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Delay-Tolerant Rendezvous-Based Data Collection for Target Tracking in Large-Scale Wireless Sensor networks with UGV 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Jian Xu, Jiang Wang, Tianbao College of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing210044 China School of Electrical and Information Engineering Jiangsu University Zhenjiang212013 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

Energy efficiency receives significant attention in wireless sensor networks. In this paper, a UGV is employed as an energy-efficient solution to prolong the network lifetime in target tracking. Data collection strategies for target tracking are investigated including the amount of data and the transmitted distances. For contributed data, the quantization technology is exploited for energy efficiency. Considering the uncertainty of sensing data, we determine a group from intra-cluster members to gather data. And then, we formulate our design a selection optimization problem, maximizing the utilization of the quality of contributed data using information matrix. As a result, we develop an optimization algorithm named rendezvous-based data collection (RDC). Furthermore, two stages of data collection for target tracking are analyzed with a UGV. Simulations verify that the proposed scheme achieves network energy saving as well as energy balance in the framework of target tracking. © 2019, Springer Nature Switzerland AG.

关键词： Target tracking

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Identifying Materials of Photographic Images and Photorealistic computer Generated Graphics Based on Deep CNNs

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computers, Materials & Continua 2018年第5期55卷 229-241页

作者： Qi Cui Suzanne McIntosh Huiyu Sun School of Computer and Software Nanjing University of Information Science and TechnologyNing Liu RoadNo.219Nanjing210044China Jiangsu Engineering Centre of Network Monitoring Ning Liu RoadNo.219Nanjing210044China Computer Science Department New York UniversityNew YorkNY 10012USA

Currently,some photorealistic computer graphics are very similar to photographic *** computer generated graphics can be forged as photographic images,causing serious security *** aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime *** paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network *** results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.

关键词： Image identification CNN DNN DCNNs computer generated graphics

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Air Quality Forcasting based on Gated Recurrent Long Short-Term Memory Model 19

Air Quality Forcasting based on Gated Recurrent Long Short-T...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Wang, Baowei Kong, Weiwen Guan, Hui School of Computer and Software Jiangsu Engineering Centre of Network Monitoring Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781450371582

With the continuous development of Chinese economy and the gradual acceleration of urbanization, it has caused tremendous damage to the *** effect of smog on people's lives has become more apparent. The bad air environment seriously damages the physical and mental health of people. Predicting smog becomes very ***,?how to accurately predict PM2.5 has become an important issue in recent years. The existing prediction models have limitations. They don't accurately capture the law between the concentration of haze and the factors affecting reality. It is difficult to accurately predict nonlinear smog data. One algorithm proposed in this paper is a two-layer model prediction algorithm based on Long Short Term Memory Neural network and Gated Recurrent Unit(LSTM&GRU). This algorithm is an improvement and enhancement of the existing prediction method Long Short Term Memory. The Recurrent Neural network (RNN) is used to process important events in the series of prediction intervals and time delays. LSTM&GRU is a time-characteristic neural network algorithm. The data of this experiment was obtained from Zhenqi Net(***). We selected daily smog data from 2014/1/1 to 2018/1/1 as a train and test dataset. The first 80% of the data was used for training and the rest for testing. The results of this experiment show that our model can play a better prediction. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Long short-term memory

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Classification based on brain storm optimization algorithm 11th

Classification based on brain storm optimization algorithm

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Xue, Yu Tang, Tao Ma, Tinghuai Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789811036101

As one of the important issues of data classification, classification has attracted the attention of many researchers in the field of data mining. Different from clustering research issues, in classification research issues, evolutionary clustering algorithms (EAs) were only used to improve the performance of classifiers either by optimizing the parameters or structure of the classifiers, or by pre-processing the inputs of the classifiers. Lots of evolutionary algorithms are employed to solve unsupervised classification, i.e., clustering. In this article, we will create a new mathematical model for supervised classification problem and use brain storm optimization algorithm (BSO) to search the global optimal solution, which resolved the problem of supervised classification with new ideas. The main objective is to find a better method. By using a new classification algorithm based on BSO, we are looking forward to optimizing the result. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Storms

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Security system construction of land and resources network based on intrusion detection 8

Security system construction of land and resources network b...

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8th International Conference on BioMedical engineering and Informatics, BMEI 2015

作者： Shen, Chao Chen, Zhongyang Xue, Yu Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology NUIST Nanjing China School of Computer and Software Nanjing University of Information Science and Technology NUIST Nanjing China

ISBN: (纸本)9781509000227

Intrusion detection can decide whether there are dangerous user behaviors. It can also timely detect network security vulnerabilities of inner attacks, and it can make quick response and improve network security. Therefore, it is necessary to research about the intrusion detection in land and resources information systems. For the security status of land and resources network, we place the security architecture platform and select different types of attacks to test the systems. The test results verify the stability, reliability and scalability of the systems, achieving the needed results. © 2015 IEEE.

关键词： Intrusion detection

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Ground nephogram classification based on Gabor wavelet and SVM

Ground nephogram classification based on Gabor wavelet and S...

