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Aluminum alloy rings are widely used in aerospace, energy, petrochemical, and equipment applications. The loss of roundness often occurs because of the weak rigidity and unstable plasticity of aluminum alloy rings. Therefore, an online measurement technique for the roundness of aluminum alloy rings is critical for ensuring the processing quality of aluminum alloy rings. In this paper, an online roundness measurement method based on the laser ranging principle and improved particle swarm optimization (IPSO) is proposed with the aid of the rotation of the ring during the rolling process. First, the distance from the outer surface of the ring to the laser rangefinder was measured using a triangular laser rangefinder with the help of the rotation of the ring in the rolling process. Second, the distance data were denoized using an improved Kalman filter algorithm to reduce the influence of random noise on the measurement results. Finally, the roundness and geometric center were evaluated using the IPSO algorithm combined with the minimum zone circle (MZC) method and compared with the results obtained using the standard particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and least square (LS) algorithm. The experimental results show that the roundness measured by the IPSO algorithm is less than 0.06 mm, demonstrating better performance in the roundness evaluation. The measurement method for roundness proposed in this paper is suitable for the online evaluation of large aluminum alloy rings, avoiding the influence of ring size on roundness measurement.

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.

In accordance with the Beer-Lambert law, absorbance is proportional to concentration and optical path length of the absorbers in the sample, and in a linear relationship with total column concentration (product of concentration and optical path length) at a single wavelength. However, limitation of spectral resolution will result in linear deviation with the Beer-Lambert law in actual measurement. Regarding additivity of polychromatic light intensity as the theoretical basis, this paper attributed linear deviation with the Beer-Lambert law to spectral resolution, concentration and light intensity, and verified this explanation by measuring sulfur dioxide at various total column concentrations using spectrometers with different spectral resolutions in the waveband range of 216-230 nm. It was found that linear deviation with the Beer-Lambert law was in negative correlation with spectral resolution, and in positive correlation with total column concentration, and absorbance could be considered to be linear with total column concentration (below 171.4 mg/m

Blended vegetable oil is a vital product in the vegetable oil market, and quantifying high-value vegetable oil is of great significance to protect the rights and interests of consumers. In this study, we established a one-dimensional convolutional neural network (1D CNN) quantitative identification model based on Raman spectra to identify the amount of olive oil in a corn-olive oil blend. The results show that the 1D CNN model based on 315 extended average Raman spectra can quantitatively identify the content of olive oil, with R

In this study, a highly sensitive monitoring system for dual sensing of oxygen and pressure has been developed. The absorption of oxygen in the wavelength range of 186–196 nm was observed and the corresponding absorption cross-section of oxygen in this band was achieved. An approach based on differential optical absorption spectroscopy was used to describe the concentration of oxygen. The detection limit of oxygen was determined to be 150 ppm per meter. No relation between pressure and oxygen absorption line shape in the related wavelength band was confirmed. Based on the absorption of oxygen, pressure detection was realized and a detection limit of 180 Pa was obtained. A dual sensing system for oxygen and pressure was constructed.

Stochastic resonance (SR), as a typical noise-assisted signal processing method, has been extensively studied in weak signal detection by virtue of the advantage of using noise to enhance the feature of periodic signal. And how to better apply the SR method in engineering signal processing has always been the research hotspot. In this paper, a novel adaptive SR method based on coupled bistable systems (ACBSR) is proposed to further improve the performance of SR method for weak signal detection. Compared with the cascaded bistable SR system, the suggested coupled bistable SR (CBSR) system can enhance the target signal while suppressing the low-frequency and high-frequency interferences, whose output behavior exhibits good bandpass filtering characteristic. In addition, a weighted spectral peak signal-to-noise ratio (WPSNR) index is constructed by using spectral peak SNR, spectral correlation coefficient and zero-crossing ratio to serve as the measurement index of SR for periodic signal detection. The constructed WPSNR index is simple in form and easy to calculate, which can quantitatively evaluate the resonance effect of periodic signal, even though the target signal frequency cannot be estimated accurately. And the combination of WPSNR index and the CBSR system is beneficial to improve the performance of SR method in rolling bearing fault diagnosis. Experiments and engineering application are performed to verify the effectiveness and superiority of the proposed ACBSR method in comparison with the traditional SR method.

