800 publications from this institution
With the rapid development of cloud computing in recent years, data centers have become the mainstay that can support large-scale virtual computing of computers. Therefore, reducing energy consumption and carbon emissions of data centers has attracted extensive attention from academia and industry. Reduce server power consumption according to data center energy consumption characteristics. Reducing CPU utilization in the server can be achieved by scheduling the workload, so the workload approach used in this article is to change the load completion time. In contains in the energy management of energy storage devices and photovoltaic power generation system, the time-sharing electricity environment, using the workload in the scheduling server scheduling factors, and increase the constraint conditions and intermediate variables, the minimum purchase electricity as the goal, the establishment and refrigeration equipment and mid-cooling power load data center under the management environment of collaborative optimization scheduling model, and USES the branch and bound method to solve. Through the example analysis, the basic data of typical days in four seasons are collected to get the optimization results of the whole year. According to the calculation, the electricity price of the data center in the whole year has been reduced by 361,570 yuan, and the cost has been reduced by about 0.58%. This model, through collaborative optimization of renewable energy and technical means, has increased the economic benefits of the data center and better fits the concept of "green data center".
The splicing design of the existing road and the new road in the expansion project is an important part of the design work. Based on the analysis of the characteristics and the load effect of pavement structure on splicing, this paper points out that tensile crack or shear failure may occur at the splicing under the repeated action of the traffic load on the new/old pavement. According to the current structure design code of asphalt pavement in China, it is proposed that the horizontal tensile stress at the bottom of the splicing layer and the vertical shear stress at other layers of the splicing line should be controlled by adjusting the position and size of the excavated steps in addition to the conventional design index, and put forward the corresponding technical requirements and design process. It can be used for reference in the design of asphalt pavement reconstruction and expansion projects.
In a extensometer, a fiber Bragg grating is attached at two ends of the inner tube, therein the separation of these two bonding ends defines the gauge length of the sensor. To measure compressive and tensile strain, the grating is required to exist in a state of permanent tensile strain equal to the largest compressive strain it is ever likely to experience. The fiber Bragg grating strain sensors are bonded on the concrete surfaces of H154 and the reinforcing steel bar surfaces of H158 girders to measure the compression and tension strain separately. When the reinforced concrete girders are loaded by the jack, theirs strain are measured by the shift quantity of reflected Bragg wavelength in these sensors. The experiment indicates that as an absolute measurement component, the fiber Bragg grating offers the effective monitoring for RC girder, whereinto, the tension strain is ~1000 me, and the compression strain is ~1500 me.
The statistics and quality of reads throughout all samples in the Leptobrachium boringii transcriptome. Table S2. Significant enrichment of GO terms in the three tissues. Table S3. Differentially expressed genes in the upper jaw skin. Table S4. Differentially expressed genes in the testis. Table S5. Differentially expressed genes in the brain. Table S6. Top 10 most differentially expressed genes in testis and brain of Leptobrachium boringii. Table S7. Specific primers used for quantitative PCR validation. (XLSX 769Â kb)
Shear connectors are generally used to link the slab and girder together in slab-on-girder bridge structures. Damage of shear connectors in such structures will result in shear slippage between the slab and girder, which significantly reduces the load-carrying capacity of bridges. A damage detection approach based on transmissibility in frequency domain is proposed in this paper to identify the damage of shear connectors in slab-on-girder bridge structures with or without reference data from the undamaged structure. The transmissibility, which is an inherent system characteristic, indicates the relationship between two sets of response vectors in frequency domain. Measured input force and acceleration responses from hammer tests are analyzed to obtain the frequency response functions at the slab and girder sensor locations by the experimental modal analysis. The transmissibility matrix that relates the slab response to the girder response is then derived. By comparing the transmissibility vectors in undamaged and damaged states, the damage level of shear connectors can be identified. When the measurement data from the undamaged structure are not available, a study with only the measured response data in the damaged state for the condition assessment of shear connectors is also conducted. Numerical and experimental studies on damage detection of shear connectors linking a concrete slab to two steel girders are conducted to validate the accuracy and efficiency of the proposed approach. The results demonstrate that the proposed method can be used to identify shear connector damages accurately and efficiently. The proposed method is also applied to the condition evaluation of shear connectors in a real composite bridge with in-field testing data.
