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Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11,   Pages 1535-1670 doi: 10.1631/FITEE.1900481

Abstract: has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for . While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary . The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.

Keywords： 人群计数；卷积神经网络；密度估计；语义分割；多任务学习    

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A novel convolutional neural network method for crowd counting Research Articles

Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen,18s004055@hit.edu.cn,dixiaoguang@hit.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8,   Pages 1119-1266 doi: 10.1631/FITEE.1900282

Abstract: Crowd , in general, is a challenging task due to the large variation of head sizes in the crowds. Existing methods always use a multi-column convolutional neural network (MCNN) to adapt to this variation, which results in an average effect in areas with different densities and brings a lot of noise to the density map. To address this problem, we propose a new method called the segmentation-aware prior network (SAPNet), which generates a high-quality density map without noise based on a coarse head-segmentation map. SAPNet is composed of two networks, i.e., a foreground-segmentation convolutional neural network (FS-CNN) as the front end and a crowd-regression convolutional neural network (CR-CNN) as the back end. With only the single dot annotation, we generate the ground truth of segmentation masks in heads. Then, based on the ground truth, FS-CNN outputs a coarse head-segmentation map, which helps eliminate the noise in regions without people in the density map. By inputting the head-segmentation map generated by the front end, CR-CNN performs accurate estimation and generates a high-quality density map. We demonstrate SAPNet on four datasets (i.e., ShanghaiTech, UCF-CC-50, WorldExpo’10, and UCSD), and show the state-of-the-art performances on ShanghaiTech part and UCF-CC-50 datasets.

Keywords： Crowd counting     Density estimation     Segmentation prior map     Uniform function    

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Gaze Estimation via a Differential Eyes’ Appearances Network with a Reference Grid Article

Song Gu, Lihui Wang, Long He, Xianding He, Jian Wang

Engineering 2021, Volume 7, Issue 6,   Pages 777-786 doi: 10.1016/j.eng.2020.08.027

Abstract:

A person’s eye gaze can effectively express that person’s intentions. Thus, gaze estimation is an important approach in intelligent manufacturing to analyze a person’s intentions. Many gaze estimation methods regress the direction of the gaze by analyzing images of the eyes, also known as eye patches. However, it is very difficult to construct a person-independent model that can estimate an accurate gaze direction for every person due to individual differences. In this paper, we hypothesize that the difference in the appearance of each of a person’s eyes is related to the difference in the corresponding gaze directions. Based on this hypothesis, a differential eyes’ appearances network (DEANet) is trained on public datasets to predict the gaze differences of pairwise eye patches belonging to the same individual. Our proposed DEANet is based on a Siamese neural network (SNNet) framework which has two identical branches. A multi-stream architecture is fed into each branch of the SNNet. Both branches of the DEANet that share the same weights extract the features of the patches; then the features are concatenated to obtain the difference of the gaze directions. Once the differential gaze model is trained, a new person’s gaze direction can be estimated when a few calibrated eye patches for that person are provided. Because personspecific calibrated eye patches are involved in the testing stage, the estimation accuracy is improved. Furthermore, the problem of requiring a large amount of data when training a person-specific model is effectively avoided. A reference grid strategy is also proposed in order to select a few references as some of the DEANet’s inputs directly based on the estimation values, further thereby improving the estimation accuracy. Experiments on public datasets show that our proposed approach outperforms the state-of-theart methods.

Keywords： Gaze estimation     Differential gaze     Siamese neural network     Cross-person evaluations     Human–robot collaboration    

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Exploration for the Synthesis Route of Superior High Energy Density Materials (SHEDM)

Yu Yongzhong

Strategic Study of CAE 1999, Volume 1, Issue 2,   Pages 91-94

Abstract:

The development of high explosives could be reviewed as TNT, RDX, and HMX as three historical stages, and now we are coming in the fourth stage which marked by the appearance of CL-20. However, the increment of energy denicty of the CL-20 is only about 10% greater than that of HMX.

For the needs of the improvement of the weapon systems in the next century, it is necessary to search and develop the superior high energy density materials, and it would be some quite new classes of substance.

Performance of nitrogen cluster, such as octaazacubane N8, was calculated by quantum chemists and demonstrated its remarkable prospects, which is represented as an example of the imaginary SHEDM. And it was emphasized that new allotropic forms of nitrogen Nn would be the most promissing substance as SHEDM. Recently N5+ was found in the MS, and it is already prepared as a salt by Christe.

Nitrogen polymer Nn, in which n is a large number and N atoms are connected by covalent bonds in a three dimentioal net structure just as a polymer, is should be paid the greatest attention and would become the most powerful SHEMD in the next century.

