Recent Patents on Engineering - Volume 13, Issue 4, 2019

Background: Nanoporous filtering membranes can be used for super purification including a liquid-liquid separation. It is the aim of engineers to design these membranes with good filtration capabilities, high fluxes and satisfying mechanical strengths. Realistic membranes need to have a balance among these performances to achieve satisfactory overall performances. Objective: The study aims to show a tree-type cylindrical-shaped nanoporous filtering membrane with good characteristics. Methods: According to the principle of the nanotube tree for transportation presented previously, here the design method of a tree-type cylindrical shaped nanoporous filtering membrane is presented and the flow resistances of this membrane have been calculated for varying operational parameter values. Results: It is shown that the invented membrane possesses nanoscale filtration pores and larger flow-resistance-reducing pores. These pores are densely evenly distributed on the membrane surface. The membrane practically has a low flow resistance and thus a high flux if its thickness is as small as possible. It can also be used for a liquid-liquid separation if the mixed liquids have largely different interactions with the pore walls of the membrane. Conclusion: By an appropriate design, the invented membrane has a good overall performance including the capabilities of super purification or a liquid-liquid separation, the high flux and a satisfactory mechanical strength.

Background: Seawater greenhouse (SWGH) is a technology established to overcome issues related to open field cultivation in arid areas like high temperatures and freshwater shortage. So far, five pilot Seawater greenhouses were built around the world; in Spain, United Arab Emirates, Oman, Australia and Somaliland. All the patents related to the Seawater greenhouse components and designs mentioned were reviewed. Methods: The Seawater greenhouse adopts the humidification-dehumidification (HDH) concept where evaporated moisture from saline water source is condensed to produce freshwater within the greenhouse body. Many advancements have been made throughout the past 25 years to optimize the Seawater greenhouse by means of structural improvement, heat distribution, condenser design and material, source of feed water and the evaporator via both trial-and-error and simulation approaches. The latter included numerical, mathematical, analytical and artificial neural network simulations. Various condenser designs were adopted in order to increase freshwater production to meet the irrigation demand of the seawater greenhouse. Results and Conclusion: To make the Seawater greenhouse self-sufficient in terms of energy production, the use of renewable energies and nonconventional sources was also investigated like the use of geothermal, solar and wind energy to produce electricity for the greenhouse operation and for other requirements as well. The use of reverse osmosis along with reverse electro dialysis to produce freshwater and electricity in the seawater greenhouse, was also one of the ideas suggested to improve and solve the associated constraints. Direct contact dehumidification is another development suggested to improve the condensation rate. This new approach seems to be very promising as it involves low capital, operation and maintenance costs, high freshwater production, and fouling- and corrosion-free.

Background: User behavior models have been widely used to simulate attack behaviors in the security domain. We revised all patents related to response to attack behavior models. How to decide the protected target against multiple models of attack behaviors is studied. Methods: We utilize one perfect rational and three bounded rational behavior models to simulate attack behaviors in cloud computing, and then investigate cloud provider’s response based on Stackelberg game. The cloud provider plays the role of defender and it is assumed to be intelligent enough to predict the attack behavior model. Based on the prediction accuracy, two schemes are built in two situations. Results: If the defender can predict the attack behavior model accurately, a single-objective game model is built to find the optimal protection strategy; otherwise, a multi-objective game model is built to find the optimal protection strategy. Conclusion: The numerical results prove that the game theoretic model performs better in the corresponding situation.

Background: Subspace learning-based dimensionality reduction algorithms are important and have been popularly applied in data mining, pattern recognition and computer vision applications. They show the successful dimension reduction when data points are evenly distributed in the high-dimensional space. However, some may distort the local geometric structure of the original dataset and result in a poor low-dimensional embedding while data samples show an uneven distribution in the original space. Methods: In this paper, we propose a supervised dimension reduction method by local neighborhood optimization to disposal the uneven distribution of high-dimensional data. It extends the widely used Locally Linear Embedding (LLE) framework, namely LNOLLE. The method considers the class label of the data to optimize local neighborhood, which achieves better separability inter-class distance of the data in the low-dimensional space with the aim to abstain holding together the data samples of different classes while mapping an uneven distributed data. This effectively preserves the geometric topological structure of the original data points. Results: We use the presented LNOLLE method to the image classification and face recognition, which achieves a good classification result and higher face recognition accuracy compared with existing manifold learning methods including popular supervised algorithms. In addition, we consider the reconstruction of the method to solve noise suppression for seismic image. To the best of our knowledge, this is the first manifold learning approach to solve high-dimensional nonlinear seismic data for noise suppression. Conclusion: The experimental results on forward model and real seismic data show that LNOLLE improves signal to noise ratio of seismic image compared with the widely used Singular Value Decomposition (SVD) filtering method.

