Certificate that Joseph Kingsmill, sheriff, has sold 4 acres in Lot no.33 in the 4th Concession of Wainfleet to W.H. Dickson

Certificate that Joseph Kingsmill, sheriff, has sold 4 acres in Lot no.33 in the 4th Concession of Wainfleet to W.H. Dickson, May 23, 1857.

Certificate that Joseph Kingsmill, sheriff, has sold 7 acres in Lot no. 24 in the 2nd Concession of Caistor to W. H. Dickson

Certificate that Joseph Kingsmill, sheriff, has sold 7 acres in Lot no. 24 in the 2nd Concession of Caistor to W. H. Dickson, May 23, 1857.

Certificate that Joseph Kingsmill, sheriff, has sold 6 acres in Lot 13 in the 3rd Concession of Crowland to Walter H. Dickson

Certificate that Joseph Kingsmill, sheriff, has sold 6 acres in Lot 13 in the 3rd Concession of Crowland to Walter H. Dickson, May 23, 1857.

Certificate that Joseph Kingsmill, sheriff, has sold 4 acres in Broken Front Lot no.3 in the 3rd Concession of Gainboro to W.H. Dickson

Certificate that Joseph Kingsmill, sheriff, has sold 4 acres in Broken Front Lot no.3 in the 3rd Concession of Gainsboro to W.H. Dickson, May 23, 1857.

Province of Upper Canada Grant (vellum) to Thomas Fraser of the Township of Edwardsburgh granted 1 acre in Lot no.9 in the County of Stormont

Province of Upper Canada Grant (vellum) to Thomas Fraser of the Township of Edwardsburgh granted 1 acre in Lot no.9 in the County of Stormont. Signed by William Jarvis, Sir Isaac Brock, Prideaux Selby and John Macdonell. There are some holes in the document and there are small pieces missing on the right hand side. William Jarvis was the Provincial Secretary of the Lt. Governor of Upper Canada; Sir Isaac Brock. Jarvis was an officer in the Queen’s Rangers. He also served as Provincial Secretary of Upper Canada. A partial crown seal is attached, Mar. 26, 1812.

Processus d'acquisition de nouvelles connaissances en urbanisme : le cas de l'îlot de chaleur urbain

Dans le contexte du changement climatique, la chaleur est, depuis le début des années 2000, une préoccupation grandissante, d’abord en tant qu’enjeu sanitaire puis comme problématique affectant la qualité de vie des citoyens. Au Québec, le concept d’îlot de chaleur urbain, issu de la climatologie urbaine, a graduellement émergé dans le discours des autorités et de certains acteurs de l’aménagement. Or, on constate l’existence d’un certain décalage entre les connaissances scientifiques et l’interprétation qu’en font les urbanistes. Dans le cadre de ce mémoire, on a tenté d’identifier les facteurs explicatifs de ce décalage en s’intéressant au processus d’acquisition des connaissances des urbanistes québécois. Par le biais d’entretiens réalisés auprès des principaux acteurs ayant contribué à l’émergence de l’ICU au Québec, on a été en mesure d’identifier les éléments ayant entraîné certaines distorsions des connaissances. L’absence d’interdisciplinarité entre la climatologie urbaine et l’urbanisme tout au long du processus d’acquisition des connaissances ainsi qu’une interprétation tronquée de la carte des températures de surface expliquent principalement la nature du décalage observé.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