Convolutional neural network has been successfully applied to image denoising. In particular, dilated convolution, which expands the network's receptive field, has been widely used and has achieved good results in image denoising. Losing some image information, a standard network cannot effectively reconstruct tiny image details from noisy images. To solve this problem, we propose a pyramid dilated CNN, which mainly has three pyramid dilated convolutional blocks (PDCBs) and a gated fusion unit (GFU). PDCB uses dilated convolution to expand the network's receptive field and the pyramid structure to obtain more image details. GFU fuses and enhances the feature maps from different blocks. Experiments demonstrate that the proposed method outperforms the comparative state-of-the-art denoising methods for gray and color images. In addition, the proposed method can effectively deal with real-world noisy images.
Plane wave imaging has widespread applications in non-destructive testing due to its fast data acquisition speed and simple system architecture. However, traditional plane wave imaging employs an unfocused transmission scheme. This results in dispersed acoustic energy distribution, low imaging resolution, and poor image quality. Although coherent plane wave compounding (CPWC) improves imaging performance through multi-angle coherent summation, it still has shortcomings in image resolution, contrast, and artifact suppression when detecting defects far from the acoustic axis center. To break through these limitations, this paper proposes a coherent plane wave compounding with delay multiplication and sum (CPWC-DMAS) method in which multi-angle plane wave is combined with DMAS beamforming technology to enhance imaging quality and resolution capability. First, coherent summation of multi-angle plane wave signals is performed to achieve comprehensive angular information fusion, ensuring effective coverage of the detection region. Subsequently, the DMAS method is used to perform cross-multiplication and summation of signals acquired from all angles by different array elements, utilizing the spatial coherence between received signals from different array elements to effectively enhance the target echo signals, while suppressing incoherent noise and reducing artifacts. Finally, to validate the correctness and effectiveness of the proposed method, experimental verification is conducted on defects embedded in steel rail and wheel components. The results indicate that compared with the total focusing method and CPWC algorithms, the proposed CPWC-DMAS algorithm achieves significant improvements of 51.18% and 50% in array performance index, 50.8% and 46.52% in contrast ratio, and 25.14% and 21.56% in signal-to-noise ratio, respectively. In summary, the proposed CPWC-DMAS algorithm demonstrates significant advantages over traditional methods in resolution enhancement, contrast improvement, and artifact suppression, achieving high-quality imaging for multi-angle coherent plane wave compounding. This method provides a novel approach for detecting defects both near and away from the center of acoustic axis, offering new insights into defect detection in complex structures with broad engineering applications.
This paper proposes a vibration-based structural damage detection approach considering the effects of uncertainties, including environmental variations and random errors that possibly stem from measurement and automatic modal identification. The existing methods that only employ the classical Principle Component Analysis (PCA) have been demonstrated effective to remove the effects of environmental variations while extremely sensitive to random errors. Therefore, the robust PCA is firstly introduced to remove the random errors, especially outliers, that significantly corrupt the low-rank property of the stacked damage sensitive feature (DSF) matrix. Then, the classical PCA is used to extract the environmental variation-free residues, which are inherently damage-dependent and can be used to detect the existence of damage. The problem of missing data is also considered in this study. It is tackled by adding virtual random errors to the locations of missing entities and thus can be addressed by the introduced robust PCA. The advantages of the proposed approach include: (1) Handling the random error-contaminated DSF data regardless of the error’s amplitude, which is an intractable problem for the existing classical PCA-based methods to consider the environmental effects; (2) Damage detection process can be automatic since the missing data can be automatically predicted and the random errors are not required to be manually distinguished. The effectiveness and performance of the proposed method are demonstrated on a numerical beam structure and an experimentally tested wooden bridge model.
Shear connectors are generally used to link the slab and girders together in slab-on-girder bridge structures. Damage of shear connectors in such structures will result in shear slippage between the slab and girders, which significantly reduces the load-carrying capacity of the bridge. Because shear connectors are buried inside the structure, routine visual inspection is not able to detect conditions of shear connectors. A few methods have been proposed in the literature to detect the condition of shear connectors based on vibration measurements. This paper proposes a different dynamic condition assessment approach to identify the damage of shear connectors in slab-on-girder bridge structures based on power spectral density transmissibility (PSDT). PSDT formulates the relationship between the auto-spectral densities of two responses in the frequency domain. It can be used to identify shear connector conditions with or without reference data of the undamaged structure (or the baseline). Measured impact force and acceleration responses from hammer tests are analyzed to obtain the frequency response functions at sensor locations by experimental modal analysis. PSDT from the slab response to the girder response is derived with the obtained frequency response functions. PSDT vectors in the undamaged and damaged states can be compared to identify the damage of shear connectors. When the baseline is not available, as in most practical cases, PSDT vectors from the measured response at a reference sensor to those of the slab and girder in the damaged state can be used to detect the damage of shear connectors. Numerical and experimental studies on a concrete slab supported by two steel girders are conducted to investigate the accuracy and efficiency of the proposed approach. Identification results demonstrate that damages of shear connectors are identified accurately and efficiently with and without the baseline. The proposed method is also used to evaluate the conditions of shear connectors in a real composite bridge with in-field testing data.