For a class of production processes, a modeling method based on pattern recognition is introduced, and state feedback stabilization of the system is studied in a pattern-moving state space. At the first part of the article, the modeling method of the system is introduced, which is based on pattern moving, and the system dynamics are presented by using the so-named pattern class variable(PCV) instead of a system output or state variable. A discrete-time and discrete-state nonlinear state-space model (DNSM) is given in Euclidean space based on the modeling method. And soon a new control and stability analysis method is put forward. Finally, validity of the stabilization method is demonstrated by using data of an actual production process.

Carbon disulfide, an important sulfur-containing species, has strong absorption lines in the wavelength range of 188 nm to 215 nm. It is difficult to accurately measure the absorption cross sections of carbon disulfide because carbon disulfide will be easily converted into carbon sulfide when it is exposed to ultraviolet light. In this study, the absorption cross sections of carbon disulfide were measured by reducing carbon disulfide conversion. The factors affecting carbon disulfide conversion, including gas flow rate, ultraviolet light intensity, and duration of illumination, were studied to reduce the conversion of carbon disulfide by controlling experimental conditions in the experiment. Finally, the absorption cross sections of carbon disulfide at room temperature and atmospheric pressure were calculated using the absorption spectrum and the carbon disulfide concentration in the absence of carbon disulfide conversion. The wavelengths of 16 absorption peaks on the carbon disulfide absorption cross sections of the vibration change were marked. Carbon disulfide has the maximum absorption cross section of 4.5 × 10

Additional file 3. The Most enriched GO terms during the different time points as compared to the control.

Additional file 2. Detailed GO function classification of differentially expressed unigenes between control and different treatments.

Sparse representation of signals have become an important tool in computer vision. In many computer vision applications such as image denoising, image super-resolution and object recognition, sparse representations have produced remarkable performances. Sparse representation models often contain two stages: sparse coding and dictionary learning. In this paper, we propose a non-linear non-negative sparse representation model: NNK-KSVD. In the sparse coding stage, a non-linear update rule is proposed to obtain the sparse matrix. In the dictionary learning stage, the proposed model extends the kernel KSVD by embedding the non-negative sparse coding. The proposed non-negative kernel sparse representation model was evaluated on several public image datasets for the task of classification. Experimental results show that by exploiting the non-linear structure in images and utilizing the ‘additive’ nature of non-negative sparse coding, promising classification performance can be obtained. Moreover, the proposed sparse representation method was also evaluated in image retrieval tasks, competitive results were obtained.

A highly sensitive sulfur dioxide detection system based on broadband absorption spectroscopy was developed using the 198–222 nm wavelength range. The compact and simple measurement system was constructed utilizing fiber optoelectronic sensing device. A modified approach based on differential optical absorption spectroscopy was used for the computation of sulfur dioxide concentrations. The results of sulfur dioxide concentration were calibrated and displayed using a LabVIEW-based software. A system detection limit of 17 ppb per meter has been reached, and a ten-fold improvement in sensitive and uncertainty has been achieved for measuring sulfur dioxide concentrations. The system is suitable for monitoring sulfur dioxide concentration in air as well as for the fault diagnosis of gas insulated switchgears.

During the recent years, fashion landmark detection has attracted great attention of many researchers. The main task of Fashion landmark detection is to recognize the components of fashion in images, therefore it is essential for other fashion applications such as fashion classification, retrieval, design and recommendation. By utilizing various techniques in deep learning, the recently proposed landmark detection methods for fashion achieve notable performance, this motivates us to review the fashion landmark detection works. In this paper, we aim at roughly categorizing these recently proposed methods, discussing how these methods can improve detection performance. The fashion benchmark datasets used for landmark detection are reviewed in this paper as well. To our best knowledge, there is no a single review in the current literature that has discussed and reviewed fashion landmark detection.

