Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.     

Multireference object pose retrieval with volume feedback

Pose retrieval of a rigid object from monocular video sequences or images is addressed. Initially, the object pose is estimated in each image assuming flat depth maps. Shape-from-silhouette is then applied to make a 3-D model (volume), which is used for a new round of pose estimations, this time by a model-based method that gives better estimates. Before repeating this process by building a new volume, pose estimates are adjusted to reduce error by maximizing a novel quality factor for shape-from-silhouette volume reconstruction. The feedback loop is terminated when pose estimates do not change much, as compared with those produced by the previous iteration. Based on a theoretical study of the proposed system, a test of convergence to a given set of poses is devised. Reliable performance of the system is also proved by several experiments on both synthetic and real image sequences. No model is assumed for the object and no feature point is detected or tracked as there is no problematic feature matching or correspondence. Our method can be used for 3-D object tracking in video, 3-D modeling, and volume reconstruction from video.     

An optimal residency-aware scheduling technique for cluster tools with buffer module

Cluster tools provide a flexible, reconfigurable, and efficient environment for several manufacturing processes (e.g., semiconductor manufacturing). A new timing constraint (distinct from a simple deadline), referred to as residency constraint, puts a timing limit on the time that a wafer can stay in a processing module in a cluster tool. The authors demonstrate that a solution that does not address residency constraints can be found easily. However, when residency constraints are added to the model, the problem becomes complex and a scheduling technique may spend a long time searching for a good solution. Also, in some cases, one may need to decrease throughput to satisfy residency constraints. The authors introduce a new technique to address cluster tool scheduling in the presence of residency constraints. The proposed technique uses a buffer resource for temporarily holding wafers to release other resources such as the robot arm. This resource is usually available in the tool for maintenance reasons. A tradeoff is discussed in using the buffer resource and a scheduling algorithm is presented that will use this resource when it can help to increase throughput under residency constraints. The experiments show that in many cases that are common in semiconductor manufacturing, use of their proposed technique can improve throughput.     

Incremental one-class classifier based on convex–concave hull

Pattern Analysis and Applications - One subject that has been considered less is a binary classification on data streams with concept drifting in which only information of one class (target class)...     

Aspen plus simulation of heavy oil gasification in a fluidized bed gasifier

Gasification is a clean technology to convert fuels to high-quality syngas in presence of a gasifying agent. In this study, an Aspen Plus model of heavy oil gasification was developed to produce the hydrogen rich syngas. Effect of some parameters such as gasification temperature and steam/fuel ratio on the hydrogen yield and was investigated. Results showed that the temperature plays a major role in the process; higher temperatures produce the higher hydrogen content. It was also found that the operation under high steam/fuel ratio can cause a significant increase in the hydrogen yield. The modeling results were compared with the experimental data available in the literature and found to be in good agreement.     

To Schedule or to Execute: Decision Support and Performance Implications

This paper addresses a fundamental trade-off in dynamic scheduling between the cost of scheduling and the quality of the resulting schedules. The time allocated to scheduling must be controlled explicitly, in order to obtain good-quality schedules in reasonable times. As task constraints are relaxed, the algorithms proposed in this paper increase scheduling complexity to optimize longer and obtain high-quality schedules. When task constraints are tightened, the algorithms adjust scheduling complexity to reduce the adverse effect of long scheduling times on the schedule quality. We show that taking into account the scheduling time is crucial for honoring the deadlines of scheduled tasks. We investigate the performance of our algorithms in two scheduling models: one that allows idle-time intervals to exist in the schedule and another that does not. The model with idle-time intervals has important implications for dynamic scheduling which are discussed in the paper. Experimental evaluation of the proposed algorithms shows that our algorithms outperform other candidate algorithms in several parameter configurations.     

