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Methods with controllable numerical damping are designed to regulate the oscillation rate within a system to prevent numerical instability and improve convergence during a simulation. Mitigating the numerical errors using numerical damping can ensure reliable and accurate results, however, there is still a lack of model-based methods that exhibit robust numerical damping capabilities. To address this gap, this paper proposes a new model-based integration method with controllable numerical damping, namely the dual-explicit method with controllable damping-$\gamma$ (DEMCD-$\gamma$). The dual-explicit method is designed to inherit the spectral characteristics of the recently developed implicit Li–Yu method by matching its temporal discrete error equations. The numerical properties of the DEMCD-$\gamma$ are investigated and several numerical examples are given to showcase the advantages of the DEMCD-$\gamma$ over the Li–Yu method, the ${\beta }_1$/${\beta }_2$ Bathe method, and other existing model-based methods such as the CR and the MKR-$\alpha$ methods. Overall, the proposed DEMCD-$\gamma$ method presents a promising solution to the issue of numerical damping encountered in model-based simulations.

Model-Based 3D object recognition systems have a variety of potential applications, but widespread use of such systems has not occurred, due to a number of factors including the representational limitations of models. One historical limitation is the discriminatory representation of free-form objects. The system described in this paper recognizes free-form objects in dense range data acquired by a structured light rangefinder. Images and object models are represented as a network of salient segments which are then brought into correspondence until a reliable pose estimate is available. Experiments with a database of images and object models highlight the contributions of this system.

In this paper, an agent-based open and adaptive system development process has been proposed which continuously change and evolve to meet new requirements. The proposed methodology is based on a model-based technique that provides a specific model for the type of information to be gathered and uses this model to drive the domain specific analysis process. The focus is on a clear separtion between the requirement gathering and analysis phases. The analysis methodology further splits the analysis phase into the user_centric analysis and the system_centric analysis phases. Optimization of the system performance has also been proposed by exploiting the relationships and dependencies among roles and mapping criteria between roles to agents. The Gaia and ROADMAP models have been used as a basis to the proposed agent-based modeling method.

The engineering data of a large enterprise is typically distributed over a wide area and archived in a variety of databases and file systems. Access to such information is crucial to a team member, particularly in a concurrent engineering setting. However, this is not easy, because (1) a model of the relevant information is not available, and (2) there is no simple way to access the information without being knowledgeable about various computer data formats, file systems, and networks. We have developed a system called the Information Sharing System (ISS) to enable access to diverse and distributed information within a corporation. Such data could be stored in different repositories such as databases and file systems including those that contain multiple media. Our paper describes the methodology of the ISS, the details of the implementation and extensions planned for the future.

There are very few integration methods satisfying the explicit displacement formulation, unconditional stability, and higher-order accuracy, simultaneously. This paper proposes a new predictor–corrector explicit (NPCE) integration method with unconditional stability and higher-order accuracy. The NPCE is developed by matching its temporal discrete equation (TDE) with that of a recently developed fourth-order new dual-explicit (NDE) integration method. The accuracy and stability of the NPCE scheme are analyzed. It is found that the NPCE method possesses the same stability property as the NDE method. In addition, the NPCE method can achieve the same accuracy level as the NDE method for the undamped system, and it is more accurate than the NDE method for the damped system. The performance of the NPCE method is illustrated by four numerical examples. Some representative integration methods are adopted for comparison. The results indicate that the NPCE method has excellent accuracy with low relative absolute and squared errors.

This paper presents methods for performing steganography and steganalysis using a statistical model of the cover medium. The methodology is general, and can be applied to virtually any type of media. It provides answers for some fundamental questions that have not been fully addressed by previous steganographic methods, such as how large a message can be hidden without risking detection by certain statistical methods, and how to achieve this maximum capacity. Current steganographic methods have been shown to be insecure against simple statistical attacks. Using the model-based methodology, an example steganography method is proposed for JPEG images that achieves a higher embedding efficiency and message capacity than previous methods while remaining secure against first order statistical attacks. A method is also described for defending against "blockiness" steganalysis attacks. Finally, a model-based steganalysis method is presented for estimating the length of messages hidden with Jsteg in JPEG images.

The diverse nature of cerebral activity, as measured using neuroimaging techniques, has been recognised long ago. It seems obvious that using single modality recordings can be limited when it comes to capturing its complex nature. Thus, it has been argued that moving to a multimodal approach will allow neuroscientists to better understand the dynamics and structure of this activity. This means that integrating information from different techniques, such as electroencephalography (EEG) and the blood oxygenated level dependent (BOLD) signal recorded with functional magnetic resonance imaging (fMRI), represents an important methodological challenge. In this work, we review the work that has been done thus far to derive EEG/fMRI integration approaches. This leads us to inspect the conditions under which such an integration approach could work or fail, and to disclose the types of scientific questions one could (and could not) hope to answer with it.

DNA copy number (DCN) is the number of copies of DNA at a region of a genome. The alterations of DCN are highly associated with the development of different tumors. Recently, microarray technologies are being employed to detect DCN changes at many loci at the same time in tumor samples. The resulting DCN data are often very noisy, and the tumor sample is often contaminated by normal cells. The goal of computational analysis of array-based DCN data is to infer the underlying DCNs from raw DCN data. Previous methods for this task do not model the tumor/normal cell mixture ratio explicitly and they cannot output segments with DCN annotations.

We developed a novel model-based method using the minimum description length (MDL) principle for DCN data segmentation. Our new method can output underlying DCN for each chromosomal segment, and at the same time, infer the underlying tumor proportion in the test samples. Empirical results show that our method achieves better accuracies on average as compared to three previous methods, namely Circular Binary Segmentation, Hidden Markov Model and Ultrasome.

One of the most promising approaches in the field of Recommender Systems (RSs) is Collaborative Filtering (CF). CF techniques are commonly divided in the two general classes of memory-based and model-based. A wise strategy would be to combine these two methods to increase their performance while leveling out the weakness of each one. Otherwise, the uncertainty pervaded throughout the different steps of the recommendation process should not be ignored. Handling uncertainty is very challenging and important for more reliable and intelligible predictions. That is why, we propose in this paper a new CF approach which combines these two categories under the belief function theory while dealing with the uncertainty pervaded in the prediction process. The effectiveness of our proposal is validated on a real-word data set and compared to state-of-the-art CF approaches under certain and uncertain frameworks.