Series Of Experimental Simulations Research Articles

Evaluation of individual innovation and entrepreneurship effect based on linear space model and grey correlation

This study goes into the complexities of innovation and entrepreneurial skills by developing a detailed linear model and exploring the essential components that make up these talents. A multi-objective function model is presented to assess the effectiveness of using and distributing educational resources in this setting. For this assessment, the study uses the grey correlation method. Through a series of experimental simulations, the study demonstrates that the optimisation approach significantly improves the utilisation and allocation efficiency of educational resources committed to innovation and entrepreneurship by 18.72% and 20.98%, respectively. This results in a more balanced resource utilisation, which helps to enhance the allocation of educational resources. A major conclusion of this study is the correlation value of 0.3177 with ideal entrepreneurship, which indicates a high degree of excellence in innovation and entrepreneurship education reached across the population analysed..

A Novel Anti-Saturation Model-Free Adaptive Control Algorithm and Its Application in the Electric Vehicle Braking Energy Recovery System

In this work, a novel anti-saturation model-free adaptive control (AS-MFAC) algorithm is proposed for the problem of pure electric vehicle’s braking energy recovery with the uncertain dynamic external factors. In the process of energy recovery during vehicle braking, actuator saturation and error accumulation occur due to various external conditions. In AS-MFAC, the non-linear dynamics of the vehicle braking energy recovery process is firstly linearized via a novel dynamical linearization technique with a time-varying parameter pseudo partial derivative (PPD). Then, by proposing the concept of the saturation parameter, the AS-MFAC controller is designed. Consequently, the stability and safety of the braking system are guaranteed while ensuring energy recovery. The major advantages of the AS-MFAC algorithm are that the controller uses only input and output data from the regenerative braking control system and this approach addresses the actuator saturation problem as well as provides a feasible solution. Moreover, the stability of the AS-MFAC algorithm is proven by rigorous mathematical theory and its effectiveness is verified by a series of experimental simulations. Remarkably, the proposed AS-MFAC controller has the property of symmetry since the controller structure and the corresponding parameter (time-varying PPD) estimator are both inferred based on the project algorithm. Consequently, the structures of the controller and the parameters of AS-MFAC have a symmetric similarity.

Leader-following consensus of second-order multi-agent systems with intermittent communication via persistent-hold control

To improve the leader-following consensus performance of second-order multi-agent systems with intermittent communication, a persistent-hold control protocol is designed in this paper. Some consensus conditions are derived under fixed and switching topologies, which depend upon the coupling strength, the eigenvalue of the Laplacian matrix and the communication structure. Finally, a series of experimental simulations are given to verify the correctness of the theoretical results. The current results provide some new insights into the consensus performance and coordinated control for multi-agent systems.

Memristive DeepLab: A hardware friendly deep CNN for semantic segmentation

DeepLab—one of the most critical deep neural network models for image segmentation—has achieved the most advanced performance in the field of image semantic understanding. However, the rich parameters and calculation of deep convolutional neural networks (DCNNs) demand further research on neuro-inspired computing chips specifically designed for hardware acceleration and AI applications. This motivates memristive solutions—the memristor integrates a range of merits, such as fast speed and nanoscale device, to provide an ideal implementation for building low-power neural computing chips and ultra-high-density non-volatile memory. Here, this paper proposes a memristive DeepLab (MDeepLab) system with software-hardware co-design, in which atrous convolution is used to enlarge the receptive field of neurons, the measure to avoid the gridding effect is adopted. Also, the weights are stored by leveraging the unit of a single crossbar array and the constant-term circuit, which significantly reduces the number of memristor devices, doubles the density, and nearly halves the power consumption conventional crossbar array. Finally, the effectiveness of the proposed scheme is verified by a series of experimental simulations and result analysis, showing the superiority of MDeepLab. This study is expected to provide a new solution for low-power consumption and real-time image processing of edge devices.

