Abstract:In order to research the topological structure of parking network, a new method of parking network connection based on attractive relationship is proposed. By improving the existing parking demand forecasting model and considering the factors such as parking utilization, parking turnover, and urban center index, a new parking demand algorithm is established. Drawing a conventional Voronoi diagram and using the key properties of the fracture point theory and physical parameters of the conventional Voronoi diagram to divide the influence range of each parking lot, the concept of attractiveness is introduced, and the parameters in the formula of attractiveness calculation are modified and calibrated. The degree of attraction of each node in the parking lot network and the edge weight of the connection between nodes were calculated. Then the connection situation between parking lots was determined. In this paper, the model of parking lot network is constructed under the attraction relationship of some parking lots in Harbin. The complex network parameter analysis method is adopted to analyze the parking lot network. The macro and micro node parameters of parking lot under the scale assignment calculation method and further calculation through the attraction relation are analyzed. Results show that the parking lot network degree distribution conforms to the power law distribution, and the scale-free network features are obvious. Compared with the global coupled network and the unilateral coupled network, the parking lot network under the attraction relationship has a high clustering coefficient and global effectiveness and a small average path length. This method is a practical mode whose performance exhibits equilibrium under the existing parameter evaluation system.

Abstract:Nowadays, more and more MMICs (e.g., limiters and RF switches) have adopted both balanced and unbalanced test pad structures to address the challenging size restrictions and integration requirements of MMICs, which needs hybrid balanced-unbalanced RF probes for on-wafer test. However, the thru standard based on a single balanced or unbalanced structure cannot meet the impedance matching requirement of the hybrid RF probes at the same time, which leads to significant reduction of the calibration accuracy and fails to satisfy the requirement of MMIC test. Therefore, in this paper, the calibration error of hybrid balanced-unbalanced RF probes based on traditional SOLR calibration method is estimated, and an on-wafer test approach of MMICs based on hybrid balanced-unbalanced RF probes is proposed, which combines OSL second-order de-embedding technique with error correction and matrix transformation technique. Meanwhile, we designed an on-wafer test system which consists of a vector network analyzer and SG and GSG RF probes and so on to validate the solution. After vector error correction and de-embedding, the calibration reference plane can be accurately shifted to the probe tip, which greatly improves the test accuracy. In addition, an automatic test system was built by utilizing the object-oriented C# language to improve the efficiency. The program can realize fully automatic operation with instrument control, data acquisition, results correction, data analysis, and so on, which avoids human interference and solves the test efficiency problems faced with the wafer-scale test.

Abstract:This paper aims to study the effect of low nitrogen doping on the microstructure and mechanical properties of hydrogenated diamond-like carbon films. By using unbalanced magnetron sputtering and plasma enhanced chemical vapor deposition (PECVD) composite technology, tungsten carbide transition layer and hydrogenated diamond-like carbon films with different nitrogen content (a-C:H(N)) were prepared on 316 stainless steel and silicon substrates. The microstructures of the films were characterized by Raman spectroscopy, X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscope (SEM). The hardness and residual stress of the films were characterized by micro and nano mechanical comprehensive measurement system and film stress measuring instrument. Results show that with nitrogen doping, carbon-nitrogen bond (CN) was formed in the films and mainly existed in the form of C=N bond, and the ratio of C=N/CN decreased with the increase of nitrogen content. When the nitrogen content increased from 0 to 0.12 at%, the ID/IG ratio of the film decreased rapidly, the ratio of sp²C=C/sp³C-C decreased from 0.65 to 0.563, whereas the hardness of the films remained unchanged at about 20.4 GPa, and the residual stress decreased from 3.35 GPa to 1.31 GPa. With the increase of nitrogen content, the ratio of sp²C=C/sp³C-C increased, the hardness of the films decreased rapidly, and the residual stress decreased slowly. The effect of nitrogen doping on the microstructure of DLC films has a critical value of 0.12 at%. When the amount of nitrogen doping is lower than this value, it promotes the formation of sp³ hybridization, and the film has high sp³ hybridization, while the sp³ hybridization of the films decreases with the increase of nitrogen, and the sp³ hybridization decreases. The films with higher hardness and lower residual stress can be obtained with low nitrogen doping.

