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The detection of gas leaks using acoustic signals is often compromised by environmental noise, which significantly impacts the accuracy of subsequent leak identification. Current noise reduction algorithms based on non-negative matrix factorization (NMF) typically utilize the Euclidean distance as their objective function, which can exacerbate noise anomalies. Moreover, these algorithms predominantly rely on simple techniques like Wiener filtering to estimate the amplitude spectrum of pure signals. This approach, however, falls short in accurately estimating the amplitude spectrum of non-stationary signals. Consequently, this paper proposes an improved non-negative matrix factorization (INMF) noise reduction algorithm that enhances the traditional NMF by refining both the objective function and the amplitude spectrum estimation process for reconstructed signals. The improved algorithm replaces the conventional Euclidean distance with the Kullback–Leibler (KL) divergence and incorporates noise and sparse constraint terms into the objective function to mitigate the adverse effects of signal amplification. Unlike traditional methods such as Wiener filtering, the proposed algorithm employs an adaptive Minimum Mean-Square Error-Log Spectral Amplitude (MMSE-LSA) method to estimate the amplitude spectrum of non-stationary signals adaptively across varying signal-to-noise ratios. Comparative experiments demonstrate that the INMF algorithm significantly outperforms existing methods in denoising leakage acoustic signals. [ABSTRACT FROM AUTHOR]

The adaptable-function mechanical product is not only of multiple functions, but also of more complex physical structure than products of fixed functions; hence it is feasible to apply modular design methodology in its design and development, where module identification is critical to the success of the design. For the modular design of this kind of product, it is often necessary to formulate a module by combining components from multiple product functions, rather than from one function alone, let alone by decomposing a function first and then making multiple modules from that function. To this end, this paper attempts to study the strategies and methodologies for functional combination-oriented module identification suitable for the adaptable-function product design and other similar design problems. By investigation on the similarities of function, executing behavior, and physical structure, respectively, a similarity matrix is established first, from which the fuzzy clustering method is exploited to derive various module schemes. After that, an evaluation model is proposed to calculate the module scheme complexity and its redesign complexity, with which the schemes are evaluated and the optimal one is obtained. The identified modules can then be used for further modular design. Finally, the feasibility of the proposed methods is demonstrated by study of two cases, including a household toolkit consisting of a variety of tools, as well as a Francis turbine that is structurally more complex. [ABSTRACT FROM AUTHOR]

The generalized assignment problem (GAP) is an open problem in which an integer k is given and one wants to assign k ′ agents to k k ′ ≤ k jobs such that the sum of the corresponding cost is minimal. Unlike the traditional K -cardinality assignment problem, a job can be assigned to many, but different, agents and an agent may undertake several, but different, jobs in our problem. A network model with a special structure of GAP is given and an algorithm for GAP is proposed. Meanwhile, some important properties of the GAP are given. Numerical experiments are implemented, and the results indicate that the proposed algorithm can globally and efficiently optimize the GAP with a large range cost. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for solving a minimum cost flow (MCF) problem with a dual approach. The algorithm holds the complementary slackness at each iteration and finds an augmenting path by updating node potential iteratively. Then, flow can be augmented at the original network. In contrast to other popular algorithms, the presented algorithm does not find a residual network, nor find a shortest path. Furthermore, our algorithm holds information of node potential at each iteration, and we update node potential within finite iterations for expanding the admissible network. The validity of our algorithm is given. Numerical experiments show that our algorithm is an efficient algorithm for the MCF problem, especially for the network with a small interval of cost of per unit flow. [ABSTRACT FROM AUTHOR]

Due to the intricate mechanism of Abbe error's impact on spatial accuracy, the accumulated Abbe error in the traditional spatial accuracy model is difficult to identify and eliminate, resulting in reduced modeling accuracy and limiting accuracy improvement. This paper proposes a data-driven spatial accuracy modeling approach for machine tools under the influence of Abbe error, using a three-axis coupling measurement optical path to directly measure the comprehensive spatial accuracy data of the machine tool containing Abbe error. Firstly, to effectively identify Abbe error in the comprehensive spatial accuracy, an Abbe error quantization function is established, which analyzes its formation mechanism in the measurement process to eliminate Abbe error in the machine tool's spatial accuracy data. Furthermore, to address the issue of limited data samples after eliminating Abbe error, data samples are extended based on the degradation mechanism of machine tool spatial accuracy at different coordinate positions, resulting in a high-precision spatial error model for machine tools. Finally, the experiment is conducted on a three-axis CNC machine tool, with a model accuracy of over 95%. The example application verification demonstrates that the developed model scheme is feasible and effective. [ABSTRACT FROM AUTHOR]

Chen, G.; Sun, X.; Hu, H., and Hu, Y., 2015. Research on modeling and algorithm of supply chain's reliability based on CCFSM. Reliability is one of the most important characteristics of the functioning of supply chains. Modeling supply chain's reliability based on common causes of failure between supplier(s) and manufacturers (abbreviated as CCFSM) is a complex and important problem. To identify key factors which cause the failure in the supply chain, the principle of fault tree analysis and the method of Monte Carlo simulation are proposed to apply in the modeling and the algorithm of supply chain's reliability based on CCFSM. After analyzing detailed failure factors which lead to supply chain failure, its diagnostic model is established with the principle of fault tree analysis and the diagnostic algorism of key factors is studied by the method of the Monte Carlo simulation, which can calculate unreliability and the key importance of supply chain. Lastly, the case is presented to prove the model and algorithm's feasibility and validity. [ABSTRACT FROM AUTHOR]