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检索条件"任意字段=Conference on Neural Network and Distributed Processing"

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distributed image processing for automatic target recognition

Distributed image processing for automatic target recognitio...

引用

conference on Machine Vision and 3-Dimensional Imaging Systems for Inspection and Metrology

作者： Cozien, RF Ecoles Mil St Cyr Coetquidan French Dept Def Ctr Rech Equipe Informat & Simulat F-56281 Guer France

ISBN: (纸本)0819438545

Our purpose is, in medium term, to detect in air images, characteristic shapes and objects such as airports, industrial plants, planes, tanks, trucks, ... with great accuracy and low rate of mistakes. However, we also want to value whether the link between neural networks and multi-agents systems is relevant and effective. If it appears to be really effective, we hope to use this kind of technology in other fields. That would be an easy and convenient way to depict and to use the agents' knowledge which is distributed and fragmented. After a first phase of preliminary tests to know if agents are able to give relevant information to a neural network, we verify that only a few agents running on an image are enough to inform the network and let it generalize the agents' distributed and fragmented knowledge. In a second phase, we developed a distributed architecture allowing several, multi-agents systems running at the same time on different computers with different images. All those agents send information to a "multi neural networks system" whose job is to identify the shapes detected by the agents. The name we gave to our project is Jarod.

关键词： multi-agents neural network TCP/IP communications image processing

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Learning Task Specific distributed Paragraph Representations Using a 2-Tier Convolutional neural network 22nd

Learning Task Specific Distributed Paragraph Representations...

引用

22nd International conference on neural Information processing (ICONIP)

作者： Chen, Tao Xu, Ruifeng He, Yulan Wang, Xuan Harbin Inst Technol Shenzhen Grad Sch Shenzhen Engn Lab Performance Robots Digital Stag Shenzhen Peoples R China Aston Univ Sch Engn & Appl Sci Birmingham B4 7ET W Midlands England

ISBN: (纸本)9783319265322;9783319265315

We introduce a type of 2-tier convolutional neural network model for learning distributed paragraph representations for a special task (e.g. paragraph or short document level sentiment analysis and text topic categorization). We decompose the paragraph semantics into 3 cascaded constitutes: word representation, sentence composition and document composition. Specifically, we learn distributed word representations by a continuous bag-of-words model from a large unstructured text corpus. Then, using these word representations as pre-trained vectors, distributed task specific sentence representations are learned from a sentence level corpus with task-specific labels by the first tier of our model. Using these sentence representations as distributed paragraph representation vectors, distributed paragraph representations are learned from a paragraph-level corpus by the second tier of our model. It is evaluated on DBpedia ontology classification dataset and Amazon review dataset. Empirical results show the effectiveness of our proposed learning model for generating distributed paragraph representations.

关键词： Natural language processing distributed representation Convolutional neural network

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Online distributed Nonlinear Regression via neural networks 25

Online Distributed Nonlinear Regression via Neural Networks

引用

25th Signal processing and Communications Applications conference (SIU)

作者： Ergen, Tolga Kozat, Suleyman S. Ihsan Dogramaci Bilkent Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781509064946

In this paper, we study the nonlinear regression problem in a network of nodes and introduce long short term memory (LSTM) based algorithms. In order to learn the parameters of the LSTM architecture in an online manner, we put. the LSTM equations into a nonlinear state space form and then introduce our distributed particle filtering (DPF) based training algorithm. Our training algorithm asymptotically achieves the optimal training performance. In our simulations, we illustrate the performance improvement achieved by the introduced algorithm with respect to the conventional methods.

关键词： nonlinear regression online learning distributed particle filtering long short term memory network

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Extending Gossip Algorithms to distributed Estimation of U-Statistics 29

Extending Gossip Algorithms to Distributed Estimation of <i>...

