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Due to the excessive concentration of computing resources in the traditional centralized cloud service system, there will be three prominent problems of management confusion, construction cost and network delay. Therefore, we propose to virtualize regional edge computing resources in intelligent buildings as edge service pooling, then presents a hierarchical cloud platform with dual-service pooling structure and a dynamic strategy for the proposed model. The analytic hierarchy process (AHP) based quality of service (QoS) evaluation mechanism and the dynamic normal distribution selection method are adopted for service deployment. And the dynamic inertia particle swarm optimization (DI-PSO) algorithm is employed to realize task scheduling. Furthermore, the cloud platform and existing terminal server group are used to conduct platform structure comparison experiments, and the popular task scheduling algorithms are selected for simulation experiments. Experimental results of platform measurement show that the average service response time of different services can be improved by about 17.3% to 37.4%. The average occupancy ratio of computing resources can be reduced by about 5%. The simulation results show that the earliest completion time of single task list can be decreased by 11.3% to 20.9%, and the makespan of 100 task lists can be improved by 0.3 times.

To increase productivity and the material removal rate, an increasing number of factories have been employing multiple tools for simultaneous cutting. Among these tools is parallel orthogonal turn-milling, which is an important parallel-processing method. However, a dynamic interaction occurs during the cutting processes due to the waviness induced on the shared cutting surface and the dynamic coupling through the machine structure, creating machining vibrations, or chatter, which affect the quality of the machined surface. Therefore, to reduce or avoid the vibration problem during the cutting process, chatter stability of parallel orthogonal turn-milling was examined in this study. Initially, a chatter mechanism model of parallel orthogonal turn-milling was designed, and the limit-critical axial depth of the cut formula was obtained. Next, the model parameters of the machine tool were obtained based on the hammer test method. A parallel orthogonal turn-milling stability lobe diagram (SLD) with tool tip runout was constructed. Finally, the experimental results were verified on a high-efficiency turn-milling machine tool. The results showed that chatter can be predicted for parallel orthogonal turn-milling and an SLD can provide a reference for the selection of machining parameters. In addition, the SLD can also guide the formulation of processing technology specifications. Our results are extremely significant for ongoing research in the field of machining methods.

Micro-linear motor, is the key feed components which is based on macro–micro-motion control technology, the accuracy of its movement directly determines the accuracy of ultra-precision machining level. Based on the study of the open servo characteristics of POWER program multiple axis controller (PMAC), a fuzzy proportional integral derivative (PID) composite control technology is applied to the servo control of the motor. The fuzzy control is used to improve the response speed and dynamic performance of the system, and the precise PID control is used to realise the precise position control. In this study, based on the analysis of linear motor installation and load characteristics, the use of smooth switching fuzzy PID composite control algorithm is applied to the micro-feed motor control. Due to the introduction of the smoothing weighting function, the composite fuzzy PID servo control mode of dual mode switching is implemented without disturbance switching. Finally, the effect of the control algorithm is verified by experiments, especially in the case of small errors, this algorithm still reflects the ability to adapt to the impact of interference.

PRS-XY Hybrid machine tool has the dual advantages of the flexibility of parallel machinery and the ductility of the series structure. Due to the complexity of its structure and kinematics algorithms, it is meaningful and difficult to improve the precision of this kind of machine tool. In this paper, Virtual-real biaxial kinematics principle of PRS-XY hybrid machine tool.is introduced as the theoretical principle of the compensation. On this basis, a virtual- real biaxial real-time error compensation strategy, which is based on fuzzy control theory, is proposed and theoretically discussed in this paper. Both the Matlab simulation and compensation experiment is carried out. And the comparative analysis to the precision data is given at the end of the paper, which shows that the error compensation strategy has an obvious effect on improving the precision of of the hybrid machine tool.

