Synthetic evaluation method for bridge priority ranking

In order to maintain the transportation operation, proper monitoring systems should be established on road structures, especially bridges. Since these systems need enormous investments, only a part of bridges should be equipped. Thus, the priorities of the bridges should be ranked. In this paper, a method based on two-level synthetic evaluation is proposed. First, the importance of each bridge is analyzed through the economic analysis. Six factors are considered for the bridges in a network, including construction cost, service duration, length, location importance coefficient, traffic volume, and reconstruction time. Second, the safety condition of the bridge is evaluated by using improved entropy method (IEM) which combines subjective weight with objective entropy weight. Five indices are incorporated in this step, i.e., design and construction condition, technical condition, level of overloading, hazard of wind and earthquake and environmental factors. Finally, the priorities of all the bridge in one network can be ranked and classified through a judge matrix. To demonstrate the effectiveness of the proposed method, a main highway including 16 bridges is taken as an illustrative example. The results show that the bridges can be ranked and classified quickly by using the proposed method.

Mentoring and the transfer of organizational memory within the context of an aging workforce : cultural implications for competitive advantage

Organizational memory, the knowledge gained from organizational experience, has significant potential for competitive advantage. Many authors in the knowledge management and human resource management literatures consider mentoring to be a particularly effective method of transferring organizational memory. In addition, older workers are often considered ideal mentors in organizations because of their experience and alleged willingness to pass on their knowledge to less experienced employees. There is an associated assumption that these workers also anticipate and experience positive outcomes when mentoring others. This chapter considers whether these assumptions hold up in the workplaces of the 21st century, particularly within Western countries. Individualistic cultural norms and some discriminatory practices towards older workers, along with a changing career contract that no longer guarantees employment in one organization for life, may discourage knowledge sharing in organizations. This chapter discusses the constraints and motivations that may operate when older experienced workers consider mentoring others. It considers relevant global and organizational cultural characteristics that may influence mentoring to transfer knowledge, and accordingly suggests strategies for those eager to capitalise on the knowledge experienced employees possess.

Projection-pursuit-based method for blind separation of nonnegative sources

This paper presents a projection pursuit (PP) based method for blind separation of nonnegative sources. First, the available observation matrix is mapped to construct a new mixing model, in which the inaccessible source matrix is normalized to be column-sum-to-1. Then, the PP method is proposed to solve this new model, where the mixing matrix is estimated column by column through tracing the projections to the mapped observations in specified directions, which leads to the recovery of the sources. The proposed method is much faster than Chan's method, which has similar assumptions to ours, due to the usage of optimal projection. It is also more advantageous in separating cross-correlated sources than the independence- and uncorrelation-based methods, as it does not employ any statistical information of the sources. Furthermore, the new method does not require the mixing matrix to be nonnegative. Simulation results demonstrate the superior performance of our method.

Automatic deformation transfer for data-driven haptic rendering

This paper addresses a major challenge in datadriven haptic modeling of deformable objects. Data-driven modelling is done for specific objects and is difficult to generalize for nearly isometric objects that have similarities in semantics or topology. This limitation prevents the wide use of the data-driven modeling techniques when compared with parametric methods such as finite element methods. The proposed solution is to incorporate deformation transfer methods when processing similar instances. The contributions of this work are focused on the novel automatic shape correspondence method that overcomes the problems of symmetry and semantics presence requirement. The results shows that the proposed algorithm can efficiently calculate the correspondence and transfer deformations for a range of similar 3D objects.

Blind Source Separation by Nonnegative Matrix Factorization with Minimum-Volume Constraint

Recently, nonnegative matrix factorization (NMF) attracts more and more attentions for the promising of wide applications. A problem that still remains is that, however, the factors resulted from it may not necessarily be realistically interpretable. Some constraints are usually added to the standard NMF to generate such interpretive results. In this paper, a minimum-volume constrained NMF is proposed and an efficient multiplicative update algorithm is developed based on the natural gradient optimization. The proposed method can be applied to the blind source separation (BSS) problem, a hot topic with many potential applications, especially if the sources are mutually dependent. Simulation results of BSS for images show the superiority of the proposed method.

Nonnegative Matrix Factorization Applied to Nonlinear Speech and Image Cryptosystems

Nonnegative matrix factorization (NMF) is widely used in signal separation and image compression. Motivated by its successful applications, we propose a new cryptosystem based on NMF, where the nonlinear mixing (NLM) model with a strong noise is introduced for encryption and NMF is used for decryption. The security of the cryptosystem relies on following two facts: 1) the constructed multivariable nonlinear function is not invertible; 2) the process of NMF is unilateral, if the inverse matrix of the constructed linear mixing matrix is not nonnegative. Comparing with Lin's method (2006) that is a theoretical scheme using one-time padding in the cryptosystem, our cipher can be used repeatedly for the practical request, i.e., multitme padding is used in our cryptosystem. Also, there is no restriction on statistical characteristics of the ciphers and the plaintexts. Thus, more signals can be processed (successfully encrypted and decrypted), no matter they are correlative, sparse, or Gaussian. Furthermore, instead of the number of zero-crossing-based method that is often unstable in encryption and decryption, an improved method based on the kurtosis of the signals is introduced to solve permutation ambiguities in waveform reconstruction. Simulations are given to illustrate security and availability of our cryptosystem.

Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing (SU), which aims at obtaining the endmembers and corresponding fractional abundances, knowing only the collected mixing spectral data. It is noted that the abundance may be sparse (i.e., the endmembers may be with sparse distributions) and sparse NMF tends to lead to a unique result, so it is intuitive and meaningful to constrain NMF with sparseness for solving SU. However, due to the abundance sum-to-one constraint in SU, the traditional sparseness measured by L0/L1-norm is not an effective constraint any more. A novel measure (termed as S-measure) of sparseness using higher order norms of the signal vector is proposed in this paper. It features the physical significance. By using the S-measure constraint (SMC), a gradient-based sparse NMF algorithm (termed as NMF-SMC) is proposed for solving the SU problem, where the learning rate is adaptively selected, and the endmembers and abundances are simultaneously estimated. In the proposed NMF-SMC, there is no pure index assumption and no need to know the exact sparseness degree of the abundance in prior. Yet, it does not require the preprocessing of dimension reduction in which some useful information may be lost. Experiments based on synthetic mixtures and real-world images collected by AVIRIS and HYDICE sensors are performed to evaluate the validity of the proposed method.

Online blind source separation using incremental nonnegative matrix factorization with volume constraint

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.

Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources

In blind source separation, many methods have been proposed to estimate the mixing matrix by exploiting sparsity. However, they often need to know the source number a priori, which is very inconvenient in practice. In this paper, a new method, namely nonlinear projection and column masking (NPCM), is proposed to estimate the mixing matrix. A major advantage of NPCM is that it does not need any knowledge of the source number. In NPCM, the objective function is based on a nonlinear projection and its maxima just correspond to the columns of the mixing matrix. Thus a column can be estimated first by locating a maximum and then deflated by a masking operation. This procedure is repeated until the evaluation of the objective function decreases to zero dramatically. Thus the mixing matrix and the number of sources are estimated simultaneously. Because the masking procedure may result in some small and useless local maxima, particle swarm optimization (PSO) is introduced to optimize the objective function. Feasibility and efficiency of PSO are also discussed. Comparative experimental results show the efficiency of NPCM, especially in the cases where the number of sources is unknown and the sources are relatively less sparse.

A hazardous substance exposure prevention rating method for intervention needs assessment and effectiveness evaluation: the small business exposure index

Aims This paper describes the refinement and adaptation to small business of a previously developed method for systematically prioritizing needs for intervention on hazardous substance exposures in manufacturing worksites, and evaluating intervention effectiveness. Methods We developed a checklist containing six unique sets of yes/no variables organized in a 2 × 3 matrix of exposure potential versus exposure protection at three levels corresponding to a simplified hierarchy of controls: materials, processes, and human interface. Each of the six sets of indicator variables was reduced to a high/moderate/low rating. Ratings from the matrix were then combined to generate an exposure prevention 'Small Business Exposure Index' (SBEI) Summary score for each area. Reflecting the hierarchy of controls, material factors were weighted highest, followed by process, and then human interface. The checklist administered by an industrial hygienist during walk-through inspection (N = 149 manufacturing processes/areas in 25 small to medium-sized manufacturing worksites). One area or process per manufacturing department was assessed and rated. A second hygienist independently assessed 36 areas to evaluate inter-rater reliability. Results The SBEI Summary scores indicated that exposures were well controlled in the majority of areas assessed (58% with rating of 1 or 2 on a 6-point scale), that there was some room for improvement in roughly one-third of areas (31% of areas rated 3 or 4), and that roughly 10% of the areas assessed were urgently in need of intervention (rated as 5 or 6). Inter-rater reliability of EP ratings was good to excellent (e.g., for SBEI Summary scores, weighted kappa = 0.73, 95% CI 0.52–0.93). Conclusion The SBEI exposure prevention rating method is suitable for use in small/medium enterprises, has good discriminatory power and reliability, offers an inexpensive method for intervention needs assessment and effectiveness evaluation, and complements quantitative exposure assessment with an upstream prevention focus.

Estimating wetland values: A comparison of benefit transfer and choice experiment values

The main purpose of thesis is to estimate the value of the ecosystem services of the Bung Khong Long through two techniques. The per hectare value estimated by economic valuation method is US$ 976 per annum. MRA-benefit transfer approach produces values of between US$ 396 and US$ 1,369 per annum.