Images are possibly degraded by various reasons, the typical forms of degradation are: blur, noise, low resolution, and etc. Image restoration techniques try to recover the degraded images to the original images with maximum fidelity. Image restoration is a challenging task and also an import research area in image processing. During the decades, researchers have proposed many restoration methods such as inverse filter, Weiner filter, wavelet analysis, support vector machine, and etc. Recently, deep learning has been increasingly popular among researchers and has obtained remarkable results. In this paper, we briefly review the approaches based on generative adversarial networks (GANs) for image restoration. The typical GANs based restoration methods for image super-resolution, image denoising, image inpainting and image deblurring are introduced and discussed.

Rolling bearings are important supporting components widely used in rotating machinery and are prone to failure, it is thus important to perform fault detection of rolling bearing quickly and accurately. Aiming at the problem that it is difficult to extract the weak impulses buried in strong background noise in rolling bearing fault diagnosis, this paper proposes an enhanced fault detection method combining sparse code shrinkage denoising with fast spectral correlation according to the cyclic statistical properties of defective bearing vibration signals. First, in view of the non-Gaussian statistical properties of the periodic impulses caused by the localized bearing defect in vibration signals, the sparse code shrinkage algorithm is employed to denoise the original noisy signal, thereby highlighting the periodic impulses. Then, the Fast Spectral Correlation (Fast-SC) algorithm is used to process the denoised signal to get the cyclic spectral correlation. Finally, the squared enhanced envelope spectrum (SEES) is presented to effectively detect and identify the rolling bearing faults. Experimental results demonstrate the validity and superiority of the proposed method in rolling bearing fault detection through the comparison with the Fast-SC, spectral kurtosis and Infogram.

Gearboxes are widely used in engineering machinery, but tough operation environments often make them subject to failure. And the emergence of periodic impact components is generally associated with gear failure in vibration analysis. However, effective extraction of weak impact features submerged in strong noise has remained a major challenge. Therefore, the paper presents a new adaptive cascaded stochastic resonance (SR) method for impact features extraction in gear fault diagnosis. Through the multi-filtered procession of cascaded SR, the weak impact features can be further enhanced to be more evident in the time domain. By analyzing the characteristics of non-dimensional index for impact signal detection, new measurement indexes are constructed, and can further promote the extraction capability of SR for impact features by combining the data segmentation algorithm via sliding window. Simulation and application have confirmed the effectiveness and superiority of the proposed method in gear fault diagnosis.

Traditional intelligent fault diagnosis techniques based on artificially selected features fail to make the most of the raw data information, and are short of the capabilities of feature self-learning. Moreover, the most informative and distinguished parts of the different faults signals only account for a small portion in the time domain and frequency domain signals. Therefore, in order to learn the discriminative features from the raw data adaptively, this paper proposes a multiscale local feature learning method based on back-propagation neural network (BPNN) for rolling bearings fault diagnosis. Based on the local characteristics of the fault features in the time domain and the frequency domain, the BPNN is used to locally learn meaningful and dissimilar features from signals of different scales, thus improving the fault diagnosis accuracy. Two sets of rolling bearing datasets are adopted to verify the validity and superiority of the proposed method by comparing with other methods.

This paper investigated a potential instrument for carbon disulfide in situ measurement with high precision and strong anti-interference capability. A compact and automated carbon disulfide detection system was developed using a fiber opto-electronic sensing device. A custom software interface based on LabVIEW was developed. The multi-wavelength least-squares method based on differential optical absorption spectroscopy was employed for improved detection and the anti-interference capabilities of the system. The detection limit of the system (signal-to-noise ratio=3) was determined to be 1 ppb per meter. Using this scheme, the reliability of a liquid-seal was verified to have carbon disulfide leakage. Although the liquid level of the liquid-sealed carbon disulfide showed no significant change over 24 h, a residue concentration of over 30 ppm remained detectable on the surface.

