DESIGN OF OPTIMAL PROCEDURAL CONTROLLERS FOR CHEMICAL PROCESSES MODELLED AS STOCHASTIC DISCRETE EVENT SYSTEMS

This thesis presents a formal method for the the design of optimal and provably correct procedural controllers for chemical processes modelled as Stochastic Discrete Event Systems (SDESs). The thesis extends previous work on Procedural Control Theory (PCT) [1], which used formal techniques for the design of automation Discrete Event Systems (DESs). Many dynamic processes for example, batch operations and the start-up and shut down of continuous plants, can be modelled as DESs. Controllers for these systems are typically of the sequential type. Most prior work on characterizing the behaviour of DESs has been restricted to deterministic systems. However, DESs consisting of concurrent interacting processes present a broad spectrum of uncertainty such as uncertainty in the occurrence of events. The formalism of weighted probabilistic Finite State Machine (wp-FSM) is introduced for modelling SDESs and pre-de ned failure models are embedded in wp-FSM to describe and control the abnormal behaviour of systems. The thesis presents e cient algorithms and procedures for synthesising optimal procedural controllers for such SDESs. The synthesised optimal controllers for such stochastic systems will take into consideration probabilities of events occurrence, operation costs and failure costs of events in making optimal choices in the design of control sequences. The controllers will force the system from an initial state to one or more goal states with an optimal expected cost and when feasible drive the system from any state reached after a failure to goal states. On the practical side, recognising the importance of the needs of the target end user, the design of a suitable software implementation is completed. The potential of both the approach and the supporting software are demonstrated by two industry case studies. Furthermore, the simulation environment gPROMS was used to test whether the operating speci cations thus designed were met in a combined discrete/continuous environment

Human motion retrieval based on freehand sketch

In this paper, we present an integrated framework of human motion retrieval based on freehand sketch. With some simple rules, the user can acquire a desired motion by sketching several key postures. To retrieve efficiently and accurately by sketch, the 3D postures are projected onto several 2D planes. The limb direction feature is proposed to represent the input sketch and the projected-postures. Furthermore, a novel index structure based on k-d tree is constructed to index the motions in the database, which speeds up the retrieval process. With our posture-by-posture retrieval algorithm, a continuous motion can be got directly or generated by using a pre-computed graph structure. What's more, our system provides an intuitive user interface. The experimental results demonstrate the effectiveness of our method. © 2014 John Wiley & Sons, Ltd

Sketch-based Human Motion Retrieval via 2D Geometric Posture Descriptor.

AbstractSketch-based human motion retrieval is a hot topic in computer animation in recent years. In this paper, we present a novel sketch-based human motion retrieval method via selected 2-dimensional (2D) Geometric Posture Descriptor (2GPD). Specially, we firstly propose a rich 2D pose feature call 2D Geometric Posture Descriptor (2GPD), which is effective in encoding the 2D posture similarity by exploiting the geometric relationships among different human body parts. Since the original 2GPD is of high dimension and redundant, a semi-supervised feature selection algorithm derived from Laplacian Score is then adopted to select the most discriminative feature component of 2GPD as feature representation, and we call it as selected 2GPD. Finally, a posture-by-posture motion retrieval algorithm is used to retrieve a motion sequence by sketching several key postures. Experimental results on CMU human motion database demonstrate the effectiveness of our proposed approach

Design of optimal procedural controllers for chemical processes modelled as stochastic discrete event systems

This thesis presents a formal method for the the design of optimal and provably correct procedural controllers for chemical processes modelled as Stochastic Discrete Event Systems (SDESs). The thesis extends previous work on Procedural Control Theory (PCT) [1], which used formal techniques for the design of automation Discrete Event Systems (DESs). Many dynamic processes for example, batch operations and the start-up and shut down of continuous plants, can be modelled as DESs. Controllers for these systems are typically of the sequential type. Most prior work on characterizing the behaviour of DESs has been restricted to deterministic systems. However, DESs consisting of concurrent interacting processes present a broad spectrum of uncertainty such as uncertainty in the occurrence of events. The formalism of weighted probabilistic Finite State Machine (wp-FSM) is introduced for modelling SDESs and pre-de ned failure models are embedded in wp-FSM to describe and control the abnormal behaviour of systems. The thesis presents e cient algorithms and procedures for synthesising optimal procedural controllers for such SDESs. The synthesised optimal controllers for such stochastic systems will take into consideration probabilities of events occurrence, operation costs and failure costs of events in making optimal choices in the design of control sequences. The controllers will force the system from an initial state to one or more goal states with an optimal expected cost and when feasible drive the system from any state reached after a failure to goal states. On the practical side, recognising the importance of the needs of the target end user, the design of a suitable software implementation is completed. The potential of both the approach and the supporting software are demonstrated by two industry case studies. Furthermore, the simulation environment gPROMS was used to test whether the operating speci cations thus designed were met in a combined discrete/continuous environment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Effect of Cationic Modified Microcrystalline Cellulose on the Emulsifying Properties and Water/Oil Interface Behavior of Soybean Protein Isolate

Stabilizing emulsion using complex biopolymers is a common strategy. It would be very interesting to characterize the impact of charge density on the emulsifying properties of complex polyelectrolytes carrying opposite charges. In this study, cationic modified microcrystalline celluloses (CMCC) of different charge densities were prepared and mixed with soy protein isolate (SPI) for emulsion applications. CMCC-1 to 3 with various cationic charge values were successfully prepared as characterized by zeta-potential and FTIR. The positive charge density’s effects on solubility, thermogravimetric properties, and rheological properties were studied. Complexes of SPI-CMCC with various zeta-potential values were then obtained and used to stabilize soybean oil emulsions. The results show that emulsions stabilized by complexes of SPI and CMCC-3 at a ratio of 1:3 had the best emulsification ability and stability. However, the interfacial tension-reducing ability of complexes decreased continuously with increasing cationic charge value, while the rheological results show that complexes of SPI-CMCC-3 at a ratio of 1:3 formed a stronger viscoelastic network than other complexes. Our results indicate that this SPI-CMCC complex formula showed excellent emulsification performance, which could be adjusted and promoted by changing the charge density. This complex formula is promising for fabrication of emulsion-based food and cosmetic products