Background: The unique individual biological characteristics is used for identification in biometrics, which is safe and difficult to forge. Therefore, it can help to enhance the safety of access control system. Since the developing of modern information science and technology, computerbased signature verification system enables signature verification more efficiently and automatically in comparison with traditional human identification method. Methods: In order to improve the accuracy of Chinese signature verification, an off-line Chinese signature verification method based on deep convolutional neural network is proposed. First, the machine learning library Tensorflow is build, and the volunteers are invited to establish the offline Chinese signature dataset. Second, the dataset is pre-processed, including denoising, binarization and size normalization. Finally, three different CNN architectures (AlexNet, GoogleNet, VGGNet) are adopted to implement the signature verification. Results: Experimental results show that the performance of AlexNet is better than that of the other two convolutional neural network architectures, the accuracy of classification has been up to 99.77%, and verification rate is 87.5%. Conclusion: Compared with the traditional offline Chinese signature recognition method, the method based on the convolutional neural network Alex Net-f is better than other methods to some extent, and avoids the complicated feature engineering.

Background: Cloud storage is generally used to provide on-demand services with sufficient scalability in an efficient network environment, and various encryption algorithms are typically applied to protect the data in the cloud. However, it is non-trivial to obtain the original data after encryption and efficient methods are needed to access the original data. Methods: In this paper, we propose a new user-controlled and efficient encrypted data sharing model in cloud storage. It preprocesses user data to ensure the confidentiality and integrity based on triple encryption scheme of CP-ABE ciphertext access control mechanism and integrity verification. Moreover, it adopts secondary screening program to achieve efficient ciphertext retrieval by using distributed Lucene technology and fine-grained decision tree. In this way, when a trustworthy third party is introduced, the security and reliability of data sharing can be guaranteed. To provide data security and efficient retrieval, we also combine active user with active system. Results: Experimental results show that the proposed model can ensure data security in cloud storage services platform as well as enhance the operational performance of data sharing. Conclusion: The proposed security sharing mechanism works well in an actual cloud storage environment.

Background: In the actual cognitive radio network, there exist the imperfect sensing such as false alarm and missed detection which can lead to the inaccurate spectrum handoff and cause more uncertain problems. Meanwhile, the predetermined target channel has high risk of being unavailable in the proactive-decision spectrum handoff scheme. Objective: To solve the problem that the state of the predetermined target channel is updated in the proactive-decision spectrum handoff scheme, we present a novel hybrid spectrum handoff scheme based on Preemptive Resume Priority (PRP) M/G/m queuing network model in this paper. Methods: The proposed scheme combines the advantages of proactive-decision spectrum handoff and reactive-decision spectrum handoff, and performs the spectrum sensing during the waiting time of the cognitive user on the target channel. On this basis, we consider the influence of multiple cognitive users, multiple authorization channels, multiple spectrum handoffs and other factors on the spectrum handoff delay of cognitive user. Then, the detailed analysis and derivations of cognitive user’s extended data delivery time are conducted. Furthermore, the effect of imperfect spectrum sensing on the spectrum handoff performance of cognitive users is discussed. Results: Compared with traditional spectrum handoff schemes, numerical results show that the extended data delivery time of the proposed scheme is smaller, and the performance of spectrum handoff is further improved. In addition, the performance of the proposed scheme is less affected by the probability of false alarm and missed detection with frequent channel states variations. Simulation results are given to validate the analytical results. Conclusion: The hybrid spectrum handoff scheme provide better performance on the spectrum sharing between primary users and cognitive users in cognitive radio network under imperfect sensing condition.