An optical sensor based on chemical conversion and partition differential optical absorption spectroscopy is developed to detect hydrogen sulfide (H

To meet and analyze the herd mentality requirements in comprehensive performance incentive management activities, the psychological mechanisms of information cascade syndrome and self-perception bias are introduced and represented in detail by using an incentive expectation model and a subjective evaluation model. As a result, a highly applicable quantitative performance incentive indicator is given, and three comparison tests are illustrated and discussed to check its validity, so that some further considerations can be introduced subsequently.

Deblurring is a classical problem for remote sensing images, which is known to be difficult as an ill-posed problem. A feasible solution for the problem is incorporating various priors into restoration procedure as constrained conditions. However, the learning of priors usually assumes that the blurs in an image are produced by fixed types of reasons, and thus a possible decrease in model's description ability. In this paper, an end-to-end learned method based on generative adversarial networks (GANs) is proposed to tackle the deblurring problem for remote sensing images. The proposed deblurring model does not need any prior assumptions for the blurs. The proposed method was evaluated on a satellite map image data set and state-of-the-art performance was obtained.

Image super-resolution is an important research field in image analysis. The techniques of image super-resolution has been widely used in many computer vision applications. In recent years, the success of deep learning methods in image super-resolution have attracted more and more researchers. This paper gives a brief review of recent deep learning based methods for single image super-resolution (SISR), in terms of network type, network structure, and training methods. The advantages and disadvantages of these methods are analyzed as well.

Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a “global” network. The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.

A novel kind of green solid propellant containing H2O2 cured at room temperature (SPHP) was prepared by a “two-steps” method, and its stability, mechanical properties, sensitivity, and combustion behavior were characterized. Stable storage of SPHP can be achieved at room temperature by selecting propellant ingredients with perfect compatibility with H2O2 and the addition of a complex stabilizer into propellant formulation. In addition, the obtained SPHP showed a lower tensile strength and higher elongation compared with conventional solid propellant. The experimental results showed that the sensitivity of SPHP was considerably low, but its combustion efficiency and C* efficiency were really high. The hot firing tests demonstrated the stable and smokeless combustion of SPHP, as well as its combustion behavior within the pressure range of 2–10 MPa was in agreement with Vieille’s law and thus can be represented by r=9.95P0.57.

Oxygen sensing properties of a luminescent material gadolinium labeled hematoporphyrin monomethyl ether (Gd-HMME) based on filter paper were studied. Photoluminescence of Gd-HMME at 712 nm was confirmed to be phosphorescence by a time-resolved spectroscopic analysis and its dependence on oxygen. Quantum yield of the phosphorescence was determined to be 0.014 in air saturated methanol solution ( λ exc = 532 nm). A linear range was found from 10% to 100% oxygen concentration in Stern–Volmer plots for Gd-HMME based on filter paper. Fast response time, recovery time ( t ↓ , 0.4 ± 0.2 s; t ↑ , 1.4 ± 0.2 s) and high photostability were achieved. Our results indicate that Gd-HMME is a promising indicator for optical oxygen sensing.

To study the diversity and cultivar-specific of phytochemicals in sweet potato, Liquid Chromatography-Electrospray Ionization-Mass Spectrometry was used to analyze the metabolic profiles of five sweet potato cultivars exhibiting different flesh colors: purple, yellow/orange, and white. A total of 213 metabolites, including 29 flavonoids and 27 phenolic acids, were characterized. The flavonoid profiles of the five different cultivars were distinguished using PCA, the results suggested the flesh color accounted for the observed metabolic differences. In addition to anthocyanins, quinic acids and ferulic acids were the prominent phenolic acids, O-hexoside of quercetin, chrysoeriol were the prominent flavonoids in sweet potato tubers, and they were all higher in the OFSP and PFSP than WFSP. The main differential metabolic pathways between the OFSP, PFSP and the WFSP included those relating to phenylpropanoid and flavonoid biosynthesis. This study provides new insights into the differences in metabolite profiles among sweet potatoes with different flesh colors.

