approach to data-flow-based testing of object-oriented libraries using RCI . We show how the information computed by RCI can be used for generating relevant test cases.Technical report DCS-TR-40
Scalable, flow-sensitive type inference for statically typed object-oriented languages
No full textAn important problem in the analysis of programs written in object-oriented languages like C++/Java is to determine the values that a pointer variable ( a reference to an object in Java ) can have at run-time. This information can be used for applications such as virtual function resolution, side-e ect analysis, detecting memory errors etc. We address issues involved in designing a scalable, ow-sensitive solution for this and related compile-time analysis questions. First we present a ow-sensitive solution which uses conditional points-to analysis. Although we show that it is better than a solution using alias analysis, it has several limitations which a ect its scalability and precision. Next we identify properties necessary for any scalable, ow-sensitive algorithm solving these problems. Using this characterization, we propose two techniques: preprocessing using types and analysis-usingabstract-values, which remove many of these limitations. We show that these techniques can represent a part of the solution implicitly, and thus facilitate ecient demand-driven computation. We introduce analysis-using-abstract-values by presenting an algorithm for nding the precise solution for may points-to in the presence of only single-level pointers (pointers with a single level of indirection). We show that this algorithm improves the worst-case bound from O(n5 ) to O(n4 ). Finally, we present an extension of this technique for multi-level pointers (pointers with multiple levels of indirection), with examples to show improvements in eciency as well as precision. We also show that this technique can adaptively change ow-sensitivity when it is pro table to do so. We also show that analysis-using-abstract-values aids in modular analysis by allowing the analysis of part of a program and avoiding the need to keep the whole program in memory.Technical report DCS-TR-32
Data-flow-based testing of object-oriented libraries
No full textData-flow-based testing is a well-established approach to program testing. Much object-oriented code is written as libraries; hence data-flow-based testing of object-oriented libraries is of great importance. However, finding def-use relationships in libraries written in object-oriented languages (e.g., Java and C++) is difficult because of unknown aliasing between parameters, unknown concrete types of the parameters, dynamic dispatch, and exceptions. We present the first algorithm for finding def-use relationships in object-oriented libraries that overcomes the above difficulties. We also show how the information computed by our algorithm can be used in generating relevant test cases. Our algorithm is flow- and context-sensitive and based on our earlier points-to analysis [CRL99]Technical report DCS-TR-43
Modular Concrete Type-Inference for Statically Typed Object-oriented Programming Languages
No full textThe problem of concrete type-inference for statically typed object-oriented programming languages (e.g., Java, C++) determines at each program point, those objects to which a reference may refer or a pointer may point during execution. We present a new technique called analysis-using-abstract-values which performs modular and demand driven concrete type-inference of a robust subset of Java without threads and exceptions and C++ without exceptions. Our algorithm is provably precise on programs with only single-level types2 and without dynamic dispatch, and has the worst-case complexity of O(n4 ) which is an improvement over the O(n7 ) worst-case bound achievable by applying previous approaches of [RHS95] and [LR91] to this case. For general programs, the algorithm is polynomial-time and computes a safe solution.Technical report DCS-TR-34
Construction of plant friendly input excitations for ill-conditioned multivariable process identification
No full textInput designs for identification of Ill-conditioned multivariable systems
Get PDFIdentification of an industrial system is generally a costly and complex process. Making an identification procedure efficient enough is an ongoing demand. Typically, designing the excitation for ill-conditioned and directional systems is a challenging task. Tailor-made input excitation using process knowledge is one way to make the identification more efficient. The focus of this thesis is to develop a better identification process for multipleinput multiple-output (MIMO) systems with proper design of input excitation signals. A distillation column simulator has been constructed as a testbed for the research. Data from a real-life distillation column system have been used for calibrating the simulator.
In model-based control (e.g., model predictive control, MPC), the model quality has a critical effect on the performance of the controller. To construct a control-relevant model via identification, the system has to be excited adequately in all gain directions. In order to reduce the relative uncertainties associated with these gain directions, it is especially important to excite the system in the weak gain directions. In this study, we consider both designs by rotated signals as well as methods based on design in the frequency domain. We study multiple input design methods using basic signals like steps, PRBS, and sinusoidal signals with multiple frequencies and compare them through two case studies. The case studies are a 4 X 4 column stripper system and a 2 X 2 nonlinear distillation column system. As most of the previous studies have focused on 2 X 2 systems, a comparative study to choose the proper excitation for identification of an ill-conditioned system with more inputs and outputs (n n;n > 2) was chosen.
Practically every system has physical limitations as well as limitations due to the operation of the process. A useful measure related to input limitations is the plantfriendliness of an input signal. Several parameters that characterize the plant-friendliness are calculated and compared for the designed inputs. These parameters depend only on the inputs, not the rest of the system.
For the model identification, it is desirable that outputs are excited equally in all directions. We propose two different tools for analyzing the excitation of the outputs, namely, projections of the outputs along the gain directions and the determinant of the correlation matrix of the outputs. Both methods yield measures on how well-balanced the output distribution is. We show that experiments where the gain directions have been considered in the input design produce output data that are better balanced than output data produced by other designs. The models obtained from directional experiments perform better in cross-validation with data from other experiments than other models.ei tietoa saavutettavuudest
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