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.     

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.     

Reliability analysis of multi-state repairable system with dynamic transition probabilities

Inadequate maintenance decisions lead to incremental overall costs. In order to minimize costs in maintenance of the multi-state repairable system, we model a preventive maintenance (PM) scheme of the multistate repairable system using non-Markov process. The periodically decreasing reliability model of the non-Markov dynamic system with dynamic transition probabilities is established to satisfy the probability change. The diesel engine system is taken as an example to illustrate the model. The reliability of the diesel engine is analyzed and its PM scheme is worked out. RENO software is used to simulate the diesel engine system. The maintenance cost of components and the optimal PM interval data of the system are obtained by using the minimal average cost as the objective function. The adaptability of PM is judged, and the optimal PM scheme is presented.     

Online fault diagnosis of modern process industry system based on color-spectrum

This paper proposes a new method to diagnose the system fault of the process industry based on the monitor data set of distributed control system (DCS). Firstly, it defines a homeomorphism product space named color phase space which is a Cartesian product of two-dimensional Euclidean space and three-dimensional color phase space. Secondly, it maps the DCS data to the color phase space in order to get a system color-spectrum which displays the inherent relationship of the whole system. Then, it diagnoses the system fault by observing the color change on the color-spectrum depending on the physiological characteristics that human’s eyes are more sensitive for the color change than data change.     

Effect of interference fits on the fatigue lives of bolted composite joints

The effect of interference fits on the fatigue lives of bolted composite joints is investigated by conducting mechanical tests. Static and fatigue tests are carried out on specimens made of carbon-bismaleimide composites joined together as double-lap single-bolt joints. The bolts having interference fits ranging of 0 (neat fit), 0.5%, 0.75% and 1% are performed. The results demonstrate the relationship between fatigue life and different values of interference fits. After the fatigue tests, non-destructive evaluation (NDE) and scanning electron microscope (SEM) are used to observe the damage of the surrender and surface of the hole. The test results show that the interference fitted specimens have improved fatigue life compared to the neat fitted specimen. The NDE and SEM results reveal that the damage degree of interference fitted specimen is weaker than that for the neat fitted one.     

Automatic semantic analysis of software requirements through machine learning and ontology approach

Nowadays, software requirements are still mainly analyzed manually, which has many drawbacks (such as a large amount of labor consumption, inefficiency, and even inaccuracy of the results). The problems are even worse in domain analysis scenarios because a large number of requirements from many users need to be analyzed. In this sense, automatic analysis of software requirements can bring benefits to software companies. For this purpose, we proposed an approach to automatically analyze software requirement specifications (SRSs) and extract the semantic information. In this approach, a machine learning and ontology based semantic role labeling (SRL) method was used. First of all, some common verbs were calculated from SRS documents in the E-commerce domain, and then semantic frames were designed for those verbs. Based on the frames, sentences from SRSs were selected and labeled manually, and the labeled sentences were used as training examples in the machine learning stage. Besides the training examples labeled with semantic roles, external ontology knowledge was used to relieve the data sparsity problem and obtain reliable results. Based on the SemCor and WordNet corpus, the senses of nouns and verbs were identified in a sequential manner through the K-nearest neighbor approach. Then the senses of the verbs were used to identify the frame types. After that, we trained the SRL labeling classifier with the maximum entropy method, in which we added some new features based on word sense, such as the hypernyms and hyponyms of the word senses in the ontology. Experimental results show that this new approach for automatic functional requirements analysis is effective.