A new approach to efficient and safe gas production from unsealed marine hydrate deposits

The exploitation of unsealed marine hydrate deposits is of greater commercial value, but also more challenging because of the risks of the invading of huge body of sea water and subsidence of the seabed. In this work, we proposed a new method to recover natural gas from the usealed hydrate deposits saturated with water by pressure-retaining gas (CO2 + H2) injection. A series of experimental simulations was performed to prove its feasibility through establishing a highly simulated unsealed marine hydrate-bearing sediments system. The results demonstrated that overlying water invading was almost completely inhibited for the pressure-retaining approach while unimpeded for the simple depressrization process. Further, we designed a temporary depressurization process before gas injection to establish a flow field for gas in the sediments. This combined operation effectively enhanced the migration and spread of the injected gas, eventually increasing gas recovery ratio from 25% to 60% while the produced water/gas ratio remains as low as 12.6 ST kg/m3. Additionally, it is proved that the balance between CO2 sequestered and CH4 recovered could be achieved, which was an excellent support for reservoir stability. The combination method realized a higher CH4 concentration in the produced gas and a lower injection-production ratio, which greatly reduced the costs of gas injection and subsequent treatment of produced gas. For comparison, similar simulation work was also performed with respect to sealed gas-rich hydrate-bearing sediments. Overall, this temporary depressurization aided pressure-retaining gas injection approach is very promising for high efficient and safe exploitation of marine hydrtates.

Knowledge Graph Construction and Application of Power Grid Equipment

Recent development of artificial intelligence (AI) technology enquires the traditional power grid system involving additional information and connectivity of all devices for the smooth transit to the next generation of smart grid system. In an AI-enhanced power grid system, each device has its unique name, function, property, location, and many more. A large number of power grid devices can form a complex power grid knowledge graph through serial and parallel connection relationships. The scale of power grid equipment is usually extremely large, with thousands and millions of power devices. Finding the proper way of understanding and operating these devices is difficult. Furthermore, the collection, analysis, and management of power grid equipment become major problems in power grid management. With the development of AI technology, the combination of labeling technology and knowledge graph technology provides a new solution understanding the internal structure of a power grid. As a result, this study focuses on knowledge graph construction techniques for large scale power grid located in China. A semiautomatic knowledge graph construction technology is proposed and applied to the power grid equipment system. Through a series of experimental simulations, we show that the efficiency of daily operations, maintenance, and management of the power grid can be largely improved.

Geochemistry effects of supercritical CO2 and H2O on the mesopore and macropore structures of high-rank coal from the Qinshui Basin, China

Abstracts The effects of supercritical CO2 (scCO2) on coal pores play a critical role in the geological storage of CO2 and enhanced coalbed methane recovery (CO2-ECBM). To investigate the effects of scCO2 - H2O on the mesopore - macropore structure of high-rank coals, a series of experimental simulations and analyses were performed. Coal samples from the Qinshui Basin, China were exposed to scCO2 and deionized water for 240 h at 45 °C and 10 MPa and at 80 °C and 20 MPa. The influences on the pore structure were analyzed by performing a mercury intrusion capillary pressure (MICP) measurement. Using a field emission scanning electron microscope (FE-SEM) and an advanced mineral identification control system (AMICS), the geochemical effects of scCO2-H2O on the same minerals and the same pore or fracture (nanometer to micrometer scale) before and after the reaction were investigated. Based on the changes of the minerals, pores and fractures, the types, targets, expressions, and scopes of the geochemical effects were explored. The experimental results show that after the injection of scCO2 into the coal samples, the pores and fractures were newly generated or expanded under the interactions of mineral dissolution-precipitation, clay hydration and swelling, coal swelling, and pyrite oxidation with gypsum precipitation. Moreover, the largest improvement of the pore volume was observed in pores larger than 30 μm or smaller than 100 nm, and higher temperatures and pressures were beneficial to the formation of large pores and fractures. The change in the specific surface area (SSA) was mainly caused by the change in pore volume. The corrosion of carbonate minerals showed the most significant contribution to the pore volume of full-scale macropores and mesopores, while most geochemical effects on other minerals had impacts on the mesopores and the macropores smaller than 1 μm. The coal swelling had the most significant effect on the connectivity of the pores in the coal reservoir.