Keywords： HEDM     nitrogen cluster     nitrogen polymer    

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An Estimate Method of Parametric in Reliability Engineering

Han Ming

Strategic Study of CAE 2003, Volume 5, Issue 3,   Pages 51-56

Abstract:

In this paper, the Bayesian method, an estimate method for parameter in reliability engineering is put forward. The author gives definition of the new Bayesian estimate for failure probability and failure rate, and shows the estimate of the failure probability and the failure rate by new Bayesian method. Finally, calculations are performed regarding to practical problems, which show that the new Bayesian method is feasible, easy to operate, and convenient to use for engineers and technicians in fieldwork.

Keywords： reliability engineering     parameter estimate     new Bayesian estimate     failure probability    

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Motor speed estimation and failure detection of a small UAV using density of maxima Research Articles

Jefferson S. Souza, Moises C. Bezerril, Mateus A. Silva, Frank C. Veras, Abel Lima-Filho, Jorge Gabriel Ramos, Alisson V. Brito,alissonbrito@ci.ufpb.br

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 1002-1009 doi: 10.1631/FITEE.2000149

Abstract: This work presents the application of the technique named signal analysis based on using density of maxima to analyze brushless direct current motors. It uses a correlation coefficient estimated from the density of maxima of the current signal. This study demonstrates in experiments the speed estimation of a brushless motor on a testbench and in a small flying drone. The experimental results demonstrate that it is possible to estimate the speed in 97.8% of the cases and to detect failure in 82.75% of the analyzed cases.

Keywords： 无人机；速度识别；故障检测；混沌    

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Development Status and Strategic Research of Graphene-Base Supercapacitors

Chen Jing, Guo Hongxia, Mao Weiguo, Liu Jianhong, Fu Yongsheng, Ouyang Xiaoping

Strategic Study of CAE 2018, Volume 20, Issue 6,   Pages 75-81 doi: 10.15302/J-SSCAE-2018.06.012

Abstract:

Supercapacitors integrate advantages of both common capacitors and chemical batteries, and have significant advantages such as high power density, fast charging & discharging speed, and long cycle life. The key to developing supercapacitors is electrode materials, among which the most ideal one is graphene due to its excellent properties in mechanical, electrical, thermal and other aspects. Based on statistical analysis of academic papers and patents related to graphene-based supercapacitors, this paper discusses the development status and direction of graphene-based supercapacitors and briefly describes its domestic industrial development. The difficulties and challenges faced by the future development of graphene-based supercapacitors are discussed and the future development direction and suggestions are proposed.

Keywords： graphene     supercapacitor     electrode material     power density     energy density    

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A review: the influence of stocking density on fish welfare

Liu Baoliang,Lei Jilin,Jia Rui,Liu Bin

Strategic Study of CAE 2014, Volume 16, Issue 9,   Pages 100-105

Abstract:

Stocking density is widely recognized as a critical husbandry factor in modern intensive aquaculture and inappropriate stocking density is a potential source of chronic stress that may affect the welfare of farmed fish, and increase breeding risk. So far the effect of stocking density on the fish welfare was studied widely. In order to provide reference for selecting suitable stocking density and further researching, this review will focus on the fish welfare indicator such as growth performance, health status, stress response and behavior, to explore how stocking densities affect fish welfare.

Keywords： Stocking density     fish welfare     intensive aquaculture     growth     health states     stress response    

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A data-driven method for estimating the target position of low-frequency sound sources in shallow seas Research Articles

Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang,robin_sun@qut.edu.cn,jiaxinming_123@163.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 1020-1030 doi: 10.1631/FITEE.2000181

Abstract: Estimating the target position of low-frequency sound sources in a environment is difficult due to the high cost of hydrophone placement and the complexity of the propagation model. We propose a compressed (C-RNN) model that compresses the signal received by a into a dynamic sound intensity signal and compresses the target position of the sound source into a GeoHash code. Two types of data are used to carry out prior training on the , and the trained network is subsequently used to estimate the target position of the sound source. Compared with traditional mathematical models, the C-RNN model functions independently under the complex sound field environment and terrain conditions, and allows for real-time positioning of the sound source under low-parameter operating conditions. Experimental results show that the average error of the model is 56 m for estimating the target position of a low-frequency sound source in a environment.

