Energy efficiency in cloud computing based on mixture power spectral density prediction

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive analysis was further enhanced by deriving the mixture power spectral density to model the complex cloud monitoring statistics. By engaging the improved technique to the predictive analysis, the prediction process was more adaptive to handle the fluctuation in system utilization. As a consequence, the optimization process could subsequently produce more appropriate setting for energy savings. After the infrastructure setting has been made available, the instruction of virtual machine migration was created and implemented by the cloud orchestrator. This instruction condensed the services into the pool of active facilities, satisfying the objective of power efficiency. Eventually, any physical machine out of the power configuration would be gradually terminated. Compared to our former method, the effectiveness of the proposed technique has been proven by cutting down 4.92% of energy consumption, while still maintaining a similar quality of services.

     

Evaluating a color-based active basis model for object recognition

Wu and coworkers introduced an active basis model (ABM) for object recognition in 2010, in which the learning algorithm tends to sketch edges in textures. A grey-value local power spectrum was used to find a common template and deformable templates from a set of training images and to detect an object in new images by template matching. In this paper, we propose a color-based active basis model (color-based ABM for short), which incorporates color information. We adopt the framework of Wu et al. in the learning, detection, and classification of the color-based ABM. However, in order to improve the performance in object recognition, we modify the framework of Wu et al. by using different color-based features in both the learning and template matching algorithms. In this color-based ABM approach, two types of learning (i.e., supervised learning and unsupervised learning) are also explored. Moreover, the usefulness of the color-based ABM for practical object recognition in computer vision applications is demonstrated and its significant improvement in recognizing objects is reported.     

A technique for extracting behavioral sequence patterns from GPS recorded data

The mobile wireless market has been attracting many customers. Technically, the paradigm of anytime-anywhere connectivity raises previously unthinkable challenges, including the management of million of mobile customers, their profiles, the profiles-based selective information dissemination, and server-side computing infrastructure design issues to support such a large pool of users automatically and intelligently. In this paper, we propose a data mining technique for discovering frequent behavioral patterns from a collection of trajectories gathered by Global Positioning System. Although the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and temporal constraints on spatiotemporal sequences makes the computation feasible. Specifically, the mined patterns are incorporated with synthetic constraints, namely spatiotemporal sequence length restriction, minimum and maximum timing gap between events, time window of occurrence of the whole pattern, inclusion or exclusion event constraints, and frequent movement patterns predictive of one ore more classes. The algorithm for mining all frequent constrained patterns is named cAllMOP. Moreover, to control the density of pattern regions a clustering algorithm is exploited. The proposed method is efficient and scalable. Its efficiency is better than that of the previous algorithms AllMOP and GSP with respect to the compactness of discovered knowledge, execution time, and memory requirement.     

Modélisation d'un four de calcination du coke de pétrole: II. Simulation du procédé

A one-dimensional steady-state mathematical model has been developed to simulate the process of calcination of petroleum coke in a rotary kiln. A simulation is made to reproduce the operating conditions prevailing during a normal working day, in order to validate the model, and to study the effects of the main process control variables. It has been found that of the energy provided by combustion, 59.5 % comes from hydrogen, 14.6 % from the carbon of the coke bed, 13.8 % from coke dust, 11.3 % from methane and 0.8 % from tar. A comparison with existing models is made to identify eventual similarities between the various kilns under study. It appears that maximum coke temperature occurs at 45 to 55 % of kiln length, while volatile matters evolution takes place between 5 and 60 % of kiln length, measured from feed end.     

Activities of MFI-Supported Rhenium Catalysts for the Aromatization of Methane: Effect of Cationic Form of the Inorganic Carrier

MFI type inorganic carrier was used in two different cationic forms, hydrogen and calcium respectively. MFI-supported molybdenum and rhenium catalysts were prepared. The activities of the catalysts were compared for the aromatization reaction of methane. Higher activity values were attained with the catalysts supported on HZSM-5. Aromatics were also observed with the catalysts supported on CaZSM-5, despite their deficiency in acid sites. Highly dispersed rhenium is expected to be formed with the use of the inorganic carrier in calcium form. On the other hand, lower reaction rates were observed with rhenium supported on CaZSM-5, in spite of the improved dispersion of the active rhenium species on this catalyst. This was interpreted in terms of the critical role of the acid sites in the conversion of methane to aromatics, compared to the improved dispersion of the active metal.     

