Ladle plays an important role in the metallurgical industry whose maintenance directly affects the production efficiency of enterprises. In view of the problems such as low maintenance efficiency and untimely maintenance in the current ladle passive maintenance scheme, the life prediction mechanism for ladle composite structures is established which bases on the stress analysis of steel shell and ladle lining in the production process, combining conventional fatigue analysis and extended fracture theory. The mechanism is accurate and effective according to the simulation results. Through which, the useful life of steel shell can be accurately predicted by detecting the crack length of it. Due to the large number of factors affecting the life of the lining of the ladle, it is difficult to accurately predict the life of the ladle lining, so a forecasting mean based on the thermal shock method is proposed to predict the service life of the ladle lining in this paper. The life prediction mechanism can provide data support and theoretical guidance for the active maintenance of the ladle, which is the prerequisite for scientifically formulating ladle initiative maintenance program.

A kind of generalized proportional-integral(GPI) observer for descriptor linear systems is introduced. We first propose two complete parametric solutions to generalized Sylvester matrix equation corresponding to the left eigenvector matrices in the case of Jordan form. Then a parametric design approach for the observer is presented. The proposed method provides all parametric expression of the gain matrices and the corresponding finite left eigenvector matrix and guarantees the regularity and impulse-freeness of the expanded error system. Two numerical examples are given to explain the design procedure and illustrate the effectiveness of the proposed method.

At present, with the increase of production capacity and the promotion of production, the reserves

of most mining enterprises under the original industrial indexes are rapidly consumed, and the full

use of low-grade resources is getting more and more attention. If mining enterprises want to make

full use of low-grade resources simultaneously and obtain good economic benefits to strengthening

the analysis and management of costs is necessary. For metal underground mines, with the gradual

implementation of exploration and mining projects, capital investment and labor consumption are

dynamic and increase cumulatively in stages. Consequently, in the evaluation of ore value, we should

proceed from a series of processes such as: exploration, mining, processing and the smelting of

geological resources, and then study the resources increment in different stages of production and the

processing. To achieve a phased assessment of the ore value and fine evaluation of the cost, based on

the value chain theory and referring to the modeling method of computer integrated manufacturing

open system architecture (CIMOSA), the analysis framework of gold mining enterprise value chain is

established based on the value chain theory from the three dimensions of value-added activities, value

subjects and value carriers. A value chain model using ore flow as the carrying body is built based on

Petri nets. With the CPN Tools emulation tool, the cycle simulation of the model is carry out by the

colored Petri nets, which contain a hierarchical structure. Taking a large-scale gold mining enterprise

as an example, the value chain model is quantified to simulate the ore value formation, flow, transmission

and implementation process. By analyzing the results of the simulation, the ore value at different

production stages is evaluated dynamically, and the cost is similarly analyzed in stages, which can improve mining enterprise cost management, promote the application of computer modeling and

simulation technology in mine engineering, more accurately evaluate the economic feasibility of ore

utilization, and provide the basis for the value evaluation and effective utilization of low-grade ores.

This paper proposes a speech enhancement method using the multi-scales and multi-thresholds of the auditory perception wavelet transform, which is suitable for a low SNR (signal to noise ratio) environment. This method achieves the goal of noise reduction according to the threshold processing of the human ear's auditory masking effect on the auditory perception wavelet transform parameters of a speech signal. At the same time, in order to prevent high frequency loss during the process of noise suppression, we first make a voicing decision based on the speech signals. Afterwards, we process the unvoiced sound segment and the voiced sound segment according to the different thresholds and different judgments. Lastly, we perform objective and subjective tests on the enhanced speech. The results show that, compared to other spectral subtractions, our method keeps the components of unvoiced sound intact, while it suppresses the residual noise and the background noise. Thus, the enhanced speech has better clarity and intelligibility.