In image super-resolution area, the effectiveness of sparse learning methods have been demonstrated, especially in single image super-resolution applications. A novel sparse learning method is proposed in this paper for the task of single image super-resolution. The proposed non-linear and nonnegative sparsity-based learning model is used to capture the nonlinear data distributions in images. Moreover, a boosting ensemble strategy is used to improve the learned ‘weak’ sparse model. In each round of boosting, the training images are chosen according to the reconstruction errors of the learned models from previous rounds. The obtained dictionary ensemble contains dictionaries which have different reconstruction capability on different types of image patches. Then the support vector regression is applied for obtaining the final result image. The proposed non-negative kernel sparse model was tested on popular benchmark images, the experimental results illustrate the competitiveness of the proposed method.

Sparse representations are widely used tools in image super-resolution (SR) tasks. In the sparsity-based SR methods, linear sparse representations are often used for image description. However, the non-linear data distributions in images might not be well represented by linear sparse models. Moreover, many sparsity-based SR methods require the image patch self-similarity assumption; however, the assumption may not always hold. In this paper, we propose a novel method for single image super-resolution (SISR). Unlike most prior sparsity-based SR methods, the proposed method uses non-linear sparse representation to enhance the description of the non-linear information in images, and the proposed framework does not need to assume the self-similarity of image patches. Based on the minimum reconstruction errors, support vector regression (SVR) is applied for predicting the SR image. The proposed method was evaluated on various benchmark images, and promising results were obtained.

a b s t r a c t A precise method for the measurement of diffuse reflectance by using gas absorption spectroscopy technique with an integrating sphere is demonstrated. A quantitative relationship between the diffuse reflectance and the gas absorption spectrum is formulated, which has been further validated by exper- iments. The precision of the reflectivity measurement depends on the diameter and port fraction of the integrating sphere as well as gas concentration, and it increases linearly with the magnitude of the reflectivity. A high precision of 0.005% was achieved at the reflectivity of 0.98844(5).

ZnO micro/nanocrystals with different percentages of the exposed (0001) facets were synthesized by a facile chemical bath deposition method. Various characterizations were carried out to understand...

As an important data structure model, ontology has become one of the core contents in information science. Multi-dividing ontology algorithm combines the advantages of graph structure and learning algorithms proved to have high efficiency. In this paper, we investigate some theoretical problems of ontology algorithm in multi-dividing setting. The relationship between two versions of low noise assumptions is established. The risk excess and Lq-error are given. Specifically, the upper bound and lower bound minimax learning rate are obtained based on assumptions we describe in Section 2.

Osmotic stress caused by drought and soil salinity is one of the factors that affect plant root system growth and development. Previous studies have shown that microtubule plays a critical role in plant roots response to osmotic stress, however, the underlying mechanism remains unclear. In the present study, the microtubule orientations in Arabidopsis roots growing under osmotic stress were determined using confocal fluorescence microscopy. The results showed that osmotic stress could significantly inhibit primary root elongation in Arabidopsis, and pharmacological tests confirmed that microtubules were involved in Arabidopsis roots response to osmotic stress. In vivo visualization of microtubule structures with the microtubule-binding domain–green fluorescent protein (GFP) reporter revealed altered microtubule orientation in rhizodermal cells under osmotic stress. These results above indicated that osmotic stress could inhibit the elongation growth of Arabidopsis primary root, and the inhibition effects might result from the changes in microtubule orientation.

Distinct from the common well faceted ZnO nanorods (R-ZnO), ZnO nanotetrapods (T-ZnO) exhibited a remarkable catalytic activity for the thermal decomposition of ammonium perchlorate (AP): the activation energy at high temperature decomposition (HTD) was significantly decreased to 111.9 kJ mol−1, much lower than 162.5 kJ mol−1 for pure AP and 156.9 kJ mol−1 for AP with R-ZnO. This was attributed to more abundant atomic steps on the surface of T-ZnO than that of R-ZnO, as evidenced by HRTEM and density function theory (DFT) calculations. It was shown that the initiation step of perchloric acid (PA) decomposition happened much faster on stepped T-ZnO edges, resulting in the formation of active oxygen atoms from HClO4. The formed oxygen atoms would subsequently react with NH3 to produce HNO, N2O and NO species, thus leading to an obvious decrease in the activation energy of AP decomposition. The proposed catalytic mechanism was further corroborated by the TG-IR spectroscopy results. Our work can provide atomic insights into the catalytic decomposition of AP on ZnO nanostructures.