Keywords： 矢量水听器；浅海；低频；位置估计；循环神经网络    

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Efficient parallel implementation of a density peaks clustering algorithm on graphics processing unit Article

Ke-shi GE, Hua-you SU, Dong-sheng LI, Xi-cheng LU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 915-927 doi: 10.1631/FITEE.1601786

Abstract: The density peak (DP) algorithm has been widely used in scientific research due to its novel and effective peak density-based clustering approach. However, the DP algorithm uses each pair of data points several times when determining cluster centers, yielding high computational complexity. In this paper, we focus on accelerating the time-consuming density peaks algorithm with a graphics processing unit (GPU). We analyze the principle of the algorithm to locate its computational bottlenecks, and evaluate its potential for parallelism. In light of our analysis, we propose an efficient parallel DP algorithm targeting on a GPU architecture and implement this parallel method with compute unified device architecture (CUDA), called the ‘CUDA-DP platform’. Specifically, we use shared memory to improve data locality, which reduces the amount of global memory access. To exploit the coalescing accessing mechanism of GPU, we convert the data structure of the CUDA-DP program from array of structures to structure of arrays. In addition, we introduce a binary search-and-sampling method to avoid sorting a large array. The results of the experiment show that CUDA-DP can achieve a 45-fold acceleration when compared to the central processing unit based density peaks implementation.

Keywords： Density peak     Graphics processing unit     Parallel computing     Clustering    

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Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method Article

Rui Xiong, Ju Wang, Weixiang Shen, Jinpeng Tian, Hao Mu

Engineering 2021, Volume 7, Issue 10,   Pages 1471-1484 doi: 10.1016/j.eng.2020.10.022

Abstract:

Lithium-ion batteries (LIBs) have emerged as the preferred energy storage systems for various types of electric transports, including electric vehicles, electric boats, electric trains, and electric airplanes. The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge (SOC) and capacity in real-time. This study proposes a multistage
model fusion algorithm to co-estimate SOC and capacity. Firstly, based on the assumption of a normal distribution, the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters. Secondly, a differential error gain with forward-looking ability is introduced into a proportional–integral observer
(PIO) to accelerate convergence speed. Thirdly, a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer (PIDO) to co-estimate SOC and capacity under a complex application environment. Fourthly, the convergence and anti-noise performance of the fusion algorithm are discussed. Finally, the hardware-in-the-loop platform is set up to verify the performance
of the fusion algorithm. The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2% and 3.3%, respectively.

Keywords： State of charge     Capacity estimation     Model fusion     Proportional–integral–differential observer     Hardware-in-the-loop    

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The Improving Characteristics of the Gradual Gaussian Multidimensional Pre-filter for Optical Flow Estimation

Fu Jun,Xu Weipu

Strategic Study of CAE 2004, Volume 6, Issue 12,   Pages 56-61

Abstract:

Based on the unified estimation-theoretic framework, an effective method of using the gradual Gaussian multidimensional pre-filter to improve the optical flow estimation is presented. The pre-filtering and smoothing effect, which attenuate the temporal aliasing and the interesting signal structure of the optical flow field, are altered with adjusting the spatiotemporal standard deviation parameters. The first 50 frames of the standard Flower Garden and Football video sequence are tested as the reference image sequences, and the LK algorithm as the reference optical flow computing method. Experimental results in objective evaluation show that the optimum temporal standard deviation parameter is 0.4, the optimum spatial standard deviation parameter is in a range of 1.6～2.0 under the condition that the pre-filtering window size is 5 × 5 pixels. After pre-filtering the image sequence by the Gaussian multidimensional filter, the average PSNR of the reconstructed frames enhance 2.572 dB, higher than that using the standard optical flow computing method by nearly 13.6 % .

Keywords： optical flow computing     Gaussian multidimensional filter     PSNR     motion estimation    

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Anenhanced mixedmodulated Lagrange explicit time delay estimator with noisy input Article

Wei XIA,Ju-lei ZHU,Wen-ying JIANG,Ling-feng ZHU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10,   Pages 1067-1073 doi: 10.1631/FITEE.1500417

Abstract: The mixed modulated Lagrange explicit time delay estimation (MMLETDE) algorithm provides an efficient time delay estimation between narrowband or sinusoidal signals. However, it does not explicitly consider the additive measurement noise at the input, which actually exists in practice. Aiming at this issue, an enhanced MMLETDE algorithm is proposed for noisy inputs based on the unbiased impulse response estimation technique, assuming that the noise power ratio is known a priori. Simulation results show that for narrowband signals or sinusoids over a wide frequency range, the proposed algorithm with a small filter order performs well in moderate and high noise scenarios.

Keywords： Time delay estimation     Adaptive filter     Noisy input     Modulated Lagrange     Unbiased impulse response estimation    

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Effect of stocking density on growth and quality in muscles of Cynoglossus semilaevis Günther adult fish in industrial recirculating aquaculture

Wang Feng and Lei Jilin

Strategic Study of CAE 2015, Volume 17, Issue 1,   Pages 19-26

Abstract:

In order to collect more basic data of intensive aquaculture of Cynoglossus semilaevis Günther， 4 groups of different stocking densities were set up in industrial recirculating aquaculture system， the adult fish in rapid growth period (0.64±0.063) kg were observed， the parameters of growth and quality in muscles were detected. The results were shown as follows. a. The experiment continued for 7 months， the survival rate of Cynoglossus semilaevis Günther in recirculating aquaculture mode was in 88.01 %～93.34 %， weight gain rate was 139.69 %～191.09 %. With the decrease of stocking density， survival rate was on a declining curve， while growth rate and fatness were on a rising trend. The concentration of growth hormone was 2.522～2.862 4 μg/L， which was on a rising trend in earlier stage and then on a declining curve in later stage with the decrease of stocking density. b. There is no regular change of moisture， ash content， crude protein， crude fat， amino acid and fatty acid with the change of stocking density. c. In this experimental condition， the group whose intial stocking density is 210 got the best culture effect； the average survival rate per month was 98.72 %； the average gaining in weight per month was 0.152 6 kg/pcs； after 7 months breeding， the specific yield reached 23.03 kg/m3. This study showed that Cynoglossus semilaevis Günther has a good adaptability in industrial recirculating aquaculture， and the growth and quality in muscles were all in a better condition. It also showed that industrial recirculating aquaculture can greatly develop the culture potential of Cynoglossus semilaevis Günther， which was a preponderant culture mode for intensive aquaculture of Cynoglossus semilaevis Günther.

Keywords： Cynoglossus semilaevis Günther； recirculating aquaculture； stocking density； growth； nutrient composition    

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Target height and multipath attenuation joint estimation with complex scenarios for very high frequency radar Research Articles

Sheng CHEN, Yongbo ZHAO, Yili HU, Chenghu CAO, Xiaojiao PANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6,   Pages 937-949 doi: 10.1631/FITEE.2100003

Abstract:

for is a difficult problem due to the in the radar field, especially in where the reflection condition is unknown. To deal with this problem, we propose an algorithm of target height and multipath attenuation joint estimation. The amplitude of the surface reflection coefficient is estimated by the characteristic of the data itself, and it is assumed that there is no reflected signal when the amplitude is very small. The phase of the surface reflection coefficient and the phase difference between the direct and reflected signals are searched as the same part, and this represents the multipath phase attenuation. The Cramer-Rao bound of the proposed algorithm is also derived. Finally, computer simulations and real data processing results show that the proposed algorithm has good estimation performance under and works well with only one snapshot.

Keywords： Low-angle estimation     Very high frequency (VHF) radar     Complex scenarios     Multipath effect     Height estimation    

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Title Author Date Type Operation

Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Journal Article

A novel convolutional neural network method for crowd counting

Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen,18s004055@hit.edu.cn,dixiaoguang@hit.edu.cn

Journal Article

Gaze Estimation via a Differential Eyes’ Appearances Network with a Reference Grid

Song Gu, Lihui Wang, Long He, Xianding He, Jian Wang

Journal Article

Exploration for the Synthesis Route of Superior High Energy Density Materials (SHEDM)

Yu Yongzhong

Journal Article

An Estimate Method of Parametric in Reliability Engineering

Han Ming

Journal Article

Motor speed estimation and failure detection of a small UAV using density of maxima

Jefferson S. Souza, Moises C. Bezerril, Mateus A. Silva, Frank C. Veras, Abel Lima-Filho, Jorge Gabriel Ramos, Alisson V. Brito,alissonbrito@ci.ufpb.br

Journal Article

Development Status and Strategic Research of Graphene-Base Supercapacitors

Chen Jing, Guo Hongxia, Mao Weiguo, Liu Jianhong, Fu Yongsheng, Ouyang Xiaoping

Journal Article

A review: the influence of stocking density on fish welfare

Liu Baoliang,Lei Jilin,Jia Rui,Liu Bin

Journal Article

A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang,robin_sun@qut.edu.cn,jiaxinming_123@163.com

Journal Article

Efficient parallel implementation of a density peaks clustering algorithm on graphics processing unit

Ke-shi GE, Hua-you SU, Dong-sheng LI, Xi-cheng LU

Journal Article

Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

Rui Xiong, Ju Wang, Weixiang Shen, Jinpeng Tian, Hao Mu

Journal Article

The Improving Characteristics of the Gradual Gaussian Multidimensional Pre-filter for Optical Flow Estimation

Fu Jun,Xu Weipu

Journal Article

Anenhanced mixedmodulated Lagrange explicit time delay estimator with noisy input

Wei XIA,Ju-lei ZHU,Wen-ying JIANG,Ling-feng ZHU

Journal Article

Effect of stocking density on growth and quality in muscles of Cynoglossus semilaevis Günther adult fish in industrial recirculating aquaculture

Wang Feng and Lei Jilin

Journal Article

Target height and multipath attenuation joint estimation with complex scenarios for very high frequency radar

Sheng CHEN, Yongbo ZHAO, Yili HU, Chenghu CAO, Xiaojiao PANG

Journal Article