Site selection of LNG terminal based on cloud matter element model and principal component analysis

With the development of liquefied natural gas(LNG) port, as one of the crucial LNG port sitting process, the LNG terminal site’s condition assessment method has always received attention from experts, scholars concern more and more about the method’s practicality and reliability. In the traditional condition assessment method, due to the characteristics of the complex and extensive factors in the comprehensive assessment of the LNG terminal site, the assessment system is not comprehensive enough, or the assessment is too complex, the indexes are not easy to quantify, such problems are emerging. In view of the above reasons, the principal component analysis(PCA) method is used to transform the multi-indicators that affect the comparison of terminal sites into a few comprehensive indicators. A comprehensive evaluation model of the LNG terminal site based on cloud matter element theory and subjective and objective comprehensive weighting method was constructed. By the subjective and objective comprehensive weighting method, the comprehensive weight of each index is determined and the LNG terminal site comprehensive assessment standard cloud element model is constructed with the combination of cloud model and matter-element theory. The cloud matter-element correlation function is established to determine the degree of association between the matter element to be evaluated and the standard cloud matter element model. In order to eliminate random errors and improve the credibility of the results, the algorithm is used for multiple calculations and analysis to achieve the purpose of simultaneously giving the evaluation results and coefficients of credible degree. Finally, the reliability and rationality of the method are verified by an example

Synthesis of variable dancing styles based on a compact spatiotemporal representation of dance

Dance as a complex expressive form of motion is able to convey emotion, meaning and social idiosyncrasies that opens channels for non-verbal communication, and promotes rich cross-modal interactions with music and the environment. As such, realistic dancing characters may incorporate crossmodal information and variability of the dance forms through compact representations that may describe the movement structure in terms of its spatial and temporal organization. In this paper, we propose a novel method for synthesizing beatsynchronous dancing motions based on a compact topological model of dance styles, previously captured with a motion capture system. The model was based on the Topological Gesture Analysis (TGA) which conveys a discrete three-dimensional point-cloud representation of the dance, by describing the spatiotemporal variability of its gestural trajectories into uniform spherical distributions, according to classes of the musical meter. The methodology for synthesizing the modeled dance traces back the topological representations, constrained with definable metrical and spatial parameters, into complete dance instances whose variability is controlled by stochastic processes that considers both TGA distributions and the kinematic constraints of the body morphology. In order to assess the relevance and flexibility of each parameter into feasibly reproducing the style of the captured dance, we correlated both captured and synthesized trajectories of samba dancing sequences in relation to the level of compression of the used model, and report on a subjective evaluation over a set of six tests. The achieved results validated our approach, suggesting that a periodic dancing style, and its musical synchrony, can be feasibly reproduced from a suitably parametrized discrete spatiotemporal representation of the gestural motion trajectories, with a notable degree of compression

Cloud Infrastructure Services Selection and Evaluation

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant increase of new cloud services almost every month by both large corporations (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g. Rackspace and FlexiScale), the selection scenarios become more and more complex. This is aggregated by confusing and ambiguous terminology and non-standardized interfaces. This is challenging for decision-makers such as application developers and chief information officers as they are overwhelmed by various choices available. In this thesis, I will address the above challenges by developing several techniques. Firstly, I define the Cloud Computing Ontology (CoCoOn). CoCoOn defines concepts, features, attributes and relations of Cloud infrastructure services. Secondly, I propose a service selection method that adopts an analytic hierarchy process (AHP)-based multi-criteria decision-making technique. It allows users to define multiple design-time constraints like renting costs, data centre locations, service features and real-time constraints, such as end-to-end message latency and throughput. These constraints are then matched against our model to compute the possible best-fit combinations of cloud Infrastructure, offered as a Service (IaaS). Pairwise comparisons are used to help users determine a relative preference among a pool of nonnumerical attributes. Criteria that are taken into consideration during comparison can be grouped into two categories: the benefit and the cost. Based on this, I define a cost-benefit-ratio-based evaluation function to calculate the ranking for Cloud service options. Thirdly, I suggest a theory-based queuing approach for estimating IaaS usage. Queuing theory is a widely studied method in QoS modelling and optimization. From the infrastructure system administrator perspective, I explore several ways to apply the queuing theory model to estimate the best-fit resource allocation for achieving the desired SLA. Finally, the thesis shows how an integrated system, CloudRecommender, can be built from our proposed approaches

Automatic Data Migration into the Cloud

Relational databases have been used for decades to store data. Using scale up, relational databases require a bigger and bigger server with more CPUs, more memory, and more disk storage to keep all the tables to support more concurrent users. However, big servers tend to be highly complex, proprietary, and disproportionately expensive, unlike the low-cost, commodity hardware. Therefore, it becomes important to store data efficiently and compute with massive amount of data, providing high scalability, providing high performance and availability at low costs. This leads to the invention of cloud databases, for instance NoSQL databases. NoSQL databases have many advantages such as reading and writing data quickly, supporting massive storage and low cost. The scaling approach in cloud databases is scale out, which is used to add multiple servers, and the data structure of storage is in the form of key-value pairs. However, it can be a challenge for enterprises to migrate existing relational databases to highly scalable NoSQL databases on clouds. In this thesis, we propose an automatic data migration model which will assist enterprises to migrate their relational databases efficiently and transparently to the cloud databases. We propose four migration methods to migrate data in four different ways. Each migration method is independent of the others and stores the migrated relational database in different formats in the cloud database. We design a system to implement the automatic data migration model. As a proof of concept, we successfully migrated a relational database from Microsoft SQL Server to a cloud database Amazon SimpleDB using four different migration methods. Furthermore, we have conducted extensive experiments on Amazon SimpleDB to evaluate the performance of our model in terms of computational time, storage cost, sharding and redundancy. Based on these experiments and detailed analysis of each migration method, our system allows enterprises to determine which method is suitable for their data migration. Furthermore, our experimental evaluation shows that our solution is promising and can migrate data from the relational databases to the cloud databases

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

A WOA-based optimization approach for task scheduling in cloud Computing systems

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks

Point cloud management techniques for a multihit ladar imaging camera system

Lidar imaging is a powerful measurement technique where a laser pulse is shone onto an object and the beam reflected back is recovered at some solid-state detector. The time elapsed is counted so an automated measurement of the distance to the target is obtained, without any further calculation. The concept is also referred to as ladar or time of-flight imaging. Different scanning mechanisms have been proposed to recover complete 3D images out of this pointwise approach. Most popular recent applications involve landing aids, object recognition, self-guided vehicles and safeWith the incorporation of optical sensors into the machine vision technology, a full new field has emerged to revolutionize different technologies such as self-driving, 3D scanners and printers or virtual reality. However, new technologies come with new techniques and methodologies to manipulate them. Point Clouds were born as the data storage system and a collection of challenges came with them. One of these challenges consists in processing them in order to obtain the best description of the real world. Hence, it is necessary to have a tool to evaluate the quality of those Point Cloud in order to analyze their quality. In this MSc thesis we developed a mathematical approach for Point Cloud quality evaluation and implanted by Matlab. The full mathematical development as well as the structure of the code and the different tools used to acquire and manipulate Point Clouds are described and introduced along the thesis. A final analysis of the methodology showed there is still a lot of work to do. Several questions appeared and need to be solved in order to grow in this field