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Sound Source Localization Based on SRP-PHAT Spatial Spectrum and deep neural network

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Computers, Materials & Continua 2020年第7期64卷 253-271页

作者： Xiaoyan Zhao Shuwen Chen Lin Zhou Ying Chen School of Information and Communication Engineering Nanjing Institute of TechnologyNanjing211167China School of Mathematics and Information Technology Jiangsu Second Normal UniversityNanjing210013China School of Information Science and Engineering Southeast UniversityNanjing210096China Department of Psychiatry Columbia University and NYSPINew York10032USA

Microphone array-based sound source localization(SSL)is a challenging task in adverse acoustic *** address this,a novel SSL algorithm based on deep neural network(DNN)using steered response power-phase transform(SRP-PHAT)spatial spectrum as input feature is presented in this *** the SRP-PHAT spatial power spectrum contains spatial location information,it is adopted as the input feature for sound source *** is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level ***-PHAT at each steering position within a frame is arranged into a vector,which is treated as DNN input.A DNN model which can map the SRP-PHAT spatial spectrum to the azimuth of sound source is learned from the training *** azimuth of sound source is estimated through trained DNN model from the testing *** results demonstrate that the proposed algorithm significantly improves localization performance whether the training and testing condition setup are the same or not,and is more robust to noise and reverberation.

关键词： Sound source localization microphone array steered response power-phase transform(SRP-PHAT)spatial spectrum deep neural network

Image Information Hiding Method Based on Image Compression and deep neural network

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Computer Modeling in Engineering & Sciences 2020年第8期124卷 721-745页

作者： Xintao Duan Daidou Guo Chuan Qin College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China

Image steganography is a technique that hides secret information into the cover image to protect information *** current image steganography is mainly to embed a smaller secret image in an area such as a texture of a larger-sized cover image,which will cause the size of the secret image to be much smaller than the cover ***,the problem of small steganographic capacity needs to be solved *** paper proposes a steganography framework that combines image *** this framework,the Vector Quantized Variational AutoEncoder(VQ-VAE)is used to achieve the compression of the secret *** compressed and reconstructed image is visually indistinguishable from the original image and facilitates more embedded data information ***,the compressed image is transmitted to a SegNet deep neural network that contains a set of encoders and decoders to achieve image hiding and *** results show that the steganographic framework guarantees the quality of steganography while its relative steganographic capacity reaches ***,Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM)values can reach 42 dB and 0.94,respectively.

关键词： Image steganography deep neural network VQ-VAE SegNet

A Stacking-Based deep neural network Approach for Effective network Anomaly Detection

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Computers, Materials & Continua 2021年第2期66卷 2217-2227页

作者： Lewis Nkenyereye Bayu Adhi Tama Sunghoon Lim Department of Computer and Information Security Sejong UniversitySeoul05006Korea Data Science Group Institute for Basic Science(IBS)Daejeon34126Korea Department of Industrial Engineering Ulsan National Institute of Science and TechnologyUlsan44919Korea

An anomaly-based intrusion detection system(A-IDS)provides a critical aspect in a modern computing infrastructure since new types of attacks can be *** prevalently utilizes several machine learning algorithms(ML)for detecting and classifying network *** date,lots of algorithms have been proposed to improve the detection performance of A-IDS,either using individual or ensemble *** particular,ensemble learners have shown remarkable performance over individual learners in many applications,including in cybersecurity ***,most existing works still suffer from unsatisfactory results due to improper ensemble *** aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS,where deep learning(e.g.,deep neural network[DNN])is used as base learner *** effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics,i.e.,Matthew’s correlation coefficient,accuracy,and false alarm *** results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature.