Thread Scheduling Mechanisms for Multiple-Context Parallel Processors

Scheduling tasks to efficiently use the available processor resources is crucial to minimizing the runtime of applications on shared-memory parallel processors. One factor that contributes to poor processor utilization is the idle time caused by long latency operations, such as remote memory references or processor synchronization operations. One way of tolerating this latency is to use a processor with multiple hardware contexts that can rapidly switch to executing another thread of computation whenever a long latency operation occurs, thus increasing processor utilization by overlapping computation with communication. Although multiple contexts are effective for tolerating latency, this effectiveness can be limited by memory and network bandwidth, by cache interference effects among the multiple contexts, and by critical tasks sharing processor resources with less critical tasks. This thesis presents techniques that increase the effectiveness of multiple contexts by intelligently scheduling threads to make more efficient use of processor pipeline, bandwidth, and cache resources. This thesis proposes thread prioritization as a fundamental mechanism for directing the thread schedule on a multiple-context processor. A priority is assigned to each thread either statically or dynamically and is used by the thread scheduler to decide which threads to load in the contexts, and to decide which context to switch to on a context switch. We develop a multiple-context model that integrates both cache and network effects, and shows how thread prioritization can both maintain high processor utilization, and limit increases in critical path runtime caused by multithreading. The model also shows that in order to be effective in bandwidth limited applications, thread prioritization must be extended to prioritize memory requests. We show how simple hardware can prioritize the running of threads in the multiple contexts, and the issuing of requests to both the local memory and the network. Simulation experiments show how thread prioritization is used in a variety of applications. Thread prioritization can improve the performance of synchronization primitives by minimizing the number of processor cycles wasted in spinning and devoting more cycles to critical threads. Thread prioritization can be used in combination with other techniques to improve cache performance and minimize cache interference between different working sets in the cache. For applications that are critical path limited, thread prioritization can improve performance by allowing processor resources to be devoted preferentially to critical threads. These experimental results show that thread prioritization is a mechanism that can be used to implement a wide range of scheduling policies.

Optimal Methodology for Synchronized Scheduling of Parallel Station Assembly with Air Transportation

We present an optimal methodology for synchronized scheduling of production assembly with air transportation to achieve accurate delivery with minimized cost in consumer electronics supply chain (CESC). This problem was motivated by a major PC manufacturer in consumer electronics industry, where it is required to schedule the delivery requirements to meet the customer needs in different parts of South East Asia. The overall problem is decomposed into two sub-problems which consist of an air transportation allocation problem and an assembly scheduling problem. The air transportation allocation problem is formulated as a Linear Programming Problem with earliness tardiness penalties for job orders. For the assembly scheduling problem, it is basically required to sequence the job orders on the assembly stations to minimize their waiting times before they are shipped by flights to their destinations. Hence the second sub-problem is modelled as a scheduling problem with earliness penalties. The earliness penalties are assumed to be independent of the job orders.

Planning and Scheduling Decisions in Supply Chains with Multiple Supply Modes: An Integrated Approach

We address the problem of jointly determining shipment planning and scheduling decisions with the presence of multiple shipment modes. We consider long lead time, less expensive sea shipment mode, and short lead time but expensive air shipment modes. Existing research on multiple shipment modes largely address the short term scheduling decisions only. Motivated by an industrial problem where planning decisions are independent of the scheduling decisions, we investigate the benefits of integrating the two sets of decisions. We develop sequence of mathematical models to address the planning and scheduling decisions. Preliminary computational results indicate improved performance of the integrated approach over some of the existing policies used in real-life situations.

Lot i les seves filles

Estudi del quadre d'Andrea Vaccaro (1604-1670): Lot embriagat per les seves filles. Aquesta obra és l'única que té el Museu d'Art de Girona de l'artista

A Gain Scheduling Model Predictive Controller for Blood Glucose Control in Type 1 Diabetes

This paper presents a control strategy for blood glucose(BG) level regulation in type 1 diabetic patients. To design the controller, model-based predictive control scheme has been applied to a newly developed diabetic patient model. The controller is provided with a feedforward loop to improve meal compensation, a gain-scheduling scheme to account for different BG levels, and an asymmetric cost function to reduce hypoglycemic risk. A simulation environment that has been approved for testing of artificial pancreas control algorithms has been used to test the controller. The simulation results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against model–patient mismatch and errors in meal estimation

There's an awful lot of weirdos in our neighbourhood : a book of rather silly poems and pictures.

Un libro de poemas más bien tonto, en el que vemos como hay un montón de raritos a nuestro alrededor.