The cyclic CO2 capture, transient phases change, and microstructure appearance of a new kind of Ca-based regenerable CO2 sorbent, CaO/Ca12Al14O33, obtained by the integration of CaO as solid reactant with a composite metal oxide of Ca12Al14O33 as a binder, were investigated by thermogravimetric analysis, XRD, and SEM at different preparation calcination temperatures. When the calcination temperature in the preparation stage is higher than 1000 °C, the cyclic CO2 capture of this new sorbent declines. The lowered CO2 capture may mainly be attributed to the formation of Ca3Al2O6, which decreases the ratio of CaO to binder in sorbent, and the severe sintering of sorbent occurs when calcined at such high temperatures in the preparation processes. These results suggest that the calcination temperature for this new sorbent should not be higher than 1000 °C in order to obtain its high reactivity. The performance of the new sorbent over 50 cycles was evaluated under mild and severe regeneration conditions, respectively. CaO/Ca12Al14O33 attained 41 wt % CO2 capture after 50 carbonation−calcination cycles under mild calcination conditions (850 °C, 100% N2), and the results obtained here indicate that the new sorbent, CaO/Ca12Al14O33, has significantly improved CO2 capture and cyclic reaction stability over multiple carbonation−calcination cycles compared with limestone and dolomite under mild calcination conditions. When more severe calcination conditions (980 °C, 100% CO2) were used, the capture of CaO/Ca12Al14O33 decreased from 52 wt % in the first cycle to about 22 wt % in the 56th cycle; however, the capture of CaO/Ca12Al14O33 sorbent over 56 cycles is still higher than that of dolomite and limestone under the same severe calcination conditions.

Person re-identification (Re-ID) is an important task in video surveillance which automatically searches and identifies people across different cameras. Despite the extensive Re-ID progress in RGB cameras, few works have studied the Re-ID between infrared and RGB images, which is essentially a cross-modality problem and widely encountered in real-world scenarios. The key challenge lies in two folds, i.e., the lack of discriminative information to re-identify the same person between RGB and infrared modalities, and the difficulty to learn a robust metric towards such a large-scale cross-modality retrieval. In this paper, we tackle the above two challenges by proposing a novel cross-modality generative adversarial network (termed cmGAN). To handle the issue of insufficient discriminative information, we leverage the cutting-edge generative adversarial training to design our own discriminator to learn discriminative feature representation from different modalities. To handle the issue of large-scale cross-modality metric learning, we integrates both identification loss and cross-modality triplet loss, which minimize inter-class ambiguity while maximizing cross-modality similarity among instances. The entire cmGAN can be trained in an end-to-end manner by using standard deep neural network framework. We have quantized the performance of our work in the newly-released SYSU RGB-IR Re-ID benchmark, and have reported superior performance, i.e., Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP), over the state-of-the-art works [Wu et al., 2017], respectively.

The article presents a new topic in path planning for mobile robots, region filling. which involves a sweeping operation to fill a whole region with random obstacle avoidance. The approaches for global strip filling and local path searching driven by sensory data procedures are developed. A computer graphic simulation is used to verify the filling strategy available. The research was developed from the program for the design of a robot lawn mower. However, the solution appears generic. The significance is that a problem of wide application and generic solutions for general autonomous mobile robots have been developed.

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The virtual machine allocation problem is the key to build a private cloud environment. This paper presents a virtual machine mapping policy based on multi-resource load balancing. It uses the resource consumption of the running virtual machine and the self-adaptive weighted approach, which resolves the load balancing conflicts of each independent resource caused by different demand for resources of cloud applications. Meanwhile, it uses probability approach to ease the problem of load crowding in the concurrent users scene. The experiments and comparative analysis show that this policy achieves the better effect than existing approach. Keywords-private cloud; virtual machine; multi-resource load balancing; mapping policyI.