Background: As the multimedia service develops and the transmission rate in terrestrial communication systems increases rapidly, satellite communication needs to improve the transmission rate and throughput. Multiple Input Multiple Output (MIMO) techniques can increase the system capacity significantly by introducing the space dimension, as the system bandwidth remains the same. Therefore, utilization of MIMO for satellite communications to increase the capacity is an important research topic. So MIMO techniques for multibeam satellite communications are researched in the dissertation. Objective: The goal of this work is improving the capacity of the satellite system. Multi-beam and dual-polarized technologies are applied to a satellite system to improve the capacity further. Methods: In this paper, we first introduce a multi-beam dual-polarized satellite multi-put and multiout (MBDP-S-MIMO) system which combines the full frequency multiplexing and dual-polarization technologies. Then the system model and channel model are first constructed. At last, to improve the capacity further, BD and BD-ZF precoding algorithms are applied to MBDP-S-MIMO and their performance is verified by simulation. Results: Simulation results show the performance of the BD precoding algorithm gets better with the growth of the XPD at the receiver and is almost not affected by the growth of the channel polarization correlation coefficient. In addition, with the growth of the users’ speed, the performance becomes worse. Conclusion: The multi-beam dual-polarized satellite MIMO system has high capacity, and it has certain application prospects for satellite communication.

Background: Study failure in a course is very complicated which is always affected by many different factors and is characterized by uncertainties. We reviewed previous literatures and patents relating to student failure prediction, while there seems to be no ideal method or tool which can easily learn the relationship between failure results and reasons. Methods: In this study a method of artificial neural network was provided to predict student failure risk in course study. A three-layered network topology was used including input layer with nine neural nodes, hidden layer with ten optimized neural nodes, and output layer with one neural node, which is advantageous for dealing with this complicated and uncertain issue. The whole modeling process includes four stages: output inference, loss evaluation, weights and biases training, and model testing. Results: In our sample data there are 577 students in total, including 433 train cases and 144 test cases, and for each sample, there are nine input dependent variables and one output target variable. All calculation and optimization results were implemented based on the TensorFlow and Python. The model accuracy measurements of relative root mean square error on the total, test and train data sets were 0.1637, 0.1596, and 0.1607 respectively, and consistency was shown by testing the predicted results with our observed data, which indicated that the method was promising for predicting student failure risk in course study. Conclusion: It can be used to identify at-risk students with study difficulty and is of practical significance for educators to provide pedagogical supports and interventions in early time to the at-risk students to help them avoid academic failure, and it is also of theoretic significance to improve the whole efficiency of early warning education management.

Background: Low rank is a recent significant model to explore the inner subspace structure of samples, which has been successfully used in many pattern recognition tasks. Methods: In this paper, we proposed a novel spectral clustering method to address the face identification problem. There are three main contributions in our paper. Firstly, the sparse coding under a cluster-based learned dictionary is taken as the character sample of each face. Secondly, the collaborative low rank representation is incorporated in the comprehensive optimization framework to construct an effective affinity graph iteratively, which is different from the conventional ones to tackle the graph construction and spectral clustering independently. We revised all the patents relating to the face identification. Thirdly, a numerical algorithm is developed to solve the optimization framework and obtain a stable solution. Results: The experimental results showed the superior performance of the proposed method on recognition ratio. Conclusion: It means that our proposed low rank based identification algorithm outperforms the existed excellent methods.

Background: It is a classic problem that we estimate the original coefficient from the known coefficient disturbed with noise. Methods: This paper proposes an image denoising method which combines the dual-tree complex wavelet with good direction selection and translation invariance. Firstly, we determine the expression of probability density function through estimating the parameters by the variance and the fourth-order moment. Secondly, we propose two assumptions and calculate the joint confidence probability of original coefficient under the situation that the disturbed parental and present coefficients from neighborhood scale are known. Finally, we set the joint confidence probability as shrinkage function of coefficient for implementing the image denoising. Results: The simulation experiment results show that, compared to these traditional methods, this new method can reserve more detail information. Conclusion: Compared to the current methods, our novel algorithm can remove the most noise and reserve the detail texture in denoising results, which can make better visualization. In addition, our algorithm also shows advantage in PSNR.

Background: K-means algorithm is implemented through two steps: initialization and subsequent iterations. Initialization is to select the initial cluster center, while subsequent iterations are to continuously change the cluster center until it won't change any more or the number of iterations reaches its maximum. K-means algorithm is so sensitive to the cluster center selected during initialization that the selection of a different initial cluster center will influence the algorithm performance. Therefore, improving the initialization process has become an important means of K-means performance improvement. Methods: This paper uses a new strategy to select the initial cluster center. It first calculates the minimum and maximum values of the data in a certain index (For lower-dimensional data, such as twodimensional data, features with larger variance, or the distance to the origin can be selected; for higher-dimensional data, PCA can be used to select the principal component with the largest variance), and then divides the range into equally-sized sub-ranges. Next adjust the sub-ranges based on the data distribution so that each sub-range contains as much data as possible. Finally, the mean value of the data in each sub-range is calculated and used as the initial clustering center. Results: The theoretical analysis shows that although the time complexity of the initialization process is linear, the algorithm has the characteristics of the superlinear initialization method. This algorithm is applied to two-dimensional GPS data analysis and high-dimensional network attack detection. Experimental results show that this algorithm achieves high clustering performance and clustering speed. Conclusion: This paper reduces the subsequent iterations of K-means algorithm without compromising the clustering performance, which makes it suitable for large-scale data clustering. This algorithm can not only be applied to low-dimensional data clustering, but also suitable for highdimensional data.

