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Microwave-Assisted Isolation and Acetylation of Inulin from Helianthus Tuberosus L Tubers
No full textJerusalem artichoke (Helianthus tuberosus L.) tubers are industrial crop considered as a promising source for inulin production. “Green” method was performed for accelerated inulin extraction from Helianthus tuberosus L. tubers by the application of microwave irradiation. Further pretreatment of the water extract with acetone and ethanol yielded inulin (20%) with purity 89% and degree of polymerization 18. Jerusalem artichoke inulin was characterized by FTIR and NMR spectroscopy. For the first time eco-friendly synthesis of acetylated Jerusalem artichoke inulin was performed by the reaction with acetic anhydride, without toxic solvent, but only with sodium acetate as catalyst under microwave irradiation for 60 s. The degree of acetylation (DA=2.1) and the structure of inulin esters were confirmed by 1H and 13C NMR. The suggested microwave acetylation shortens significantly the esterification reaction
Stiffness Degradation Characteristics Destructive Testing and Finite-Element Analysis of Prestressed Concrete T-Beam
No full textThe failure behavior of the precast prestressed concrete T girder was investigated by destructive test and finite-element analysis, and the mid-span deflection, girder stiffness and the variation of the cross section strain in the loading process were obtained, and the mechanical properties, mechanical behavior, elastic and plastic behavior and ultimate bearing capacity of T girder with large span were revealed. Furthermore, the relationship between the beam stiffness degradation, the neutral axis in cross-section, steel yielding and concrete cracking are investigated and analyzed. A method was proposed to predict the residual bearing capacity of a bridge based on the variation of the position of the cross section strain distribution and the section neutral axis, which provided a theoretical basis for predicting the stiffness detection and carrying capacity assessment of prestressed concrete beam
Dimensionless Study on Secretion Clearance of a Pressure Controlled Mechanical Ventilation System with Double Lungs
No full textA pressure controlled mechanical ventilator with an automatic secretion clearance function can improve secretion clearance safely and efficiently. Studies on secretion clearance by pressure controlled systems show that these are suited for clinical applications. However, these studies are based on a single lung electric model and neglect the coupling between the two lungs. The research methods applied are too complex for the analysis of a multi-parameter system. In order to understand the functioning of the human respiratory system, this paper develops a dimensionless mathematical model of double-lung mechanical ventilation system with a secretion clearance function. An experiment is designed to verify the mathematical model through comparison of dimensionless experimental data and dimensionless simulation data. Finally, the coupling between the two lungs is studied, and an orthogonal experiment designed to identify the impact of each parameter on the system
Stability Analysis of Network Controlled Temperature Control System with Additive Delays
No full textThis paper presents, using Lyapunov-Krasovskii functional technique combined with reciprocal convex lemma is considered for a networked control temperature control system with additive time-varying-delays. In the stability analysis, a new LK functional is assumed, and take the time-derivative of the (LK) functional, using reciprocal convex combination technique was employed to obtain less conservative stability criteria. Finally, the proposed stability analysis culminates into a stability criterion in the LMI (linear matrix inequalities) framework. The results obtained are in accordance with the theoretically obtained in the temperature control system and they are closer to the standard benchmark temperature-control system
Lower Bound Limit Analysis of Anisotropic Soils
Get PDFPrevious approaches can only tackle anisotropic problems with cohesion varying with direction. A novel linearization of the Mohr-Coulomb yield criterion associated with plane strain problem has been achieved by simulating the Mohr’s circle with orientation lines in σ-τ space, which allows for lower bound solution of soils with cohesion and friction coefficient varying with direction. The finite element lower limit analysis formulation using the modified anisotropic yield criterion is then developed. Several examples are given to illustrate the capability and effectiveness of the proposed numerical procedure for computing rigorous lower bounds for anisotropic soils
Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB
