DRANET is Ad hoc Drone Network, where a Drone system is connected to a Drone peer, the system communicates a consistent manner in collaborative work. The DRANET systems are Drone systems work in equilibrium and with c...
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ISBN:
(纸本)9781728123851
DRANET is Ad hoc Drone Network, where a Drone system is connected to a Drone peer, the system communicates a consistent manner in collaborative work. The DRANET systems are Drone systems work in equilibrium and with communication sharing. The DRANET system problem is the operational flight balance and service system to the user. The algorithm design of DRANET consists of twenty-two logical functions of the system. The paper is the results of the Drone logic flight investigations in the herd. algorithms provide rules about how DRANET in a system does not collide with each other in forming flight formations, flies equilibrium. DRANET systems are designed to serve communication when people gather for public meetings or support communication links in order to restore an emergency.
Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and h...
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ISBN:
(纸本)9789811033735;9789811033728
Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three "V" or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL.
Contributing to a growing attention to algorithms and algorithmic interaction in the CHI and CSCW communities, this workshop aims to deal centrally with the topic of human "participation" and its changing ro...
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ISBN:
(纸本)9781450360180
Contributing to a growing attention to algorithms and algorithmic interaction in the CHI and CSCW communities, this workshop aims to deal centrally with the topic of human "participation" and its changing role to data-driven, algorithmic ecosystems. Such a focus includes projects that involve users in the design of algorithms and "human-in-the-loop" systems, broader investigations into the ways in which "participation" is situated in data-driven activities, as well as conceptual concerns about participation's changing contours in contemporary social computing landscapes. This one-day workshop will be led by academic and industry researchers and sets out to achieve three goals: identify cases and ongoing projects on the topic of participation in algorithmic ecosystems;create a tactical toolkit of key challenges and strategies in this space;and set a forward-facing agenda to provoke further attention to the changing role of participation in contemporary sociotechnical systems.
With a view to future large space telescopes, we investigate image-based wavefront correction with active optics. We use an image-sharpness metric as merit function to evaluate the image quality, and the Zernike modes...
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ISBN:
(纸本)9781510630772
With a view to future large space telescopes, we investigate image-based wavefront correction with active optics. We use an image-sharpness metric as merit function to evaluate the image quality, and the Zernike modes as control variables. In severely aberrated systems, the Zernike modes are not orthogonal to each other with respect to this merit function. Using wavefront maps, the PSF, and the MTF, we discuss the physical causes for the non-orthogonality of the Zernike modes with respect to the merit function. We show that for combinations of Zernike modes with the same azimuthal order, a flatter wavefront in the central region of the aperture is more important than the RMS wavefront error across the full aperture for achieving a better merit function. The non-orthogonality of the Zernike modes with respect to the merit function should be taken into account when designing the algorithm for image-based wavefront correction, because it may slow down the process or lead to premature convergence.
The paper considers a problem statement for the synthesis of a robust stochastic regulator based on the principles of control on manifolds and a new algorithm for designing a regulator for stochastic discrete objects....
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The paper considers a problem statement for the synthesis of a robust stochastic regulator based on the principles of control on manifolds and a new algorithm for designing a regulator for stochastic discrete objects. The object of study is presented in the form of a system of stochastic difference nonlinear equations. The new algorithm for the analytical design of a stochastic nonlinear control system is based on the classical method for the analytical design of aggregated regulators, previously developed for a deterministic nonlinear object with a complete description. The robust stochastic nonlinear regulator provides the following characteristics of the control system: a) the minimal variance of the output variable;b) the minimal dispersion of the target invariant;c) the minimum of the average value of the quality functional.
To empower an autonomous robot to perform long-term navigation in a given area, a concurrent localization and map update algorithm is required. In this paper, we tackle this problem by providing both theoretical analy...
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ISBN:
(纸本)9781538679012;9781538679265
To empower an autonomous robot to perform long-term navigation in a given area, a concurrent localization and map update algorithm is required. In this paper, we tackle this problem by providing both theoretical analysis and algorithm design for robotic systems equipped with 2D laser range finders. The first key contribution of this paper is that we propose a hybrid signed distance field (SDF) framework for laser based localization. The proposed hybrid SDF integrates two methods with complementary characteristics, namely Euclidean SDF (ESDF) and Truncated SDF (TSDF). With our framework, accurate pose estimation and fast map update can be performed simultaneously. Moreover, we introduce a novel sliding window estimator which attains better accuracy by consistently utilizing sensor and map information with both scan-to-scan and scan-to-map data association. Real-world experimental results demonstrate that the proposed algorithm can be used for commercial robots in various environments with long-term usage. Experiments also show that our approach outperforms competing approaches by a wide margin.
