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Dissertations / Theses on the topic 'Constraint-based method'
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Lilja, Robin. "Constraint-Based Patterns : An examination of an algorithmic composition method." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik, konst och samhälle, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85001.
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This thesis examines the composition of three different musical works through the use of my constraint-based patterns. I have explored the patterns through spreadsheets and also SuperCollider: a software for algorithmic composition and audio synthesis. The aim is to find how the patterns can be used to reach clear contrasts while maintaining coherence in the music, as well as finding challenges and possibilities within the patterns, while exploring how evaluation of the artistic results can contribute to improved methods. While I see the main method as autoethnographical, with the core focus on composing, I have also used feedback from other composers, and through focus groups, as a way to collect data. Throughout this thesis I describe my process of constructing patterns and composing music, accompanied by my reasoning and relevant feedback. My results from analyzing feedback, score and patterns are that while some ways of using the patterns are well suited for achieving contrast and coherence, problems arose related to (among other things) musical form and predictability. Evaluation through feedback and interviews resulted in a better understanding of the patterns, and different workflows allowed for different viability in the evaluation. The most valuable insight is that the greater the amount of composition parameters which are controlled through constraint-based patterns, the simpler each individual composition parameter has to be in order to reach contrasting results that I find satisfying. My conclusion is that I can therefore design each individual composition parameter with high coherence to reach contrasting results when the composition parameters are applied on the same musical structure.
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Leach, Austin M. "A NEW CONSTRAINT BASED FRACTURE PREDICTION METHODOLOGY FOR DUCTILE MATERIALS CONTAINING SURFACE CRACKS [FLAWS]." Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-07122004-155332.
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Köckemann, Uwe. "Constraint-based Methods for Human-aware Planning." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-51437.
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As more robots and sensors are deployed in work and home environments, there is a growing need for these devices to act with some degree of autonomy to fulfill their purpose. Automated planning can be used to synthesize plans of action that achieve this. The main challenge addressed in this thesis is to consider how the automated planning problem changes when considered in the context of environments that are populated by humans. Humans have their own plans, and automatically generated plans should not interfere with these. We refer to this as social acceptability. Opportunities for proactive behavior often arise during execution. The planner should be able to identify these opportunities and proactively plan accordingly. Both social acceptability and proactivity require the planner to identify relevant situations from available information. We refer to this capability as context-awareness, and it may require complex inferences based on observed human activities. Finally, planning may have to consider cooperation with humans to reach common goals or to enable robots and humans to support one another. This thesis analyzes the requirements that emerge from human-aware planning — what it takes to make automated planning socially acceptable, proactive, context aware, and to make it support cooperation with humans. We formally state the human-aware planning problem, and propose a planning and execution framework for human-aware planning that is based on constraint reasoning and flaw-resolution techniques, and which fulfills the identified requirements. This approach is modular and extendable: new types of constraints can be added and solvers can be exchanged and re-arranged. This allows us to address the identified requirements for humanaware planning. In particular, we introduce Interaction Constraints (ICs) for this purpose, and propose patterns of Ics for social acceptability, proactivity, and contextawareness. We also consider cooperative plans in which certain actions are assigned to humans and the implications that this has. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system. introduce Interaction Constraints (ICs) for this purpose, and propose patterns of ICs for social acceptability, proactivity, and context-awareness. We also consider cooperative plans in which certain actions are assigned to humans and the implications that this has. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system.
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Westphal, Matthias [Verfasser], and Bernhard [Akademischer Betreuer] Nebel. "Qualitative constraint-based reasoning: methods and applications." Freiburg : Universität, 2015. http://d-nb.info/1119805627/34.
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Kim, Dae Gyu. "Mapping based constraint handling methods for evolutionary algorithms." Thesis, University of Sussex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311406.
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Niu, Fei. "Learning-based Software Testing using Symbolic Constraint Solving Methods." Licentiate thesis, KTH, Teoretisk datalogi, TCS, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-41932.
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Software testing remains one of the most important but expensive approaches to ensure high-quality software today. In order to reduce the cost of testing, over the last several decades, various techniques such as formal verification and inductive learning have been used for test automation in previous research. In this thesis, we present a specification-based black-box testing approach, learning-based testing (LBT), which is suitable for a wide range of systems, e.g. procedural and reactive systems. In the LBT architecture, given the requirement specification of a system under test (SUT), a large number of high-quality test cases can be iteratively generated, executed and evaluated by means of combining inductive learning with constraint solving. We apply LBT to two types of systems, namely procedural and reactive systems. We specify a procedural system in Hoare logic and model it as a set of piecewise polynomials that can be locally and incrementally inferred. To automate test case generation (TCG), we use a quantifier elimination method, the Hoon-Collins cylindric algebraic decomposition (CAD), which is applied on only one local model (a bounded polynomial) at a time. On the other hand, a reactive system is specified in temporal logic formulas, and modeled as an extended Mealy automaton over abstract data types (EMA) that can be incrementally learned as a complete term rewriting system (TRS) using the congruence generator extension (CGE) algorithm. We consider TCG for a reactive system as a bounded model checking problem, which can be further reformulated into a disunification problem and solved by narrowing. The performance of the LBT frameworks is empirically evaluated against random testing for both procedural and reactive systems (executable models and programs). The results show that LBT is significantly more efficient than random testing in fault detection, i.e. less test cases and potentially less time are required than for random testing.QC 20111012
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Llaneras, Estrada Francisco. "Interval and Possibilistic Methods for Constraint-Based Metabolic Models." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/10528.