Modeling of macroscopic delamination of plywood using design of experiments method

The complexity of damage of plywood makes the prediction of his rupture a very difficult task. Among these predominant damages, we find the delamination which is certainly the most unknown and the most difficult to model. To model this phenomenon, we opted for an empirical method that of design of experiments. This method aims to establish and analyze relationships between the quantities studied and their sources of variation. It allows determining mathematical models of the responses observed as a function of the factors studied. This modeling is deduced from the values obtained by a series of experimental simulations. Obtaining these models requires at first to limit the study to factors and interactions having a significant influence on the responses observed using screening plans. Finally, to determine quantitatively the variations in response with respect to influential factors and interactions identified in a first screening study, we used the response surface methodology.

Gas hydrate exploitation using CO2/H2 mixture gas by semi-continuous injection-production mode

The CO2 replacement technique is considered as a promising approach for both gas hydrate recovery and CO2 sequestration. Based on this technique, the continuous CO2/H2 injection-production mode had been studied in our previous work. However, there are limitations on this continuous mode, such as lower CH4 recovery and CO2 sequestration ratios, lower CH4 concentration in produced gas and higher injection-production ratio caused by the fast breakthrough of injected gas. Here we proposed a so called semi-continuous injection-production mode, in which continuous injection-production process is interrupted periodically by stopping injection and production operations for letting injected gas diffuse sufficiently, delaying its breakthrough and increasing its effective sweep region. A series of experimental simulations were performed with respect to this mode in a three-dimensional simulator with a volume of 10.6 L. The results indicated that for the injected gas with low CO2 concentration, the CH4 recovery and concentration in the produced gas could be enhanced dramatically by the semi-continuous gas injection method. Additionally, the fast decomposition stage of CH4 hydrate could be retained separately in the replacement process, thereby effectively improving production efficiency. However, the corresponding CH4 concentration in the produced gas decreased and the injection production ratio increased. Notably, the process combining the CH4 steam reforming with cyclic injection-production was first simulated. The results showed an excellent CO2 storage capability and CH4 hydrate recovery ratio, which was mainly controlled by the gas composition of the re-injected gas. Through systematically comparing and analyzing the CH4 concentration, recovery ratio, production efficiency and injection-production ratio, it is demonstrated that the semi-continuous injection-production mode is superior to the continuous one. The results obtained in this work are of significance for guiding future NGHs exploitation.

Deep Belief Network and Auto-Encoder for Face Classification

The Deep Learning models have drawn ever-increasing research interest owing to their intrinsic capability of overcoming the drawback of traditional algorithm. Hence, we have adopted the representative Deep Learning methods which are Deep Belief Network (DBN) and Stacked Auto-Encoder (SAE), to initialize deep supervised Neural Networks (NN), besides of Back Propagation Neural Networks (BPNN) applied to face classification task. Moreover, our contribution is to extract hierarchical representations of face image based on the Deep Learning models which are: DBN, SAE and BPNN. Then, the extracted feature vectors of each model are used as input of NN classifier. Next, to test our approach and evaluate its performance, a simulation series of experiments were performed on two facial databases: BOSS and MIT. Our proposed approach which is (DBN,NN) has a significant improvement on the classification error rate compared to (SAE,NN) and BPNN which we get 1.14% and 1.96% in terms of error rate with BOSS and MIT respectively

NOMA‐based multiple‐antenna and multiple‐relay networks over Nakagami‐ m fading channels with imperfect CSI and SIC error

In this study, non-orthogonal multiple access (NOMA)-based multiple-antenna and multiple-relay networks over Nakagami- m fading channels with imperfect channel state information (CSI) and successive interference cancellation (SIC) error are investigated. In order to measure the performance of the system, the exact outage probability and the ergodic sum rate of the system are explored. By analysing the impact of the partial relay selection scheme, it is shown that the performance gain can be achieved significantly by increasing the number of relays from one to two, but when the number of relays is equal to or greater than three, the increment of the performance gain becomes smaller and smaller, and finally saturated as the number of relays increases. Furthermore, due to the complexity of the derivation process for the ergodic sum rate, an upper bound of the ergodic sum rate is taken into account. Finally, by carrying out a series of experimental simulations, the authors prove the correctness of the theoretical derivations above, and from the comparison of NOMA and orthogonal multiple access (OMA), it is found that NOMA can provide better spectral efficiency and user fairness than OMA.

