Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network
TL;DR: The theoretical analysis proves the convergence of a proposed algorithm and efficient convergence during the first and second steps of the algorithm to effectively prevent parking navigation from a gridlock situation and demonstrates that the proposed algorithm performs more efficiently than existing algorithms.
About: This article is published in Information Sciences.The article was published on 2017-10-01. It has received 169 citations till now. The article focuses on the topics: Parking guidance and information & Wireless sensor network.
Citations
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TL;DR: This paper adopts Random Forest to select the important feature in classification and compares the result of the dataset with and without essential features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination to get the best percentage accuracy and kappa.
Abstract: Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection is essential. First, to simplify the model by reducing the number of parameters, next to decrease the training time, to reduce overfilling by enhancing generalization, and to avoid the curse of dimensionality. Besides, we evaluate and compare each accuracy and performance of the classification model, such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). The highest accuracy of the model is the best classifier. Practically, this paper adopts Random Forest to select the important feature in classification. Our experiments clearly show the comparative study of the RF algorithm from different perspectives. Furthermore, we compare the result of the dataset with and without essential features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination (RFE) to get the best percentage accuracy and kappa. Experimental results demonstrate that Random Forest achieves a better performance in all experiment groups.
271 citations
Cites background from "Gradient-driven parking navigation ..."
...Besides providing point estimates, it also considers estimating the variability of an error rate estimate [110]....
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Wei Wei1, Xu Xia2, Marcin Wozniak3, Xunli Fan4, Robertas Damaševičius5, Robertas Damaševičius3, Ye Li1 •
TL;DR: Simulation results show that the new algorithm exhibits superior connectivity, power consumption validity, clustering interference, and network performance, can effectively reduce the overall power consumption and prolong the lifetime of the network, thus ensure the monitoring data transmitted to the monitoring center rapidly and quickly.
117 citations
TL;DR: These studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.
Abstract: This paper uses big data technologies to study base stations’ behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, first we exploit big data platform and technologies to analyze cellular network big data, i.e., Call Detail Records (CDRs). Our CDRs data set, which includes more than 1,000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Second, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations’ behaviors and dissect their predictability. Third, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations’ activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations’ behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.
112 citations
Cites methods from "Gradient-driven parking navigation ..."
...Additionally, other some methods are also used to analyze big data [22], [23], [24], [25], [26], [27], and other relevant topics and approaches [28], [29], [30] are also be discussed....
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TL;DR: A Semantic SLAM system which builds the semantic maps with object-level entities, and it is integrated into the RGB-D SLAM framework, and an improved Octomap based on the Fast Line Rasterization Algorithm is constructed to improve the computational efficiency.
Abstract: Due to the development of the computer vision, machine learning, and deep learning technologies, the research community focuses not only on the traditional SLAM problems, such as geometric mapping and localization, but also on semantic SLAM. In this paper, we propose a Semantic SLAM system which builds the semantic maps with object-level entities, and it is integrated into the RGB-D SLAM framework. The system combines object detection module that is realized by the deep-learning method, and localization module with RGB-D SLAM seamlessly. In the proposed system, object detection module is used to perform object detection and recognition, and localization module is utilized to get the exact location of the camera. The two modules are integrated together to obtain the semantic maps of the environment. Furthermore, to improve the computational efficiency of the framework, an improved Octomap based on the Fast Line Rasterization Algorithm is constructed. Meanwhile, for the sake of accuracy and robustness of the semantic map, conditional random field is employed to do the optimization. Finally, we evaluate our Semantic SLAM through three different tasks, i.e., localization, object detection, and mapping. Specifically, the accuracy of localization and the mapping speed is evaluated on TUM data set. Compared with ORB-SLAM2 and original RGB-D SLAM, our system, respectively, got 72.9% and 91.2% improvements in dynamic environments localization evaluated by root-mean-square error. With the improved Octomap, the proposed Semantic SLAM is 66.5% faster than the original RGB-D SLAM. We also demonstrate the efficiency of object detection through quantitative evaluation in an automated inventory management task on a real-world data sets recorded over a realistic office.
92 citations
Cites methods from "Gradient-driven parking navigation ..."
...RGBD_SLAM [6] can also generate dense point clouds, it tracks ORB features and optimizes camera poses by G2O algorithm....
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...Therefore we use the constant velocity motion model to predict the position of the current frames as a G2O initial value before optimization....
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...We choose the LevenbergMarquardt method, from G2O [45] which contains several optimization algorithms to optimize the pose of the current frame....
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TL;DR: The adaptive neural fuzzy inference system is designed, which is trained by extracted feature vectors and can effectively predict the grip force, which can be used to guide rehabilitation therapy in virtual space, combined with an electrical stimulator.