In this paper, we propose and experimentally demonstrate a new method for optical frequency transfer over fibre. Instead of dual acousto-optic modulators (AOMs) as adopted in the traditional fibre phase noise compensation setup, here an active fibre phase noise compensation scheme with a single acousto-optic modulator (AOM) is used. The configuration simplifies the equipment of the user end while maintaining a high-performance optical frequency transfer stability. We demonstrate an actively stabilized coherent transfer at an optical frequency of 193.55THz over 10-km spooled fibre, obtaining a relative frequency stability (Allan deviation) of 3.84 × 10−16/1 s and 4.08 × 10−18/104 s, which is improved by about 2∼3 orders of magnitude in comparison with the one without any phase noise compensation that achieves a relative frequency stability of 1.81 × 10−14/1 s and 2.48 × 10−15/104 s.

In this paper, a new lifting wavelet domain audio watermarking algorithm based on the statistical characteristics of sub-band coefficients is proposed. First of all, an original audio signal was segmented and each segment was divided into two sections. Then, the Barker code was used for synchronization, the LWT (lifting wavelet transform) was performed on each section, a synchronization code and a watermark were embedded into the first section and the second section, respectively, by modifying the statistical average value of the sub-band coefficients. The embed strength was determined adaptively according to the auditory masking property. Experiments show that the embedded watermark has better robustness against common signal processing attacks than present algorithms based on LWT and can resist random cropping in particular.

A ceria loaded carbon nanotubes (CeO2/CNTs) nanocomposites photocatalyst was prepared by chemical precipitation, and the preparation conditions were optimized using an orthogonal experiment method. HR-TEM, XRD, UV-Vis/DRS, TGA and XPS were used to characterize the photocatalyst. Nitrogen adsorption-desorption was employed to determine the BET specific surface area. The results indicated that the photocatalyst has no obvious impurities. CeO2 was dispersed on the carbon nanotubes with a good loading effect and high loading efficiency without agglomeration. The catalyst exhibits a strong ability to absorb light in the ultraviolet region and some ability to absorb light in the visible light region. The CeO2/CNTs nanocomposites photocatalyst was used to degrade azo dye Acid Orange 7 (40 mg/L). The optical decolorization rate was 66.58% after xenon lamp irradiation for 4 h, which is better than that of commercial CeO2 (43.13%). The results suggested that CeO2 loading on CNTs not only enhanced the optical decolorization rate but also accelerated the separation of CeO2/CNTs and water.

The detection of transformer winding deformation caused by short-circuit current is of great significance to the realization of condition based maintenance. Considering the influence of environment and measurement errors, an online deformation detection method is proposed based on the analysis of leakage inductance changes. First, the operation expressions are derived on the basis of the equivalent circuit and the leakage inductance parameters are identified by the partial least squares regression algorithm. Second, the amount of the leakage inductance samples in a detection time window is determined using the Monte Carlo simulation thought, and then the samples in the confidence interval are obtained. Last, a criteria is built by the mean value changes of the leakage inductance samples and the winding deformation is detected. The online detection method considers the random fluctuation characteristics of the leakage inductance samples, adjust the threshold value automatically, and can quantify the change range to assess the severity. Based on the field data, the distribution of the leakage inductance samples is analyzed to obey the normal function approximately. Three deformation experiments are done by different sub-winding connections and the detection results verify the effectiveness of the proposed method.

This paper presents a geomagnetic detection method for pipeline defects using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet energy product (WEP) – Teager energy operator (TEO), which improves detection accuracy and defect identification ability as encountering strong inference noise. The measured signal is first subtly decomposed via CEEMDAN into a series of intrinsic mode functions (IMFs), which are then distinguished by the Hurst exponent to reconstruct the filtered signal. Subsequently, the scale signals are obtained by using gradient calculation and discrete wavelet transform and are then fused by using WEP. Finally, TEO is implemented to enhance defect signal amplitude, completing geomagnetic detection of pipeline defects. The simulation results created by magnetic dipole in a noisy environment, indoor experiment results and field testing results certify that the proposed method outperforms ensemble empirical mode decomposition (EEMD)-gradient, EEMD-WEP-TEO, CEEMDAN-gradient in terms of detection deviation, peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR).