Matsuoka's CPG With Desired Rhythmic Signals for Adaptive Walking of Humanoid Robots

The desired rhythmic signals for adaptive walking of humanoid robots should have proper frequencies, phases, and shapes. Matsuoka's central pattern generator (CPG) is able to generate rhythmic signals with reasonable frequencies and phases, and thus has been widely applied to control the movements of legged robots, such as walking of humanoid robots. However, it is difficult for this kind of CPG to generate rhythmic signals with desired shapes, which limits the adaptability of walking of humanoid robots in various environments. To address this issue, a new framework that can generate desired rhythmic signals for Matsuoka's CPG is presented. The proposed framework includes three main parts. First, feature processing is conducted to transform the Matsuoka's CPG outputs into a normalized limit cycle. Second, by combining the normalized limit cycle with robot feedback as the feature inputs and setting the required learning objective, the neural network (NN) learns to generate desired rhythmic signals. Finally, in order to ensure the continuity of the desired rhythmic signals, signal filtering is applied to the outputs of NN, with the aim of smoothing the discontinuous parts. Numerical experiments on the proposed framework suggest that it can not only generate a variety of rhythmic signals with desired shapes but also preserve the frequency and phase properties of Matsuoka's CPG. In addition, the proposed framework is embedded into a control system for adaptive omnidirectional walking of humanoid robot NAO. Extensive simulation and real experiments on this control system demonstrate that the proposed framework is able to generate desired rhythmic signals for adaptive walking of NAO on fixed and changing inclined surfaces. Furthermore, the comparison studies verify that the proposed framework can significantly improve the adaptability of NAO's walking compared with the other methods

Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

We present a novel method for publishing differentially private synthetic attributed graphs. Unlike preceding approaches, our method is able to preserve the community structure of the original graph without sacrificing the ability to capture global structural properties. Our proposal relies on C-AGM, a new community-preserving generative model for attributed graphs. We equip C-AGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release community-preserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments, we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph, as well as degree sequences and clustering coefficients

Dynamics-Based Vibration Signal Modeling for Tooth Fault Diagnosis of Planetary Gearboxes

Vibration analysis has been widely used to diagnose gear tooth fault inside a planetary gearbox. However, the vibration characteristics of a planetary gearbox are very complicated. Inside a planetary gearbox, there are multiple vibration sources as several sun-planet gear pairs, and several ring-planet gear pairs are meshing simultaneously. In addition, due to the rotation of the carrier, distance varies between vibration sources and a transducer installed on the planetary gearbox housing. Dynamics-based vibration signal modeling techniques can simulate the vibration signals of a planetary gearbox and reveal the signal generation mechanism and fault features effectively. However, these techniques are basically in the theoretical development stage. Comprehensive experimental validations are required for their future applications in real systems. This chapter describes the methodologies related to vibration signal modeling of a planetary gear set for gear tooth damage diagnosis. The main contents include gear mesh stiffness evaluation, gear tooth crack modeling, dynamic modeling of a planetary gear set, vibration source modeling, modeling of transmission path effect due to the rotation of the carrier, sensor perceived vibration signal modeling, and vibration signal decomposition techniques. The methods presented in this chapter can help understand the vibration properties of planetary gearboxes and give insights into developing new signal processing methods for gear tooth damage diagnosis

The Impacts of the CEO’s Network Effect on Digitalization and Agile Leadership in China

Digitalization as a business enabler has speeded and scaled innovation in many firms. As the corporate leader, the CEO is there to set the stage for a learning process that facilitates strategic agility and enhances network effects to create value. This study uses innovation efficiency as the proxy of digitalization to examine the contribution of the CEO networks to firm-level innovation efficiency in Chinese listed firms. We apply a frontier analysis approach (e.g., DEA and SFA) and measure innovation efficiency based on the scale ratio of innovation output (i.e., patent counts) and input (R&D investment and R&D personnel). First, we find that innovation is more efficient when CEO has more outside directorships by considering 13,516 firm-year observations in Chinese listed high-tech firms between 2007 and 2017. Second, a significant and positive relationship exists between a well-connected CEO and innovation efficiency when the newly appointed CEO has larger networks than the predec essor. Third, it is found out that the positive correlation between a well-connected CEO and innovation efficiency will become non-significant when the number of outside directorships is above the yearly median level. This empirical study provides evidence for the network effects of a CEO for improving innovation efficiency. The findings emphasize the contingent value of the CEO\u27s external social capital on agility, especially the multiple directorships in a transitional economy

A DECISION-TREE APPROACH TO ANALYZING CHANNEL ALLOCATION ALGORITHMS FOR TWO-TIER WIRELESS LOCAL LOOPS

A wireless local loop (WLL) uses radio signals to connect customer premise equipment (CPE) to a public switched telephone network (PSTN). It has the potential to help the telephony providers overcome the “last mile” problem in delivering telephony services. A typical WLL consists of a base station controller (BSC), a base station (BS), and subscriber terminals (STs). A WLL can be single-tier, two-tier, or threetier, based on the configuration of the cells within it. There are numerous channel allocation algorithms for two-tier WLLs. These algorithms include no repacking, always repacking, repacking on demand— random, repacking on demand—least load, and repacking on demand—subscriber terminal. This paper provides a decision-tree approach to analyzing these channel allocation algorithms for designing two-tier WLLs. The generated decision-trees can not only help us understand these channel allocation algorithms better, but can also serve as a basis for constructing simulation models and eventually implementing simulation programs for the purpose of comparing the performance of the different network designs

A multilingual dataset of COVID-19 vaccination attitudes on Twitter

Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. A fast and accurate grasp of public attitudes toward vaccination is critical to addressing vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators’ vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualization of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes in public COVID-19 vaccination attitudes