Objective To evaluate four detectors for the off-axis ratio profile measurements of a CyberKnife system, and provide reference and suggestions for selecting and using the correct detectors. Methods Profiles were acquired by using four detectors, PTW-60017, PTW-60018, PTW-60019 and IBA-SFD, at different depths for different collimator sizes, with the detector stem being oriented both perpendicular and parallel to the central beam axis. The differences of profiles and the influence of detector orientation on measurement result were analyzed. Results All full width at half maximum (FWHM) of field measured by four detectors in parallel orientation was larger than that in actual field size. The deviation was increased with the size of collimator and measurement depth, with the maximum deviation of 1.9 mm. The maximum deviation of FWHM among four detectors was 0.2 mm. The penumbra was the smallest for IBA-SFD, and the largest for PTW-60019. The maximum deviation of penumbra was 0.3 mm. The IBA-SFD tended to over-respond in the out-of-field region when the collimator size was larger than 30 mm. Both FWHM and penumbra in perpendicular orientation were smaller than those in parallel orientation for PTW-60017, PTW-60018 and PTW-60019, especially at 5 mm collimator. However, the trend was opposite for IBA-SFD. With the increase of collimator aperture, the difference between the right and left penumbra acquired by four detectors was increased, with more obvious stem effects. Conclusions Similar profiles were acquired by four detectors, but the detector characteristics and effects of detector orientations should be considered. Key words: CyberKnife; Off axis ratio profiles; Volume averaging; Detector orientation; Stem effects
The phase identification and travel time picking are critical for seismic tomography, yet it will be challenging when the numbers of stations and earthquakes are huge. We here present a method to quickly obtain P and S travel times of pre-determined earthquakes from mobile dense array with the aid from long term phase records from co-located permanent stations. The records for 1 768 M ≥ 2.0 events from 2011 to 2013 recorded by 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and rectilinearity. The reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage, which are further refined with AR-AIC. Totally, 718 573 P picks and 512 035 S picks are obtained from mobile stations, which are 28 and 22 times of those from permanent stations, respectively. By comparing the automatic picks with manual picks from 88 permanent stations, for M ≥ 3.0 events, 81.5% of the P-pick errors are smaller than 0.5 second and 70.5% of S-pick errors are smaller than 1 second. For events with a lower magnitude, 76.5% P-pick errors fall into 0.5 second and 69.5% S-pick errors are smaller than 1 second. Moreover, the Pn and Sn phases are easily discriminated from directly P/S, indicating the necessity of combining traditional auto picking and integrating machine learning method.
To ensure the fulfilling hospital's medical income, it is necessary to reinforce the management of in patients' charge. To raise both economic and social benefits of hospitals , many measures need to be considered: management through net works; raising the quality of the staff of shroff; establish rules and regulations; set up 3 level auditing system; consummate the management course of the patients' charge; make up of various possible leaks; reduce the patients' arrearage.
An ultrasensitive gas refractive index (RI) sensor based on fiber Mach–Zehnder interferometers (MZIs) and Vernier effect is proposed and demonstrated. The sensor consists of two cascaded fiber MZIs, one of which serves as a reference unit and is fabricated by fusion splicing a section of symmetrical side-hole fiber in between two short pieces of multi-mode fiber (MMF); the other acts as a sensing unit, which is composed of a section of tapered single-mode fiber sandwiched between two sections of MMF. The two MZIs are designed to have similar free spectral range for generating optical Vernier effect. Because the two MZIs have different responses to external RI, the RI-related Vernier effect can be obtained and the RI sensitivity can be improved, while there is no temperature sensitivity enhancement because the two MZIs have the same response to temperature. Thus the structure has low temperature crosstalk when obtaining high RI sensitivity. Experimental results show that the gas RI sensor has an ultrahigh sensitivity of 4.2 × 104 nm RIU−1 (RI unit). At the same time, the temperature cross-sensitivity is only 2.2 × 10−6 RIU °C−1. Therefore, the proposed sensor has potential applications for modern gas concentration monitoring in a large dynamic range.
