Hierarchical Porous Nitrogen Doped Carbon Derived from Horn Comb as Anode for Sodium-Ion Storage with High Performance

Junke Ou,Lin Yang,Xianghui Xi 대한금속·재료학회 2017 ELECTRONIC MATERIALS LETTERS Vol.13 No.1

Horn comb, an abundant biomass waste, has been successfully converted intoa hierarchical porous nitrogen doped carbon (HPNDC) via a simple and costeffectiveapproach. Tested as anode for sodium ion batteries (SIBs), horncomb derived carbon shows good rate capability and cycling stability,delivering a high initial charge capacity of 400 mAh g−1 at 100 mA g−1,retaining a reversible capacity of 112 mAh g−1 at 5 A g−1, and exhibiting acapacity of 241 mAh g−1 at 100 mA g−1 after 100 cycles. These superiorelectrochemical performances can be ascribed to its unique hierarchical porestructure combined with appropriate nitrogen doping effects. We believe thatour works will be helpful in promoting the development of high-rate and lowcostsodium ion batteries for large-scale energy storage systems.

Low-power Scheduling Framework for Heterogeneous Architecture under Performance Constraint

( Junke Li ),( Bing Guo ),( Yan Shen ),( Deguang Li ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.5

Today’s computer systems are widely integrated with CPU and GPU to achieve considerable performance, but energy consumption of such system directly affects operational cost, maintainability and environmental problem, which has been aroused wide concern by researchers, computer architects, and developers. To cope with energy problem, we propose a task-scheduling framework to reduce energy under performance constraint by rationally allocating the tasks across the CPU and GPU. The framework first collects the estimated energy consumption of programs and performance information. Next, we use above information to formalize the scheduling problem as the 0-1 knapsack problem. Then, we elaborate our experiment on typical platform to verify proposed scheduling framework. The experimental results show that our proposed algorithm saves 14.97% energy compared with that of the time-oriented policy and yields 37.23% performance improvement than that of energy-oriented scheme on average.

Junction Temperature Prediction of IGBT Power Module Based on BP Neural Network

Junke Wu,Luowei Zhou,Xiong Du,Pengju Sun 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.3

In this paper, the artificial neural network is used to predict the junction temperature of the IGBT power module, by measuring the temperature sensitive electrical parameters (TSEP) of the module. An experiment circuit is built to measure saturation voltage drop and collector current under different temperature. In order to solve the nonlinear problem of TSEP approach as a junction temperature evaluation method, a Back Propagation (BP) neural network prediction model is established by using the Matlab. With the advantages of non-contact, high sensitivity, and without package open, the proposed method is also potentially promising for on-line junction temperature measurement. The Matlab simulation results show that BP neural network gives a more accuracy results, compared with the method of polynomial fitting.

Power Modeling Approach for GPU Source Program

Junke Li,Bing Guo,Yan Shen,Deguang Li,Yanhui Huang 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.1

Rapid development of information technology makes our environment become smarter and massive high performance computers are providing powerful computing for that. Graphics Processing Unit (GPU) as a typical high performance component is being widely used for both graphics and general-purpose applications. Although it can greatly improve computing power, it also delivers significant power consumption and need sufficient power supplies. To make high performance computing more sustainable, the important step is to measure it. Current power technologies for GPU have some drawbacks, such as they are not applicable for power estimation at the early stage. In this article, we present a novel power technology to correlate power consumption and the characteristics at the programmer perspective, and then to estimate power consumption of source program without prerunning. We conduct experiments on Nvidia’s GT740 platform; the results show that our power model is more accurately than regression model and has an average error of 2.34% and the maximum error of 9.65%.

A ResNet based multiscale feature extraction for classifying multi-variate medical time series

Junke Zhu,Le Sun,Yilin Wang,Sudha Subramani,Dandan Peng,Shangwe Charmant Nicolas 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.5

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

Mechanical Performance and Environmental Effect of Coal Fly Ash on MICP-Induced Soil Improvement

Junke Zhang,Peidong Su,Kejun Wen,Yadong Li,Lin Li 대한토목학회 2020 KSCE JOURNAL OF CIVIL ENGINEERING Vol.24 No.11

