A relaxed-PPA contraction method for sparse signal recovery

Sparse signal recovery is a topic of considerable interest, and the literature in this field is already quite immense. Many problems that arise in sparse signal recovery can be generalized as a convex programming with linear conic constraints. In this paper, we present a new proximal point algorithm (PPA) termed as relaxed-PPA (RPPA) contraction method, for solving this common convex programming. More precisely, we first reformulate the convex programming into an equivalent variational inequality (VI), and then efficiently explore its inner structure. In each step, our method relaxes the VI-subproblem to a tractable one, which can be solved much more efficiently than the original VI. Under mild conditions, the convergence of the proposed method is proved. Experiments with l ₁ analysis show that RPPA is a computationally efficient algorithm and compares favorably with the recently proposed state-of-the-art algorithms.     

Nonlinear analyses of exchange rates of six emerging markets

This paper presents some empirical evidences on the presence of nonlinearity of exchange rates of six emerging markets by using Brock-Dechert-Scheinkman (BDS) test and Volterra-Wiener-Korenberg (VWK) model, respectively. The nonlinear dependences are found in the exchange rates of six emerging markets. Furthermore, this paper applies the VWK model with surrogate data method to detect if their nonlinear dependences are deterministic or not. The results show that the above exchange rates are deterministic and nonlinear time series. These imply that the exchange rate markets do not conform to the requirements of the random walk hypothesis. Therefore, the nonlinear dynamic model should be used to analyze the exchange rates.     

Image fusion scheme based on nonsubsampled contourlet and block-based cosine transform

A kind of nonsubsampled contourlet and block-based cosine transform (NSCBCT) is developed, and its application in image fusion is studied in this paper. The construction of filtering banks is based on the nonsubsampled contourlet transform (NSCT) and block-based discrete cosine transform (B-DCT). We combine NSCT and B-DCT to design filters that lead to NSCBCT with better singularity representation than either of them in isolation. A design framework based on the hybrid approach is proposed, which allows for the fast implementation based on NSCT and B-DCT respectively. In addition, a new image fusion scheme based on NSCBCT for multispectral and panchromatic satellite images is proposed. Firstly, because it adopts NSCT, the fused satellite images have higher spatial resolution than those based on wavelets. Secondly, based on the localized high frequency information provided by B-DCT, the proposed fusion scheme can reduce the spectral distortion of the fused image further. Experimental results show that the proposed fusion method is able to increase the spatial resolution and reduce the spectral distortion of the fused image at the same time.     

Five-axis interpolation of continuous short linear trajectories for 3[PP]S-<Emphasis Type="Italic">XY</Emphasis> hybrid mechanism by dual Bezier blending

A novel five-axis real-time interpolation algorithm for 3[PP]S-XY hybrid mechanism is proposed in this paper. In the algorithm, the five-axis tool path for controlling this hybrid mechanism is separated into two sub-paths. One sub-path describes the movement of 3[PP]S parallel kinematic mechanism module, and the other one describes the movement of XY platform. A pair of cubic Bezier curves is employed to smooth the corners in those two sub-paths. Based on the homogenous Jacobian matrix of 3[PP]S mechanism, a relationship between the position errors of every driving joint in hybrid mechanism and the position deviation of the tool tip center point at the moving platform is established. This relationship is used to estimate the approximation error for the corners smoothing according to the accuracy requirement of tool tip center in interpolation. Due to the high computational efficiency of this corner smoothing method, it is integrated into the look-ahead module of computer numerical control (CNC) system to perform online tool path smoothing. By performing the speed planning based on a floating window scheme, a jerk limited S-shape speed profile can be generated efficiently. On this basis, a realtime look-ahead scheme, which is comprised of path-smoothing and feedrate scheduling, is developed to acquire a speed profile with smooth acceleration. A monotonic cubic spline is employed for synchronization between those two smoothed sub-paths in tool path interpolation. This interpolation algorithm has been integrated into our own developed CNC system to control a 3PRS-XY experimental instrument (P, R and S standing for prismatic, revolute and spherical, respectively). A club shaped trajectory is adopted to verify the smoothness and efficiency of the five-axis interpolator for hybrid mechanism control.     