Background: Aiming at the problem of user information security authentication in cloud manufacturing, a user information security authentication model is proposed. The model is introduced in detail. Methods: In the security authentication model, the point set topology group fractal transformation algorithm is combined with the biometric identification technology to acquire the user biometric information to generate the secret key. Combined with the AES (Advanced Encryption Standard) encryption algorithm, the user biometric information is encrypted. Result: It provides a stronger guarantee for the user's security certification. We revised all patents relating to pharmaceutical formulations of applicability in authentication of user information in cloud manufacturing. Conclusion: Experiments show that the research has credibility and practicability, which improves the overall security performance of cloud manufacturing user information authentication and access.

Background: Patents suggest that efficient hybrid information scheduling algorithm is critical to achieve high performance for heterogeneous multi-core processors. Because the commonly used list scheduling algorithm obtains the approximate optimal solution, and the genetic algorithm is easy to converge to the local optimal solution and the convergence rate is slow. Methods: To solve the above two problems, the thesis proposes a hybrid algorithm integrating list scheduling and genetic algorithm. Firstly, in the task priority calculation phase of the list scheduling algorithm, the total cost of the current task node to the exit node and the differences of its execution cost on different processor cores are taken into account when constructing the task scheduling list, then the task insertion method is used in the task allocation phase, thus obtaining a better scheduling sequence. Secondly, the pre-acquired scheduling sequence is added to the initial population of the genetic algorithm, and then a dynamic selection strategy based on fitness value is adopted in the phase of evolution. Finally, the cross and mutation probability in the genetic algorithm is improved to avoid premature phenomenon. Results: With a series of simulation experiments, the proposed algorithm is proved to have a faster convergence rate and a higher optimal solution quality. Conclusion: The experimental results show that the ICLGA has the highest quality of the optimal solution than CPOP and GA, and the convergence rate of ICLGA is faster than that of GA.

Background: According to the characteristics of phase noise, phase noise power spectrum feature was used for emitter individual identification. Methods: For different emitter individuals, we established a phase noise model with the influence of both transmitter and receiver based on the research of its characteristics. Using power spectrum of phase noise, the corresponding scattered information entropy was proposed. The same type of communication equipments can be identified by Minimum Error Minimax Probability Machine (MEMPM) classifier through extracting this feature at a different frequency offset. Results: Simulation results show that the new features can be effectively used to classify emitter individuals with stable classification performance. Conclusion: According to the simulation, when the SNR was higher than 10dB, the accuracy rate was higher than 90%. It proved that the method is useful and effective. In addition, the recognition performance of the proposed method is very stable, showing the stability of the device phase noise. Therefore, it can be used in practice.

Background: For the traditional Fourier Transform (FT), it cannot effectively detect the frequency of non-stationary signals with time. Analyzing the local characteristics of time-varying signal by using FT is hard to achieve. Therefore, many time-frequency analysis methods which can meet different needs have been proposed on the basis of the traditional Fourier transform, like the Short Time Fourier Transform (STFT), the widely used Continuous Wavelet Transform (CWT), Wigner-Ville Distribution (WVD) and so on. However, the best time and frequency resolution cannot be achieved at the same time due to the uncertainty criterion. Methods: From the point of view of optimizing time-frequency performance, a new Generalized S Transform (GST) window function optimization method is proposed by combining time frequency aggregation with an improved genetic algorithm in this paper. Results: In the noiseless condition, the Linear Frequency Modulation (LFM), Nonlinear Frequency Modulation (NLFM) and binary Frequency Shift Keying (2FSK) signals are simulated. The simulation results prove that the method can improve the time-frequency concentration and decreasing Rényi entropy effectively. Compared with other four traditional time-frequency analysis methods, the improved GST has more advantages. Conclusion: In this paper, a new method of optimizing the window function in GST based on improved GA is proposed in this paper. Experiments on LFM, NLFM and 2FSK signals show that the proposed method not only has the advantages of high resolution, but also determines the optimal parameters via the time frequency focusing criterion and the Rényi entropy. Compared with the other four kinds of time-frequency analysis methods, the optimized GST based on improved GA in this paper has the best time-frequency focusing.

