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A Localized Inter-Actuator Network Topology Repair Scheme for Wireless Sensor and Actuator Networks 预览
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作者 Yong Feng Hai Liu +1 位作者 Jie Yang Xiaodong Fu 《中国通信:英文版》 SCIE CSCD 2019年第2期215-232,共18页
Node failure is one of the most severe problems that wireless sensor and actuator networks(WSANs)have to deal with.The failure of actuator nodes,in particular,may result in substantial consequences such as network par... Node failure is one of the most severe problems that wireless sensor and actuator networks(WSANs)have to deal with.The failure of actuator nodes,in particular,may result in substantial consequences such as network partitioning,incorrect and incomplete decision execution for WSANs.This paper proposes an efficient localized scheme,called LANTR,to repair the damaged topology of inter-actuator network while single actuator node paralyzes.For the failure of an ordinary actuator node,LANTR can rapidly repair the topology through relocating only one-hop neighbors of the failure node,meanwhile,keep the original topology structure as much as possible.Given the magnitude of cut vertex actuators playing on the connectivity,LANTR designs a novel method for each cut vertex to select out a specific guardian node with the minimum degree or minimum cumulative degree from its neighbors,which can reduce the repair influence on the original topology and effectively reduce the coverage loss rate.The performance of the proposed scheme is evaluated and compared with several existing representative topology repair schemes,and the results indicate that LANTR can more effectively and efficiently repair the topology of inter-actuator networks. 展开更多
关键词 wireless sensor and ACTUATOR NETWORKS inter-actuator NETWORKS topology REPAIR COVERAGE loss rate
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Automated brain tumor segmentation on multi-modal MR image using SegNet
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作者 Salma Alqazzaz Xianfang Sun +1 位作者 Xin Yang Len Nokes 《计算可视媒体(英文版)》 CSCD 2019年第2期209-219,共11页
The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation.Glioma, a type of brain tumor, can appear at different locations with... The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation.Glioma, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only timeconsuming but also prone to human error, and its performance depends on pathologists’ experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3 D data sets for four MRI modalities(Flair, T1, T1 ce, and T2)for automated segmentation of brain tumor and subtumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature representation.Taking the combined feature as input, a decision tree(DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017(BraTS 2017)challenge, we achieved F-measure scores of 0.85, 0.81,and 0.79 for whole tumor, tumor core, and enhancing tumor, respectively.Experimental results demonstrate that using SegNet models with 3 D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation. 展开更多
关键词 brain tumor SEGMENTATION MULTI-MODAL MRI convolutional neural NETWORKS fully convolutional NETWORKS DECISION tree
A comparative study of name resolution and routing mechanisms in 网 information-centric networks 预览
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作者 Hang Liu Koorosh Azhandeh +1 位作者 Xavier de Foy Robert Gazda 《数字通信与网络:英文版》 2019年第2期69-75,共7页
Information-Centric Networking (ICN) is an innovative paradigm for the future internet architecture, which addresses IP network limitations in supporting content distribution and information access by decoupling conte... Information-Centric Networking (ICN) is an innovative paradigm for the future internet architecture, which addresses IP network limitations in supporting content distribution and information access by decoupling content from hosts and providing the ability to retrieve a content object by its name (identifier), rather than its storage location (IP address). Name resolution and routing is critical for content retrieval in ICN networks. In this research, we perform a comparative study of two widely used classes of ICN name resolution and routing schemes, namely flooding and Distributed Hash Table (DHT). We consider the flooding-based routing in Content-Centric Networks due to its wide acceptance. For the DHT scheme, we design a multi-level DHT that takes into account the underlying network topology and uses name aggregation to further reduce control overhead and improve network efficiency. Then, we compare the characteristics and performance of these two classes of name resolution and routing through extensive simulations. The evaluation results show that the performances of these two approaches are reliant on several factors, including network size, content location dynamics, and content popularity. Our study reveals insights into the design tradeoffs and offers guidelines for design strategies. 展开更多
