Control of a 3D piezo-actuating table by using an adaptive sliding-mode controller for a drilling process

AbstractRecently, the micropositioner has become an important developing target for achieving the requirements of precision machinery. The piezo-actuating device plays a very important role in this application area. In this paper, a model-free adaptive sliding-mode controller is proposed for a 3D piezo-actuating system because of the system’s hysteresis nonlinearity and time-varying characteristics. This control strategy employs the functional approximation technique to establish the unknown function for releasing the model based requirements of the sliding-mode control. The update laws for the coefficients of the Fourier series function parameters are derived from a Lyapunov function to guarantee the control system stability. To verify the effectiveness of the proposed controller, drilling process control using the designed controller is investigated in this paper

Fourth Generation Leptons and Muon g−2g-2

We consider the contributions to $g_\mu-2$ from fourth generation heavy neutral and charged leptons, $N$ and $E$, at the one-loop level. Diagrammatically, there are two types of contributions: boson-boson-$N$, and $E$-$E$-boson in the loop diagram. In general, the effect from $N$ is suppressed by off-diagonal lepton mixing matrix elements. For $E$, we consider flavor changing neutral couplings arising from various New Physics models, which are stringently constrained by $\mu\to e\gamma$. We assess how the existence of a fourth generation would affect these New Physics models.Comment: Minor changes, with references update

Augmented Reality Applied in Road Excavation System of Government

As one of the presentative novel technologies in recent years, Augmented Reality (AR) has gotten the ROC government’s attention, and thereby the AR-related applications have been taken into official account in facilitating citizen life. In this research, first, a sort-out of the definitions and the scope of three types of Realities—AR, VR, MR—will be offered to tell the much more realistic dimension of AR. Under the official road excavation context in X city via observation method, second, specific AR applications will be proposed by illustrating it in the pre-excavation, the excavation, and the post-excavation phases, respectively. By cross-referencing between the official road excavation system and public infrastructure pipeline databases, third, the related data of pipeline maps could endow the current AR positioning with better accuracy and directionality

Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201