Abstract:In response to the principle of achieving shared improvement through collaboration, relevant actions taken in the aspect of the digital education resources have become important contents and research highlights of the present information and communications technology (ICT) in education. After years of research and construction, digital education resources are still not fully valued. Even worse, comments such as “resource island” and “digital ruins” have emerged. This is mainly due to the fact that existing resource platforms lack sufficient openness and scientific correlation. This paper proposes a model of the educational resources open platform based on the PaaS structure. By combining the techniques such as dynamic geometry, computer algebra and automated geometric theorem proving, a dynamic mathematical educational resources open platform is designed and implemented by means of micro-service architecture. Moreover, the OAuth2.0 technology is applied to provide third-party applications and users with authentication and authorization, hierarchically customized source materials and the capability to edit dynamic mathematical contents. This paper proposes an overall design of the open platform, gives a hierarchical strategy and an authentication process for applications and user permissions thereof and introduces the process of customizing the open resources and the tools. Currently, the open platform serves a plurality of third-party applications and is widely applied.

Abstract:The effects of porosity and external loading on hygrothermal tensile mechanical properties and interfacial failure mechanism of CFRP laminates were studied by high and low temperature alternating accelerated hygrothermal cyclical test and finite element simulation. 3 kinds of laminates with different porosities were prepared by controlling molding pressure. The load on specimen was 30%, 40% and 60% of the maximum bending load of laminates, respectively. The results showed that the increase of porosity was the main reason leading to the sharp decrease of tensile properties of CFRP laminates after hot-humid aging. The greater the porosity was, the more hygrothermal tensile strength decreased. The external load could accelerate the debonding of the interface between fibers and resin matrix, further degraded the hygrothermal tensile strength of the composites, and had different effects on the laminates with different porosity. Among of them, the most influential one was the laminate with porosity of 0.08, the second was the laminate with porosity of 0.04, and the least was the laminate with porosity of 0.11. And the greater the load, the greater the impact, but the influence of load on the hygrothermal tensile properties of the laminates was far less than that of the porosity. ABAQUS software was employed to simulate tensile properties of composite specimen. The tensile stress distribution of the laminates was investigated, and it was found that the tensile stress increased at 90 degree layer due to the hydrothermal cycle. Therefore, the matrix cracking and fiber/matrix interface debonding were prone to occur, resulting in the decrease of mechanical properties, which was consistent with the experimental results. The variation trend of tensile mechanical strength calculated by the ABAQUS software was consistent with the experimental results. Keywords: CFRP; porosity; bending load; hygrothermal environment; mechanical properties 〖FQ(+25mm。22,ZX-W〗收稿日期: 2017-12-20 基金项目: 黑龙江省自然科学基金（E201311） 作者简介: 张东兴(1961—),男,教授; 贾近（1976—）,女,高级工程师通信作者: 贾近,jiajin@hit.edu.cn 碳纤维增强聚合物基复合材料（简称CFRP）具有比重小、比强度高、比模量高、耐高温、抗疲劳及耐化学腐蚀性能好等优点而广泛应用于飞行器的主、次受力构件及特殊部位的功能件^[1-3]. 飞行器用复合材料在运输、贮存、发射或飞行过程中要面临如温度、湿度、复杂的外载荷等特殊环境,这些环境因子以不同的机制作用于复合材料,造成其降质退化直至损坏变质^[4-9]. 例如,在超音速飞机飞行时,所用的复合材料要承受长期的温度在-55~130℃与湿度在0~80%RH交变的环境,经历多次飞行循环结束后,飞机进入维修护理期,在超音速飞机整个飞行服役期间,复合材料处于温度与湿度交变的湿热循环中^[10-13]. 并且,由于CFRP制备工艺的特殊性,通常会形成孔隙、夹杂、分层等制造缺陷,其中,孔隙是复合材料结构中最为常见的缺陷之一. 大量的研究表明,孔隙会使CFRP在服役期间,因湿热等环境因素而加速材料性能的退化^[14-16]. 目前国内外大多数学者多采用水浸、湿热试验箱等简单的恒温恒湿加速试验方法研究复合材料的湿热性能,试验湿热环境没有真实体现飞行器实际服役环境,同时未考虑外载荷的影响,得到的试验结果在实际应用上具有很大的局限性^[17-19]. 因此,本文以含孔隙的碳纤维增强环氧树脂基复合材料(CF/EP)层合板为研究对象,基于材料服役期间的吸湿状态和内应力状态,通过模拟超音速飞机服役环境的高低温交变加速湿热循环试验,并结合有限元分析方法,研究湿热交变环境下孔隙率和外加载荷对CFRP层合板拉伸力学性能及界面破坏机理的影响,为CFRP在先进飞机中的应用提供理论设计依据. 1试验

