As the crown of the scholar’s four jewels, the brush has occupied a cherished position in the culture of traditional Chinese painting and calligraphy since its invention. In virtual painting, virtual brush modeling plays the most important role. A powerful virtual brush model can truly reflect the characteristics of a real brush and enhance the reality of virtual painting. By reviewing the state of the art in virtual brush modeling, we summarize the basic principles, merits, and drawbacks of typical modeling methods, and discuss simulation results based on empirical methods and physical methods separately. The influences of brush-paper, paper-ink, and human-computer interactive devices on virtual brush modeling are analyzed briefly. The main chal-lenges and problems in virtual painting are analyzed, and the prospects for research on virtual brush modeling in the future are put forward.
We study the balance problem caused by forward leaning of the wearer’s upper body during rehabilitation training with a lower limb rehabilitation exoskeleton. The instantaneous capture point is obtained by modeling the human-exoskeleton system and using the capture point theory. By comparing the stability region with instantaneous capture points of different gait phases, the balancing characteristics of different gait phases and changes to the equilibrium state in the gait process are analyzed. Based on a model of the human-exoskeleton system and the condition of balance of different phases, a trajectory correction strategy is proposed for the instability of the human-exoskeleton system caused by forward leaning of the wearer’s upper body. Finally, the reliability of the trajectory correction strategy is verified by carrying out experiments on the Zhejiang University Lower Extremity Exoskeleton. The proposed trajectory correction strategy can respond to forward leaning of the upper body in a timely manner. Additionally, in the process of the center of gravity transferred from a double-support phase to a single-support phase, the ratio of gait cycle to zero moment point transfer is reduced correspondingly, and the gait stability is improved.
Cabled ocean networks with tree or ring topologies play an important role in real-time ocean exploration. Due to the time-consuming need for field maintenance, cable switching technology that can actively switch the power on/off on certain branches of the network becomes essential for enhancing the reliability and availability of the network. In this paper, a novel switching-control method is proposed, in which we invert the power transmission polarity and use the current on the power line as the digital signal at low frequency to broadcast information with the address and commands to the network, and the corresponding branching unit (BU) can decode and execute the switching commands. The cable’s parasitic parameters, the network scale, and the number of BUs, as the influencing factors of the communication frequency on the power line, are theoretically studied and simulated. An optimized frequency that balances the executing accuracy and rate is calculated and proved on a simulated prototype. The results showed that the cable switching technology with optimized frequency can enhance the switching accuracy and configuring rate.
The Griewank function is a typical multimodal benchmark function, composed of a quadratic convex function and an oscillatory nonconvex function. The comparative importance of Griewank’s two major parts alters in different dimensions. Different from most test functions, an unusual phenomenon appears when optimizing the Griewank function. The Griewank function first becomes more difficult and then becomes easier to optimize with the increase of dimension. In this study, from the methodology perspective, this phenomenon is explained by structural, mathematical, and quantum analyses. Furthermore, frequency transformation and amplitude transformation are implemented on the Griewank function to make a generalization. The multi-scale quantum harmonic oscillator algorithm (MQHOA) with quantum tunnel effect is used to verify its characteristics. Experimental results indicate that the Griewank function’s two-scale structure is the main reason for this phenomenon. The quantum tunneling mechanism mentioned in this paper is an effective method which can be generalized to analyze the generation and variation of solutions for numerous swarm optimization algorithms.
Current blockchain consensus protocols have a triangle of contradictions in aspects of decentralization, security, and energy consumption, and cannot be synchronously optimized. We describe a design of two new blockchain consensus protocols, called “CHB-consensus” and “CHBD-consensus,” based on a consistent hash algorithm. Honest miners can fairly gain the opportunity to create blocks. They do not consume any extra computational power resources when creating new blocks, and such blocks can obtain the whole blockchain network to confirm consensus with fairness. However, malicious miners have to pay massive computational power resources for attacking the new block creation privilege or double-spending. Blockchain networks formed by CHB-consensus and CHBD-consensus are based on the same security assumption as that in Bitcoin systems, so they save a huge amount of power without sacrificing decentralization or security. We analyze possible attacks and give a rigorous but adjustable validation strategy. CHB-consensus and CHBD-consensus introduce a certification authority (CA) system, which does not have special management or control rights over blockchain networks or data structures, but carries the risk of privacy breaches depending on credibility and reliability of the CA system. Here, we analyze the robustness and energy consumption of CHB-consensus and CHBD-consensus, and demonstrate their advantages through theoretical derivation.
