Recently, triboelectric generator(TEG) has attracted a lot of attention due to its high output voltage and low-cost fabrication process. Here, a novel cubic TEG box is designed, which has separated electrodes on diffe...Recently, triboelectric generator(TEG) has attracted a lot of attention due to its high output voltage and low-cost fabrication process. Here, a novel cubic TEG box is designed, which has separated electrodes on different surfaces. Thanks to the specially designed structure, it can scavenge vibration energy from all directions. Firstly the device is investigated through finite element method(FEM) simulation. Then the device is evaluated by experiments. The measuremental results show that this device can generate an amount of 25 n C charge during once shake by charging a 10 n F capacitor. Besides, an output voltage about 100 V is obtained, which is able to directly light up several light-emitting diodes(LEDs) simultaneously. At last, the device is utilized as a self-powered orientation sensor, which shows explicit directivity. This work extends the applications of TEG for ambient vibration energy harvesting techniques and the self-powered orientation sensor.展开更多
Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the I...Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61176103,91323304)the National High-Tech Research and Development Program of China("863"Project)(Grant No.2013AA041102)the Beijing Natural Science Foundation of China(Grant No.4141002)
文摘Recently, triboelectric generator(TEG) has attracted a lot of attention due to its high output voltage and low-cost fabrication process. Here, a novel cubic TEG box is designed, which has separated electrodes on different surfaces. Thanks to the specially designed structure, it can scavenge vibration energy from all directions. Firstly the device is investigated through finite element method(FEM) simulation. Then the device is evaluated by experiments. The measuremental results show that this device can generate an amount of 25 n C charge during once shake by charging a 10 n F capacitor. Besides, an output voltage about 100 V is obtained, which is able to directly light up several light-emitting diodes(LEDs) simultaneously. At last, the device is utilized as a self-powered orientation sensor, which shows explicit directivity. This work extends the applications of TEG for ambient vibration energy harvesting techniques and the self-powered orientation sensor.
基金the Project"The Basic Research on Internet of Things Architecture"supported by National Key Basic Research Program of China(No.2011CB302704)supported by National Natural Science Foundation of China(No.60802034)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20070013026)Beijing Nova Program(No.2008B50)"New generation broadband wireless mobile communication network"Key Projects for Science and Technology Development(No.2011ZX03002-002-01)
文摘Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.