Abstract:To study the factors and regularities affecting the penetration depth of steel target subjected to impact by deflagration-driving-type nail, the penetration process of 20 mm thickness Q235 steel target by 7 mm diameter nail was studied with numerical simulation. The Johnson-Cook constitutive model and the Gruneison state equation were used to simulate the penetration process of nails into steel target based on LS-DYNA finite element software, and the rationality of this model was verified through penetration test. Using the established simulation model, the influences of the cone angle of the nail’s nose, material strength of the nail, deflagration driving force, and the initial gap between the nail and the steel target on the penetration depth of Q235 steel target were analyzed. Results indicate that with the increase of the cone angle of the nail’s nose and the increase of penetration resistance, the maximum speed of the nail was decreased, so the penetration depth and the effective penetration depth were continuously reduced. When the static yield strength of the nail was less than 700 MPa, the penetration depth was relatively shallow due to the obvious mushrooming deformation of the nail, and when it was higher than 700 MPa, the static yield strength of the nail had little effect on the penetration depth. When the pressure produced by the deflagration was less than 150 MPa, the penetration depth increased approximately linearly with the increase of pressure, and when it was higher than 150 MPa, the stress was released due to the deformation of the back of the target plate, and the penetration depth was significantly improved. With the increase of the initial gap between the nail and the steel target, nail could achieve higher velocity before touching the target plate, and the penetration depth was increased, but when it was more than 20 mm, the initial gap no longer affected the penetration depth.

Abstract:The design of a high-performance and low-cost direct digital frequency synthesizer (DDS) by using complementary metal oxide semiconductor (CMOS) technology is a challenging task. This paper presents a programmable modular and ultra-low latency DDS circuit design. By adding an auxiliary phase-accumulator, the input of the auxiliary accumulator and the analog-digital configuration can be set to generate the fractional composite frequency control word based on the need of the output frequency, so that accurate output of various frequencies can be performed and the output frequency error is completely eliminated. The coordinate rotation digital computer (CORDIC) algorithm was optimized and improved to propose a CORDIC implementation which only needs a small capacity lookup table and simple angle correction. It eliminates the iterative operation process and designs an ultra-low delay phase-amplitude conversion circuit. Under the premise that the circuit resource consumption is not increased, the design circuit not only realized the accurate frequency output, but also greatly reduced the output delay of the circuit. The verification results show that the output frequency of the DDS design circuit did not have a frequency error, and it took only two clock cycles to obtain a high-precision sine cosine wave output. This design improves the phase-accumulator and phase-amplitude conversion circuit, eliminates output frequency error, reduces output delay, and has the advantages such as accurate output frequency, small output delay, and low cost. Therefore, it is more suitable for signal processing applications with high output frequency accuracy and real-time performance.

Abstract:With the development of information technology and social media, user sentiment analysis tends to play an increasingly important role in public opinion monitoring, information prediction and product evaluation. However, collecting sufficient manual sentiment labels in supervised learning is still difficult and costly, and unsupervised learning is lack of label guidance. Therefore, a semi-supervised sentiment analysis model based on sociological theory is established in this paper, which is mainly divided into two parts: label addition and emotion analysis. First, a UR-S (User Relationship using Social relations) model was built, which was inspired by sentiment consistency and emotional contagion. Then a TRS-SAT (Text Relationship Strength using Social relations, user Attribute and Text similarities) model based on UR-S model and add labels was established. Finally, the TRS-SAT model and CNN (convolutional neural network) were combined to construct SA-SRS-CNN (Sentiment Analysis using Social Relationship Strength and Convolutional Neural Network) model. The model uses CNN to mine the deep connection between the feature set and the emotional labels to improve the emotional performance. Experiments show that the accuracy, recall, and the F value of the proposed model increased by 11.40%, 5.90% and 8.65%, respectively compared with SVM, and increased 4.12%, 4.17%, and 4.14%, respectively compared with CNN, which suggests that the model is innovative and practical and can provide a good theoretical basis for public opinion analysis.