Structure and properties of polyimide‐bonded magnets processed from prepolymers based on diacetyl derivatives of aromatic diamines and dianhydrides

We report a novel method of polyimide (PI) synthesis from prepolymers based on dianhydrides and diacetyl derivatives of aromatic diamines that facilitate the preparation of a melt processable mixture at 300 ± 10°C of the prepolymer and magnetic Nd‐Fe‐B alloy to provide PI‐bonded magnets with enhanced properties. It is shown that chemical structure of the prepolymers strongly influences viscosity behavior via crystallization of the oligoimide in the melt, leading to formation of PI with rigid‐rod like structure. This structural ordering of the prepolymers based on diacetyl derivative of diamine used in this study, if not controlled, leads to exponential increase of melt viscosity with time, making it practically impossible to prepare melt processable mixture of the magnetic particles and the PI prepolymers at elevated temperatures. The results obtained demonstrate that appropriate dianhydrides and diacetyl derivatives of diamines that do not lead to crystallization of oligoimides in prepolymer mixture can be used under controlled processing conditions to prepare melt‐processable PI‐bonded magnets containing rigid‐rod like PI structure that significantly increases thermal stability of the magnets. The temperature dependencies of the magnetic properties of the PI‐bonded magnets under conditions that they are likely to encounter during their service life were found to be remarkably similar to that of commercial thermoplastic magnets such as injection‐molded nylon magnets. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 478–485, 2006     

Synthesis and Characterization of Segmented Block Copolymers Based on Hydroxyl‐Terminated Liquid Natural Rubber and α,ω‐Diisocyanato Telechelics

Summary: Segmented block copolymers, consisting of non‐polar soft segments from hydroxyl‐terminated liquid natural rubber (HTNR) and polar hard segments from α,ω‐diisocyanato telechelics obtained by “criss‐cross”‐cycloaddition, have been synthesized. The block copolymer formation took place under relatively mild reaction conditions at 80 °C in dichloroethane in the presence of dibutyltin dilaurate as a catalyst. The resulting block copolymers were characterized by spectroscopic techniques (¹H NMR, FTIR, UV‐vis spectroscopy) as well as GPC for molar mass determination. The block copolymers were compression molded in a hot stage press, and the resulting samples were characterized by DSC and stress‐strain measurement. The solubility and phase morphology of the materials have also been studied.

Segmented block copolymer from HTNR and α,ω‐diisocyanato telechelics     

Modélisation Mathématique de la Décantation de la Boue Rouge

A good performance of the solid‐liquid unit operation is required for the economical exploitation of the Bayer process. A computational fluid dynamics (CFD) model simulating the operation of the last washing stage mud thickener of a large Canadian alumina plant is presented. The parametric study of the impact of changes in four parameters shows that the diameter of flocculated red mud particles, the feed flow rate and the radius of the feed well are critical parameters for the operation of the thickener.     

Acetals as nucleating agents for polypropylene

Nucleating agents increase the impact strength, tensile strength, and tensile elasticity modulus of semicrystalline polymers. Nucleating agents also decrease product cycle times, resulting in a cost savings per product unit. We have synthesized and tested 15 compounds as nucleactors for polypropylene. Of these, trinapthylidene sorbitol, tri-(4-methyl-1-naphthylidene)sorbitol, tri-(4-methoxy-1-naphthylidene) sorbitol, and dibenzylidene xylitol are efficient nucleators of polypropylene. Trinaphthylidene sorbitol (tns) has two major diastereomers: The “S” diastereomer yields a faster crystallization rate for polypropylene than does the commercial nucleator dibenzylidene sorbitol (Millad 3905). Crystallization rates are 208 and 88, respectively (t min^?1 × 1000). The “R” diastereomer, however, is a poor nucleator and interferes with the nucleating activity of the “S” diastereomer. A 52/48 mixture of diastereomers does not nucleate polypropylene, even at twice the concentration. This is first time that the importance of stereochemistry has been demonstrated in the nucleating action. © 1994 John Wiley & Sons, Inc.