关键词 Information-centric NETWORKS Content-centric NETWORKS NAME RESOLUTION Name-based ROUTING
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Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Net works
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作者 Brian D. O. Anderson Mengbin Ye 《国际自动化与计算杂志:英文版》 EI CSCD 2019年第2期129-149,共21页
Abs tract: A fundamental aspect of society is the exchange and discussion of opinions bet ween individuals, occurring in situations as varied as company boardrooms, eleme nt ary school classrooms and online social med... Abs tract: A fundamental aspect of society is the exchange and discussion of opinions bet ween individuals, occurring in situations as varied as company boardrooms, eleme nt ary school classrooms and online social media. After a very brief introduction to the established results of the most fundamental opinion dynamics models, which seek to mathematically capture observed social phenomena, a brief discussion follows on several recent themes pursued by the authors building on the fundamental ideas. In the first theme, we study the way an individuaFs self-confidence can develop through contributing to discussions on a sequence of topics, reaching a consensus in each case, where the consensus value to some degree reflects the contribution of that individual to the conclusion. During this process, the individuals in the network and the way they interact can change. The second theme introduces a novel discrete-time model of opinion dynamics to study how discrepancies between an individual's expressed and private opinions can arise due to stubbornness and a pressure to conform to a social norm. It is also shown that a few extremists can create "pluralistic ignorance^^, where people believe there is majority support for a position but in fact the position is privately rejected by the majority. Last, we consider a group of individuals discussing a collection of logically related topics. In particular, we identify that for topics whose logical interdependencies take on a cascade structure, disagreement in opinions can occur if individuals have competing and/or heterogeneous views on how the topics are related, i.e., the logical interdependence structure varies between individuals. 展开更多
关键词 OPINION dynamics social NETWORKS influence NETWORKS AGENT-BASED models multi-agent SYSTEMS NETWORKED SYSTEMS
Identifying topologies and system parameters of uncertaintime-varying delayed complex networks
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作者 WANG Xiong GU HaiBo +1 位作者 WANG QianYao LU JinHu 《中国科学:技术科学英文版》 SCIE EI CAS CSCD 2019年第1期94-105,共12页
Node dynamics and network topologies play vital roles in determining the network features and network dynamical behaviors.Thus it is of great theoretical significance and practical value to recover the topology struct... Node dynamics and network topologies play vital roles in determining the network features and network dynamical behaviors.Thus it is of great theoretical significance and practical value to recover the topology structures and system parameters of uncertain complex networks with available information. This paper presents an adaptive anticipatory synchronization-based approach to identify the unknown system parameters and network topological structures of uncertain time-varying delayed complex networks in the presence of noise. Moreover, during the identification process, our proposed scheme guarantees anticipatory synchronization between the uncertain drive and constructed auxiliary response network simultaneously. Particularly, our method can be extended to several special cases. Furthermore, numerical simulations are provided to verify the effectiveness and applicability of our method for reconstructing network topologies and node parameters. We hope our method can provide basic insight into future research on addressing reconstruction issues of uncertain realistic and large-scale complex networks. 展开更多
关键词 SYSTEM parameters and network TOPOLOGIES identification anticipatory synchronization UNCERTAIN time-varying delayed COMPLEX NETWORKS noise-perturbed COMPLEX NETWORKS
An Energy-Efficient Data Collection Scheme Using Denoising Autoencoder in Wireless Sensor Networks