Abstract:To solve the problem that the swarm intelligence clustering methods are time-consuming in detecting functional modules in protein-protein interaction networks, this paper proposes a method based on fireworks algorithm for functional module detection in protein-protein interaction networks (FWA-FMD). First, each firework individual was initialized as a candidate solution based on the label propagation idea by combining the topological and functional information. Then in each generation of evolution, each firework individual was optimized by using explosion operation with local search and global search self-adjustment capabilities, and the next generation of fireworks individuals were selected by using elite retention and roulette strategy. Finally, the nodes with the same label in the optimal firework were divided into the same function module to obtain the final function module detection result. Functional module detection results on the four protein-protein interaction network datasets of Saccharomyces cerevisiae and Homo sapiens were evaluated by using two standard functional module datasets as benchmarks, which shows that the FWA-FMD algorithm not only costs less time than GA-PPI, ACC-FMD, and BFO-FMD, but also has obvious advantages in many evaluation indicators compared with some representative algorithms, which can better identify functional modules.

Abstract:In order to study the aseismic performance of prefabricated light steel frame with light steel truss structure and the seismic failure mechanism of truss-type L-shaped column, full-scale shaking table tests were performed on a two-story building model. Seismic waves in 42 events were input to model structure. The dynamic properties of model structure was quantified by model analysis theories. The change regularity of acceleration responses, displacement responses, and strain responses were analyzed. Results show that with the peak acceleration of seismic waves increasing from 0.07g to 0.9g, both the fundamental frequency and the acceleration magnification factor of the structure decreased gradually. The maximum inter-story drift occurred in the first floor, and the maximum inter-story drift ratios under basis ground motion and rare ground motion of 8-degree was 1/268 and 1/164, respectively, which were lower than the elastic and elastic-plastic inter-story drift ratio limits of multistory steel structure. The model structure can basically satisfy the two-phase and three-level seismic fortification requirements. The truss-type L-shaped column in the model was in elastic state under basis ground motion of 8 degree. Crack was found at the junctions between end column and diagonal member under rare ground motion of 8 degree. At the same time, the L-shaped column enter plastic stage, although the plastic drift increased under rare ground motion of 9 degree and a certain safety margin was remained. The lateral stiffness of L-shaped column degraded severely under very rare ground motion of 9 degree. After the gradual process of plastic deformation, the L-shaped column retained a certain level of carrying capacity, so it has good ductility and can be used in seismic fortification region of high degree.

Abstract:Learning brain effective connectivity (EC) networks is an important topic within the community of human brain connectome. It is of great significance for the early diagnosis and pathological study of brain diseases to accurately identify the brain EC network structure. This paper combines the Firefly Algorithm (FA) with Bayesian network, and proposes a new method to learn brain EC networks by FA with a reproductive mechanism. The new method uses K2 score as the evaluation method of absolute brightness of fireflies, uses the optimization of firefly population to complete the learning of brain EC networks, and uses reproductive mechanism to further optimize the population. First, a firefly individual represented a brain EC network with a few edges, which was gradually constructed through the directional movements and random movements of the firefly individual. Then, a reproductive mechanism was employed to optimize the quality of networks after a certain number of evolution iterations. Finally, the network structure represented by the individuals with the highest absolute brightness in the population was used as the learning brain EC network. Experimental results on many simulated datasets verified the effectiveness of the reproductive mechanism, and the new algorithm has obvious advantages on the whole performance compared with other algorithms. Experimental results on real datasets also show the potential practicability of the new algorithm.

Abstract:In order to study the influencing factors and the evolution mechanism of network group behavior choice in current society, this paper designs 4 influencing variables, i.e., event fit variable, government influence variable, individual capability variable, and information oriented variable, by applying social physics research methods based on the similarity between particles in dynamics and behavior individual in network society. The event fit variable includes the fit between event sensitivity and public experience. The government influence variable includes communication channels, credibility, and disposition. The individual capability variable includes cognitive ability, mental state, and behavioral state, and the information oriented variable includes information symmetry, impartiality, and authority. We described the attributes of each variable, presented the function of each influencing factor, and established the influence model between different factors. On the basis of the survey of the hot events on the Internet and the statistics of the reports of major media at different stages in the past five years, the paper analyzes the evolution process of network group behavior and verifies the results by Matlab simulation according to the text of “grab salt incident.” Results show that the intensity of fuse incident control directly affects the evolution of secondary events, in which the government plays a leading role. The impartiality and authority of the mainstream media are important factors that influence the evolution of network group behavior. The fit between fuse incident and the public has a certain impact on the network group behavior, but it does not play an absolute role. Individual cognitive ability, psychological endurance, and behavior ability of the public have certain impact on the network group behavior. The rational and objective public who have stronger cognitive ability are less likely to be influenced by others’ opinions.