关键词： Anomaly detection deep neural network intrusion detection system stacking ensemble

A deep neural network Coordination Model for Electric Heating and Cooling Loads Based on IoT Data

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CSEE Journal of Power and Energy Systems 2020年第1期6卷 22-30页

作者： Hongyang Jin Yun Teng Tieyan Zhang Zedi Wang Zhe Chen Shenyang University of Technology Shenyang 110870China Aalborg University Aalborg 9220Denmark

As the ubiquitous electric power internet of things(UEPIoT)evolves and IoT data increases,traditional scheduling modes for load dispatch centers have yielded a variety of chal-lenges such as calculation of real-time optimization,extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling *** this paper,a deep neural network coor-dination model for electric heating and cooling loads based on the situation awareness(SA)of thermostatically controlled loads(TCLs)is ***,a sliding window is used to adaptively preprocess the IoT node data with *** to personal thermal comfort(PTC)and peak shaving contribution(PSC),a dynamic model for loads is proposed;meanwhile,personalized behavior and consumer psychology are integrated into a flexible regulation model of ***,a deep Q-network(DQN)-based approach,using the thermal comfort and electricity cost as the comprehensive reward function,is proposed to solve the sequential decision ***,the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads,by using UEPIoT intelligent dispatching system *** case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.

关键词： deep neural network electric heating and cooling load IoT data reinforcement learning

Leveraging Pima Dataset to Diabetes Prediction: Case Study of deep neural network

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Journal of Computer and Communications 2022年第11期10卷 15-28页

作者： Pélagie Hounguè Annie Ghylaine Bigirimana Laboratory of Data Science and Digital Inclusion Institut de Mathématiques et de Sciences Physiques Université d’Abomey-Calavi Abomey-Calavi Bénin

Diabetes is a chronic disease. In 2019, it was the ninth leading cause of death with an estimated 1.5 million deaths. Poorly controlled, diabetes can lead to serious health problems. That explains why early diagnosis of diabetes is very important. Several approaches that use Artificial Intelligence, specifically deep Learning, have been widely used with promising results. The contribution of this paper is in two-folds: 1) deep neural network (DNN) approach is used on Pima Indian dataset to predict diabetes using 10 k-fold cross validation and 89% accuracy is obtained;2) comparative analysis of previous work is provided on diabetes prediction using DNN with the tested model. The results showed that 10 k-fold cross-validation could decrease the efficiency of diabetes prediction models using DNN.

关键词： deep Learning Artificial Intelligence deep neural network k-Fold Cross-Validation Diabete Mellitus

Machine learning and deep neural network-based learning in osteoarthritis knee

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World Journal of Methodology 2023年第5期13卷 419-425页

作者： Harish V K Ratna Madhan Jeyaraman Naveen Jeyaraman Arulkumar Nallakumarasamy Shilpa Sharma Manish Khanna Ashim Gupta Department of Orthopaedics Rathimed Speciality HospitalChennai 600040Tamil NaduIndia Department of Orthopaedics ACS Medical College and HospitalDr MGR Educational and Research InstituteChennai 600077Tamil NaduIndia Department of Orthopaedics South Texas Orthopaedic Research InstituteLaredoTX 78045United States Department of Paediatric Surgery All India Institute of Medical SciencesNew Delhi 110029India Department of Orthopaedics Autonomous State Medical CollegeAyodhya 224133Uttar PradeshIndia Department of Regenerative Medicine Regenerative OrthopaedicsNoida 201301Uttar PradeshIndia Department of Regenerative Medicine Future BiologicsLawrencevilleGA 30043United States Department of Regenerative Medicine BioIntegarteLawrencevilleGA 30043United States