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作者 Guorui li Sancheng Peng +2 位作者 Cong Wang Jianwei Niu Ying Yuan 《清华大学学报自然科学版(英文版)》 EI CAS CSCD 2019年第1期86-96,共11页
As one of the key operations in Wireless Sensor Networks(WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data i... As one of the key operations in Wireless Sensor Networks(WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data is usually chosen empirically, and the transformed results are not always the sparsest. In this paper, we propose a Data Collection scheme based on Denoising Autoencoder(DCDA) to solve the above problem. In the data training phase, a Denoising AutoEncoder(DAE) is trained to compute the data measurement matrix and the data reconstruction matrix using the historical sensed data. Then, in the data collection phase, the sensed data of whole network are collected along a data collection tree. The data measurement matrix is utilized to compress the sensed data in each sensor node, and the data reconstruction matrix is utilized to reconstruct the original data in the sink.Finally, the data communication performance and data reconstruction performance of the proposed scheme are evaluated and compared with those of existing schemes using real-world sensed data. The experimental results show that compared to its counterparts, the proposed scheme results in a higher data compression rate, lower energy consumption, more accurate data reconstruction, and faster data reconstruction speed. 展开更多
关键词 wireless sensor NETWORKS DATA COLLECTION NEURAL NETWORKS autoencoder DATA reconstruction
Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders 预览
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作者 Emanuele Principi Damiano Rossetti +1 位作者 Stefano Squartini Francesco Piazza 《自动化学报:英文版》 CSCD 2019年第2期441-451,共11页
Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literatu... Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors’ knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract LogMel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multilayer perceptron(MLP) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory(LSTM) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine(OC-SVM) algorithm. The performance has been evaluated in terms area under curve(AUC) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OCSVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11 %. 展开更多
关键词 Autoencoder convolutional NEURAL NETWORKS electric motor fault DETECTION long short-term memory NEURAL NETWORKS NOVELTY DETECTION
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Learning Hand Latent Features for Unsupervised 3D Hand Pose Estimation 预览
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作者 Jamal Banzi Isack Bulugu Zhongfu Ye 《自主智能(英文)》 2019年第1期1-10,共10页
Recent hand pose estimation methods require large numbers of annotated training data to extract the dynamic information from a hand representation.Nevertheless,precise and dense annotation on the real data is difficul... Recent hand pose estimation methods require large numbers of annotated training data to extract the dynamic information from a hand representation.Nevertheless,precise and dense annotation on the real data is difficult to come by and the amount of information passed to the training algorithm is significantly higher.This paper presents an approach to developing a hand pose estimation system which can accurately regress a 3D pose in an unsupervised manner.The whole process is performed in three stages.Firstly,the hand is modelled by a novel latent tree dependency model (LTDM) which transforms internal joints location to an explicit representation.Secondly,we perform predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision.A mapping is then performed between an image depth and a generated representation.Thirdly,the hand joints are regressed using convolutional neural networks to finally estimate the latent pose given some depth map.Finally,an unsupervised error term which is a part of the recurrent architecture ensures smooth estimation of the final pose.To demonstrate the performance of the proposed system,a complete experiment was conducted on three challenging public datasets,ICVL,MSRA,and NYU.The empirical results show the significant performance of our method which is comparable or better than the state-of-the-art approaches. 展开更多
关键词 HAND Pose Estimation Convolutional NEURAL NETWORKS Recurrent NEURAL NETWORKS HUMAN-MACHINE Interaction Predictive Coding UNSUPERVISED LEARNING
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论恐怖主义网络
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作者 阿里·佩里格 吴乐(译) 《河南警察学院学报》 2019年第3期44-54,共11页