Effects of various barium precursors and promoters on catalytic activity of Ba-Ti perovskite catalysts for oxidative coupling of methane

The influences of barium precursor and promoter type on the catalytic performance of perovskite catalysts in OCM reaction were studied. Catalysts (BaTiPO₃, P: promoter) were prepared by carbonate, hydroxide and propionate precursors of barium and SnCl₂ and CeO₂ as promoters by sol-gel method, tested in a fixed-bed microreactor and characterized by XRD, BET, CO₂-TPD, FT-IR and UV-Visible analysis. The experiment results showed that based on the extent of effect upon catalyst efficiency, the barium anions can be ranked as; propionate > carbonate > hydroxide, and the CeO₂ promoted catalysts were more active than the SnCl₂ promoted ones. The characterization results showed that the substitution of metal precursors caused formation of different phases with different particle sizes, influenced the basicity of the catalysts, resulted in the appearance of the peaks corresponding to different groups in IR spectroscopy, and shifted the absorption peaks in UV-Visible spectra. These results suggested that OCM reaction over perovskite catalysts is structure sensitive and depended on the type of used precursor and promoter.     

DDC: distance-based decision classifier

This paper presents a new classification method utilizing distance-based decision surface with nearest neighbor projection approach, called DDC. Kernel type of DDC has been extended to take into account the effective nonlinear structure of the data. DDC has some properties: (1) does not need conventional learning procedure (as k-NN algorithm), (2) does not need searching time to locate the k-nearest neighbors, and (3) does not need optimization process unlike some classification methods such as Support Vector Machine (SVM). In DDC, we compute the weighted average of distances to all the training samples. Unclassified sample will be classified as belonging to a class that has the minimum obtained distance. As a result, by such a rule we can derive a formula that can be used as the decision surface. DDC is tested on both synthetic and real-world data sets from the UCI repository, and the results were compared with k-NN, RBF Network, and SVM. The experimental results indicate DDC outperforms k-NN in the most experiments and the results are comparable to or better than SVM with some data sets.     

Dynamic real-time scheduling strategies for interactive continuous media servers

In this paper, we propose and study a dynamic approach to schedule real-time requests in a video-on-demand (VOD) server. Providing quality of service in such servers requires uninterrupted and on-time retrieval of motion video data. VOD services and multimedia applications further require access to the storage devices to be shared among multiple concurrent streams. Most of the previous VOD scheduling approaches use limited run-time,0 information and thus cannot exploit the potential capacity of the system fully. Our approach improves throughput by making use of run-time information to relax admission control. It maintains excellent quality of service under varying playout rates by observing deadlines and by reallocating resources to guarantee continuous service. It also reduces start-up latency by beginning service as soon as it is detected that deadlines of all real-time requests will be met. We establish safe conditions for greedy admission, dynamic control of disk read sizes, fast initial service, and sporadic services. We conduct thorough simulations over a wide range of buffer capacities, load settings, and over varying playout rates to demonstrate the significant improvements in quality of service, throughput and start-up latency of our approach relative to a static approach.     

Efficient sequencing techniques for variable-length messages in WDMnetworks

Message sequencing and channel assignment are two important issues that need to be addressed when scheduling variable-length messages in a wavelength division multiplexing (WDM) network. Channel assignment addresses the problem of choosing an appropriate data channel via which a message is transmitted to a node. This problem has been addressed extensively in the literature. On the other hand, message sequencing which addresses the order in which messages are sent, has rarely been addressed. In this paper, we propose a set of scheduling techniques for single-hop WDM passive star networks, which address both the sequencing aspect and the assignment aspect of the problem. In particular, we develop two priority schemes for sequencing messages in a WDM network in order to increase the overall performance of the network. We evaluate the proposed algorithms, using analytical modeling and extensive discrete event simulations, by comparing their performance with state-of-the-art scheduling algorithms that only address the assignment problem. We find that significant improvement in performance can be achieved using our scheduling algorithms where message sequencing and channel assignment are simultaneously taken into consideration. This suggests that, when scheduling messages in WDM networks, one has to consider message sequencing, as well as channel assignment. As a result, we anticipate that this research will open new directions into the problem of on-line scheduling in WDM networks