Accurate building energy consumption (BEC) prediction plays an increasingly significant role in energy control and conservation. However, owing to the high level of randomness of BEC data, acquiring accurate prediction results is difficult. To further enhance the predicted precision and robustness, we herein present a non-iterative decomposition–integration model to realise BEC prediction. Our proposed method merges the ensemble empirical mode decomposition (EEMD), learning using a privileged information (LUPI) paradigm-based random vector functional link network (RVFL+), and support vector regression (SVR) to generate satisfactory results. In this model, EEMD is first adopted for the decomposition of historical energy consumption data. Subsequently, the decomposed subsignals are input into the RVFL+ network, and the features of high correlation with BEC are selected as privileged information to constrain the weight of the output layer of RVFL+ network in order to obtain their corresponding output RVFL+ models of each subsignal. Finally, the resulting RVFL+ output models are aggregated and input into the SVR model to acquire the prediction results, thereby breaking through the limitation of the single-prediction model and improving prediction accuracy. To verify our proposed model, five actual BEC datasets were employed for experiments: Jinan in China, Fairbanks and California in the USA, Vancouver in Canada, and Sydney in Australia. Experiment results indicated that our proposed EEMD-RVFL+-SVR method had better accuracy and anti-noise performance. Specially, the mean absolute percentage error of the proposed method, compared with the EMD-SVR, EEMD-SVR, EEMD-PSO-GA-SVR, EEMD-RVFL, ARIMA-SVM, FLS-SVM, EMD-RVFL+-SVR, CEEMD-RVFL+-SVR, SVR, RVFL, RVFL+, BPNN, and Wavelet neural network, was reduced by 65.07%, 37.20%, 40.61%, 52.27%, 44.66%, 34.76%, 50.01%, 39.06%, 43.04%, 53.77%, 46.38%, 41.82% and 56.14% respectively.

Energy audit is considered as an important aspect for building energy saving, and the significant building energy saving is conducted in university first.According to a survey of the end-use energy of various premises in the college, a detailed study of building energy audit is conducted. An excellent array of energy use data are provide and, the characteristics of building energy consumption for the university are shown. More data about the lighting, equipment and air conditioning system loads of the typical buildings in various premises are also gathered for the analysis, the comparison between the results and the energy efficiency of other 7 buildings of Guangzhou is presented based on the definited energy efficiency. The energy-saving potential for the typical buildings is analyzed, and then some effective measures are proposed to increase energy efficiency.

This paper analyzed the inverse kinematics for the new Parallel rotate slider-XY axes (PRS-XY) style hybrid machining tool and educed the five axes linkage inverse kinematics transform formula on the basis of the coordinates of the X, Y, Z, A and B virtual axes. The program for the PRS-XY style hybrid machining tool in accordance with the program manner for the common numerical control (NC) machine tool was made. The results of the experiments prove that the inverse kinematics transform formula is correct.

Consistent state of the system is the basis of reaching the target area at the same time in rendezvous of multi-UAV, and consensus algorithm is an effective method of multi-UAV system to achieve a consistent state. In order to shorten the convergence time of system state, on the base of the mathematical model of multi-UAV rendezvousing problem, the basic consensus algorithm and the consensus algorithm based on the past state difference have been improved. A new fast consensus algorithm was designed with a parameter, and theorems about efficiency and convergence of the new algorithm were mathematically proved. Simulation results verify the effectiveness and feasibility of the new algorithm.

The impact fault of the hybrid machine tool were considered as the analysis object in this paper. The kinematics control principle of the hybrid machine tool was given as well as the causes of the impact based on the hybrid structure. The impact suppression method was given, which based on the preview-control algorithm. It was confirmed by experiment that the impact was reduced which was account for the application of preview-control algorithm and the performance of the control strategy was improved.

A novel realtime look-ahead velocity control strategy, which was based on Turbo PMAC for parallel kinematics mechanism, was proposed to avoid gouging and mechanical vibration that caused by kinematics calculation failure in virtual axis machine tool machining. In terms of configuration of look-ahead control strategy, the basic is acceleration and deceleration algorithm. Then according to the ultiformity and practicality of polynomial, the acceleration and deceleration construction algorithm of polynomial were proposed. Finally, it was confirmed by experiment that the impact was reduced, which was account for the application of look-ahead control strategy and the performance of the control strategy was improved.