Abstract:The plate-impact experiment was used to measure the response of shock pressure and the equation of state (EOS) of Zr₄₁Ti₁₄Ni_12.5Cu₁₀Be_22.5 amorphous alloy. The particle velocities of free surface were tested under impact from copper plate with the velocity from 350 m/s to 550 m/s. The Hugoniot parameters of the material were calculated by using impedance matching method, and the experimental results were compared with the computational results calculated by ideal mixture model. The problem to solve the Gruneisen EOS of the material was turned into nonlinear optimization. A novel numerical method based on simulated annealing algorithm was developed to analyze the Gruneisen EOS of the material, and the annealing scheme was designed. The Birch-Murnaghan equation and the Trinomial equation of the material were proposed by analytical method, and compared with Gruneisen EOS and the experimental results. The experimental and theoretical results show that within the pressure between 5 GPa and 10GPa, the zero-pressure volume velocity of Zr₄₁Ti₁₄Ni_12.5Cu₁₀Be_22.5 amorphous alloy was 4267 m/s, the slope of D-u curve was 4.376, which is much larger than normal metal, and the Hugoniot limit of the material was around 5.6 GPa. The ideal mixture model is only applicable to calculate the Hugoniot parameters of Zr-based amorphous alloy in high-pressure phase, whereas considerable errors will be caused under low pressure. The Gruneisen EOS and Trinomial EOS matched the experimental results well within the pressure between 5 GPa and 10 GPa, while there were major errors between the experimental results and the Birch-Murnaghan equation.

Abstract:Weakly cemented conglomerate of Tertiary system is a kind of special geotechnical material between soil and rock. It has low diagenesis degree and poor self-stability, and its characteristics of particle gradation and cementation degree have significant impact on engineering characteristics of the rock. The relationship among fractal characteristics, cementation degree, and shear strength of conglomerate was analyzed by screening test, particle size fractal characteristics study, and large-scale direct shear test on undisturbed and remolded samples taken from the weakly cemented conglomerate of Tertiary system in the tunnel exit of Menghua Railway of Zhongtiao Mountain. Research results show that weakly cemented conglomerate generally had binary fractal features in the boundary of particle size 1.0 mm, and the fractal dimension of fine particles was mostly less than that of coarse particles, which indicates that there are relatively more fine particles in weakly cemented conglomerate of Tertiary system, and the conglomerate with 1D fractal has optimal gradation and maximum shear strength. According to the degree of cementation, weakly cemented conglomerate can be divided into weak cementation and extremely weak cementation. The cohesion of extremely weak cemented conglomerate was negligible and its strength characteristics were similar to those of non-cohesive soil. Under the condition of similar cementation degree, the relationship between fractal dimension and tangent of internal friction angle of the conglomerate could be described by negative exponent function. The shear strength and internal friction angle of extremely weakly cemented conglomerate were only 33.46%-40.85% and 58.64% compared with weakly cemented conglomerate without considering the difference in particle gradation, which shows that the cementation degree is an important factor affecting the shear strength of weakly cemented conglomerate of Tertiary system.

Abstract:The space-time distribution characteristics of clutter under the airborne bistatic radar system are heavily dependent on the configuration of bistatic radar. For the airborne bistatic radar system, to solve the problem that the radar configuration scene is in time-varying state due to the relative motion between the carriers, the fixed ground coordinate system was constructed. By using the coordinate transformation method, the analytical expression of bistatic range sum unit and the clutter echo expression were derived, and the mathematical model of clutter in a time-invariant coordinate system was established. Then the space-time distribution characteristics of clutter under airborne bistatic radar system and the influence of bistatic radar configuration on clutter distribution were theoretically analyzed and simulated, which verified the validity of the clutter model. The simulation results show that clutter distribution is greatly different under four flight configurations, i.e., consistent heading, parallel heading, vertical heading, and intersecting heading. The smaller the bistatic range sum is, the more the angle-Doppler trace disperses, the faster the Doppler frequency changes with the range, and the stronger the range correlation is. However, with the increase of bistatic range sum, clutter angle-Doppler trace gradually approaches, and range dependence of clutter is relatively reduced. The radar clutter model with fixed coordinate system is independent of the transceiver platform, which avoids the repeated modeling caused by the platform movement and extends the range of clutter model, which lays a foundation for the study of clutter suppression for airborne bistatic radar.

