Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach

A Review on the Little Ice Age and Factors to Glacier Changes in the Tian Shan, Central Asia

Mountain glaciers are a reliable and unequivocal indicator of climate change due to their sensitive response to changes in temperature and precipitation. The importance of mountain glaciers is best reflected in regions with limited precipitation, such as arid and semi-arid central Asia. High concentration of glaciers and meltwater from the Tian Shan contribute considerably to the freshwater resource in Xinjiang (China), Kyrgyzstan and nearby countries. Documenting glacier distribution and research on glacier changes can provide insights and scientific support for water management in central Asia. As the most recent glacial event, the Little Ice Age (LIA, approximately AD 1300–1850) signifies the cold periods prior to the warming trend in the twentieth century. Here we present an overview of topics recently studied on the modern and LIA glaciers in the Tian Shan of the central Asia. With data sets of the Glacier Inventory of China and the presumed LIA glacial extents, we applied statistical models in a case study of the eastern Tian Shan to examine the impact of local topographic and geometric factors on glacier area changes. The findings of glacier size and elevation as key local factors are representative and consistent with other studies

A Review of Researches on Blockchain

Analyzing 242 articles related to the study of blockchain which were published in China and abroad from 2014 to 2016, and from the aspects of literature sources, research subjects, research methods and western countries, the basic frame of blockchain research classification is put forward. Summarize the current blockchain technology progress, research limitations and future development trends. The research shows that the domestic research on the blockchain is more decentralized, non-systematic, and has not reached a certain research depth. What’s more, it is lack of quantitative analysis. Digital currency, Internet finance, and the risk of blockchain technology research will be the focus of future research

Covalent Organic Frameworks for Ion Conduction

Covalent organic frameworks (COFs) are an emerging class of crystalline porous materials constructed by the precise reticulation of organic building blocks through dynamic covalent bonds. Due to their facile preparation, easy modulation and functionalization, COFs have been considered as a powerful platform for engineering molecular devices in various fields, such as catalysis, energy storage and conversion, sensing, and bioengineering. Particularly, the highly ordered pores in the backbones with controlled pore size, topology, and interface property provide ideal pathways for the long-term ion conduction. Herein, we summarized the latest progress of COFs as solid ion conductors in energy devices, especially lithium-based batteries and fuel cells. The design strategies and performance in terms of transporting lithium ions, protons, and hydroxide anions are systematically illustrated. Finally, the current challenges and future research directions on COFs in energy devices are proposed, laying the groundwork for greater achievements for this emerging material

Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning

Traffic flow prediction is an important part of smart transportation. The goal is to predict future traffic conditions based on historical data recorded by sensors and the traffic network. As the city continues to build, parts of the transportation network will be added or modified. How to accurately predict expanding and evolving long-term streaming networks is of great significance. To this end, we propose a new simulation-based criterion that considers teaching autonomous agents to mimic sensor patterns, planning their next visit based on the sensor's profile (e.g., traffic, speed, occupancy). The data recorded by the sensor is most accurate when the agent can perfectly simulate the sensor's activity pattern. We propose to formulate the problem as a continuous reinforcement learning task, where the agent is the next flow value predictor, the action is the next time-series flow value in the sensor, and the environment state is a dynamically fused representation of the sensor and transportation network. Actions taken by the agent change the environment, which in turn forces the agent's mode to update, while the agent further explores changes in the dynamic traffic network, which helps the agent predict its next visit more accurately. Therefore, we develop a strategy in which sensors and traffic networks update each other and incorporate temporal context to quantify state representations evolving over time