Geodesic Distance on Riemannian Manifold using Jacobi Iterations in 3D Face Recognition System

In this paper, we present an automatic application of 3D face recognition system using geodesic distance in Riemannian geometry. We consider, in this approach, the three dimensional face images as residing in Riemannian manifold and we compute the geodesic distance using the Jacobi iterations as a solution of the Eikonal equation. The problem of solving the Eikonal equation, unstructured simplified meshes of 3D face surface, such as tetrahedral and triangles are important for accurately modeling material interfaces and curved domains, which are approximations to curved surfaces in R<sup>3</sup>. In the classifying steps, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this method and evaluate its performance, a simulation series of experiments were performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).<strong></strong>

Experimental and Numerical Simulations of Phase Transformations Occurring During Continuous Annealing of DP Steel Strips

Due to their exceptional strength properties combined with good workability the Advanced High-Strength Steels (AHSS) are commonly used in automotive industry. Manufacturing of these steels is a complex process which requires precise control of technological parameters during thermo-mechanical treatment. Design of these processes can be significantly improved by the numerical models of phase transformations. Evaluation of predictive capabilities of models, as far as their applicability in simulation of thermal cycles thermal cycles for AHSS is considered, was the objective of the paper. Two models were considered. The former was upgrade of the JMAK equation while the latter was an upgrade of the Leblond model. The models can be applied to any AHSS though the examples quoted in the paper refer to the Dual Phase (DP) steel. Three series of experimental simulations were performed. The first included various thermal cycles going beyond limitations of the continuous annealing lines. The objective was to validate models behavior in more complex cooling conditions. The second set of tests included experimental simulations of the thermal cycle characteristic for the continuous annealing lines. Capability of the models to describe properly phase transformations in this process was evaluated. The third set included data from the industrial continuous annealing line. Validation and verification of models confirmed their good predictive capabilities. Since it does not require application of the additivity rule, the upgrade of the Leblond model was selected as the better one for simulation of industrial processes in AHSS production.

Earthquake detection by new motion estimation algorithm in video processing

As increasing urbanization is taking place worldwide, earthquake hazards pose serious threats to lives and properties for urban areas. A practical earthquake prediction method appears to be far from realization. Generally, the traditional instruments for earthquake detection have the disadvantages of high cost and size. To solve these problems, this paper presents a new method which can detect earthquake intensity using video capture device. The main method is based on a new proposed motion vector algorithm with simple but effective methods to immediately calculate acceleration of a predefined target object. By estimating the motion vector variation, the movement distance of predefined target object can be computed, and therefore the earthquake amplitude can be defined. The effectiveness of the proposed scheme is demonstrated in a series of experimental simulations. It is shown that the scheme successfully detects the earthquake occurrence and identifies the earthquake amplitude from video streams.

Compression-watermarking scheme for MPEG-4 fine granularity scalability video recompression and authentication

A novel fragile watermarking scheme is proposed. This scheme not only can verify the authenticity and integrity of the video streams with fine granularity scalability, but also can reduce the size of bit streams after embedding fragile watermark information into the base-layer stream and the bit-plane coding symbol in the enhancement-layer stream. For clarifying the effectiveness of the proposed scheme, a series of experimental simulations were conducted. The results show that the scheme can detect tampering modification in the content of video streams and identify the locations at which these modifications have been made. Furthermore, the scheme can further reduce the average length of the compressed bit stream by about 12%.

Video compression by bit‐plane watermarking

AbstractThis article proposes a new fragile watermarking scheme named bit‐plane watermarking (BPW) which considers ways to improve coding efficiency whereas providing watermarking capability. Because of the use of the run‐length coding strategy for embedding watermark information, the BPW not only verifies the authenticity and integrity of video streams, but also can recompress the bit streams for improving the compression ratio. To verify the effectiveness of this new concept, we select the MPEG‐4 FGS as the platform for implementation. The watermark is embedded in the bit‐plane blocks of MPEG‐4 FGS enhancement layer bitstream, and the coefficient in each bit‐plane block is automatically selected so that the run‐length symbols can be reduced and the location of embedded bits is not required to memorize. The effectiveness of the proposed scheme is demonstrated in a series of experimental simulations. It is shown that the scheme successfully detects changes made to the content of MPEG‐4 FGS video streams and identifies the specific location at which these changes have been made. Furthermore, the results show that the scheme can further reduce the length of compressed bit stream. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 257–264, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).