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This thesis is devoted to the study and application of constraint-based metabolic models. The objective was to find simple ways to handle the difficulties that arise in practice due to uncertainty (knowledge is incomplete, there is a lack of measurable variables, and those available are imprecise). With this purpose, tools have been developed to model, analyse, estimate and predict the metabolic behaviour of cells.
The document is structured in three parts. First, related literature is revised and summarised. This results in a unified perspective of several methodologies that use constraint-based representations of the cell metabolism. Three outstanding methods are discussed in detail, network-based pathways analysis (NPA), metabolic flux analysis (MFA), and flux balance analysis (FBA). Four types of metabolic pathways are also compared to clarify the subtle differences among them.
The second part is devoted to interval methods for constraint-based models. The first contribution is an interval approach to traditional MFA, particularly useful to estimate the metabolic fluxes under data scarcity (FS-MFA). These estimates provide insight on the internal state of cells, which determines the behaviour they exhibit at given conditions. The second contribution is a procedure for monitoring the metabolic fluxes during a cultivation process that uses FS-MFA to handle uncertainty.
The third part of the document addresses the use of possibility theory. The main contribution is a possibilistic framework to (a) evaluate model and measurements consistency, and (b) perform flux estimations (Poss-MFA). It combines flexibility on the assumptions and computational efficiency. Poss-MFA is also applied to monitoring fluxes and metabolite concentrations during a cultivation, information of great use for fault-detection and control of industrial processes. Afterwards, the FBA problem is addressed.Llaneras Estrada, F. (2011). Interval and Possibilistic Methods for Constraint-Based Metabolic Models [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10528
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Andereck, Michael. "Procedural Terrain Generation Based on Constraint Paths." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1388357258.
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Bonfietti, Alessio <1981>. "Constraint based methods for allocation and scheduling of periodic applications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5503/1/Bonfietti_Alessio_Tesi.pdf.
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This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design.
The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems.
In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms.
We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity.
Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions.
The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.
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Bonfietti, Alessio <1981>. "Constraint based methods for allocation and scheduling of periodic applications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5503/.
Full textAbstract:
This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design.
The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems.
In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms.
We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity.
Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions.
The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.
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Wedig, Sean A. "SpecTackle: Inferring Partial Specifications Through Constraint-Based Dynamic Analysis." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345515046.
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Sadeghi, Rezvan. "Consistency of global and local scheduling decisions in semiconductor manufacturing." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEM023.
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Le niveau opérationnel dans la fabrication de semi-conducteurs peut être divisé en un niveau global et un niveau local. Le niveau global est associé aux décisions d’ordonnancement et de contrôle de la production pour l’ensemble de l’unité de fabrication (fab), tandis que le niveau local traite de ces problèmes dans chaque atelier. Le niveau global établit des objectifs ou des contraintes au niveau local. Dans cette thèse, nous proposons un cadre général qui vise à contrôler les décisions prises au niveau local pour assurer la cohérence entre les décisions d’ordonnancement aux niveaux global et local. Le cadre est composé de deux niveaux. Le niveau inférieur comprend les politiques locales utilisées dans chaque atelier. Le niveau supérieur comprend les objectifs globaux, les informations globales et une stratégie globale qui est au coeur de ce cadre. La stratégie globale proposée vise à contrôler les politiques locales ainsi que les processus de production. L’idée est de gérer périodiquement la stratégie globale, en même temps que que la production, pour guider le processus de production vers la réalisation des objectifs globaux et assurer ainsi une cohérence entre les décisions prises aux niveaux global et local. Nous proposons deux types de stratégie globale : (1) une stratégie basée sur l’évaluation qui vise à améliorer le processus de production sans garantie de déterminer une solution optimale et (2) une stratégie d’optimisation basée sur un modèle de programmation linéaire. Afin d’évaluer la performance du cadre proposé, nous avons développé un modèle de simulation générique basé sur les données pour les systèmes de fabrication de semi-conducteurs. Le modèle de simulation, développé avec le logiciel AnyLogic, est une combinaison de méthodes de simulation multi-agents et de simulation à événements discrets. Étant donné que le solveur standard IBM ILOG CPLEX est utilisé pour résoudre le modèle de programmation linéaire, nous décrivons son intégration avec AnyLogic. Un ensemble d’expérimentations sur des instances industrielles sont présentées et discutées. En outre, cette thèse traite de la gestion des contraintes de temps. Dans une usine de fabrication de semi-conducteurs, les contraintes de temps sont associées à deux étapes du processus pour assurer le rendement et la qualité des lots. Une contrainte de temps correspond à un temps maximal qu’un lot ne doit pas dépasser entre les deux étapes. Si une contrainte de temps n’est pas satisfaite, le lot sera mis au rebut ou traité à nouveau. Par conséquent, parce que les équipements de fabrication sont onéreux et que les temps de cycle doivent être minimisés, il est important de contrôler efficacement le