Coal fly ash (FA) is one of the main byproducts of coal burning. Nearly half of FA cannot be reused or recycled. The potential environmental leaching of trace elements in FA may limit its application. Microbially induced carbonate precipitation (MICP) is a promising technology to improve soil properties. This study was to investigate the effect of fly ash in MICP-stabilized soil on its mechanical and environmental impacts. Two kinds of fly ash were considered: FA1 was Class-F fly ash, FA2 was off-specific fly ash. The 0% (sand only), 3%, 6% and 9% content of FA were introduced to FA-sand mixtures to perform MICP process. Triaxial compression test was performed to evaluate the effect of FA content on the development of strength. The triaxial test results indicated that with 3% addition of FA, the peak deviator stress increased significantly. When MICP-treated sand mixed with 3% FA1, the deviator stress increased to 1,959 kPa compared to that of MICP-treated sand only samples of 800 kPa. The peak deviator stress increased by 154% and 115% when the additions of FA1 were 6% and 9%, respectively. The stress increase was caused by the bonding of precipitated CaCO3 in MICP. However, higher content of FA1 (9% or higher) could restrict the activity of bacteria by reducing the void spaces. MICP-treated samples with the addition of FA2 presented a better enhancement in peak stress for its higher CaO content which could lead to additional cementation besides MICP. Leaching tests by toxicity characteristic leaching procedure (TCLP) and sequential extraction tests indicated that there was no potential risk to introduce fly ash into the MICP process during the soil improvement. MICP process resulted to the fraction change of trace metals which could make trace metal more stable. Microscale images at scale of 10 μm, 100 μm and 200 μm have clearly presented the precipitated CaCO3. It showed that large amount of precipitated CaCO3 coated the particle surfaces and filled the void spaces. Small particles were buried and formed aggregates. There was a highly cemented phases produced between soil particle matrix. XRD analysis also confirmed the presence of CaCO3 crystal after the MICP process.

A Modeling Approach for Energy Saving Based on GA-BP Neural Network

Junke Li,Bing Guo,Yan Shen,Deguang Li,Yanhui Huang 대한전기학회 2016 Journal of Electrical Engineering & Technology Vol.11 No.5

To cope with the increasing scale of scientific data and computational complexity of daily data, more and more cores have been integrated into GPU(Graphic Processing Units) and its working frequency is continually upgrading, which makes it being widely used in general computing for assisting CPU to accelerate program. While GPU offers powerful computing capability, the problem of the energy consumption becomes particularly prominently and it has become one of the important issues hindering development of GPU. For the purpose of solving this problem, DVFS (Dynamic Voltage Frequency Scaling) becomes an effective solution. Because the previous works only focus on single component and use linear relationship to do DVFS without considering energy saving of other units in system at software runtime, therefore we propose an energy saving model (CDVFS) of considering the characteristics of both GPU and memory at software runtime based on GA-BP (Genetic Algorithm-Back propagation) neural network to make better use of the relationship between components for energy saving. Firstly, the model assumes that functional relation between the software runtime characteristics of GPU and memory and the appropriate frequency which corresponds to the GPU and memory as nonlinear. Secondly, we extract five characteristics and use GA-BP neural network to fit the nonlinear functional relation. At last, experiments demonstrate the effectiveness of the approach and reasonableness of assumption, and also show that CDVFS can get average energy savings of 17.06% compared with previous works within acceptable performance loss.

Junction Temperature Prediction of IGBT Power Module Based on BP Neural Network

Wu, Junke,Zhou, Luowei,Du, Xiong,Sun, Pengju The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.3

In this paper, the artificial neural network is used to predict the junction temperature of the IGBT power module, by measuring the temperature sensitive electrical parameters (TSEP) of the module. An experiment circuit is built to measure saturation voltage drop and collector current under different temperature. In order to solve the nonlinear problem of TSEP approach as a junction temperature evaluation method, a Back Propagation (BP) neural network prediction model is established by using the Matlab. With the advantages of non-contact, high sensitivity, and without package open, the proposed method is also potentially promising for on-line junction temperature measurement. The Matlab simulation results show that BP neural network gives a more accuracy results, compared with the method of polynomial fitting.

A Modeling Approach for Energy Saving Based on GA-BP Neural Network

Li, Junke,Guo, Bing,Shen, Yan,Li, Deguang,Huang, Yanhui The Korean Institute of Electrical Engineers 2016 Journal of Electrical Engineering & Technology Vol.11 No.5

Power Modeling Approach for GPU Source Program

Li, Junke,Guo, Bing,Shen, Yan,Li, Deguang,Huang, Yanhui The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.1

Rapid development of information technology makes our environment become smarter and massive high performance computers are providing powerful computing for that. Graphics Processing Unit (GPU) as a typical high performance component is being widely used for both graphics and general-purpose applications. Although it can greatly improve computing power, it also delivers significant power consumption and need sufficient power supplies. To make high performance computing more sustainable, the important step is to measure it. Current power technologies for GPU have some drawbacks, such as they are not applicable for power estimation at the early stage. In this article, we present a novel power technology to correlate power consumption and the characteristics at the programmer perspective, and then to estimate power consumption of source program without prerunning. We conduct experiments on Nvidia's GT740 platform; the results show that our power model is more accurately than regression model and has an average error of 2.34% and the maximum error of 9.65%.