Evaluation of two alternative carbon capture and storage technologies: A stochastic model

In this paper we evaluate two alternative CCS technologies at a coal-fired power plant from an investor's point of view. The first technology uses CO₂ for enhanced oil recovery (EOR) paired with storage in deep saline formations (DSF) and the second merely stores CO₂ in DSF. The paper updates and improves on an earlier publication by Tzimas et al. (2005). For projects of this type there are many sources of risk, three of which stand out: the price of electricity, the price of oil and the price of carbon allowances. In this paper we develop a general stochastic model that can be adapted to other projects such as enhanced gas recovery (EGR) or industrial plants that use CO₂ for either EOR or EGR with CCS. The model is calibrated with UK data and applied to help understand the conditions that generate the incentives needed for early investments in these technologies. Additionally, we analyse the risks of these investments. Investments with EOR and secondary DSF storage can only be profitable (NPV > 0) when there is a high long-term equilibrium price for oil of more than $56.38/barrel. When the investment decision can be made at any time, i.e. there is an option value, then the trigger value for optimal investment is significantly higher.     

A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services

Selection of strategies that help reduce riverine inputs requires numerical models that accurately quantify hydrologic processes. While numerous models exist, information on how to evaluate and select the most robust models is limited. Toward this end, we developed a comprehensive approach that helps evaluate watershed models in their ability to simulate flow regimes critical to downstream ecosystem services. We demonstrated the method using the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program–FORTRAN (HSPF) model, and Distributed Large Basin Runoff Model (DLBRM) applied to the Maumee River Basin (USA). The approach helped in identifying that each model simulated flows within acceptable ranges. However, each was limited in its ability to simulate flows triggered by extreme weather events, owing to algorithms not being optimized for such events and mismatched physiographic watershed conditions. Ultimately, we found HSPF to best predict river flow, whereas SWAT offered the most flexibility for evaluating agricultural management practices.     

A noisy-smoothing relevance feedback method for content-based medical image retrieval

In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively.     

3D video components generation using object tracking technique

2D-to-3D conversion that would be a solution of the lack of 3D contents has been a worthy and challenging research field. In this paper, we propose a computer interactive conversion method to capture components which is used to generate 3D sequences. First, we divide the key frame into foreground and background, and then label the objects by convenient computer interactive operation. Depth information of objects is labeled after segmentation. Second, we use object tracking technique which synthesizes the advantages of kernel-based mean shift tracker and contour tracker to accomplish object depth capture for non-key frame. Finally, all the 3D information is prepared to render 3D sequences. After all, we propose our future work direction: a 2D-to-3D system which can generate 3D sequence interactively.     

Synthesizing English emphatic speech for multimodal corrective feedback in computer-aided pronunciation training

Emphasis plays an important role in expressive speech synthesis in highlighting the focus of an utterance to draw the attention of the listener. We present a hidden Markov model (HMM)-based emphatic speech synthesis model. The ultimate objective is to synthesize corrective feedback in a computer-aided pronunciation training (CAPT) system. We first analyze contrastive (neutral versus emphatic) speech recording. The changes of the acoustic features of emphasis at different prosody locations and the local prominences of emphasis are analyzed. Based on the analysis, we develop a perturbation model that predicts the changes of the acoustic features from neutral to emphatic speech with high accuracy. Further based on the perturbation model we develop an HMM-based emphatic speech synthesis model. Different from the previous work, the HMM model is trained with neutral corpus, but the context features and additional acoustic-feature-related features are used during the growing of the decision tree. Then the output of the perturbation model can be used to supervise the HMM model to synthesize emphatic speeches instead of applying the perturbation model at the backend of a neutral speech synthesis model directly. In this way, the demand of emphasis corpus is reduced and the speech quality decreased by speech modification algorithm is avoided. The experiments indicate that the proposed emphatic speech synthesis model improves the emphasis quality of synthesized speech while keeping a high degree of the naturalness.     