démarrage des lots dans les contraintes de temps. Nous proposons une approche qui estime la probabilité de satisfaire une contrainte de temps avant de démarrer la première étape de la contrainte. Cette approche a été mise en oeuvre et validée sur des données industriellesThe operational level in semiconductor manufacturing can be divided into a global level and a local level. The global level refers to the scheduling decisions and production control for the whole manufacturing facility (fab), while the local level deals with those issues in each work area. The global level provides objectives or constraints for the local level. In this thesis, we propose a general framework which aims at supporting and controlling the decisions taken at the local level to deal with consistency problems between global and local scheduling decisions. The framework is composed of two layers. The bottom layer includes local policies used in each work center. The top layer consists of global objectives, global information and a global strategy which is the core of this framework. The proposed global strategy aims at controlling local policies as well as production processes. The idea is to periodically run the global strategy while production is performed to guide the production process towards achieving global objectives, and thus ensuring consistency between decisions taken at the global and local levels. We propose two types of global strategy: (1) An evaluation-based strategy which aims at improving the production process with no guarantee to determine an optimal solution and (2) An optimization-based strategy, based on a Linear Programming model. In order to evaluate the performance of the proposed framework, we develop a data-driven generic simulation model for semiconductor manufacturing facilities. The simulation model is a combination of Agent-Based and Discrete Event modelling methods developed with the software AnyLogic. Since the standard solver IBM ILOG CPLEX is used to solve the linear programming model, we describe its integration with AnyLogic. A set of experiments on industrial instances are presented and discussed. In addition, this thesis deals with the management of time constraints. In a semiconductor manufacturing facility, time constraints are associated to two process steps to ensure the yield and quality of lots. A time constraint corresponds to a maximum time that a lot can spend between the two steps. If a time constraint is not satisfied by a lot, this lot will be scrapped or reprocessed. Therefore, because manufacturing equipment is expensive and cycle times must be minimized, efficiently controlling the start of lots in time constraints is important. We propose an approach which estimates the probability of satisfying a time constraint before starting a lot in the first step of the time constraint. This approach was implemented and validated on industrial constraints
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Lu, Wei. "Integer Programming-based Methods for Computing Minimum Reaction Modifications of Metabolic Networks for Constraint Satisfaction." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199436.
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Maung, David. "Tile-based Method for Procedural Content Generation." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461077485.
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Teran, Villegas Oswaldo Ramon. "Emergent tendencies in multi-agent-based simulations using constraint-based methods to effect practical proofs over finite subsets of simulation outcomes." Thesis, Manchester Metropolitan University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343494.
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Baghaei, Nilufar. "A collaborative constraint-based intelligent system for learning object-oriented analysis and design using UML." Thesis, University of Canterbury. Computer Science and Software Engineering, 2007. http://hdl.handle.net/10092/1204.
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Web-based collaborative learning is becoming an increasingly popular educational paradigm as more individuals who are geographically isolated seek higher education. As such students do not meet face to face with their peers and teachers, support for collaboration becomes extremely important. Successful collaboration means asking questions to gain a better understanding of the main concepts, elaborating and justifying opinions and sharing and explaining ideas. When group members' combined skills are sufficient to complete the learning task, effective group work can result in greater overall achievement than individual learning. Intelligent Tutoring Systems (ITS) have been shown to be highly effective at increasing students' performance and motivation. They achieve their intelligence by representing pedagogical decisions about how to teach as well as information about the learner. Constraint based tutors are a class of ITSs that use Constraint-based Modelling(CBM) to represent student and domain models. Proposed by Ohlsson, CBM is based on learning from performance errors, and focuses on correct knowledge. In this thesis, we present COLLECT-UML, a collaborative constraint-based ITS that teaches object-oriented analysis and design using Unified Modelling Language (UML). While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on collaboration. Being a constraint-based tutor, COLLECT-UML represents the domain knowledge as a set of syntax and semantic constraints. However, it is the first system to also represent a higher-level skill such as collaboration using the same formalism. We started by developing a single-user ITS that supported students in learning UML class diagrams. The system was evaluated in a real classroom, and the results showed that students' performance increased significantly. We then extended the system to provide support for collaboration as well as domain-level support. The enhancement process included implementation of the shared workspace, modification of the pedagogical module to support groups of users, designing and implementing a group-modelling component, and developing a set of meta-constraints which are used to represent an ideal model of collaboration. The effectiveness of the system was evaluated in two studies. In addition to improved problem-solving skills, the participants both acquired declarative knowledge about effective collaboration and did collaborate more effectively. The participants enjoyed working with the system and found it a valuable asset to their learning. The results, therefore, show that CBM is an effective technique for modelling and supporting collaboration in computer-supported collaborative learning environments.
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Riahi, Vahid. "Constraint Directed Scheduling." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386545.
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Scheduling is a decision-making process, which is employed to allocate resources to
tasks in a given time. Scheduling problems are in general NP-hard. In order to solve
scheduling problems, three common types of methods have been used: exact methods
(e.g., branch & bound and dynamic programming), population based metaheuristics
(e.g., genetic algorithm and ant colony optimisation), and local search (LS) algorithms
(e.g., simulated annealing and iterated local search). Exact methods are not able to
address the practical-sized problems effectively with regard to both CPU times and
solution quality. LS algorithms have recently attracted much more attention because of
their simplicity, being easy to implement, robustness, and high effectiveness. However,
the available LS algorithms in the literature typically use a generic structure for speci fic
problems. In other words, the biggest disadvantage of those methods is the lack of
problem speci fic components into their algorithmic structures. To ll in this gap, in this
thesis, we consider constraint-based local search (CBLS) algorithms to solve scheduling
problems because of their effectiveness and also because they are not used much in the
scheduling literature. The key difference of CBLS with other LS algorithms is in the use
of the problem specifi c information in the search process. CBLS helps the search focus
more on areas where efforts will bring more effect, and thus increase the scalability of
the search. In other words, CBLS attempts to exploit the essence of the problem and,
based on the speci ficities of the problem, defi nes the procedures that will guide the
search towards better local optima. The effectiveness of our proposed CBLS techniques is shown throughout this thesis by solving several scheduling problems, such as flowshops with blocking constraints, aircraft operations, and customer order problems.
The first scheduling problem is permutation flowshop scheduling problem (PFSP). It is one of the most thoroughly studied scheduling problems. However, mixed blocking
PFSP (MBPFSP) is a generalised and more realistic version of PFSP with real-life
applications such as cider industry. MBPFSP is an important branch of `zero capacity
buffer' scheduling problems. The second scheduling problem is aircraft scheduling
problem (ASP). ASP involves allocation of aircraft to runways for arrival and departure flights, minimising total delays. In this thesis, we focus on both single-runway and
multiple-runway ASP cases. The third scheduling problem is customer order scheduling
problem (COSP), which has many applications including the pharmaceutical industries
and the paper industries.
All of the three above-mentioned scheduling problems are NP-hard. They have made
signi ficant progress in recent years. However, within practical time limits, existing algorithms
still either find low quality solutions or struggle with practical-sized problems.
In this thesis, we aim to advance their search by better exploiting the problem speci fic
structural knowledge, extracted from the constraints and the objective functions. We
run our experiments on a range of respective standard benchmark problem instances.
Experimental results and comprehensive analyses show that our new algorithms signi ficantly outperform respective state-of-the-art scheduling algorithms.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Inst Integrated&IntelligentSys
Science, Environment, Engineering and Technology
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Li, Honghao. "Interpretable biological network reconstruction from observational data." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5207.
