A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

Intelligent Space(IS)is widely regarded as a promising paradigm for improving quality of life through using service task processing.As the field matures,various state-of-the-art IS architectures have been proposed.Most of the IS architectures designed for service robots face the problems of fixedfunction modules and low scalability when performing service tasks.To this end,we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture(SOA).Specifically,we first use the distributed deployment of functional modules to solve the problem of high computing resource occupancy.Then,the Socket communication interface layer is designed to improve the calling efficiency of the function module.Next,the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing tasks.Finally,we design and deploy an interactive system based on Browser/Server(B/S)architecture,which aims to display the status of the robot in real-time as well as to expand and call the robot service.This system is integrated into the private cloud framework,which provides a feasible solution for improving the quality of life.Besides,it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right way.The results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service tasks.It is an efficient way to transmit data and reduce the computational burden on the robot.By using our cloud detection module,the robot system can save approximately 25% of the averageCPUusage and reduce the average detection time by 0.1 s compared to the locally deployed system,demonstrating the reliability and practicality of our proposed architecture.

Adaptive Proportional-Derivative Sliding Mode Control Law With Improved Transient Performance for Underactuated Overhead Crane Systems

In this paper, an adaptive proportional-derivative sliding mode control(APD-SMC) law, is proposed for 2D underactuated overhead crane systems. The proposed controller has the advantages of simple structure, easy to implement of PD control, strong robustness of SMC with respect to external disturbances and uncertain system parameters, and adaptation for unknown system dynamics associated with the feedforward parts. In the proposed APD-SMC law, the PD control part is used to stabilize the controlled system, the SMC part is used to compensate the external disturbances and system uncertainties,and the adaptive control part is utilized to estimate the unknown system parameters. The coupling behavior between the trolley movement and the payload swing is enhanced and, therefore, the transient performance of the proposed controller is improved.The Lyapunov techniques and the La Salle's invariance theorem are employed in to support the theoretical derivations. Experimental results are provided to validate the superior performance of the proposed control law.

Building an Intelligent Home Space for Service Robot Based on Multi-Pattern Information Model and Wireless Sensor Networks

This paper is concerned with constructing a prototype intelligent home environment for home service robot. In this environment, multi-pattern information can be represented by some intelligent artificial marks. Light-packs service robots can provide reliable and intelligent service by interacting with the environment through the wireless sensor networks. The intelligent space consists the following main components: smart devices with intelligent artificial mark;home server that connects the smart device and maintains the information through wireless sensor network;and the service robot that perform tasks in collaboration with the environment. In this paper, the multi-pattern information model is built, the construction of wireless sensor networks is presented, the smart and agilely home service is introduced. Fi- nally, the future direction of intelligent space system is discussed.

Composites of small Ag clusters confined in the channels of well-ordered mesoporous anatase TiO2 and their excellent solar-light-driven photocatalytic performance

Small Ag clusters confined in the channels of ordered mesoporous anatase TiO2 have been fabricated via a vacuum-assisted wet-impregnation method, utilizing well-ordered mesoporous anatase TiO2 with high thermal stability as the host. The composites have been characterized in detail by X-ray diffraction, X-ray photoelectron spectroscopy X-ray absorption fine structure （XAFS） spectroscopy, N2 adsorption, UV-visible diffuse reflectance spectroscopy and transmission electron microscopy. The results indicate that small Ag clusters are formed and uniformly confined in the channels of mesoporous TiO2 with an obvious confinement effect. The presence of strong AgO interactions involving the Ag clusters in intimate contact with the pore walls of mesoporous TiO2 is confirmed by XAFS analysis, and favors the separation of photogenerated electron-hole pairs, as shown by steady-state surface photovoltage spectroscopy and transient-state surface photovoltage measurements. The ordered mesoporous Ag/TiO2 composites exhibit excellent solar-light-driven photocatalytic performance for the degradation of phenol. This is attributed to the synergistic effects between the small Ag clusters acting as traps to effectively capture the photogenerated electrons, and the surface plasmon resonance of the Ag clusters promoting the absorption of visible light. This study clearly demonstrates the high-efficiency utilization of noble metals in the fabrication of high-performance solar-light-driven photocatalysts.

