A Hybrid Deep Grouping Algorithm for Large Scale Global Optimization

The information guidance system for parking spaces in large- and medium-sized parking lots is not efficient at present. It tends to be difficult to find an empty parking space in parking lots in big cities. One of the problems is the large amount of calculation in the traditional Dijkstra algorithm. In this paper, an improved Dijkstra algorithm is presented and optimized to find the best parking path with the purpose of looking for the nearest free parking space based on the layout model in parking lot parking guidance. The experiments show that the improved Dijkstra algorithm can find the optimal parking space and the optimal parking path and improve the parking efficiency.

Section: Discussionmentioning

confidence: 99%

Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance

Liu

Yang

et al. 2021

“…In contrast to the classical mathematical approaches, stochastic optimization methods (such as an evolutionary 7 Wireless Communications and Mobile Computing algorithm) only utilize the objective function value with lower requirements for the continuity and differentiability of the objective function, which are suitable for the most complex engineering optimization problems [24][25][26]. As one of the well-developed stochastic optimization methods, a genetic algorithm [27][28][29] is adopted to design the solving algorithm to resolve our problem defined in Equation (8).…”

Section: The Proposed Hybrid Genetic Algorithmmentioning

confidence: 99%

A New Method for Reconstructing Data on a Single Failure Node in the Distributed Storage System Based on the MSR Code

Miao

Wei

Guo

et al. 2021

As a storage method for a distributed storage system, an erasure code can save storage space and repair the data of failed nodes. However, most studies that discuss the repair of fault nodes in the erasure code mode only focus on the condition that the bandwidth of heterogeneous links restricts the repair rate but ignore the condition that the storage node is heterogeneous, the cost of repair traffic in the repair process, and the influence of the failure of secondary nodes on the repair process. An optimal repair strategy based on the minimum storage regenerative (MSR) code and a hybrid genetic algorithm is proposed for single-node fault scenarios to solve the above problems. In this work, the single-node data repair problem is modeled as an optimization problem of an optimal Steiner tree with constraints considering heterogeneous link bandwidth and heterogeneous node processing capacity and takes repair traffic and repair delay as optimization objectives. After that, a hybrid genetic algorithm is designed to solve the problem. The experimental results show that under the same scales used in the MSR code cases, our approach has good robustness and its repair delay decreases by 10% and 55% compared with the conventional tree repair topology and star repair topology, respectively; the repair flow increases by 10% compared with the star topology, and the flow rate of the conventional tree repair topology decreases by 40%.

“…It is foreseeable that intelligent speech systems will be critical to the mentioned areas. With the rapid growth of data volume, large-scale problems should be urgently solved effectively [4,5], while more opportunities are brought. Speech sensor networks, an important part of IoT, will have many application needs.…”

Section: Introductionmentioning

confidence: 99%

An Improved Unsupervised Single‐Channel Speech Separation Algorithm for Processing Speech Sensor Signals

Jiang

Lin

et al. 2021

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.