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Cette thèse porte sur les méthodes basées sur des contraintes. Nous présentons comme exemple l’algorithme PC, pour lequel nous proposons une modification qui garantit la cohérence des ensembles de séparation, utilisés pendant l’étape de reconstruction du squelette pour supprimer les arêtes entre les variables conditionnellement indépendantes, par rapport au graphe final. Elle consiste à itérer l’algorithme d’apprentissage de structure tout en limitant la recherche des ensembles de séparation à ceux qui sont cohérents par rapport au graphe obtenu à la fin de l’itération précédente. La contrainte peut être posée avec une complexité de calcul limitée à l’aide de la décomposition en block-cut tree du squelette du graphe. La modification permet d’augmenter le rappel au prix de la précision des méthodes basées sur des contraintes, tout en conservant une performance globale similaire ou supérieure. Elle améliore également l’interprétabilité et l’explicabilité du modèle graphique obtenu. Nous présentons ensuite la méthode basée sur des contraintes MIIC, récemment développée, qui adopte les idées du cadre du maximum de vraisemblance pour améliorer la robustesse et la performance du graphe obtenu. Nous discutons les caractéristiques et les limites de MIIC, et proposons plusieurs modifications qui mettent l’accent sur l’interprétabilité du graphe obtenu et l’extensibilité de l’algorithme. En particulier, nous mettons en œuvre l’approche itérative pour renforcer la cohérence de l’ensemble de séparation, nous optons pour une règle d’orientation conservatrice et nous utilisons la probabilité d’orientation de MIIC pour étendre la notation des arêtes dans le graphe final afin d’illustrer différentes relations causales. L’algorithme MIIC est appliqué à un ensemble de données d’environ 400 000 dossiers de cancer du sein provenant de la base de données SEER, comme benchmark à grande échelle dans la vie réelleThis thesis is focused on constraint-based methods, one of the basic types of causal structure learning algorithm. We use PC algorithm as a representative, for which we propose a simple and general modification that is applicable to any PC-derived methods. The modification ensures that all separating sets used during the skeleton reconstruction step to remove edges between conditionally independent variables remain consistent with respect to the final graph. It consists in iterating the structure learning algorithm while restricting the search of separating sets to those that are consistent with respect to the graph obtained at the end of the previous iteration. The restriction can be achieved with limited computational complexity with the help of block-cut tree decomposition of the graph skeleton. The enforcement of separating set consistency is found to increase the recall of constraint-based methods at the cost of precision, while keeping similar or better overall performance. It also improves the interpretability and explainability of the obtained graphical model. We then introduce the recently developed constraint-based method MIIC, which adopts ideas from the maximum likelihood framework to improve the robustness and overall performance of the obtained graph. We discuss the characteristics and the limitations of MIIC, and propose several modifications that emphasize the interpretability of the obtained graph and the scalability of the algorithm. In particular, we implement the iterative approach to enforce separating set consistency, and opt for a conservative rule of orientation, and exploit the orientation probability feature of MIIC to extend the edge notation in the final graph to illustrate different causal implications. The MIIC algorithm is applied to a dataset of about 400 000 breast cancer records from the SEER database, as a large-scale real-life benchmark
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Loxton, Ryan Christopher. "Optimal control problems involving constrained, switched, and delay systems." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/1479.
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In this thesis, we develop numerical methods for solving five nonstandard optimal control problems. The main idea of each method is to reformulate the optimal control problem as, or approximate it by, a nonlinear programming problem. The decision variables in this nonlinear programming problem influence its cost function (and constraints, if it has any) implicitly through the dynamic system. Hence, deriving the gradient of the cost and the constraint functions is a difficult task. A major focus of this thesis is on developing methods for computing these gradients. These methods can then be used in conjunction with a gradient-based optimization technique to solve the optimal control problem efficiently.The first optimal control problem that we consider has nonlinear inequality constraints that depend on the state at two or more discrete time points. These time points are decision variables that, together with a control function, should be chosen in an optimal manner. To tackle this problem, we first approximate the control by a piecewise constant function whose values and switching times (the times at which it changes value) are decision variables. We then apply a novel time-scaling transformation that maps the switching times to fixed points in a new time horizon. This yields an approximate dynamic optimization problem with a finite number of decision variables. We develop a new algorithm, which involves integrating an auxiliary dynamic system forward in time, for computing the gradient of the cost and constraints in this approximate problem.The second optimal control problem that we consider has nonlinear continuous inequality constraints. These constraints restrict both the state and the control at every point in the time horizon. As with the first problem, we approximate the control by a piecewise constant function and then transform the time variable. This yields an approximate semi-infinite programming problem, which can be solved using a penalty function algorithm. A solution of this problem immediately furnishes a suboptimal control for the original optimal control problem. By repeatedly increasing the number of parameters used in the approximation, we can generate a sequence of suboptimal controls. Our main result shows that the cost of these suboptimal controls converges to the minimum cost.The third optimal control problem that we consider is an applied problem from electrical engineering. Its aim is to determine an optimal operating scheme for a switchedcapacitor DC-DC power converter—an electronic device that transforms one DC voltage into another by periodically switching between several circuit topologies. Specifically, the optimal control problem is to choose the times at which the topology switches occur so that the output voltage ripple is minimized and the load regulation is maximized. This problem is governed by a switched system with linear subsystems (each subsystem models one of the power converter’s topologies). Moreover, its cost function is non-smooth. By introducing an auxiliary dynamic system and transforming the time variable (so that the topology switching times become fixed), we derive an equivalent semi-infinite programming problem. This semi-infinite programming problem, like the one that approximates the continuously-constrained optimal control problem, can be solved using a penalty function algorithm.The fourth optimal control problem that we consider involves a general switched system, which includes the model of a switched-capacitor DC-DC power converter as a special case. This switched system evolves by switching between several subsystems of nonlinear ordinary differential equations. Furthermore, each subsystem switch is accompanied by an instantaneous change in the state. These instantaneous changes—so-called state jumps—are influenced by control variables that, together with the subsystem switching times, should be selected in an optimal manner. As with the previous optimal control problems, we tackle this problem by transforming the time variable to obtain an equivalent problem in which the switching times are fixed. However, the functions governing the state jumps in this new problem are discontinuous. To overcome this difficulty, we introduce an approximate problem whose state jumps are governed by smooth functions. This approximate problem can be solved using a nonlinear programming algorithm. We prove an important convergence result that links the approximate problem’s solution with the original problem’s solution.The final optimal control problem that we consider is a parameter identification problem. The aim of this problem is to use given experimental data to identify unknown state-delays in a nonlinear delay-differential system. More precisely, the optimal control problem involves choosing the state-delays to minimize a cost function measuring the discrepancy between predicted and observed system output. We show that the gradient of this cost function can be computed by solving an auxiliary delay-differential system. On the basis of this result, the optimal control problem can be formulated—and hence solved—as a standard nonlinear programming problem.
