Selective laser sintering (SLS) is a type of laminating sintering technique, using CO2 laser with (metal, polymer, and ceramic) powders. In this result, the flake SUS 316L was used to achieve a high porous product, and compare to spherical type. After SLS, the porosity of flake-type sample with 34% was quite higher than that of the spherical-type one that had only 11%. The surface roughness of the flake SLS sample were also investigated in both inner and surface parts. The results show that the deviation of the roughness of the surface part is about 64.40μm, while that of the internal one was about 117.65μm, which presents the containing of high porosity in the uneven surfaces. With the process using spherical powder, the sample was quite dense, however, some initial particles still remained as a result of less energy received at the beneath of the processing layer.
Double-pass Friction Stir Processing (FSP) was applied to fabricate an AZ31/CNT nano-composite for surface hardening of lightweight structural components. The effects of double-pass FSP as well as groove depth (i.e., volume fraction of CNT) on the CNT distribution, dynamically recrystallized grain size, and resulting microhardness were studied. Double-pass FSP was performed for the CNT-filled plate-type specimen with different groove depths of 2, 3, and 4 mm. By applying double-pass FSP, the average size of CNT clusters decreased, implying a more homogeneous distribution. Compared with the FSPed specimen without CNT, grain size was refined from 19 μm to 3 μm and microhardness increased from 52 Hv to 83 Hv (i.e., 71% increase).
The movement of people can be considered as the flow of liquid, so we can use the methods employed for the flow of liquid to understand the motion of a crowd. Based on this, we present a novel framework for abnormal behavior detection in crowded scenes. We extract a topological structure from the crowd with the topology simplification algorithm. However, a conventional topology simplification algorithm can not work well if we apply it to the crowd directly because there is too much noises produced by the random motion of the people in the original image. To overcome this, we make a step forward by optimizing this model using Particle Swarm Optimization (PSO) to perform the advection of particle population spread randomly over the image frames. Then we propose two new methods for analyzing the boundary point structure and extraction of a critical point from the particle motion field; both methods can be used to describe the global topological structure of the crowd motion. The advantage of our approach is that each kind of abnormal event can be described as a specific change in the topological structure, so we do not need construct a complex classifier, but can classify the crowd anomalies dynamically and directly. Moreover, the approach monitors the crowd motion macroscopically, making it insensitive to the motion of an individual, disregarding the global movement. The result of an experiment conducted on a common data set shows that our method is both precise and stable.
This paper presents a task and message-based scheduling method to guarantee the given end-to-end constraints including precedence constraints, time constraints, and period and priority of task and message. The method is the integrated one considering both tasks executed in each node and messages transmitted via the network and is designed to apply to the general distributed control system that has multiple loops and a single loop has sensor nodes with multiple sensors, actuator nodes with multiple actuators, controller nodes with multiple tasks, and several types of constraints. The assigning method of the optimal period and priority of task and message is proposed, using the presented task and message-based scheduling method.
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