Abstract:In order to accurately analyze the mechanical properties of composite grains in fiber reinforced composite ceramics (FRCC), a prediction method of composite grain strength is proposed based on the stress concentration effect of nano-interface. On the basis of the microstructure characteristics of FRCC, considering the interaction between nano-fibers, an effective self-consistent method was applied to determine the effective stress field of the nano-interface model. It was assumed that the stress and the displacement between matrix and fiber were continuous at the interface. The uniaxial tension was decomposed into the combination of biaxial tension and pure shear stress by superposition method. The displacement field and the stress field of the matrix and the fiber on the two sides of the interface were obtained according to the displacement function of the FRCC subjected to transverse load, and the stress concentration factor generated by the nano-interface was calculated. Considering the effect of stress concentration caused by dislocation pile-up and nano-interface, a prediction model for fracture stress of composite grains was established, and the influence of the reinforced fiber radius and the volume fraction on the fracture stress was analyzed. Results show that the smaller the radius is, the greater the fracture stress is, and the higher the grain strength becomes. When the radius is larger than 50 nm, it has little effect on the fracture stress. The larger the fiber volume fraction is, the smaller the fracture stress is, and the lower the grain strength becomes.

Abstract:To solve the problem that the two-dimensional convolutive non-negative matrix factorization (2-DCNMF) algorithm is sensitive to the initial value, and the traditional random initialization is easy to converge to the relatively poor local optimal value, this paper proposes a hybrid algorithm by combining k-means clustering algorithm and singular value decomposition (SVD) algorithm. Through using k-means clustering method, clustering center was calculated as the initial value of the coefficient matrix H, which avoids the non-unity problem of the traditional decomposition result. Considering that the number of base matrix W of the 2-DCNMF algorithm is more than that of the one-dimensional convolution non-negative matrix decomposition, the singular value decomposition and the principal component analysis method were applied iteratively to obtain initial W matrix, which eliminates the initialization error from a single algorithm. Under the same parameter environment, experiments demonstrate that the proposed method has better separation performance and better convergence compared with other similar algorithms. The experimental results show that the method is capable of separating relatively independent signals in SNR environments from -1 dB to 10 dB accurately and has high robustness to noise data, which further proves that the use of hybrid algorithm is beneficial for the realization of real-time and high-performance of 2-DCNMF.

Abstract:According to the spreading characteristic of new escape virus, a novel network virus spreading model and its stability were researched under the escape mechanism to provide a theoretical reference for defenders. Firstly, three transformation modes were derived based on the exposed characteristic of new network virus from the exposed nodes, and then a novel network virus spreading model was constructed under the exposed mechanism. Secondly, by using Routh stability criterion, the stability of system equilibrium point was demonstrated and the basic reproductive number R₀was calculated. Finally, simulations were demonstrated for the influence of three transfer parameters on system stability. Theoretical research and simulations showed that if the basic reproductive number R₀≤1, the disease-free equilibrium was locally asymptotically stable; if R₀>1, the endemic equilibrium was locally asymptotically stable. By adjusting the probability of three kinds of metastasis in the escape state, the basic reproductive number of the system could be reduced, and the network was maintained in a disease-free healthy state. The network could be controlled in the disease-free equilibrium by increasing transition probability θfrom escape state to susceptible state, bfrom escape state to removed state, or decreasing γ from escape state to infected state, so as to maintain network security.

Abstract:Because of the deficiency of the same type of radar in extracting micro-motion parameters of ballistic target, a method for precession feature extraction of ballistic target was proposed by using hybrid-scheme radar network under the circumstance that the numbers of the obtained scattering centers are different. First, a ballistic target precession model and a wide band and narrow band radar signal model were established, and the echoes characteristics of the target scattering centers under narrow band radar and wide band radar were analyzed. Based on the characteristics, each scattering center in different radar system echoes was matched and identified. Then the scattering center range information obtained by the wide band radar was analyzed, the range information was transformed by using generalized Radon transform, and the precession angle was solved by parameter transform relationship. The associated systems of equations of micro-Doppler information were further established, and the visual angle and structural parameters of radars were solved by the precession angle. Dichotomy was used to optimize the parameters obtained from the three solution combinations, and the obtained parameters were constructed to obtain three-dimensional cone rotation vector. Simulation results show that when the SNR was greater than 5 dB, the validity of the proposed method was close to that of the wide band network and both were higher than that of the narrow band network, which verified the effectiveness of the proposed method.