引用

29th Annual conference on neural Information processing Systems (NIPS)

作者： Colin, Igor Salmon, Joseph Clemencon, Stephan Bellet, Aurelien Univ Paris Saclay Telecom ParisTech CNRS LTCI F-75013 Paris France INRIA Lille Nord Europe Magnet Team F-59650 Villeneuve Dascq France

Efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems. Whereas distributed estimation of sample mean statistics has been the subject of a good deal of attention, computation of U-statistics, relying on more expensive averaging over pairs of observations, is a less investigated area. Yet, such data functionals are essential to describe global properties of a statistical population, with important examples including Area Under the Curve, empirical variance, Gini mean difference and within-cluster point scatter. This paper proposes new synchronous and asynchronous randomized gossip algorithms which simultaneously propagate data across the network and maintain local estimates of the U-statistic of interest. We establish convergence rate bounds of O(1/t) and O(log t/t) for the synchronous and asynchronous cases respectively, where t is the number of iterations, with explicit data and network dependent terms. Beyond favorable comparisons in terms of rate analysis, numerical experiments provide empirical evidence the proposed algorithms surpasses the previously introduced approach.

关键词： Population statistics

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Obstacle detection for mobile vehicle using neural network and fuzzy logic

Obstacle detection for mobile vehicle using neural network a...

引用

conference on neural network and distributed processing

作者： Sun, HJ Yang, JY Nanjing Univ Sci & Technol Dept Comp Nanjing 210094 Peoples R China

ISBN: (纸本)0819442836

In our mobile vehicle project, sensors for environment modeling are a CCD color camera and two line-scan laser range finders. The CCD color camera is used to detect road edges. The two line-scan laser range finders are used to detect obstacles. Only two line-scan laser range finders increase processing speed, but there are blind zones for low obstacles, especially near the vehicle. In this paper, neural network and fuzzy logic are used to cluster and fuse obstacle points provided by two line-scan laser range finders. There is an assumption that obstacles missed by laser radar in some instant must be detected previously. A circle Adaptive Resonance neural network algorithm is used to incrementally cluster obstacle points provided by laser range finders into candidate obstacles. Every candidate obstacle is expressed by a circle, and is assigned a belief by a fuzzy logic system. Inputs of the fuzzy logic system are radius and number of points. Fuzzy rules are provided by human and can be fine-tuned with training data. The final true obstacle is the nearest one chosen from candidate obstacles whose beliefs exceed a threshold. Experiment results indicate that our mobile vehicle can safely follow road and avoid obstacles.

关键词： obstacle detection mobile vehicle neural network fuzzy logic

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Statistical Guarantees of distributed Nearest Neighbor Classification 34

Statistical Guarantees of Distributed Nearest Neighbor Class...

引用

34th conference on neural Information processing Systems (NeurIPS)

作者： Duan, Jiexin Qiao, Xingye Cheng, Guang Purdue Univ Dept Stat W Lafayette IN 47907 USA Binghamton Univ Dept Math Sci Binghamton NY USA

ISBN: (纸本)9781713829546

Nearest neighbor is a popular nonparametric method for classification and regression with many appealing properties. In the big data era, the sheer volume and spatial/temporal disparity of big data may prohibit centrally processing and storing the data. This has imposed considerable hurdle for nearest neighbor predictions since the entire training data must be memorized. One effective way to overcome this issue is the distributed learning framework. Through majority voting, the distributed nearest neighbor classifier achieves the same rate of convergence as its oracle version in terms of the regret, up to a multiplicative constant that depends solely on the data dimension. The multiplicative difference can be eliminated by replacing majority voting with the weighted voting scheme. In addition, we provide sharp theoretical upper bounds of the number of subsamples in order for the distributed nearest neighbor classifier to reach the optimal convergence rate. It is interesting to note that the weighted voting scheme allows a larger number of subsamples than the majority voting one. Our findings are supported by numerical studies.

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Field Independent Target Classification Analysis in distributed Acoustic Sensing Systems 27

Field Independent Target Classification Analysis in Distribu...