Background: With the technical development of counter-reconnaissance and antijamming, communication system becomes more and more complex, and therefore, the recognition of communication signal becomes a challenging task according to recent patents. In order to achieve successful recognition and classification of radiation source signal under variant SNR environment, the design and selection of classifier are one of the key points. Methods: Gray relation theory can solve the learning problem with a small number of samples and its algorithm is simple and can solve the issue of generality versus accuracy, which is very suitable for dealing with fuzzy mathematical problems. However, the selection of distinguishing coefficient has a direct effect on the recognition results by gray relation classifier. For conventional gray relation classifier, the distinguishing coefficient is usually set as a fixed value of 0.5, and for different types of signals, its recognition rate varies. Aiming at this issue, an improved adaptive gray relation classifier algorithm is proposed in the paper. Results: The simulation results show that the recognition rate can still reach more than 87% even at the SNR of 10dB. Conclusion: The proposed methods can improve the anti-jamming capability of the classifier, which can be widely used in the fields of electronic reconnaissance, fault diagnosis and image processing.

Background: 3D printing technology is an emerging technology based on additive ideas. Desktop-level 3D printers molded by Fused Deposition Modeling (FDM) are widely used. However, their printing accuracy is relatively low accompanied by severe warpage, which limits its application scope and fields. Therefore, analysis of the cause of warpage in the printing process and optimization, has important practical significance for promoting the application of FDM printers. Methods: The goal of this work is to improve the forming precision of 3D prints, through the finite element analysis software ANSYS, utilizing the life and death component innovation and coupling the temperature field and stress field of printing speed, one of the key factors affecting the forming precision. After the calculation and analysis, the following conclusions can be drawn: In testing with other conditions unchanged, when the printing speed is gradually increased, the accuracy of the print is improved first and then decreased. This method provides a new way to analyze the influence of other factors on the forming accuracy and also provides a new way to get the best print parameters under the combined action of many factors. We reviewed several patents related to 3D printing, its optimization, formulations, precision and accuracy in respective field. Results: So, to achieve the best results, layer thickness has great influence on the molding precision. Finally, the results were obtained by finite element analysis, finding the best printing accuracy of the print parameters and verifying them by conducting actual printing. Conclusion: The research shows that the thickness of the layer has the greatest influence on the printing accuracy in the process parameters studied.

Background: After the oilfield development coming into the middle and later periods, many oil wells only had liquid production less than 5m3 per day, therefore the present sucker-rod pumps didn’t satisfy the need for oilfield production. We revised all patents relating to small displacement pump and didn’t found any suitable pump for low production well. A new type of pump with small diameter was needed to design for solving the unbalance between the production and feed in lower production wells. Objective: To resolve the problems of supply and production imbalance of the low producing well, low pump efficiency and low system efficiency, a low displacement pump with non-coaxial delivery valve had been designed. Methods: This type pump adopted a solid plunger, and its delivery valve was not coaxial with the pump axial that would enhance its structural strength and been useful to reduce the partial flow loss of oil flowing through the static pump valve. At the down dead point of pump plunger, the delivery valve was located at the top of the pump chamber that was to the benefit of discharge of the gas and the oil from the pump chamber. The uneven distribution of the delivery hole was suitable for the wellhead diameter of the traditional oil wells. Results: The delivery valve was non-coaxial with the plunger that would decrease the flow loss through the passage and increase the pump efficiency. At the down-dead point, the plunger entered the extension nipple, and the delivery valve was located up the end of the solid plunger that promoted the remnant gas flowing out of the pump cylinder and decreased the effect of gas on the pump efficiency. Conclusion: As to this low displacement pump, we suggested: The suitable stroke was 2-3m; the stroke frequency was less than 3min-1. A reasonable range of submersible depth for low production well was 200-300m. It was used in the gas content well lower than 20%, else used the gas anchor firstly. The low displacement pump with non-coaxial delivery valve and solid plunger (NLDP) can improve the pump efficiency and reduce the polish rod load. At the same time, this NLDP provides new equipment for the production of the low oil well and improves the artificial lift design theory.