Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration

Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI). Therefore, redefining modes of collaboration between devices and servers and constructing native intelligence libraries become critically important in 6G. In this paper, we analyze the challenges of achieving 6G native AI from the perspectives of data, intelligence, and networks. Then, we propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, present a construction of a task-oriented AI toolkit, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system, and presenting preliminary evaluation results. Finally, we outline research directions for achieving native AI in 6G.Comment: 8 pages, 4 figures, 1 tabl

Visual Servoing of Humanoid Dual-Arm Robot with Neural Learning Enhanced Skill Transferring Control

This paper presents a novel combination of visual servoing (VS) control and neural network (NN) learning on humanoid dual-arm robot. A VS control system is built by using stereo vision to obtain the 3D point cloud of a target object. A least square-based method is proposed to reduce the stochastic error in workspace calibration. An NN controller is designed to compensate for the effect of uncertainties in payload and other parameters (both internal and external) during the tracking control. In contrast to the conventional NN controller, a deterministic learning technique is utilized in this work, to enable the learned neural knowledge to be reused before current dynamics changes. A skill transfer mechanism is also developed to apply the neural learned knowledge from one arm to the other, to increase the neural learning efficiency. Tracked trajectory of object is used to provide target position to the coordinated dual arms of a Baxter robot in the experimental study. Robotic implementations has demonstrated the efficiency of the developed VS control system and has verified the effectiveness of the proposed NN controller with knowledge-reuse and skill transfer features

Dual cloud point extraction coupled with hydrodynamic-electrokinetic two-step injection followed by micellar electrokinetic chromatography for simultaneous determination of trace phenolic estrogens in water samples

A dual cloud point extraction (dCPE) off-line enrichment procedure coupled with a hydrodynamic-electrokinetic two-step injection online enrichment technique was successfully developed for simultaneous preconcentration of trace phenolic estrogens (hexestrol, dienestrol, and diethylstilbestrol) in water samples followed by micellar electrokinetic chromatography (MEKC) analysis. Several parameters affecting the extraction and online injection conditions were optimized. Under optimal dCPE-two-step injection-MEKC conditions, detection limits of 7.9-8.9 ng/mL and good linearity in the range from 0.05 to 5 mu g/mL with correlation coefficients R (2) a parts per thousand yenaEuro parts per thousand 0.9990 were achieved. Satisfactory recoveries ranging from 83 to 108 % were obtained with lake and tap water spiked at 0.1 and 0.5 mu g/mL, respectively, with relative standard deviations (n = 6) of 1.3-3.1 %. This method was demonstrated to be convenient, rapid, cost-effective, and environmentally benign, and could be used as an alternative to existing methods for analyzing trace residues of phenolic estrogens in water samples.A dual cloud point extraction (dCPE) off-line enrichment procedure coupled with a hydrodynamic-electrokinetic two-step injection online enrichment technique was successfully developed for simultaneous preconcentration of trace phenolic estrogens (hexestrol, dienestrol, and diethylstilbestrol) in water samples followed by micellar electrokinetic chromatography (MEKC) analysis. Several parameters affecting the extraction and online injection conditions were optimized. Under optimal dCPE-two-step injection-MEKC conditions, detection limits of 7.9-8.9 ng/mL and good linearity in the range from 0.05 to 5 mu g/mL with correlation coefficients R (2) a parts per thousand yenaEuro parts per thousand 0.9990 were achieved. Satisfactory recoveries ranging from 83 to 108 % were obtained with lake and tap water spiked at 0.1 and 0.5 mu g/mL, respectively, with relative standard deviations (n = 6) of 1.3-3.1 %. This method was demonstrated to be convenient, rapid, cost-effective, and environmentally benign, and could be used as an alternative to existing methods for analyzing trace residues of phenolic estrogens in water samples

Collaborative human-robot assembly: Methodology, simulation and industrial validation

This paper developed an approach to integrate a human-robot collaborative assembly with a manual production line. The integration was completed effectively by exploring several collaboration algorithms. The study was conducted to enhance the working environment by improving the productivity and the quality of the production. Variability in the assembly processes was reduced, thus improving product quality and reducing rejects and reworks. The automation or robot collaboration was evaluated on one of the existing assembly lines, to help worker reduce repetitive work and increase productivity, which will also help to save labor costs over the long term. The inspection outputs from a robot are easily accessible, providing the quantitative data, analysis of which will lead to continual improvement

Visualized Stacked Denoising Auto-Encoder Model for Extracting and Evaluating the State Features of Rolling Bearings

Extracting intuitive operating state features from vibration signals without prior knowledge is a prospective requirement for health monitoring and fault diagnosis in bearings. In this paper, a visualized stacked denoising auto-encoder (VSDAE) model is proposed for the unsupervised extraction and quantitative evaluation of bearings’ state features. First, the stacked denoising auto-encoder (SDAE) was used to reconstruct vibration signals. The intermediate vector of the SDAE, which is a high-information-density representation of vibration signals, was regarded as the pending state feature. Then, the dimension of the intermediate vector was reduced by the t-distributed stochastic neighbor embedding (t-SNE) method to the two-dimensional visualization space. Finally, the silhouette coefficient of feature distribution was calculated to quantitatively evaluate the extracted features. The proposed model was evaluated using experimental bearing signals simulating various operating states. The results proved that the features, extracted and evaluated by the VSDAE, allowed the recognition of the operating states of the examined bearings

Photonic and magnetic dual responsive molecularly imprinted polymers: preparation, recognition characteristics and properties as a novel sorbent for caffeine in complicated samples

We demonstrated the construction and characteristics of photonic and magnetic dual responsive molecularly imprinted polymers (DR-MIPs) prepared by combination of stimuli-responsive polymers and a molecular imprinting technique. The resultant DR-MIPs of Fe3O4@MIPs exhibited specific affinity for caffeine and photoisomerization induced reversible uptake and release of caffeine upon alternate UV and visible light irradiation. With irradiation at 365 nm, 62.5% of the receptor-bound caffeine was released from the DR-MIPs back into solution. Subsequent irradiation with visible light caused 93.6% of the released caffeine to be rebound by the DR-MIPs. The novel DR-MIPs were used as a sorbent for the enrichment of caffeine from real water and beverage samples. Recoveries ranging from 89.5-117.6% were achieved. The magnetic property of DR-MIPs provided fast and simple separation while the photonic responsive property offered simple template elution with the assistance of UV-Vis irradiation. The simple, rapid and reliable DR-MIPs based method proved potentially applicable for trace caffeine analysis in complicated samples.We demonstrated the construction and characteristics of photonic and magnetic dual responsive molecularly imprinted polymers (DR-MIPs) prepared by combination of stimuli-responsive polymers and a molecular imprinting technique. The resultant DR-MIPs of Fe3O4@MIPs exhibited specific affinity for caffeine and photoisomerization induced reversible uptake and release of caffeine upon alternate UV and visible light irradiation. With irradiation at 365 nm, 62.5% of the receptor-bound caffeine was released from the DR-MIPs back into solution. Subsequent irradiation with visible light caused 93.6% of the released caffeine to be rebound by the DR-MIPs. The novel DR-MIPs were used as a sorbent for the enrichment of caffeine from real water and beverage samples. Recoveries ranging from 89.5-117.6% were achieved. The magnetic property of DR-MIPs provided fast and simple separation while the photonic responsive property offered simple template elution with the assistance of UV-Vis irradiation. The simple, rapid and reliable DR-MIPs based method proved potentially applicable for trace caffeine analysis in complicated samples