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Optimization of the schedule is an effective way to reduce energy consumption in hot strip manufacturing. The heating energy and the rolling energy contribute the vast majority of the energy consumption. In order to reduce the energy consumption for the whole process, an optimization method based on the multi-objective function was applied, and the heating energy and rolling energy were both taken into consideration during the function modeling. Meanwhile, process constraints and equipment limitations were taken as objective function boundary conditions, and the tapping temperature in the furnace exit and thickness of each pass were decision variables. Further, to obtain the optimal solution of the objective function, the differential evolution algorithm was used, finally, the optimal tapping temperature and the thickness were obtained successfully. Meanwhile, the validity of the calculated results was verified by comparing with the actual process in a hot rolling production line in Hebei province, the results indicated that with the proposed method, a more reasonable schedule were obtained, compared with the existing actual schedule of typical specification, the energy consumption can be reduced by about 1.5%.

The path planning problem is an important problem in the research area of robot, games and group animation. This paper shows a 2.5-dimensional terrain grid which can reduce the amount of computation. By applying the fuzzy logic theory, the terrain trafficability of the rugged road can be evaluated based on different gradient, roughness, elevation difference; the trafficability factor can be achieved and applied to the heuristic function. The improved algorithm can solve the symmetry problem of path planning on uneven surfaces, reduce the search space.

Coronary heart disease preoperative diagnosis plays an important role in the treatment of vascular interventional surgery. Actually, most doctors are used to diagnosing the position of the vascular stenosis and then empirically estimating vascular stenosis by selective coronary angiography images instead of using mouse, keyboard and computer during preoperative diagnosis. The invasive diagnostic modality is short of intuitive and natural interaction and the results are not accurate enough. Aiming at above problems, the coronary heart disease preoperative gesture interactive diagnostic system based on Augmented Reality is proposed. The system uses Leap Motion Controller to capture hand gesture video sequences and extract the features which that are the position and orientation vector of the gesture motion trajectory and the change of the hand shape. The training planet is determined by K-means algorithm and then the effect of gesture training is improved by multi-features and multi-observation sequences for gesture training. The reusability of gesture is improved by establishing the state transition model. The algorithm efficiency is improved by gesture prejudgment which is used by threshold discriminating before recognition. The integrity of the trajectory is preserved and the gesture motion space is extended by employing space rotation transformation of gesture manipulation plane. Ultimately, the gesture recognition based on SRT-HMM is realized. The diagnosis and measurement of the vascular stenosis are intuitively and naturally realized by operating and measuring the coronary artery model with augmented reality and gesture interaction techniques. All of the gesture recognition experiments show the distinguish ability and generalization ability of the algorithm and gesture interaction experiments prove the availability and reliability of the system.

Because of the complexity of procedure interface and working conditions, the further improvement of steel strip quality and production efficiency is limited. Realizing the optimization of product quality and production process in multi-process, system-level through intelligent key technology is one of the strategic directions in steel strip production. (1) Collaborative intelligent optimization and dynamic scheduling technology for steel manufacturing supply chain oriented to customized production, reducing the cost of raw material purchase and production operations and improving production efficiency and precision service ability, (2) Online monitoring, diagnosis and optimization technology of product quality. Getting through the process information flow of product, the correlation analysis of process quality parameters, the tracing of quality anomalies, the reverse optimization of process parameters and the optimization of process route will be realized, (3) Multi-process coordination optimization and quality control based on CPS architecture. The precise control technology of process quality parameters will be developed, the structure of multi-process coordination optimization system is established and optimization of key quality parameters in the through process will be realized, and (4) Microstructure and mechanical property optimization and surface quality intelligent control technology in the hot strip rolling process. Intelligent prediction of microstructure and mechanical properties, rapid dynamic design and steel grade merging technology will be formed and green process design technology of oxide scale will be developed.