Design of Distributed Temperature Measurement System for Switchgear

This paper develops a novel distributed temperature measurement system based on DSP and DS18B20 digital thermometer. The real-time temperature of each node in the switchgear is obtained by several DS18B20s which are connected on the 1-wire bus together. RS-485 master-slave communication protocol is used to centralize monitoring temperatures of several switchgear cabinets. The system also has the function of temperature alarm. The operation of simulation experiment has showed that the system is able to complete monitoring real-time temperatures in high voltage switchgear.

Deep learning and transfer learning for device-free human activity recognition:A survey

Device-free activity recognition plays a crucial role in smart building,security,and human–computer interaction,which shows its strength in its convenience and cost-efficiency.Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models,but it suffers from the limitation of manual feature design.Deep learning overcomes such issues by automatic high-level feature extraction,but its performance degrades due to the requirement of massive annotated data and cross-site issues.To deal with these problems,transfer learning helps to transfer knowledge from existing datasets while dealing with the negative effect of background dynamics.This paper surveys the recent progress of deep learning and transfer learning for device-free activity recognition.We begin with the motivation of deep learning and transfer learning,and then introduce the major sensor modalities.Then the deep and transfer learning techniques for device-free human activity recognition are introduced.Eventually,insights on existing works and grand challenges are summarized and presented to promote future research.

Exploring the potentiality of future standard candles and standard sirens to detect cosmic opacity

In this work,we explore the potentiality of future gravitational wave(GW)and Type la supermovae(SNe la)measurements to detect cosmic opacity by comparing the opacity-free luminosity distance(LD)of GW events with the opacity-dependent LD of SNe la observations.The GW data are simulated from the future measurements of the ground-based Einstein Telescope(ET)and the space-borne Deci-Herz Interferometer Gravitational wave Obser-vatory(DECIGO).The SNe la data are simulated from the observations of the Wide Field Infrared Survey Tele-scope(WFIRST)that will be collected over the next few decades.A binning method is adopted to match the GW data with the SNe la data at the same redshift z with a sclection criterion|△z|<0.005,and most of the available data from the GW measurements is employed to detect cosmic opacity due to improvements in the distribution of the fu-ture SNe la observations.Results show that the uncertainties of the constraints on cosmic opacity can be reduced toσe~0.0041 and 0.0014 at the lσconfidence level(CL)for 1000 data points from the ET and DECIGO measure-ments,respectively.Compared with the allowable limits of intergalactic opacity obtained from quasar continum ob-servations,these future astronomical observations can be used to verify the cosmic opacity.In this way,GW and SNe la measurements can be used as important and effective tools to detect cosmic opacity in the future.