Boosted charge transfer and photocatalytic CO_(2) reduction over sulfurdoped C_(3)N_(4) porous nanosheets with embedded SnS_(2)-SnO_(2) nanojunctions

Two-dimensional porous nanosheet heterostructure materials,which combine the advantages of both architecture and components,are expected to feature a significant photocatalytic performance toward CO_(2) conversion into useful fuels.Herein,we provide a facile strategy for fabricating sulfur-doped C_(3)N_(4) porous nanosheets with embedded SnO_(2)-SnS_(2) nanojunctions(S-C_(3)N_(4)/SnO_(2)-SnS_(2))via liquid impregnation-pyrolysis and subsequent sulfidation treatment using a layered supramolecular structure as the precursor of C_(3)N_(4).A hexagonal layered supramolecular structure was first prepared as the precursor of C_(3)N_(4).Then Sn^(4+) ions were intercalated into the supramolecular interlayers through the liquid impregnation method.The subsequent annealing treatment in air simultaneously realized the fabrication and efficient exfoliation of layered C_(3)N_(4) porous nanosheets.Moreover,SnO_(2) nanoparticles were formed and embedded in situ in the porous C_(3)N_(4) nanosheets.In the following sulfidation process under a nitrogen atmosphere,sulfur powder can react with SnO_(2) nanoparticles to form SnO_(2)-SnS_(2) nanojunctions.As expected,the exfoliation of sulfur-doped C_(3)N_(4) porous nanosheets and ternary heterostructure construction could be simultaneously achieved in this work.Sulfur-doped C_(3)N_(4) porous nanosheets with embedded SnO_(2)-SnS_(2) nanojunctions featured abundant active sites,enhanced visible light absorption,and efficient interfacial charge transfer.As expected,the optimized S-C_(3)N_(4)/SnO_(2)-SnS_(2) achieved a much higher gas-phase photocatalytic CO_(2) reduction performance with high yields of CO(21.68μmol g^(−1)h^(−1))and CH_(4)(22.09μmol g^(−1)h^(−1))compared with the control C_(3)N_(4),C_(3)N_(4)/SnO_(2),and S-C_(3)N_(4)/SnS_(2) photocatalysts.The selectivity of CH_(4) reached 80.30%.Such a promising synthetic strategy can be expected to inspire the design of other robust C_(3)N_(4)-based porous nanosheet heterostructures for a broad range of applications.

Synergetic enhancement of surface reactions and charge separation over holey C_(3)N_(4)/TiO_(2)2D heterojunctions

Efficient charge separation and rapid interfacial reaction kinetics are crucial factors that determine the efficiency of photocatalytic hydrogen evolution.Herein,a fascinating 2D heterojunction photocatalyst with superior photocatalytic hydrogen evolution performance–holey C_(3)N_(4)nanosheets nested with TiO_(2)nanocrystals(denoted as HCN/TiO_(2))–is designed and fabricated via an in situ exfoliation and conversion strategy.The HCN/TiO_(2)is found to exhibit an ultrathin 2D heteroarchitecture with intimate interfacial contact,highly porous structures and ultrasmall TiO_(2)nanocrystals,leading to drastically improved charge carrier separation,maximized active sites and the promotion of mass transport for photocatalysis.Consequently,the HCN/TiO_(2)delivers an impressive hydrogen production rate of 282.3 lmol h^(-1)per10 mg under AM 1.5 illumination and an apparent quantum efficiency of 13.4%at a wavelength of 420 nm due to the synergetic enhancement of surface reactions and charge separation.The present work provides a promising strategy for developing high-performance 2D heterojunctions for clean energy applications.

A deep Q-learning network based active object detection model with a novel training algorithm for service robots

This paper focuses on the problem of active object detection(AOD).AOD is important for service robots to complete tasks in the family environment,and leads robots to approach the target ob ject by taking appropriate moving actions.Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy.Therefore,an AOD model based on a deep Q-learning network(DQN)with a novel training algorithm is proposed in this paper.The DQN model is designed to fit the Q-values of various actions,and includes state space,feature extraction,and a multilayer perceptron.In contrast to existing research,a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy.In addition,a method of generating the end state is presented to judge when to stop the AOD task during the training process.Sufficient comparison experiments and ablation studies are performed based on an AOD dataset,proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

Distributed environment representation and object localization system in intelligent space

A kind of new environment representation and object localization scheme is proposed in the paper aiming to accomplish the task of object operation more efficiently in intelligent space. First, a distributed environment represen- tation method is put forward to reduce storage burden and improve the system＇s stability. The layered topological maps are separately stored in different landmarks attached to the key positions of intelligent space, so that the robot can search the landmarks on which the map information can be read from the QR code, and then the environment map can be built autonomously. Map building is an important prerequisite for object search. An object search scheme based on RFID and vision technology is proposed. The RFID tags are attached to the target objects and reference objects in the indoor environ- ment. A fixed RFID system is built to monitor the rough position （room and local area） of target and a mobile RFID system is constructed to detect the targets which are not in the covering range of the fixed system. The existing area of target is determined by the time sequence of reference tags and target tags, and the accurate position is obtained by onboard vision system at a short distance. The experiments demonstrate that the distributed environment representation proposed in the paper can fully meet the requirements of object localization, and the positioning scheme has high search efficiency, high localization accuracy and precision, and a strong anti-interference ability in the complex indoor environment.