恐怖主义领域研究最新发展是:越来越多的人认识到侧重于群体内社会进程的分析框架,在提高我们对恐怖主义集团内部动态的理解方面非常有效。许多研究恐怖主义的学者已经开始研究恐怖主义团体出现和运作的方式以及成员角色和概况之间的关... 恐怖主义领域研究最新发展是:越来越多的人认识到侧重于群体内社会进程的分析框架,在提高我们对恐怖主义集团内部动态的理解方面非常有效。许多研究恐怖主义的学者已经开始研究恐怖主义团体出现和运作的方式以及成员角色和概况之间的关系。而对网络之于理解恐怖主义团体之间关系的潜在贡献以及影响政治对恐怖主义的反应的因素的关注较少。通过解释网络如何增进对恐怖主义集团竞争、合作、合并或分裂的理解以及恐怖主义应对工作中的困境来解决这些鸿沟,这些困境主要涉及各级政府和机构在国际和国家层面的协调与合作。 展开更多
关键词 网络 恐怖主义 恐怖主义网络
A Game Theoretic Approach for Hierarchical Caching Resource Sharing in 5G Networks with Virtualization 预览
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作者 Renchao Xie Jun Wu +1 位作者 Rui Wang Tao Huang 《中国通信:英文版》 SCIE CSCD 2019年第7期32-48,共17页
Caching and virtualization have been considered as the promising techniques in 5G Networks. In 5G networks with virtualization, the caching resources deployed by infrastructure providers (InPs) can be abstracted into ... Caching and virtualization have been considered as the promising techniques in 5G Networks. In 5G networks with virtualization, the caching resources deployed by infrastructure providers (InPs) can be abstracted into isolated slices and transparently shared by mobile virtual network operators (MVNOs). In this case, one of the most important issues is how the MVNOs to share the caching resource. To solve this issue, different from previous works, a hierarchical caching architecture that core network and radio access network (RAN) have the caching capability in 5G networks with virtualization is first considered in this paper. Then, we study the problem of hierarchical caching resource sharing for MVNOs, and a competitive game to maximize their expectation revenue based on the oligopoly market model is formulated. As it is a hard problem to find the optimal solution in the hierarchical caching resource sharing problem, we decompose the optimization problem into two independent caching resource sharing problems in RAN and core network, respectively. Then the local optimal solutions are solved and the global Nash equilibrium solution is achieved. Finally, simulation results are illustrated to demonstrate the performance of the proposed scheme. 展开更多
关键词 HIERARCHICAL CACHING resource sharing GAME theory OLIGOPOLY market model 5G NETWORKS
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Model Error Correction in Data Assimilation by Integrating Neural Networks
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作者 Jiangcheng Zhu Shuang Hu +3 位作者 Rossella Arcucci Chao Xu Jihong Zhu Yi-ke Guo 《大数据挖掘与分析(英文)》 2019年第2期83-91,共9页
In this paper, we suggest a new methodology which combines Neural Networks(NN) into Data Assimilation(DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical mode... In this paper, we suggest a new methodology which combines Neural Networks(NN) into Data Assimilation(DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. The NNs are iteratively trained as observational data is updated. The main DA models used here are the Kalman filter and the variational approaches. The effectiveness of the proposed algorithm is validated by examples and by a sensitivity study. 展开更多
关键词 data ASSIMILATION deep learning neural networks KALMAN filter VARIATIONAL approach
Spreading Social Influence with both Positive and Negative Opinions in Online Networks
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作者 Jing(Selena)He Meng Han +2 位作者 Shouling Ji Tianyu Du Zhao Li 《大数据挖掘与分析(英文)》 2019年第2期100-117,共18页
Social networks are important media for spreading information, ideas, and influence among individuals.Most existing research focuses on understanding the characteristics of social networks, investigating how informati... Social networks are important media for spreading information, ideas, and influence among individuals.Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the 'word-of-mouth' effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set(MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of ?. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks. 展开更多
关键词 influence SPREAD social networks POSITIVE influential NODE set GREEDY algorithm POSITIVE and NEGATIVE influences
Three Tier Fog Networks:Enabling IoT/5G for Latency Sensitive Applications 预览
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作者 Romana Shahzadi Ambreen Niaz +4 位作者 Mudassar Ali Muhammad Naeem Joel J.P.C.Rodrigues Farhan Qamar Syed Muhammad Anwar 《中国通信:英文版》 SCIE CSCD 2019年第3期1-11,共11页