A novel Ho:LuAG ceramic laser Q-switched by a polycrystalline Cr2+:ZnS saturable absorber was reported for the first time in this paper. We took a diode-pumped Tm:YLF laser emitting at 1.9075 μm as the pump source. The laser operated in both continuous wave mode and passively Q-switching (PQS) mode. The maximum PQS output power of 2.67 W was obtained with a slope efficiency of 26.4%. When the absorbed pump power increased from 4.78 to 10.8 W, with three output couplers of T = 2%, T = 10% and T = 25%, the pulse widths decreased as the pump power increased, from 102.9 to 89.2 ns, from 147.1 to 127.6 ns, and from 173 to 150 ns, respectively, and the repetition frequency varied from 10.2 to 20.1 kHz, from 9.3 to 18.3 kHz, and from 8.45 to 16.66 kHz as well. The central wavelength remained constant 2100.64 nm with the change of output couplers and operation modes. Furthermore, the output laser had a beam quality factor M 2 of 1.1.

In this study, a sensitive system for simultaneous sensing of sulfur dioxide and carbon disulfide was developed based on absorption spectroscopy in the deep ultraviolet. An effective spectrum-unfolding approach is proposed to examine the overlapping spectral characteristics. Direct proportional relations with determination coefficients of 0.999 were obtained. The detection limit of sulfur dioxide was determined to be 42 ppb, and a detection limit of 5 ppb for carbon disulfide was achieved with an optical length of 20 cm. The interplay between the measurement results of the two components was investigated. Interference close to the detection limits was confirmed for both sulfur dioxide and carbon disulfide measurements. An automatic and reliable simultaneous sensing system for sulfur dioxide and carbon disulfide was constructed.

A simple cubic-shaped cavity with a high-diffuse-reflectivity inner coating as a novel gas detection cell was developed. The effective optical path length (EOPL) was evaluated by comparing the oxygen absorption signal in the cavity and in air based on tunable diode laser absorption spectroscopy. The law for a spherical cavity was applied and modified to a cubic cavity as a function of reflectivity ρ, port fraction f, and the side length. Single-pass average path length of the cubic cavity was 0.723(7) times the side length. EOPL can be modified conveniently by adjusting the parameters of the cavity.

For real-time video streaming systems, the video packets arriving after the display deadline of their frames are considered as late-arrival packets, and typically they are discarded. This will affect the current frame and the following ones due to error propagations. For this reason, in this paper, we propose an approach to exploit the late-arrival and out-of-order packets, which includes two mechanisms. The first mechanism will use these packets to update the reference frames to make them more consistent with the encoder side, and this will eventually reduce the error propagations. The second mechanism will use these packets to increase the chance of successfully decoding the Reed-Solomon (RS) code. In the proposed approach, a sub-GOP based systematic RS code is used and optimized to exploit these packets, where the size of each sub-GOP and the parity packet number for each sub-GOP are optimally tuned, taking into consideration the maximum end-to-end delay, the network conditions, and other system parameters, so as to make the best use of the late-arrival packets and to exploit the out-of-order packets. Finally, the experimental results show the advantage of the proposed approach over other approaches.

Tunable multi-mode diode laser absorption spectroscopy for methane detection was demonstrated. The exclusive dependence of the 1318 nm laser modes distribution on the input current and temperature was justified. Stable absorption signals related to the methane concentrations were obtained based on second-harmonic detection technique. A real-time data recording and analyzing software program was developed to realize the on-line gas concentration monitoring. A measurement sensitivity of 25 ppm m and an accuracy of 0.27% of this system were achieved.