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Hu, Yi-Ting, and 胡伊婷. "A Method-Level Unit Testing Tool Based on Object Constraint Language and Constraint Logic Programming." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/74068935574139087346.
Full textAbstract:
碩士國立中正大學
資訊工程所
98
Software testing is the main activity to ensure the quality of software. This thesis applies a black-box testing technique to automatically generate test cases based on the preconditions and postconditions in the Design by Contract software development approach. It uses Object Constraint Language to specify the preconditions and postconditions of Java methods. This thesis develops an automatic testing tool to generate Java test classes for the Java classes under test. The testing framework is based on the JUnit framework. The automatic generation of test input and expected output for each test case is based on the powerful constraint solving capability of constraint logic programming. This approach can generate the test input and expected output simultaneously.
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CHANG, CHENG-HUNG, and 張振鴻. "Constraint-Based Test Case Generation for White-Box Method-Level Unit Testing." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/36b93f.
Full textAbstract:
碩士國立中正大學
資訊工程研究所
105
This thesis defines the test case generation problem as a constraint satisfaction problem, and implements a constraint-based test case generator for white-box method-level unit testing. A test case contains a test input and the corresponding expected output. For test inputs, this tool first transforms the source program into a constraint model called a constraint logic graph. A constraint logic graph describes graphically the actual behaviors of a method or the constraint logic relationships between the inputs and the actual outputs of the method. On the constraint logic graph, this tool then performs equivalence class partitioning of the actual behaviors and test coverage criteria management using graph-based approaches to generate a small-quantity and wide-coverage group of representative behaviors for testing. The problem of generating the input for a representative behavior can be defined as a constraint satisfaction problem. This constraint satisfaction problem can be described and solved by a constraint logic programming language. For expected outputs, the expected behaviors of a method defined by the Object Constraint Language can also be viewed as a constraint satisfaction problem. Because the Object Constraint Language is non-executable, this tool will transform the specification described by the Object Constraint Language into an executable specification described by a constraint logic programming language. Given an input, the executable specification can be solved to generate the corresponding expected output. One of the features of this tool is that it uses an uniformed constraint-based approach to generate both test inputs and expected outputs for white-box method-level unit testing. Keywords- unit testing, white-box testing, constraint-based testing,test case generation, method-level testing, constraint logic programming
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Horng, Chu Bong, and 朱邦弘. "A graph-theoretical method for verifying, validating and refining constraint-based knowledge bases." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/15725063054087520550.
Full textAbstract:
碩士國立臺灣科技大學
工程技術研究所
83
Verification, validation and refinement of a knowledge base have played such an important role in the development of a knowledge-based system that researchers have recently put much emphasis on the task. To ensure the reliability and to enhance the performance of a knowledge-based system, we need formal and systematical methods to verify, validate and refine the involved knowledge base. This thesis proposes an approach based on the graph-theoretical method to verify, validate and refine a knowledge base that is represented by GASE-net, a net-based knowledge representation scheme. The approach converts a GASE- net into a constraints-based form (named V-net), and then utilizes a graph-theoretical process to find potential structural and/or functional anomalies via the constraint relationships. The approach is applied during knowledge acquisition so as to ensure the correctness and validity of a knowledge base incrementally, which in turns reduces the complexity of the involved operations. A knowledge verification, validation and refinement system (VARY) is proposed to realize the approach. Its integration into a knowledge acquisition tool is also discussed.
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Lin, Chih-Yuan, and 林志遠. "A Black-Box Method-Level Test Case Generator Based on Constraint Logic Programming." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/69222561756871618394.