Following the progression in Internet of Things(IoT)and 5G communication networks,the traditional cloud computing model have shifted to fog computing.Fog computing provides mobile computing,network control and storage... Following the progression in Internet of Things(IoT)and 5G communication networks,the traditional cloud computing model have shifted to fog computing.Fog computing provides mobile computing,network control and storage to the network edges to assist latency critical and computation-intensive applications.Moreover,security features are improved in fog paradigm by processing critical data on edge devices instead of data centres outside the control plane of users.However,fog network deployment imposes many challenges including resource allocation,privacy of users,non-availability of programming model and testing software and support for the heterogenous networks.This article highlights these challenges and their potential solutions in detail.This article also discusses threetier fog network architecture,its standardization and benefits in detail.The proposed resource allocation mechanism for three tier fog networks based on swap matching is described.Results show that by practicing the proposed resource allocation mechanism,maximum throughput with reduced latency is achieved. 展开更多
关键词 cloud COMPUTING FOG NETWORKS MATCHING GAMES internet of THINGS
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A Link-Based Similarity for Improving Community Det ection Based on Label Propaga tion Algorithm
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作者 BERAHMAND Kamal BOUYER Asgarali 《系统科学与复杂性学报:英文版》 SCIE EI CSCD 2019年第3期737-758,共22页
Community structure is one of the most best-known proper ties of complex networks. Finding communities help us analyze networks from a mesoscopic viewpoints instead of microscopic or macroscopic one. It helps to under... Community structure is one of the most best-known proper ties of complex networks. Finding communities help us analyze networks from a mesoscopic viewpoints instead of microscopic or macroscopic one. It helps to understand behavior grouping. Various community detection algorithms have been proposed with some shortcomings in time and space complexity, accuracy, or stability. Label Propagation Algorithm (LPA) is a popular method used for finding communities in an almost-linear time-consuming process. However, its performance is not satisfactory in some metrics such as accuracy and stability. In this paper, a new modified version of LPA is proposed to improve the stability and accuracy of the LPA by defining two concepts -nodes and link strength based on semi-local similarity-, while preserving its simplicity. In the proposed method a new initial node selection strategy, namely the tiebreak strategy, updating order and rule update are presented to solve the random behavior problem of original LPA. The proposed algorithm is evaluated on artificial and real networks. The experiments show that the proposed algorithm is close to linear time complexity with better accuracy than the original LPA and other compared met hods. Furthermore, the proposed algorithm has the robustness and stability advantages while the original LPA does not have these features. 展开更多
关键词 COMMUNITY detection complex networks LPA SIMILARITY MEASURE
Periodicity of non-autonomous inertial neural networks involving proportional delays and non-reduced order method
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作者 Chuangxia Huang Hua Zhang 《生物数学学报:英文版》 2019年第2期101-113,共13页
This paper,mainly explores a class of non-autonomous inertial neural networks with proportional delays and time-varying coefficients.By combining Lyapunov function method with differential inequality approach,non-redu... This paper,mainly explores a class of non-autonomous inertial neural networks with proportional delays and time-varying coefficients.By combining Lyapunov function method with differential inequality approach,non-reduced order method is used to establish some novel assertions on the existence and generalized exponential stability of periodic solutions for the addressed model.In addition,an example and its numerical simulations are given to support the proposed approach. 展开更多
关键词 INERTIAL neural networks periodic solution generalized exponential stability proportional delay non-reduced order METHOD
QUANTIZATION AND TRAINING OF LOW BIT-WIDTH CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION
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作者 Penghang Yin Shuai Zhang +1 位作者 Yingyong Qi Jack Xin 《计算数学:英文版》 SCIE CSCD 2019年第3期349-359,共11页