Abstract:Aiming at the problem that the moth-flame optimization algorithm is easy to fall into the local optimal trap and the global optimization ability is insufficient, chaotic sequences, simulated annealing algorithm, and genetic algorithm were utilized to propose a tent chaos and simulated annealing improved moth-flame optimization algorithm. First, the tent chaotic sequences were used to initialize the population and increase the diversity of the population. Then the new solution was obtained by adding disturbance to the current optimal solution which was further proportionally hybridized to generate a final new solution. According to the Metropolis criterion in the simulated annealing algorithm, the ultimate solution was finally obtained by determining whether the new hybridized solution was accepted. The performance of the proposed algorithm was tested through complex high-dimensional benchmark functions and trajectory planning problems. The results of six complex benchmark function optimization tests show that for the 10-dimensional benchmark function, the algorithm converged to the optimal value after about 0.25 seconds, and for 50 dimensions, it was about 0.5 seconds. Compared with the standard moth-flame optimization algorithm and other intelligent optimization algorithms, the proposed algorithm could effectively jump out of the local optimal solution in the discussed scenarios, and the accuracy precision and the convergence speed were improved in certain degree. The trajectory planning simulation results show that for an airspace environment with four no-fly zones and two threat sources, the proposed algorithm could obtain the optimal trajectory after about 100 iterations, which is more accurate than the standard moth-flame optimization algorithm. Therefore, it is valuable for engineering applications and has better performance.

Abstract:In this research, the Abaqus finite element simulation software was adopted to calculate and analyze the critical height of the section rib-ring in supporting the sandwich composite pressure shell. First, the simulation method was verified by comparing the simulated result with related experimental results. Then three series of T section rib structure models were established in different layer methods and different sizes to calculate their capability to support sandwich composite pressure shell long cabin section. The buckling loads of these models were carried out and the conclusions are drawn as follows. By setting rib-ring, the stability of the long section pressure shell can be effectively increased. When the rib-ring support stiffness increased to a certain value, the overall buckling mode of the long section would be limited by the connection section with the rib-ring as boundary and turn into several single section buckling modes. The certain value of the rib-ring height is called critical height, and the calculation method is given in this paper. When the rib-ring support stiffness is greater than the critical stiffness, the critical buckling load of the structure has little relation to the properties of the rib-ring, while it is related to the size and material properties of the sandwich cylindrical shell. This critical buckling load of the structure is called section buckling load, which can reach 7.0 MPa for the sandwich composite pressure shell structure analyzed in this paper.

Abstract:Hydrological Simulation Program-Fortran (HSPF) model has many parameters and complex interactions. The traditional parameter optimization is insensitive to the optimization parameters and the optimization algorithm is easy to trap into local problems, which affects the precision and efficiency of optimization. In this paper, a new optimization approach is explored by integrating Qinglong River watershed, parameter sampling, sensitivity analysis, and parameter optimization. Response surface optimization software Design Expert was applied to sample the parameters of 9 HSPF models, and 130 sets of parameter sets were obtained. Multiple quadratic regression models were used to establish the nonlinear relationship between the parameter sets and the efficiency coefficient of nash-sutcliffe (NSE), and the optimal parameters and their dense value ranges were identified by contour lines and response surface. The NSE mean value, maximum value, and minimum value of the response surface optimization parameters as well as the optimized interval reduction rate were all superior to the orthogonal range analysis method. LZSN, INFILT, and AGWRC were extremely sensitive parameters, while DEEPFR was sensitive parameters. The interactions between LZSN and INFILT, INFILT and AGWRC, INFILT and UZSN, and INFILT and IRC had significant impacts on the results. The dense value range of parameters were optimized as follows: LZSN[2.0,2.65], INFILT[0.0,0.475], AGWRC[0.0,0.885], DEEPFR[0.1,0.176], BASETP[0.1,0.120], AGWETP[0,3,0.120], CEPSC[0.6,0.244], UZSN[0.3,1.22], IRC[0.3,0.63]. The response surface method synthesized three aspects, i.e., parameter sampling, parameter sensitivity analysis, and parameter optimization, which considers the nonlinear relationship of parameters, the interaction of parameters, and the optimization accuracy and efficiency, thus opening up a new way for parameter optimization of HSPF model in Qinglong River watershed.