Get PDFSteganography technology has been widely used in data transmission with secret information. However, the existing steganography has the disadvantages of low hidden information capacity, poor visual effect of cover images, and is hard to guarantee security. To solve these problems, steganography using reversible texture synthesis based on seeded region growing and LSB is proposed. Secret information is embedded in the process of synthesizing texture image from the existing natural texture. Firstly, we refine the visual effect. Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture. We use seeded region growing algorithm to ensure texture’s similar local appearance. Secondly, the size and capacity of image can be decreased by introducing the information segmentation, because the capacity of the secret information is proportional to the size of the synthetic texture. Thirdly, enhanced security is also a contribution in this research, because our method does not need to transmit parameters for secret information extraction. LSB is used to embed these parameters in the synthetic texture
A Spark Scheduling Strategy for Heterogeneous Cluster
Get PDFAs a main distributed computing system, Spark has been used to solve problems with more and more complex tasks. However, the native scheduling strategy of Spark assumes it works on a homogenized cluster, which is not so effective when it comes to heterogeneous cluster. The aim of this study is looking for a more effective strategy to schedule tasks and adding it to the source code of Spark. After investigating Spark scheduling principles and mechanisms, we developed a stratifying algorithm and a node scheduling algorithm is proposed in this paper to optimize the native scheduling strategy of Spark. In this new strategy, the static level of nodes is calculated, the dynamic factors such as the length of running tasks, and CPU usage of work nodes are considered comprehensively. And through a series of comparative experiments in alienation cluster, the new strategy costs less running time and lower CPU usage rate than the original Spark strategy, which verifies that the new schedule strategy is more effective one
Real-Time Visual Tracking with Compact Shape and Color Feature
Get PDFThe colour feature is often used in the object tracking. The tracking methods extract the colour features of the object and the background, and distinguish them by a classifier. However, these existing methods simply use the colour information of the target pixels and do not consider the shape feature of the target, so that the description capability of the feature is weak. Moreover, incorporating shape information often leads to large feature dimension, which is not conducive to real-time object tracking. Recently, the emergence of visual tracking methods based on deep learning has also greatly increased the demand for computing resources of the algorithm. In this paper, we propose a real-time visual tracking method with compact shape and colour feature, which forms low dimensional compact shape and colour feature by fusing the shape and colour characteristics of the candidate object region, and reduces the dimensionality of the combined feature through the Hash function. The structural classification function is trained and updated online with dynamic data flow for adapting to the new frames. Further, the classification and prediction of the object are carried out with structured classification function. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark dataset OTB-100 and OTB-13
Modification of Nano Tourmaline Surface Treatment Agent and Its Performance on Negative Ion Release
Get PDFIn this paper, a kind of wall fabric’s surface treatment agent modified with nonionic surfactant was reported. This surface treatment agent was prepared by using nano tourmaline powder dispersion in water with surfactant as dispersants by sand milling. Under the influence of different dispersants, the negative ions releasing amount of functional wall fabrics, the milling process and the storage stability of nano tourmaline powder dispersion were discussed. The results showed that nano tourmaline powder dispersion achieved the smallest average diameter of 44 nm and had best storage stability that the average diameter maintained below 200 nm in 17 days when the addition amount of dispersant was 20 percent of the tourmaline powders’ weight. What is more, the quantity of negative ion releasing achieved 6500 ion/cm3 when addition amount of dispersant was 30 percent. This technique could be used to strengthen productivity of nano tourmaline powder dispersion
Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder
Get PDFWith the rapid development of mechanical equipment, mechanical health monitoring field has entered the era of big data. Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities, this also brings influence to the mechanical fault diagnosis field. Therefore, according to the characteristics of motor vibration signals (nonstationary and difficult to deal with) and mechanical ‘big data’, combined with deep learning, a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed. The frequency domain signals obtained by the Fourier transform are used as input to the network. This method can extract features adaptively and unsupervised, and get rid of the dependence of traditional machine learning methods on human extraction features. A supervised fine tuning of the model is then carried out by backpropagation. The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object, the effectiveness of the proposed method was verified by a large number of data, and research on visualization of network output, the results shown that the SDAE method is more efficient and more intelligent