An adaptive watermarking scheme for e-government document images

This paper proposes an adaptive watermarking scheme for e-government document images. The adaptive scheme combines the discrete cosine transform (DCT) and the singular value decomposition (SVD) using luminance masking. As a core of masking model in the human visual system (HVS), luminance masking is implemented to improve noise sensitivity. Genetic algorithm (GA), subsequently, is employed for the optimization of the scaling factor of the masking. Involving a number of steps, the scheme proposed through this study begins by calculating the mask of the host image using luminance masking. It is then continued by transforming the mask on each area into all frequencies domain. The watermark image, following this, is embedded by modifying the singular values of DCT-transformed host image with singular values of mask coefficient of host image and the control parameter of DCT-transformed watermark image using Genetic Algorithm (GA). The use of both the singular values and the control parameter respectively, in this case, is not only to improve the sensitivity of the watermark performance but also to avoid the false positive problem. The watermark image, afterwards, is extracted from the distorted images. The experiment results show the improved adaptive performance of the proposed scheme is in resistant to several types of attacks in comparison with the previous schemes; the adaptive performance refers to the adaptive parameter of the luminance masking functioned to improve the performance or robustness of an image from any attacks.     

An intensive case study on kernel-based relation extraction

Relation extraction refers to a method of efficiently detecting and identifying predefined semantic relationships within a set of entities in text documents. Numerous relation extractionfc techniques have been developed thus far, owing to their innate importance in the domain of information extraction and text mining. The majority of the relation extraction methods proposed to date is based on a supervised learning method requiring the use of learning collections; such learning methods can be classified into feature-based, semi-supervised, and kernel-based techniques. Among these methods, a case analysis on a kernel-based relation extraction method, considered the most successful of the three approaches, is carried out in this paper. Although some previous survey papers on this topic have been published, they failed to select the most essential of the currently available kernel-based relation extraction approaches or provide an in-depth comparative analysis of them. Unlike existing case studies, the study described in this paper is based on a close analysis of the operation principles and individual characteristics of five vital representative kernel-based relation extraction methods. In addition, we present deep comparative analysis results of these methods. In addition, for further research on kernel-based relation extraction with an even higher performance and for general high-level kernel studies for linguistic processing and text mining, some additional approaches including feature-based methods based on various criteria are introduced.     

A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem

This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard’s benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms.     

Changes in pore properties of phenol formaldehyde-based carbon with carbonization and oxidation conditions

Porous carbon beads were prepared by carbonizing at 700 and 1000 °C under N₂ (NN-series) or CO₂ atmospheres (CO-series) and a subsequent oxidization with boiling nitric acid solution (13%, v/v) for 3 h (ANN- and ACO-series). BET surface area of both CO- and NN-series samples tends to increase with increasing carbonization temperature, but CO-series samples show higher BET surface areas (700 °C: 610 m²/g, 1000 °C: 780 m²/g) compared with those of NN-series samples (700 °C: 380 m²/g, 1000 °C: 580 m²/g). After acid oxidation, BET surface areas of NN-series samples increased from 580 to 650 m²/g, whereas those of CO-series samples decreased from 780 to 600 m²/g. On subsequent acid oxidation, acidic surface functional groups increased in both CO- and NN-series samples, but the CO-series samples tend to have much more acidic surface functional groups. Adsorption of halogenated methanes, such as tetra-, tri- and dichloromethanes, onto the samples of CO-, NN-, ACO- and ANN-series was thought to be driven initially by the basicity, but the acidic functional groups that could attract adsorbates via dipole-dipole interaction might hinder the adsorption of subsequently incoming adsorbates.     

聚合物合成及成型加工中协同学的应用

综述了协同学在聚合物领域的最新研究进展，介绍了加工制备一些聚合物合成及复合材料所显示出的协同效应，以及用协同学方法定量求解一类合成反应体系的动力学模型。提出通过改变振动力场等作用方式，在聚合物动态塑化成型加工时，协同学方法具有广阔的应用前景。