Recursive least squares algorithm for a nonlinear additive system with time delay

This paper proposes a recursive least squares algorithm for a nonlinear additive system with time delay. By the Weierstrass approximation theorem and the key term separation principle, the model can be simplified as an identification model. Based on the identification model, a recursive least squares identification algorithm is used to estimate all the unknown parameters of the time-delayed additive system. An example is provided to show the effectiveness of the proposed algorithm.     

A bayesian based process monitoring and fixture fault diagnosis approach in the auto body assembly process

The auto body process monitoring and the root cause diagnosis based on data-driven approaches are vital ways to improve the dimension quality of sheet metal assemblies. However, during the launch time of the process mass production with an off-line measurement strategy, the traditional statistical methods are difficult to perform process control effectively. Based on the powerful abilities in information fusion, a systematic Bayesian based quality control approach is presented to solve the quality problems in condition of incomplete dataset. For the process monitoring, a Bayesian estimation method is used to give out-of-control signals in the process. With the abnormal evidence, the Bayesian network (BN) approach is employed to identify the fixture root causes. A novel BN structure and the conditional probability training methods based on process knowledge representation are proposed to obtain the diagnostic model. Furthermore, based on the diagnostic performance analysis, a case study is used to evaluate the effectiveness of the proposed approach. Results show that the Bayesian based method has a better diagnostic performance for multi-fault cases.     

Space enterprise strategy guiding-based study on project portfolio management and flow optimization

Project portfolio management (PPM) is the centralized management method, process and technology in multiple projects. When multiple projects in the space industry are implemented, it provides an effective methodology to resolve the problems at the same time such as conflicts among models, decrease in design efficiency, and increase in target deviation. Hence, a PPM dedicated to multiple projects management in space enterprise is presented in this paper. Firstly, an analysis of features and contents in space enterprise portfolio management mode is performed by using PPM based on its specific strategic characteristics. Then, the principle and selection methods of PPM are provided as a reference for the future development of an enterprise. Finally, a multiple-level organization architecture including decision making layers, function management layers and project execution layers is proposed so as to adapt to possible changes in the multiple projects and correspond to the strategic development. As a consequence, a perfect matching mechanism to fit the changes in PPM modes is reached. In addition, the flow chart of PPM is designed and optimized by analyzing the implementation procedure of strategic target and project portfolio life-cycle, which is expected to realize the purpose of improving space enterprise management efficiency, project management capacity, innovation development and economic benefits.     

Deliberative collision avoidance for unmanned surface vehicle based on the directional weight

The purpose of this study is to realize the intelligent deliberative obstacle avoidance for unmanned surface vehicle (USV), based on the International Regulations for Preventing Collisions at Sea called COLREGS. Firstly, a three-level system architecture is designed for the ship intelligent collision avoidance system. Secondly, an intelligent collision avoidance algorithm is presented based on the calculation of motion parameters and the rules of COLREGS. Thirdly, according to COLREGS, the marine environment is modeled from the electronic chart data and radar information, and a deliberative collision avoidance algorithm is written. Finally, the deliberative collision avoidance simulation is carried out for the USV. The feasibility and reliability of intelligent collision avoidance algorithm are verified by the vivid simulation results.     

A dynamic computable general equilibrium simulation of China’s innovation-based economy under the new normal

Researching China’s innovative economic and financial innovation issues under the background of the New Normal, we need to carefully analyze the internal structure and interaction of China’s macroeconomics. The computable general equilibrium (CGE) model has outstanding advantages on predicting the external shock influences on economic system, but previous studies on forecast for China’s future economy mostly considered a high growth rate which is hard to comply with the New Normal scene. By constructing China’s macroeconomic dynamic CGE (DCGE) model and anticipating the economic impact of the New Normal, this paper finds that the New Normal has a certain extent inhibition on China’s macro-economy and innovation. However, after adding the research and development (R&D) subsidy policy, the negative impacts of the New Normal on macro-economy can be eliminated to realize the optimization of economic structure. In addition, after combining the financial innovation promoting policy and the KeQiang index through the simulation of macro-economy, we find that the quality of economic growth is improved. Finally, we provide the policy recommendations for the realization of an innovative economy under China’s New Normal.     

Non-probabilistic reliability research on multi-variable hydraulic turbine blade model

Based on the interval mathematics and possibility theory, the variables existing in hydraulic turbine blade are described. Considering the multi-failure mode in turbine blade, multi-variable model is established to meet the actual situation. Thus, non-probabilistic reliability index is presented by comparing with the output range and the given range.