We presen t LBW-Net,an efficient optimization based method for qua nt ization and training of the low bit-width convolutional neural networks(CNNs).Specifically,we quantize the weights to zero or powers of 2 by minimi... We presen t LBW-Net,an efficient optimization based method for qua nt ization and training of the low bit-width convolutional neural networks(CNNs).Specifically,we quantize the weights to zero or powers of 2 by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation(weight learning).We characterize the combinatorial nature of the low bit-width quantization problem.For 2-bit(ternary)CNNs,the quantization of N weights can be done by an exact formula in O(N log N)complexity.When the bit-width is 3 and above,we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally inexpensive.The free parameter is further determined by network retraining and object detection tests.The LBW-Net has several desirable advantages over full-precision CNNs,including considerable memory savings,energy efficiency,and faster deployment.Our experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart,the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks,and can even do better in real world visual scenes,while empirically enjoying more than 4× faster deployment. 展开更多
关键词 QUANTIZATION LOW BIT WIDTH deep neural networks Exact and approximate analytical FORMULAS Network training Object detection
A survey of web resources and tools for the study of TCM network pharmacology
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作者 Jing Zhao Jian Yang +1 位作者 Saisai Tian Weidong Zhang 《中国电气与电子工程前沿:英文版》 CSCD 2019年第1期17-29,共13页
Background: Traditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi-component, multi-target agents at the molecular level. Thus there are many parallels between the key id... Background: Traditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi-component, multi-target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM? Results: In this paper we introduce state-of-the-art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis. Conclusions: This review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM. 展开更多
关键词 TCM network PHARMACOLOGY molecular networks SIGNALING PATHWAYS DATABASES web servers
Impaired brain white matter and functional networks in healthy individuals with auditory verbal hallucinations
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作者 Xiao-Dong Lin De-Guo Jiang +3 位作者 Lang-Lang Cheng Ce Chen Chong-Guang Lin Chuan-Jun Zhuo 《中华医学杂志:英文版》 SCIE CAS CSCD 2019年第5期606-608,共3页
To the Editor:Auditory verbal hallucinations (AVHs) are experienced concomitantly with various neuropsychiatric diagnoses including schizophrenia,bipolar disorder,major depression disorder,post-traumatic stress disord... To the Editor:Auditory verbal hallucinations (AVHs) are experienced concomitantly with various neuropsychiatric diagnoses including schizophrenia,bipolar disorder,major depression disorder,post-traumatic stress disorder,and borderline personality disorder. 展开更多
关键词 BRAIN white MATTER functional networks AUDITORY VERBAL HALLUCINATIONS
Finite-Time Synchronization for a Class of Dynamical Complex Net works with Nonidentical Nodes and Uncertain Disturbance
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作者 LI Qingbo GUO Jin +2 位作者 SUN Changyin WU Yuanyuan DING Zhengtao 《系统科学与复杂性学报:英文版》 SCIE EI CSCD 2019年第3期818-834,共17页
This paper investigates the finite-time synchronization for a class of linearly coupled dynamical complex networks with both nonidentical nodes and uncertain disturbance. A set of controllers are designed such that th... This paper investigates the finite-time synchronization for a class of linearly coupled dynamical complex networks with both nonidentical nodes and uncertain disturbance. A set of controllers are designed such that the considered system can be finite-timely synchronized onto the target node. Based on the stability of the error equation, the Lyapunov function method and the linear matrix inequality technique, several sufficient conditions are derived to ensure the finite-time synchronization, and applied to the case of identical nodes and tlie one without uncertain disturbance. Also the adaptive finite-time synchronization is discussed. A numerical example is given to show the effectiveness of the main results obtained. 展开更多
关键词 DISTURBANCE DYNAMICAL COMPLEX networks FINITE-TIME synchronization nonidentical NODES
Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment
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作者 Da-qiZHU YunQU Simon X. YANG 《信息与电子工程前沿:英文版》 SCIE EI CSCD 2019年第3期330-341,共12页
There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles(AUVs) is proposed for a three-dimensi... There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles(AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given. 展开更多
关键词 AUTONOMOUS UNDERWATER vehicles SELF-ORGANIZING neural networks Azimuths Ocean current
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