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2nd International Conference on Information engineering and Applications, IEA 2012

作者： Li, Hanguang Wang, Qi Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China College of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

ISBN: (纸本)9781447148555

Techniques called feature extraction and pattern recognition are implemented for nephogram representation and recognition. In this approach, the Gabor wavelets are used to extract features of ground nephograms. The principal component analysis (PCA) is applied on the Gabor transformed matrices to reduce the dimensions of these features. Finally by using these features, we can train support vector machine (SVM) classifiers for classifying cloud types. After finishing training, we can use these trained SVM classifiers to classify these cloud types. During these experiments, we used different Kernels and parameters for comparison. Experiment results indicated that the solution proposed in this paper was able to get a good accurate rate which was higher than the solution using Haar-like features and AdaBoost algorithm. © 2013 Springer-Verlag.

关键词： Principal component analysis

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Training pool selection for semi-supervised learning

Training pool selection for semi-supervised learning

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9th International Symposium on Neural networks, ISNN 2012

作者： Ge, Jian Ma, Tinghuai Yan, Qiaoqiao Yan, Yonggang Tian, Wei Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology 210044 Nanjing China College of Computer and Software Nanjing University of Information Science and Technology 210044 Nanjing China

ISBN: (纸本)9783642313455

Semi-supervised leaning deals with methods for automatically exploiting unlabeled samples in addition to labeled set. The data selection is an important topic in active learning. It addresses the selection the valuable unlabeled data to label, considering that labeling data is a costly job. In this paper, we want to discuss in detail three aspects of technology in data selection, which includes how to select the unlabeled sample, how many unlabeled samples should be selected and how to define the capacity of the training pool. Experiments which use self-training based on C4.5 show that while the L labeled ratio lager continuous, the initial error value becomes smaller. Also when L labeled ratio is less than 10%, the selection ratio value should be set in less than *** error value has no significant change while selection ratio value larger than 1.0. © 2012 Springer-Verlag.

关键词： Supervised learning

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High Efficiency Quantum Image Steganography Protocol Based on ZZW Framework 7th

High Efficiency Quantum Image Steganography Protocol Based o...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Sun, Hanrong Qu, Zhiguo School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783030786113

This paper proposes an efficient quantum image steganography protocol based on Zhang Weiming, Zhang Xinpeng and Wang Shuozhong’s steganography framework (ZZW). Based on the high embedding efficiency of the classical ZZW steganography framework, the new protocol combined with the quantum carrier, not only greatly improves the embedding efficiency of the existing quantum image steganography protocols, but also has high security. In order to reflect the practicability of the new protocol, we also designed dedicated quantum circuits to embed and extract secret information. In addition, the simulation results based on Matlab also show that the new protocol not only has good imperceptibility, but also has very high embedding efficiency. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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Hybrid multiplicative watermarking and its decoders

Hybrid multiplicative watermarking and its decoders

引用

3rd International Conference on Multimedia Information networking and Security, MINES 2011

作者： Wang, Jinwei Lian, Shiguo Yan, Leiming Wang, Yuxiang Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China Beijing Beijing 10080 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

ISBN: (纸本)9780769545592

With the rapid development and the richer content of the Internet, the copyright protection of the works should be further developed. The watermarking has become a possible solution. In this paper, firstly, a novel hybrid multiplicative rule is presented and utilized to embed the watermark into DWT coefficients controlled by the secret key. Secondly, the optimum and locally optimum hybrid multiplicative multi-watermarking decoders are proposed, respectively, which are based on the minimum Bayesian risk criterion. The DWT coefficients are modeled as the generalized Gaussian distribution. Nextly, the performance of the optimum hybrid watermarking decoder, i.e. the average bit error rate is theoretically analyzed. Then the security of the hybrid multiplicative watermarking scheme is compared with the existing schemes. Finally, experimental results prove the theoretical analysis valid. © 2011 IEEE.

关键词： Watermarking

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Impact Force Magnitude and Location Recognition of Composite Materials

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computers, Materials & Continua 2020年第9期64卷 1647-1656页

作者： Yajie Sun Yanqing Yuan Qi Wang Sai Ji Lihua Wang Shao’en Wu Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information and Control Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Science Ball State UniversityMuncieUSA

In order to identify the location and magnitude of the impact force accurately,determine the damage range of the structure and accelerate the health monitoring of key components of the composite,this paper studies the location and magnitude of the impact force of composite plates by an inverse ***,a PZT sensor mounted on the material plate is used to collect the response signal generated by the impact force,which is from several impact locations,and establish transfer functions between the impact location and the PZT ***,this paper applies several forces to any location on the material plate,and collects the corresponding response signals,and reconstructs the impact force of several locations in ***,according to the reconstruction result of each location,the correct impact location is ***,an improved regularization method is used to optimize the reconstructed impact force and accurate the magnitude of the impact *** comparison experiments prove that the recognition error of this method is smaller.

关键词： Impact force identification transfer function regularization

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