Full textAbstract:
碩士國立中正大學
資訊工程研究所
99
Software testing is the main activity to ensure the quality of software. This article proposes a testing framework that applies a black-box testing technique to automatically generate test cases for Java methods. The specification of Java methods is based on the Design by Contract software development approach. This article uses UML class diagrams and the Object Constraint Language to describe the specification of Java methods. The automatic generation of test cases is based on the unification mechanism and the powerful constraint solving mechanism of constraint logic programming. A novel characteristic of this approach is that the test input and its corresponding expected output of a test case are simultaneously generated in a unified fashion.
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Huang, Chin-Jan, and 黃群然. "Skull Stripping Based on a Cluster-Constraint Watershed Method for MR T1-Weighted Image." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/87206207958444014060.
Full textAbstract:
碩士國立陽明大學
醫學工程研究所
99
The purpose of skull-stripping is to remove non-brain tissue from a head image and retain brain tissue only. It has played an important role in neuroimage analyses because it’s a basic preliminary step in many clinical applications. There are also many applications benefit from the ability to accurately segment brain from non-brain tissue, such as registration, cortical flattening algorithm and brain atrophy estimation. But skull-stripping has been a tricky and challenging problem all along due to the complexity of human brain structure, variety of individual characteristics and variable parameters of MR scanners. This study proposes a semi-automatic method to perform skull-stripping in T1-weighted magnetic resonance（MR）head images. We first apply a series of pre-processing steps combining bilateral filtering and fuzzy possibilistic c-means to smooth noise and cluster images. Then we apply watershed algorithm to segment clustered images into several regions. Finally, a post-merging step merges brain-like regions, and then a morphology processing refines the brain region to a final result. Experimental results suggest that our algorithm is potentially accurate, robust, and fast for automatically skull-stripping MR head image volumes.
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Chang, Chi-Kuang, and 張智光. "Design and Implementation of a Constraint-Based Test Case Generator for Black-Box Method-Level Unit Testing." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/06190123901503326640.
Full textAbstract:
博士國立中正大學
資訊工程研究所
103
Software testing is still the main approach to ensuring software quality. Software testing involves the formulation of the test case generation problem: The specification of the software behaviors; the partitioning of software behaviors into equivalence classes; the management of test coverage criteria to achieve specified software quality; the determination of test input and expected output for a representative software behavior in an equivalence class; the construction of a test fixture for this representative software behavior. We design and implement a constraint-based test case generator for black-box method-level unit testing. The test case generation problem is formulated as a constraint satisfaction problem. The software behaviors are specified using the Universal Modeling Language class diagrams and the Object Constraint Language. The partitioning of software behaviors into equivalence classes and the management of test coverage criteria are based on constraint logic graphs. A constraint logic graph can be viewed as a succinct graphical representation of the disjunctive normal form of a logic constraint expression. Each complete path in the constraint logic graph corresponds to a conjunctive clause in the disjunctive normal form and corresponds to a test case. The determination of the test input and expected output for this test case is formulated as a constraint satisfaction problem in constraint logic programming predicates. Constraint logic programming provides powerful constraint solving capability and can solve the test input and expected output simultaneously. The construction of the test fixture for this test case is also formulated as a constraint satisfaction problem and solved by constraint logic programming. The constraint-based test case generator can run in console mode or in a plugin for Eclipse integrated development environment. Console mode supports fast test case generation and verification. The plugin for Eclipse supports a user-friendly interface integrated with development environment. In the future, this constraint-based test case generator can be easily extended to support test case generation for white-box method-level unit testing, test case generation for class-level unit testing. and test case
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WANG, CHIEN-LUNG, and 王建瓏. "Supporting Array Data Type in Constraint-Based Test Case Generation for Black-Box Method-Level Unit Testing." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/h6b857.
Full textAbstract:
碩士國立中正大學
資訊工程研究所
107
Test case generation for collections like arrays is more sophisticated than that for scalars. This issue is more challenging in black-box testing than in white-box testing because the specification usually does not describe how collections will be processed. This thesis proposes a constraint-based approach to generate test cases for arrays in black-box method-level unit testing. The constraint-based framework uses the object constraint language as the specification language. This thesis extends the object constraint language to support array data type. This thesis supports static arrays, dynamic fixed-size arrays, and dynamic variable-szie arrays. The constraint-based framework then converts the constraint-based specification for a method into a constraint-based test model, called a constraint logic graph. This thesis performs equivalence class partitioning, boundary value analysis, and test coverage criteria management of black-box unit testing on the constraint logic graph. The constraint-based framework then formulates the test case generation for a method as a set of constraint satisfaction problems generated from the constraint logic graph. The test case generation for arrays involves both the size of the array and the values of array elements. These constraint satisfaction problems are then solved using the constraint logic programming to generate the test cases. This thesis implements the corresponding constraint logic predicate for each array operation supported in the object constraint language. This thesis also performs a preliminary evaluation of the constraint-based framework using a suite of Java benchmarks. Keyword: Constraint-based testing; black-box testing; method-level unit testing; test case generation; array data type
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Chen, Yu-Ting, and 陳昱廷. "Regularized Semi-Dense Map Reconstruction from a Monocular Sequence based on Piecewise Planar Constraint and Gauss Newton Method." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/69236153826950334115.
