A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

Automation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.postprin

Job shop scheduling with artificial immune systems

The job shop scheduling is complex due to the dynamic environment. When the information of the jobs and machines are pre-defined and no unexpected events occur, the job shop is static. However, the real scheduling environment is always dynamic due to the constantly changing information and different uncertainties. This study discusses this complex job shop scheduling environment, and applies the AIS theory and switching strategy that changes the sequencing approach to the dispatching approach by taking into account the system status to solve this problem. AIS is a biological inspired computational paradigm that simulates the mechanisms of the biological immune system. Therefore, AIS presents appealing features of immune system that make AIS unique from other evolutionary intelligent algorithm, such as self-learning, long-lasting memory, cross reactive response, discrimination of self from non-self, fault tolerance, and strong adaptability to the environment. These features of AIS are successfully used in this study to solve the job shop scheduling problem. When the job shop environment is static, sequencing approach based on the clonal selection theory and immune network theory of AIS is applied. This approach achieves great performance, especially for small size problems in terms of computation time. The feature of long-lasting memory is demonstrated to be able to accelerate the convergence rate of the algorithm and reduce the computation time. When some unexpected events occasionally arrive at the job shop and disrupt the static environment, an extended deterministic dendritic cell algorithm (DCA) based on the DCA theory of AIS is proposed to arrange the rescheduling process to balance the efficiency and stability of the system. When the disturbances continuously occur, such as the continuous jobs arrival, the sequencing approach is changed to the dispatching approach that involves the priority dispatching rules (PDRs). The immune network theory of AIS is applied to propose an idiotypic network model of PDRs to arrange the application of various dispatching rules. The experiments show that the proposed network model presents strong adaptability to the dynamic job shop scheduling environment.postprin

Багатозначний аналіз еволюційних систем хвильового типу з нерегулярними обмеженнями

Дисертаційна робота присвячена дослідженню асимптотичної поведінки розв’язків класу дисипативних динамiчних систем хвильового типу з нерегулярними обмеженнями. В обмеженій області дослiджено якiсну поведiнку слабких розв’язкiв еволюцiйної системи хвильового типу з розривною нелiнiйнiстю в скалярному випадку та поширено отриманi результати на бiльш загальне диференціально-операторне включення. Дослiджено асимптотичну поведiнку розв’язкiв стохастично збуреної дисипативної динамiчної системи. Побудувано алгоритм розв’язання задач дослiдження глобальної поведiнки функцiй стану для еволюцiйних задач з нерегулярними обмеженнями, який може застосовуватись до класiв математичних моделей, що описують поведiнку процесiв та полiв рiзної природи. Отримані результати застосовано до дослiдження п’єзоелектричної системи. Це дозволило забезпечити стiйке функцiонування дослiджуваного об’єкту. Отриманi теоретичнi результати можуть бути використанi в процесах керування для зменшення або компенсацiї небажаних ефектiв, для обґрунтування чисельних алгоритмiв пошуку слабких розв’язкiв, при виведеннi дослiджуваних систем на заданi стацiонарнi рiвнi

Advances in Multiscale and Multifield Solid Material Interfaces

Interfaces play an essential role in determining the mechanical properties and the structural integrity of a wide variety of technological materials. As new manufacturing methods become available, interface engineering and architecture at multiscale length levels in multi-physics materials open up to applications with high innovation potential. This Special Issue is dedicated to recent advances in fundamental and applications of solid material interfaces

Techniques d'optimisation non lisse avec des applications en automatique et en mécanique des contacts

L'optimisation non lisse est une branche active de programmation non linéaire moderne, où l'objectif et les contraintes sont des fonctions continues mais pas nécessairement différentiables. Les sous-gradients généralisés sont disponibles comme un substitut à l'information dérivée manquante, et sont utilisés dans le cadre des algorithmes de descente pour se rapprocher des solutions optimales locales. Sous des hypothèses réalistes en pratique, nous prouvons des certificats de convergence vers les points optimums locaux ou critiques à partir d'un point de départ arbitraire. Dans cette thèse, nous développons plus particulièrement des techniques d'optimisation non lisse de type faisceaux, où le défi consiste à prouver des certificats de convergence sans hypothèse de convexité. Des résultats satisfaisants sont obtenus pour les deux classes importantes de fonctions non lisses dans des applications, fonctions C1-inférieurement et C1-supérieurement. Nos méthodes sont appliquées à des problèmes de design dans la théorie du système de contrôle et dans la mécanique de contact unilatéral et en particulier, dans les essais mécaniques destructifs pour la délaminage des matériaux composites. Nous montrons comment ces domaines conduisent à des problèmes d'optimisation non lisse typiques, et nous développons des algorithmes de faisceaux appropriés pour traiter ces problèmes avec succèsNonsmooth optimization is an active branch of modern nonlinear programming, where objective and constraints are continuous but not necessarily differentiable functions. Generalized subgradients are available as a substitute for the missing derivative information, and are used within the framework of descent algorithms to approximate local optimal solutions. Under practically realistic hypotheses we prove convergence certificates to local optima or critical points from an arbitrary starting point. In this thesis we develop especially nonsmooth optimization techniques of bundle type, where the challenge is to prove convergence certificates without convexity hypotheses. Satisfactory results are obtained for two important classes of nonsmooth functions in applications, lower- and upper-C1 functions. Our methods are applied to design problems in control system theory and in unilateral contact mechanics and in particular, in destructive mechanical testing for delamination of composite materials. We show how these fields lead to typical nonsmooth optimization problems, and we develop bundle algorithms suited to address these problems successfully

Advances in Optimization and Nonlinear Analysis

The present book focuses on that part of calculus of variations, optimization, nonlinear analysis and related applications which combines tools and methods from partial differential equations with geometrical techniques. More precisely, this work is devoted to nonlinear problems coming from different areas, with particular reference to those introducing new techniques capable of solving a wide range of problems. The book is a valuable guide for researchers, engineers and students in the field of mathematics, operations research, optimal control science, artificial intelligence, management science and economics