Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing.

Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um.

Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning.

Introduction: Fruit losses in the supply chain owing to improper handling and a lack of proper control are common in the industry. As losses are caused by the inefficiency of the export method, selecting the appropriate export method is a possible solution. Several organizations employ only a single strategy, which is mainly based on a first-in-first-out approach. Such a policy is easy to manage but inefficient. Given that the batch of fruits may become overripe during transportation, frontline operators do not have the authority or immediate support to change the fruit dispatching strategy. Thus, this study aims to develop a dynamic strategy simulator to determine the sequence of delivery based on forecasting information projected from probabilistic data to reduce the amount of fruit loss. Methods: The proposed method to accomplish asynchronous federated learning (FL) is based on blockchain technology and a serially interacting smart contract. In this method, each party in the chain updates its model parameters and uses a voting system to reach a consensus. This study employs blockchain technology with smart contracts to serially enable asynchronous FL, with each party in the chain updating its parameter model. A smart contract combines a global model with a voting system to reach a common consensus. Its artificial intelligence (AI) and Internet of Things engine further strengthen the support for implementing the Long Short-Term Memory (LSTM) forecasting model. Based on AI technology, a system was constructed using FL in a decentralized governance AI policy on a blockchain network platform. Results: With mangoes being selected as the category of fruit in the study, the system improves the cost-effectiveness of the fruit (mango) supply chain. In the proposed approach, the simulation outcomes show fewer mangoes lost (0.035%) and operational costs reduced. Discussion: The proposed method shows improved cost-effectiveness in the fruit supply chain through the use of AI technology and blockchain. To evaluate the effectiveness of the proposed method, an Indonesian mango supply chain business case study has been selected. The results of the Indonesian mango supply chain case study indicate the effectiveness of the proposed approach in reducing fruit loss and operational costs.

A total-internal-reflection-based Fabry-Pérot resonator for ultra-sensitive wideband ultrasound and photoacoustic applications.

In photoacoustic and ultrasound imaging, optical transducers offer a unique potential to provide higher responsivity, wider bandwidths, and greatly reduced electrical and acoustic impedance mismatch when compared with piezoelectric transducers. In this paper, we propose a total-internal-reflection-based Fabry-Pérot resonator composed of a 12-nm-thick gold layer and a dielectric resonant cavity. The resonator uses the same Kretschmann configuration as surface plasmon resonators (SPR). The resonators were analyzed both theoretically and experimentally. The experimental results were compared with those for an SPR for benchmarking. The 1.9-µm-thick-PMMA- and 3.4-µm-thick-PDMS-based resonators demonstrated responsivities of 3.6- and 30-fold improvements compared with the SPR, respectively. The measured bandwidths for the PMMA, PDMS devices are 110 MHz and 75 MHz, respectively. Single-shot sensitivity of 160 Pa is obtained for the PDMS device. The results indicate that, with the proposed resonator in imaging applications, sensitivity and the signal-to-noise ratio can be improved significantly without compromising the bandwidth.

Effective bias removal for fringe projection profilometry using the dual-tree complex wavelet transform.

When reconstructing the three-dimensional (3D) object height profile using the fringe projection profilometry (FPP) technique, the light intensity reflected from the object surface can yield abruptly changing bias in the captured fringe image, which leads to severe reconstruction error. The traditional approach tries to remove the bias by suppressing the zero spectrum of the fringe image. It is based on the assumption that the aliasing between the frequency spectrum of the bias, which is around the zero frequency, and the frequency spectrum of the fringe is negligible. This, however, is not the case in practice. In this paper, we propose a novel (to our knowledge) technique to eliminate the bias in the fringe image using the dual-tree complex wavelet transform (DT-CWT). The new approach successfully identifies the features of bias, fringe, and noise in the DT-CWT domain, which allows the bias to be effectively extracted from a noisy fringe image. Experimental results show that the proposed algorithm is superior to the traditional methods and facilitates accurate reconstruction of objects' 3D models.

Efficient fringe image enhancement based on dual-tree complex wavelet transform.

In optical phase shift profilometry (PSP), parallel fringe patterns are projected onto an object and the deformed fringes are captured using a digital camera. It is of particular interest in real time three-dimensional (3D) modeling applications because it enables 3D reconstruction using just a few image captures. When using this approach in a real life environment, however, the noise in the captured images can greatly affect the quality of the reconstructed 3D model. In this paper, a new image enhancement algorithm based on the oriented two-dimenional dual-tree complex wavelet transform (DT-CWT) is proposed for denoising the captured fringe images. The proposed algorithm makes use of the special analytic property of DT-CWT to obtain a sparse representation of the fringe image. Based on the sparse representation, a new iterative regularization procedure is applied for enhancing the noisy fringe image. The new approach introduces an additional preprocessing step to improve the initial guess of the iterative algorithm. Compared with the traditional image enhancement techniques, the proposed algorithm achieves a further improvement of 7.2 dB on average in the signal-to-noise ratio (SNR). When applying the proposed algorithm to optical PSP, the new approach enables the reconstruction of 3D models with improved accuracy from 6 to 20 dB in the SNR over the traditional approaches if the fringe images are noisy.

Improved MPEG-4 still texture image coding under noisy environment.

This paper describes the performance of the MPEG-4 still texture image codec in coding noisy images. As will be shown, when using the MPEG-4 still texture image codec to compress a noisy image, increasing the compression rate does not necessarily imply reducing the peak-signal-to-noise ratio (PSNR) of the decoded image. An optimal operating point having the highest PSNR can be obtained within the low bit rate region. Nevertheless, the visual quality of the decoded noisy image at this optimal operating point is greatly degraded by the so-called "cross" shape artifact. In this paper, we analyze the reason for the existence of the optimal operating point and the "cross" shape artifact when using the MPEG-4 still texture image codec to compress noisy images. We then propose an adaptive thresholding technique to remove the "cross" shape artifact of the decoded images. It requires only a slight modification to the quantization process of the traditional MPEG-4 encoder while the decoder remains unchanged. Finally, an analytical study is performed for the selection and validation of the threshold value used in the adaptive thresholding technique. It is shown that, the visual quality and PSNR of the decoded images are much improved by using the proposed technique comparing with the traditional MPEG-4 still texture image codec in coding noisy images.