Elliptic curve cryptography has been used in many security systems due to its small key size and high security compared with other cryptosystems. In many well-known security systems, a substitution box (S-box) is the only non-linear component. Recently, it has been shown that the security of a cryptosystem can be improved using dynamic S-boxes instead of a static S-box. This necessitates the construction of new secure S-boxes. We propose an efficient method to generate S-boxes that are based on a class of Mordell elliptic curves over prime fields and achieved by defining different total orders. The proposed scheme is devel-oped in such a way that for each input it outputs an S-box in linear time and constant space. Due to this property, our method takes less time and space than the existing S-box construction methods over elliptic curves. Computational results show that the pro-posed method is capable of generating cryptographically strong S-boxes with security comparable to some of the existing S-boxes constructed via different mathematical structures.
The era of big data in healthcare is here, and this era will significantly improve medicine and especially oncology. However, traditional machine learning algorithms need to be promoted to solve such large-scale realworld problems due to a large amount of data that needs to be analyzed and the difficulty in solving problems with nonconvex nonlinear settings. We aim to minimize the composite of a smooth nonlinear function and a block-separable nonconvex function on a large number of block variables with inequality constraints. We propose a novel parallel first-order optimization method, called asynchronous block coordinate descent with time perturbation (ATP), which adopts a time perturbation technique that escapes from saddle points and sub-optimal local points. The details of the proposed method are presented with analyses of convergence and iteration complexity properties. Experiments conducted on real-world machine learning problems validate the efficacy of our proposed method. The experimental results demonstrate that time perturbation enables ATP to escape from saddle points and sub-optimal points, providing a promising way to handle nonconvex optimization problems with inequality constraints employing asynchronous block coordinate descent. The asynchronous parallel implementation on shared memory multi-core platforms indicates that the proposed algorithm, ATP, has strong scalability.
Opportunity networks provide a chance to offload the tremendous cellular traffic generated by sharing popular content on mobile networks. Analyzing the content spread characteristics in real opportunity environments can discover important clues for traffic offloading decision making. However, relevant published work is very limited since it is not easy to collect data from real environments. In this study, we elaborate the analysis on the dataset collected from a real opportunity environment formed by the users of Xender, which is one of the leading mobile applications for content sharing. To discover content transmission characteristics, scale, speed, and type analyses are implemented on the dataset. The analysis results show that file transmission has obvious periodicity, that only a very small fraction of files spread widely, and that application files have much higher probability to be popular than other files. We also propose a solution to maximize file spread scales, which is very helpful for forecasting popular files. The experimental results verify the effectiveness and usefulness of our solution.
Based on the dual uniform circular array, a novel method is proposed to estimate the direction-of-arrival (DOA) and jointly calibrate gain-phase errors, position errors, and mutual coupling errors. In this paper, only one auxiliary source is required to generate three time-disjoint calibration sources with the help of the rotation platform. Subsequently, according to the principle that the signal subspace is orthogonal to the noise subspace, the cost function is constructed. The alternating iteration method is used to estimate the coefficients of the three kinds of errors. During the process, the proposed algorithm makes full use of the structural characteristics of the array when estimating mutual coupling errors, while the signal phase matrix is used to eliminate the phase influence caused by the delay in signal arrival at the antenna array when estimating gain-phase errors and position errors. Compared with the algorithm using multidimensional nonlinear search, the proposed algorithm has lower computational complexity. Moreover, our algorithm does not require additional auxiliary sensors. Simulation results demonstrate that the proposed algorithm is effective and can precisely and comprehensively calibrate the errors in a dual uniform circular array.
Phased array (PA) radar is one of the most popular types of radar. In contrast to PA, the frequency diverse array (FDA) is a potential solution to suppress range-related interference because of its time-range-angle-dependent beampattern. However, the range-angle coupling inherent in the FDA transmit beampattern may degrade the output signal-to-interference-plus-noise ratio (SINR). We propose a dot-shaped beamforming method based on the analyzed four subarray-based FDAs and subarray-based planar FDAs using a sinusoidally increasing frequency offset with elements transmitting at multiple frequencies. The numerical results show that the proposed approach outperforms the existing log-FDA with logarithmical frequency offset in transmit energy focus, sidelobe suppression, and array resolution. Comparative simulation results validate the effectiveness of the proposed method.
A differential evolution based methodology is introduced for the solution of elliptic partial differential equations (PDEs) with Dirichlet and/or Neumann boundary conditions. The solutions evolve over bounded domains throughout the interior nodes by minimization of nodal deviations among the population. The elliptic PDEs are replaced by the corresponding system of finite difference approximation, yielding an expression for nodal residues. The global residue is declared as the root-mean-square value of the nodal residues and taken as the cost function. The standard differential evolution is then used for the solution of elliptic PDEs by conversion to a minimization problem of the global residue. A set of benchmark problems consisting of both linear and nonlinear elliptic PDEs has been considered for validation, proving the effectiveness of the proposed algorithm. To demonstrate its robustness, sensitivity analysis has been carried out for various differential evolution operators and parameters. Comparison of the differential evolution based computed nodal values with the corresponding data obtained using the exact analytical expressions shows the accuracy and convergence of the proposed methodology.