引用

27th Signal processing and Communications Applications conference (SIU)

作者： Maral, Hakan Aktas, Metin ASELSAN Ulasim Guvenlik Enerji & Otomasyon Sistemleri UGES Sekt Baskanligi Ankara Turkey

ISBN: (纸本)9781728119045

In this paper, we are able to focus on the fiber-optic distributed acoustic systems required to isolate the oil and gas pipeline, remote areas, facilities and lines in the area. Threatening performances of network structures of different complexity and depth. All measurements, the criteria for working independently from the field, the results obtained and the results obtained and on-site troubleshooting methods in different geographical areas. It analyzes the performance results and shows a high performance network in the field in real time.

关键词： distributed acoustic sensing phase-OTDR deep learning convolutional neural networks CNN threat detection threat classification

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The Application of Photovoltaic Power Prediction Technology

The Application of Photovoltaic Power Prediction Technology

引用

IEEE International conference on Electronics, Communications and Control (ICECC)

作者： Wang, Zengxin Su, Shi Zhang, Shaoquan North China Elect Power Univ Kunming Peoples R China Yunnan Power Grid Co Postgrad Workstat Kunming Peoples R China Yunnan Elect Power Test Res Inst Grp Ltd Elect Power Res Inst Kunming Peoples R China

ISBN: (纸本)9781457703218

Power load forecasting is an important part of the power system planning, and accurate load forecasting can provide necessary basis data for the dispatcher, which is extremely important in the planning and operation of power system. neural network can approximate any nonlinear mapping with arbitrary precision, and its distributed information storage and processing structure have a certain fault tolerant. So neural network is suitable for complex system modeling, and it can be used as the main method to predict photovoltaic power operation state variables. This paper uses the neural network algorithm to establish photovoltaic power system's load forecast model, and except the generate historical data, meteorological forecast information is added to the algorithm, then the model is trained and tested, the high-precision prediction results can show the effectiveness of the algorithm.

关键词： distributed information load forecasting neural network

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distributed inference in dynamical systems 19

引用

20th Annual conference on neural Information processing Systems, NIPS 2006

作者： Funiak, Stanislav Guestrin, Carlos Paskin, Mark Sukthankar, Rahul Carnegie Mellon University United States Google United States Intel Research United States

ISBN: (纸本)9780262195683

We present a robust distributed algorithm for approximate probabilistic inference in dynamical systems, such as sensor networks and teams of mobile robots. Using assumed density filtering, the network nodes maintain a tractable representation of the belief state in a distributed fashion. At each time step, the nodes coordinate to condition this distribution on the observations made throughout the network, and to advance this estimate to the next time step. In addition, we identify a significant challenge for probabilistic inference in dynamical systems: message losses or network partitions can cause nodes to have inconsistent beliefs about the current state of the system. We address this problem by developing distributed algorithms that guarantee that nodes will reach an informative consistent distribution when communication is re-established. We present a suite of experimental results on real-world sensor data for two real sensor network deployments: one with 25 cameras and another with 54 temperature sensors.

关键词： Dynamical systems

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MapReduce-based Capsule networks 6

MapReduce-based Capsule Networks

引用

6th International conference on Social networks Analysis, Management and Security (SNAMS)

作者： Park, Sun Jin Park, Ho-Hyun LG CNS Seoul South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9781728129464

Currently, artificial intelligence technology is attracting much attention, and image processing field is also making remarkable progress in recognition rate through CNN models. Furthermore, Capsule network which is flexible in changing pose of image is being studied in various fields by improving disadvantage of Pooling Layer of CNN model. We propose a method to accelerate the learning of CapsNet model, which is much slower than the existing neural network model. TensorFlow, Google's deep-running library, and the Apache Foundation's Hadoop framework are run through Python. We can confirm that the learning time is decreased in inverse proportion to the increase of the number of nodes in learning CapsNet according to the proposed method of distributed processing.

关键词： distributed processing Training GPU Acceleration neural network Capsule network

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