A constrained joint optimization method for large margin HMM estimation

Spectrum detection is a critical technology in cognitive radio, which involves two very important parameters: detection period and detection time. Firstly, in order to improve spectrum efficiency and reduce the interference to authorized users, detection period optimization is adopted to maximize spectrum access opportunities and minimize the interference to authorized users. Secondly, under the target detection probability constraints, detection time optimization is adopted to maximize the achievable normalized throughput when CR user detects the channel. At last, we develop the joint-optimization algorithm of detection period and detection time. Compared with the method in literature [9], it can be found that our joint-optimization algorithm can actually improve spectrum access opportunities. Compared with single-optimization method of detection period and detection time, our algorithm can also greatly improve the achievable normalized throughput when CR user detects the channel

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Joint Optimization Method Combining Genetic Algorithm and Numerical Algorithm Based on MATLAB

International Journal of Hybrid Information Technology ◽

10.14257/ijhit.2016.9.11.06 ◽

2016 ◽

Vol 9 (11) ◽

pp. 57-66

Author(s):

Yanhua Guo ◽

Feifei Liu ◽

Ning Zhang ◽

Tao Wang

Keyword(s):

Genetic Algorithm ◽

Numerical Algorithm ◽

Optimization Method ◽

Joint Optimization

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A Joint Optimization Framework of the Embedding Model and Classifier for Meta-Learning

Scientific Programming ◽

10.1155/2021/1538914 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Zhongyu Liu ◽

Xu Chu ◽

Yan Lu ◽

Wanli Yu ◽

Shuguang Miao ◽

...

Keyword(s):

Texture Features ◽

Current Method ◽

Optimization Method ◽

Learning Model ◽

Feature Representation ◽

Joint Optimization ◽

Small Samples ◽

Semantic Features ◽

Quantum Genetic Algorithm ◽

Meta Learning

The aim of meta-learning is to train the machine to learn quickly and accurately. Improving the performance of the meta-learning model is important in solving the problem of small samples and in achieving general artificial intelligence. A meta-learning method based on feature embedding that exhibits good performance on the few-shot problem was previously proposed. In this method, the pretrained deep convolution neural network was used as the embedding model of sample features, and the output of one layer was used as the feature representation of samples. The main limitation of the method is the inability to fuse low-level texture features and high-level semantic features of the embedding model and joint optimization of the embedding model and classifier. Therefore, a multilayer adaptive joint training and optimization method of the embedding model was proposed in the current study. The main characteristics of the current method include using multilayer adaptive hierarchical loss to train the embedding model and using the quantum genetic algorithm to jointly optimize the embedding model and classifier. Validation was performed based on multiple public datasets for meta-learning model testing. The proposed method shows higher accuracy compared with multiple baseline methods.

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Joint Optimization Method of Airborne MIMO Radar Track and Radiated Power Based on Mutual Information

IEEE Access ◽

10.1109/access.2020.3011421 ◽

2020 ◽

Vol 8 ◽

pp. 136278-136295

Author(s):

Xiangyu Fan ◽

Peng Bai ◽

Hongwei Wang ◽

Jiaqiang Zhang ◽

Huanyu Li

Keyword(s):

Mutual Information ◽

Optimization Method ◽

Mimo Radar ◽

Joint Optimization ◽

Radiated Power

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Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2015.p0197 ◽

2015 ◽

Vol 19 (2) ◽

pp. 197-204 ◽

Cited By ~ 2

Author(s):

Yi Li ◽

◽

Jun Peng ◽

Fu Jiang ◽

Kaiyang Liu ◽

...

Keyword(s):

Energy Efficiency ◽

Cognitive Radio ◽

Sensor Networks ◽

Spectrum Sensing ◽

Data Transmission ◽

Cooperative Spectrum Sensing ◽

Optimization Method ◽

Joint Optimization ◽

Transmission Scheme ◽

Cognitive Radio Sensor Networks

To address the inherent energy constraint in cognitive radio sensor networks, a novel joint optimization method of spectrum sensing and data transmission for energy efficiency is investigated in this paper. To begin with, a cooperative spectrum sensing scheme based on dynamic censoring is employed to shorten sensing time and save unnecessary spectrum sensing energy. Then to jointly optimize the energy efficiency, the distortion constrained probabilistic transmission scheme is utilized. Afterwards the sensing threshold solving issue can be formulated as a nonlinear minmax optimization problem with the detection probability and false alarm probability constraints. Solving by the Matlab software with the free OPTI toolbox, simulation results demonstrate that significant energy can be saved via the the proposed joint optimization method in various mobile cloud scenarios.

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