Abstract:Rail corrugation detection is an important method to ensure traffic safety. In order to solve the problem of the extraction of the trend of track irregularity in complex rail lines, a novel de-trending method named EEMD-SVD is proposed, which uses permutation entropy (PE) to select relevant singular-value components to reconstruct the trend. Compared with the existing methods based on EMD, this method considers the problem of mixed signal components in the original IMFs, and initially proposes the use of SVD to extract the trend accurately hidden in multi-dimensional IMF matrix. Since the singular-value components are arranged in the order of energy reduction without considering low-complexity and high-energy of signal, the proposed method modifies the EEMD-SVD, uses PE to select relevant low-complexity singular-value components, and finally reconstructs the trend with the relevant singular-value components selected above. Numerical simulation and track irregularity data test were carried out to evaluate the performance of the method. The numerical simulation experimental results showed that the proposed method outperformed the low-pass filter algorithm, the linear programming de-trending algorithm combined with EMD, and the WD de-noising algorithm. In particular, in the simulation of multiple signal-noise ratio, the improvement accuracy was about 30% when the signal-noise ratio was low. The track irregularity data test proved that the method is effective for de-trending the trend of track irregularity.

Abstract:Acoustic seabed classification is significant for the study of the seabed environment and the ecosystem, in which multibeam and sidescan sonars are the most common sonar equipment. However, multibeam and sidescan sonars are both largely affected by radiometric distortion, which makes the noise of the sonar image too big to be eliminated and the seabed sediment misinterpreted. Moreover, the universal seabed classification methods for multibeam and sidescan sonars are still deficient. Therefore, this paper proposes a universal seabed classification method of multibeam and sidescan sonars in consideration of radiation distortion. Steps of the method include correction of radiometric distortion, normalization of both mulitbeam and sidescan sonars data, adaptive unsupervised seabed classification, and morphological image denoising. A complete universal seafloor classification procedure was also presented. The proposed method was applied to the same water area in Fujian Coast, where multibeam and sidescan sonars data were both measured and the seabed classification results of the water area were obtained. Experimental results show that the seabed classification results processed from multibeam and sidescan data in the same water area were in high consistency, which verifies the validity of the universal seabed classification method and improves the reliability of the classification results through mutual verification.

Abstract:The network data in the current network environment is enormous, complex, and multidimensional, which is hugely different from the past. The traditional machine learning method needs to manually extract a large number of features in the face of complex high-dimensional data, and the process is complex and computationally intensive, which is not conducive to the current real-time and accuracy requirements of intrusion detection. Thus, in order to reduce the data dimension and eliminate redundant information, an intrusion detection method based on DAN-BP which combines deep auto-encoder network (DAN) and BP algorithm is proposed. First, a DAN model was constructed by overlaying several auto-encoder networks, and the network feature data was used as the input of the model, which enables the model to intelligently extract the distribution rules of the network data layer by layer, thereby obtaining a new low-dimensional feature data set. Then the low-dimensional data was classified and identified by the BP algorithm. In this research, the regularization correction was added to the auto-encoder network to prevent the trained auto-encoder network from directly copying the input information and influencing the training effect. Moreover, noise was added to the input data, and the reconstruction error of the original data and the output data was learned to achieve the purpose of denoising so that the learned new feature data is more robust. The traditional dimensionality reduction method and the proposed intrusion detection method were compared in this paper. Results show that the proposed method has better performance in classification accuracy, false alarm rate, and detection rate.

Abstract:An important prerequisite for the study of network controllability is to prove that the system is controllable. The controllability of the network means that the entire network is controlled by applying proper external inputs or adjusting inputs to achieve the desired state. The traditional way to calculate the control input nodes of directed network is to solve the maximum matching of the bipartite graphs corresponding to the network. However, since this method does not impose restrictions on the matching manner of the network node, the node control chain is too long and the information transfer of the control input is delayed, which affects the whole controllable performance of the network. The Kalman criterion and the PBH criterion can prove whether the system is controllable within a certain period of time. However, the relationship between nodes in the network becomes more complicated as the size of the network increases, so the simple utilization of such methods increases the complexity of the operation. Therefore, the lower bound algorithm for the controllability index K (KMLA) was proposed by combining the Kalman rank criterion. Control node cluster for control input can be quickly determined by determining the lower bound of the network’s controllability index. Then based on in-degree of networks, the minimum upper bound algorithm for the controllability index K (KMUA) was proposed. It was found that the KMUA algorithm proposed in this paper could make the upper bound of the K value closer to the K value when the network reached the K-step controllable. The upper and lower bounds of the controllability index K were verified by the specific network model and the real network. Results show that the algorithm can optimize the control chain length of the driven node by combining the upper and lower bounds of the controllability index.