Full textAbstract:
碩士國立臺灣大學
電機工程學研究所
104
Three-dimensional environment reconstruction from a monocular camera has been a popular and a challenge research topic in past few years. This technique can be applied to unmanned vehicles to perform automatic navigation, environment exploration and automatic obstacle avoidance. In addition, it can also be applied to augmented reality. Since the camera is not equipped with an inertial measurement unit (IMU), it is necessary to locate the camera position and map the environment simultaneously. In this thesis, the camera pose estimation is based on feature based method [24: Lepetit et al. 2009] and direct method [1: Engel et al. 2014]. The camera localization thread is depend on the semi-dense map which is the high gradient area in image and is easily to become noisy. Hence, a method that can regularize the reconstructed semi-dense map without affect the accuracy of the camera pose localization is proposed in this thesis. The regularization method can eliminate the noise and smooth the semi-dense map. Furthermore, the regularization method is related to the photometric information between two images, unlike other methods only using the information of the depth and spatial relation. The reconstruction algorithm can be divide into three parts: stereo matching, piecewise planar constraint, and plane optimization. Since the high gradient areas are always narrow and hard to apply the piecewise planar constraint, a stereo matching method that can broaden the high gradient area by using their nearby low gradient pixels is proposed. After the semi-dense map is reconstructed, the semi-dense map will propagate to the piecewise planar constraint which can estimate the initial piecewise planes for each pixel. Finally, the optimization method is applied to optimize each estimated piecewise plane. In this thesis, the proposed stereo matching is composed of prior depth of ORB feature [27: Rublee et al. 2011], KD-Tree [36: Bentley 1975], Priority Queue and the entropy of the histogram of oriented gradient. The aim is to match the low gradient area around the high gradient area between two images correctly by using the epipolar geometry. It is hard to match two textureless areas between two images, so the best nearby texture area is searched to do the matching procedure. Firstly, if one pixel does not hold an inverse depth hypothesis, the nearby ORB features which has initial depth knowledge is used to initiate the inverse depth value, which can shorter the epipolar line searching length and improve the accuracy of the matching result. Searching the texture area which contains high gradient pixel is done by using k nearest neighbor search with KD-Tree, and sorting the searched pixels in accordance with the gradient magnitude by the priority queue. If the searched point passes the stereo searching constraint, the searched high gradient point will form a 5×5 pixels template and be used to do the stereo line searching. The corresponding points are considered to be matched if the residual between the templates in two image pass the stereo matching threshold which will change with the value of the searching region’s entropy of the histogram of oriented gradient. In the regularization part of this thesis, each tiny piece of point cloud projected from the image in 3D coordinate is assumed to fit a plane. The corresponding size in the image of each piece is set to 5×5 pixels. Since the assumption will not hold if the piece is in the border between two different objects or the discontinuous area, the planar constraint is applied to discriminate the non-planar region. After passing the planar constraint, Gauss-Newton method is used to minimize the photometric error between the two patches which projected from the piece in 3D coordinate in two images and the optimal parameters of the plane can be obtained. Afterwards, the optimal parameters are used to eliminate the noises and smooth the point cloud. The experimental results demonstrate that the proposed regularization algorithm can eliminate most of the noises and reconstruct a more clearly point cloud.
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Smith, Graham J. "Multiuser constraint based 3D scene construction." 2001. http://wwwlib.umi.com/cr/yorku/fullcit?pMQ67751.
Full textAbstract:
Thesis (M. Sc.)--York University, 2001. Graduate Programme in Computer Science.Typescript. Includes bibliographical references (leaves 92-95). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ67751.
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Michalik, Pavol Brüderlin Beat. "Methods for constraint-based conceptual free-form surface design /." 2003. http://www.gbv.de/dms/ilmenau/toc/374818258.PDF.
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Heldt, Corinna Melanie [Verfasser]. "Constraint-based methods for phasing in crystallography / Corinna Melanie Heldt." 2010. http://d-nb.info/1012625702/34.
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Michalik, Pavol [Verfasser]. "Methods for constraint based conceptual free-form surface design / vorgelegt von Pavol Michalik." 2003. http://d-nb.info/980463955/34.
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