Evaluation of different disinfection calculation methods using CFD

Computational Fluid Dynamics combined with a particle tracking technique provides valuable information concerning residence times and contact times in chemical reactors. In drinking water treatment, for example an accurate estimation of the disinfection is important to predict the microbial safety. Ozone contactors are widely used for disinfection, but the complex geometry of the system causes suboptimal hydraulics and requires optimizations of the flow. This results in a lower ozone dosage, which may reduce the formation of unwanted disinfection-by-products and the consumption of energy. To that end disinfection needs to be calculated precisely, accounting for the complex hydraulics. Several calculation methods estimating the disinfection performance of ozone contactors were evaluated using Computational Fluid Dynamics. For an accurate disinfection prediction, the full distribution of ozone exposures (CT values) is needed, only a mean CT value or residence time distribution provides insufficient information for an accurate disinfection prediction. Adjustments to the geometry of the ozone contactor that reduce the short-circuit flows resulted in an increase in disinfection capacity, whereas the mean CT value remained the same. A sensitivity analysis with respect to the kinetics was conducted. The gain in disinfection capacity obtained by optimizing the hydraulics was significant for typical values used in practice.     

Mining Web navigation patterns with a path traversal graph

Understanding the navigational behaviour of website visitors is a significant factor of success in the emerging business models of electronic commerce and even mobile commerce. However, Web traversal patterns obtained by traditional Web usage mining approaches are ineffective for the content management of websites. They do not provide the big picture of the intentions of the visitors. The Web navigation patterns, termed throughout-surfing patterns (TSPs) as defined in this paper, are a superset of Web traversal patterns that effectively display the trends toward the next visited Web pages in a browsing session. TSPs are more expressive for understanding the purposes of website visitors. In this paper, we first introduce the concept of throughout-surfing patterns and then present an efficient method for mining the patterns. We propose a compact graph structure, termed a path traversal graph, to record information about the navigation paths of website visitors. The graph contains the frequent surfing paths that are required for mining TSPs. In addition, we devised a graph traverse algorithm based on the proposed graph structure to discover the TSPs. The experimental results show the proposed mining method is highly efficient to discover TSPs.     

A virtual subnet scheme on clustering algorithms for mobile ad hoc networks

Resolving the broadcast storm problem is an important issue in mobile ad hoc networks (MANETs). In this paper, we propose an approach for constructing a virtual subnet whose nodes are logically related. The virtual subnet can be spread upon clusters of a MANET. An intelligent agent with a routing filtering table is proposed to assist the best known clustering algorithms, the original Least ID algorithm and the original Highest Connection Cluster (HCC) algorithm, to improve group communication efficiency. Our simulation covers the network factors of hop count, deprave rate, and delay time. The simulation results show that when the proposed intelligent agent is used with the HCC algorithm, the delay time was reduced by 81.84% as compared with flooding, and by 49.25% as compared with the Ad Hoc On-Demand Distance Vector (AODV) routing algorithm. The delay time for the Least ID algorithm assisted by the proposed agents reduced by 81.84% compared to that of flooding and by 50% compared to that of AODV.     

Accelerating the kernel-method-based feature extraction procedure from the viewpoint of numerical approximation

The kernel method suffers from the following problem: the computational efficiency of the feature extraction procedure is inversely proportional to the size of the training sample set. In this paper, from a novel viewpoint, we propose a very simple and mathematically tractable method to produce the computationally efficient kernel-method-based feature extraction procedure. We first address the issue that how to make the feature extraction result of the reformulated kernel method well approximate that of the naïve kernel method. We identify these training samples that statistically contribute much to the feature extraction results and exploit them to reformulate the kernel method to produce the computationally efficient kernel-method-based feature extraction procedure. Indeed, the proposed method has the following basic idea: when one training sample has little effect on the feature extraction result and statistically has the high correlation with regard to all the training samples, the feature extraction term associated with this training sample can be removed from the feature extraction procedure. The proposed method has the following advantages: First, it proposes, for the first time, to improve the kernel method through formal and reasonable evaluation on the feature extraction term. Second, the proposed method improves the kernel method at a low extra cost and thus has a much more computationally efficient training phase than most of the previous improvements to the kernel method. The experimental comparison shows that the proposed method performs well in classification problems. This paper also intuitively shows the geometrical relation between the identified training samples and other training samples.     

An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.     

A blind watermarking method using maximum wavelet coefficient quantization

This paper proposes a blind watermarking algorithm based on maximum wavelet coefficient quantization for copyright protection. The wavelet coefficients are grouped into different block size and blocks are randomly selected from different subbands. We add different energies to the maximum wavelet coefficient under the constraint that the maximum wavelet coefficient is always maximum in a block. The watermark is embedded the local maximum coefficient which can effectively resist attacks. Also, using the block-based watermarking, we can extract the watermark without using the original image or watermark. Experimental results show that the proposed method is quite robust under either non-geometry or geometry attacks.     

An expert system using rough sets theory for aided conceptual design of ship’s engine room automation

More and more complicated conceptual design of ship’s engine room (CDSER) heavily depends on designers’ engineering knowledge and existing ship data. To achieve intelligent design at the initial ship design stage, many researchers have made much significant progress in this field, however, most of them only focused on how to find the similar constructed ships. At present, how to utilize these existing data remains an untouched topic. In order to make good use of the existing data and reduce the dependence on designers’ experience, a novel system named Expert System for Aided Conceptual Design of Ship’s Engine Room Automation (ESACD), is elaborated in this study. With the support of the constructed Ship Data Warehouse System, two core subsystems Configuration Selection Assistant (CSA) and Design Scheme Decision Assistant (DSDA) are included in ESACD. A promising approach integrating Fuzzy c-means algorithm (FCM) and Rough Sets Theory (RST) to extract configuration rules from the stored data is adopted in CSA. According to engineers’ proposals, RST is utilized to reason knowledge in incomplete scheme information systems for getting design scheme rules in DSDA, which are useful suggestions for engineers to get better schemes at this stage. Finally, the validity and necessity of this interactive expert system are demonstrated through the CDSER of a new 50,000 DWT Handymax bulk carrier. It is proved that ESACD can efficiently facilitate rapid and intelligent design in CDSER, and reduce the cost of a new ship design.     

Solving the generalized Sylvester matrix equation AV +BW = VF via Kronecker map

This note considers the solution to the generalized Sylvester matrix equation AV ＋ BW = VF with F being an arbitrary matrix, where V and W are the matrices to be determined. With the help of the Kronecker map, an explicit parametric solution to this matrix equation is established. The proposed solution possesses a very simple and neat form, and allows the matrix F to be undetermined.     

Eigenvalue Modeling and Cross-Correlation Analysisfor 6.0-6.4 GHz MIMO Channel

In Multiple-Input Multiple-Output （MIMO） system, the number of positive channel matrix eigenvalues ~s directly related to system performance. In order to characterize and model channel matrix eigenvalues, channel measurements at 6. 0 -6. 4GHz by using 4 ~ 4 MIMO structure were conducted in a typical classroom environment. Based on measured data, the eigenvalues were modeled as Log-Normal distributed random variables and parameterized. Furthermore, Cross-Correlation （CC） coefficients of eigenvalues were estimated. The measurement results show that, under both Light-Of-Sight （LOS） and Non- Light-Of-Sight （NLOS） scenarios, eigenvalues are highly de-correlatod so that CC can be ignored for eigenvalue model.