Firstly, we show that in the unified structural model of the consensus-based time synchronization (CBTS) algorithm, the offset estimate is divergent in general while the drift estimate converges in a rate of 1/k, wher...
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ISBN:
(纸本)9781728113128
Firstly, we show that in the unified structural model of the consensus-based time synchronization (CBTS) algorithm, the offset estimate is divergent in general while the drift estimate converges in a rate of 1/k, where k is the discrete time. Then. we propose an offset compensation approach to guaranteeing the boundedness of the offset estimate for wireless sensor networks (WSNs) with random bounded communication delays. Two numerical experiments are presented to illustrate the availability of this algorithm and the better synchronization performance.
The viewshed of a point v on a grid terrain T, viewshedT (v), is the set of grid points in T that are visible from v. We describe a novel algorithm for computing viewshedT (v) using a multiresolution approach: Given a...
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ISBN:
(纸本)9781450358897
The viewshed of a point v on a grid terrain T, viewshedT (v), is the set of grid points in T that are visible from v. We describe a novel algorithm for computing viewshedT (v) using a multiresolution approach: Given a parameter k > 1 that represents the block size, we create a grid T' which is a lower-resolution version of T, such that each point in T' corresponds to a block of 1---j1 -by- 1---j1 points in T;T' has size 0(n/ k), where n is the size of the original grid. The key of our approach is using T' to speed up the computation of viewshedT (v) while not introducing approximation. We compute viewshedT (v) in two steps: First we compute the viewshed of v on T', while maintaining the invariant that any block in T' that is labeled as invisible may not contain any visible points. Thus, the first step's role is to use T' to filter out blocks in T that are guaranteed to be invisible. The second step considers the blocks that were labeled as visible in T' and computes the visibility of their points with full accuracy using the data in T. Overall the algorithm runs in 0 (1172 lg k+ 1.1g n), where 1 is the total size of visible blocks in T'. When k > 1 and 1 = o(n), the running time of our algorithm improves on the previous best bound of 0(n lg n) [9, 15]. We describe a suite of experimental results showing the performance of our algorithm in practice and a speedup of more than an order of magnitude compared to previous algorithms.
Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain using different electrodes, which are considered as the EEG channels that are placed on scalp. In th...
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ISBN:
(纸本)9783319942681;9783319942674
Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain using different electrodes, which are considered as the EEG channels that are placed on scalp. In this paper, we propose an effective information processing approach to explore the association among EEG channels under different circumstances. Particularly, we design four different experimental scenarios and record the EEG signals under motions of eye-opening and body-movement. With sequences of data collected in time order, we first compute the mutual conditional entropy to measure the association between two electrodes. Using the hierarchical clustering tree and data mechanics algorithm, we could effectively identify the association between particular EEG channels under certain motion scenarios. We also implement the weighted random forest to further classify the classes (experimental scenarios) of the EEG time series. Our evaluation results show that we could successfully classify the particular motions with given EEG data series.
We propose a novel classifier for a Recommender System which is based on a Kansei Model in this paper. We called this Recommender System as Kansei Recommender System (hereafter, we denoted as KRS algorithm). The purpo...
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ISBN:
(纸本)9781538672419
We propose a novel classifier for a Recommender System which is based on a Kansei Model in this paper. We called this Recommender System as Kansei Recommender System (hereafter, we denoted as KRS algorithm). The purpose of building KRS algorithm is to reduce the time of training data from database and give more precise recommender items for consumers by considering their Kansei (a Japanese word which means the consumers' psychological feeling). To build a novel classifier, we divide the KRS algorithm into two parts of algorithms: (1) algorithm 1 is proposed to extract Kansei factors (score 1) and evaluation factors (score 2) from consumers' shopping items. (2) algorithm 2 is proposed to give a training dataset that is to fit the scored value of Kansei model. Combining two algorithms, we get a novel classifier for a KRS algorithm. We give an architecture of KRS algorithm based on the database of on-line shopping market in the end of this paper.
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