Abstract: In order to resolve the problem of unstable control of force in human–computer interaction based on surface EMG signals, the adaptive neural fuzzy inference system is designed to achieve the grip strength assessment. As we know, the acquisition of surface EMG signal is non-invasive, which provides a better evaluation index for rehabilitation training in the medical process. By establishing the relationship between grip force and surface electromechanical signals, the effect of rehabilitation training can be evaluated directly while reducing the types of sensors used. Firstly, the experimental equipment are introduced, which are utilized to carry out simultaneous acquisition of surface EMG signals and forces. Then, the traditional features of sEMG and the corresponding algorithms are illustrated, based on this, supplementing the energy eigenvalue with wavelet analysis and fuzzy entropy. In which, fuzzy entropy is effective in characterizing muscle fatigue that can effectively reduce the impact of muscle fatigue on force assessment. Finally, combining fuzzy logic implication and neural network, the adaptive neural fuzzy inference system is designed, which is trained by extracted feature vectors. The experimental result shows the method used in this paper can effectively predict the grip force. Further, force prediction based on sEMG can be used to guide rehabilitation therapy in virtual space, combined with an electrical stimulator.
75 citations
References
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01 Jul 1990TL;DR: This paper reformulated the manipulator control problem as direct control of manipulator motion in operational space-the space in which the task is originally described-rather than as control of the task's corresponding joint space motion obtained only after geometric and kinematic transformation.
Abstract: This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation. Outside the obstacles' regions of influ ence, we caused the end effector to move in a straight line with an...
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01 Jan 2005
TL;DR: The purpose of this text is to offer a comprehensive and self-contained presentation of some of the most successful and popular domain decomposition preconditioners for finite and spectral element approximations of partial differential equations.
Abstract: The purpose of this text is to offer a comprehensive and self-contained presentation of some of the most successful and popular domain decomposition preconditioners for finite and spectral element approximations of partial differential equations. Strong emphasis is placed on both algorithmic and mathematical aspects. Some important methods such FETI and balancing Neumann-Neumann methods and algorithms for spectral element methods, not treated previously in any monograph, are covered in detail.
Winner of the 2005 Award for Excellence in Professional and Scholarly Publishing - Mathematics/Statistics - of the Association of American Publishers
2,313 citations
05 Nov 2003
TL;DR: TOSSIM, a simulator for TinyOS wireless sensor networks can capture network behavior at a high fidelity while scaling to thousands of nodes, by using a probabilistic bit error model for the network.
Abstract: Accurate and scalable simulation has historically been a key enabling factor for systems research. We present TOSSIM, a simulator for TinyOS wireless sensor networks. By exploiting the sensor network domain and TinyOS's design, TOSSIM can capture network behavior at a high fidelity while scaling to thousands of nodes. By using a probabilistic bit error model for the network, TOSSIM remains simple and efficient, but expressive enough to capture a wide range of network interactions. Using TOSSIM, we have discovered several bugs in TinyOS, ranging from network bit-level MAC interactions to queue overflows in an ad-hoc routing protocol. Through these and other evaluations, we show that detailed, scalable sensor network simulation is possible.
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"Gradient-driven parking navigation ..." refers methods in this paper
...Two wireless transmission modes, which are similar to the simple mode and the lossy mode in TOSSIM [20] , were adopted in our simulator....
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01 Aug 2001
TL;DR: It is shown that the RBF representation has advantages for mesh simplification and remeshing applications, and a greedy algorithm in the fitting process reduces the number of RBF centers required to represent a surface and results in significant compression and further computational advantages.
Abstract: We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from point-cloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs allow us to model large data sets, consisting of millions of surface points, by a single RBF — previously an impossible task. A greedy algorithm in the fitting process reduces the number of RBF centers required to represent a surface and results in significant compression and further computational advantages. The energy-minimisation characterisation of polyharmonic splines result in a “smoothest” interpolant. This scale-independent characterisation is well-suited to reconstructing surfaces from non-uniformly sampled data. Holes are smoothly filled and surfaces smoothly extrapolated. We use a non-interpolating approximation when the data is noisy. The functional representation is in effect a solid model, which means that gradients and surface normals can be determined analytically. This helps generate uniform meshes and we show that the RBF representation has advantages for mesh simplification and remeshing applications. Results are presented for real-world rangefinder data.
1,958 citations
25 Mar 1985
TL;DR: In this article, a real-time obstacle avoidance approach for manipulators and mobile robots based on the "artificial potential field" concept is presented, where collision avoidance, traditionally considered a high level planning problem, can be effectively distributed between different levels of control.
Abstract: This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the "artificial potential field" concept. In this approach, collision avoidance, traditionally considered a high level planning problem, can be effectively distributed between different levels of control, allowing real-time robot operations in a complex environment. We have applied this obstacle avoidance scheme to robot arm using a new approach to the general problem of real-time manipulator control. We reformulated the manipulator control problem as direct control of manipulator motion in operational space-the space in which the task is originally described-rather than as control of the task's corresponding joint space motion obtained only after geometric and kinematic transformation. This method has been implemented in the COSMOS system for a PUMA 560 robot. Using visual sensing, real-time collision avoidance demonstrations on moving obstacles have been performed.
1,088 citations