Osteoarthritis(OA)of the knee joint is considered the commonest musculoskeletal condition leading to marked disability for patients residing in various regions around the *** of machine learning(ML)in doing research regarding OA has brought about various clinical advances viz,OA being diagnosed at preliminary stages,prediction of chances of development of OA among the population,discovering various phenotypes of OA,calculating the severity in OA structure and also discovering people with slow and fast progression of disease pathology,*** publications are available regarding machine learning methods for the early detection of *** key features are detected by morphology,molecular architecture,and electrical and mechanical *** addition,this particular technique was utilized to assess non-interfering,non-ionizing,and in-vivo techniques using magnetic resonance *** is being utilized in OA,chiefly with the formulation of large cohorts viz,the OA Initiative,a cohort observational study,the Multicentre Osteoarthritis Study,an observational,prospective longitudinal study and the Cohort Hip&Cohort Knee,an observational cohort prospective study of both hip and knee *** ML has various contributions and enhancing applications,it remains an imminent field with high potential,also with its *** more studies are to be carried out to find more about the link between machine learning and knee osteoarthritis,which would help in the improvement of making decisions clinically,and expedite the necessary interventions.

关键词： Osteoarthritis Knee Artificial intelligence Machine learning deep neural network

Transfer learning for deep neural network-based partial differential equations solving

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Advances in Aerodynamics 2021年第1期3卷 635-648页

作者： Xinhai Chen Chunye Gong Qian Wan Liang Deng Yunbo Wan Yang Liu Bo Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangshaChina China Aerodynamics Research and Development Center MianyangChina.

deep neural networks(DNNs)have recently shown great potential in solving partial differential equations(PDEs).The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related ***,learning DNNs usually involves tedious training iterations to converge and requires a very large number of training data,which hinders the application of these models to complex physical *** address this problem,we propose to apply the transfer learning approach to DNN-based PDE solving *** our work,we create pairs of transfer experiments on Helmholtz and Navier-Stokes equations by constructing subtasks with different source terms and Reynolds *** also conduct a series of experiments to investigate the degree of generality of the features between different *** results demonstrate that despite differences in underlying PDE systems,the transfer methodology can lead to a significant improvement in the accuracy of the predicted solutions and achieve a maximum performance boost of 97.3%on widely used surrogate models.

关键词： deep neural network Partial differential equation Surrogate model Transfer learning

Detection of leaf folder and yellow stemborer moths in the paddy field using deep neural network with search and rescue optimization

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Information Processing in Agriculture 2021年第2期8卷 350-358页

作者： Chiranjeevi Muppala Velmathi Guruviah SENSE Department VIT UniversityChennaiIndia

In agriculture,insect pests must be identified at the initial stage of infestation to avoid their spread in the *** folders(cnaphalocrocis medinalis)and yellow stemborers(scirpophaga incertulas)are destructive pests of paddy crops,which are causing severe yield *** identification of insect pests in the crop is time-consuming,tedious,and *** paper focuses on a light trap based four-layer deep neural network with search and rescue optimization(DNN-SAR)method to identify leaf folders and yellow *** traps are designed to lure the insects in the paddy field and the images of trapped insects are analyzed using the proposed detection *** the DNN-SAR,images are contrastenhanced using deer hunting algorithm,impulse noise is removed with fast average group filter,and segmented using social ski-driver *** search and rescue optimization algorithm is used for the selection of optimal weights in the deep neural network,which has improved the convergence rate,lowered the complexity of learning,and improved the accuracy of *** proposed method outperformed the existing methods and achieved 98.29%pest detection accuracy.

关键词： Yellow stemborer Leaf folder Social ski-driver optimization Search and rescue optimization deep neural network

Towards efficient deep neural network training by FPGA-based batch-level parallelism

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Journal of Semiconductors 2020年第2期41卷 51-62页

作者： Cheng Luo Man-Kit Sit Hongxiang Fan Shuanglong Liu Wayne Luk Ce Guo State Key Laboratory of ASIC and System Fudan UniversityShanghai 200050China Department of Computing Imperial College LondonLondonUnited Kingdom

Training deep neural networks(DNNs)requires a significant amount of time and resources to obtain acceptable results,which severely limits its deployment in resource-limited *** paper proposes DarkFPGA,a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA ***,we explore batch-level parallelism to enable efficient FPGA-based DNN ***,we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit ***,an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource *** results show that the accelerator is able to perform about 10 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.

关键词： deep neural network training FPGA batch-level parallelism