Energy-efficient data gathering in multi-hop wireless sensor networks was studied,considering that different node produces different amounts of data in realistic environments.A novel dominating set based clustering pr...Energy-efficient data gathering in multi-hop wireless sensor networks was studied,considering that different node produces different amounts of data in realistic environments.A novel dominating set based clustering protocol (DSCP) was proposed to solve the data gathering problem in this scenario.In DSCP,a node evaluates the potential lifetime of the network (from its local point of view) assuming that it acts as the cluster head,and claims to be a tentative cluster head if it maximizes the potential lifetime.When evaluating the potential lifetime of the network,a node considers not only its remaining energy,but also other factors including its traffic load,the number of its neighbors,and the traffic loads of its neighbors.A tentative cluster head becomes a final cluster head with a probability inversely proportional to the number of tentative cluster heads that cover its neighbors.The protocol can terminate in O(n/lg n) steps,and its total message complexity is O(n2/lg n).Simulation results show that DSCP can effectively prolong the lifetime of the network in multi-hop networks with unbalanced traffic load.Compared with EECT,the network lifetime is prolonged by 56.6% in average.展开更多
In order to solve the problem that, the <span style="white-space:normal;">hyper-parameters</span> of the existing random forest-based classification prediction model depend on empirical settings,...In order to solve the problem that, the <span style="white-space:normal;">hyper-parameters</span> of the existing random forest-based classification prediction model depend on empirical settings, which leads to unsatisfactory model performance. We propose a based on adaptive particle swarm optimization algorithm random forest model to optimize data classification and an adaptive particle swarm algorithm for optimizing hyper-parameters in the random forest to ensure that the model can better predict unbalanced data. Aiming at the premature convergence problem in the particle swarm optimization algorithm, the population is adaptively divided according to the fitness of the population, and an adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum. The main steps of the model are as follows: Normalize the data set, initialize the model on the training set, and then use the particle swarm optimization algorithm to optimize the modeling process to establish a classification model. Experimental results show that our proposed algorithm is better than traditional algorithms, especially in terms of F1-Measure and ACC evaluation standards. The results of the six-keel imbalanced data set demonstrate the advantages of our proposed algorithm.展开更多
To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior ...To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior probability estimator for visual concepts is provided. The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models, as well as in improved robustness with respect to high-level visual semantic concept classification.展开更多
This paper presents an innovative switched-mode auto gain control （AGC） circuit with internally created reset module for DC-10Mb/s burst-mode unbalanced （BMU） optical data transmission. Conventional AGC circuit is...This paper presents an innovative switched-mode auto gain control （AGC） circuit with internally created reset module for DC-10Mb/s burst-mode unbalanced （BMU） optical data transmission. Conventional AGC circuit is inappropriate for BMU data transmission because it is based on average level detection and requires considerable time to settle on a predefined gain. Therefore, we adopt a fast switched-mode AGC based on peak level detection. After the gain is adjusted, the peak level detectors need to re-detect the peak level of the input signal. Thus, we develop an internally created reset module. This AGC with reset module exhibits a fast operation and achieves an adjusted stable gain within one-bit, avoiding any bit loss up to 10Mb/s data rate. During power-up, the peak level detectors possibly hold an uncertain level resulting in the bit-errors. We propose a power-up reset circuit to solve this problem. Designed in a 0.5μm CMOS technology, the circuit achieves an optical sensitivity of better than -30dBm and a wide dynamic range of over 30dB with a power dissipation of only 30 mW from a 5V supply.展开更多
Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise....Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LSSVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LSSVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability.展开更多
The expected mean squares for unbalanced mixed effect interactive model were derived using Brute Force Method. From the expected mean squares, there are no obvious denominators for testing for the main effects when th...The expected mean squares for unbalanced mixed effect interactive model were derived using Brute Force Method. From the expected mean squares, there are no obvious denominators for testing for the main effects when the factors are mixed. An expression for F-test for testing for the main effects was derived which was proved to be unbiased.展开更多
基金Projects(61173169,61103203)supported by the National Natural Science Foundation of ChinaProject(NCET-10-0798)supported by the Program for New Century Excellent Talents in University of ChinaProject supported by the Post-doctoral Program and the Freedom Explore Program of Central South University,China
文摘Energy-efficient data gathering in multi-hop wireless sensor networks was studied,considering that different node produces different amounts of data in realistic environments.A novel dominating set based clustering protocol (DSCP) was proposed to solve the data gathering problem in this scenario.In DSCP,a node evaluates the potential lifetime of the network (from its local point of view) assuming that it acts as the cluster head,and claims to be a tentative cluster head if it maximizes the potential lifetime.When evaluating the potential lifetime of the network,a node considers not only its remaining energy,but also other factors including its traffic load,the number of its neighbors,and the traffic loads of its neighbors.A tentative cluster head becomes a final cluster head with a probability inversely proportional to the number of tentative cluster heads that cover its neighbors.The protocol can terminate in O(n/lg n) steps,and its total message complexity is O(n2/lg n).Simulation results show that DSCP can effectively prolong the lifetime of the network in multi-hop networks with unbalanced traffic load.Compared with EECT,the network lifetime is prolonged by 56.6% in average.
文摘In order to solve the problem that, the <span style="white-space:normal;">hyper-parameters</span> of the existing random forest-based classification prediction model depend on empirical settings, which leads to unsatisfactory model performance. We propose a based on adaptive particle swarm optimization algorithm random forest model to optimize data classification and an adaptive particle swarm algorithm for optimizing hyper-parameters in the random forest to ensure that the model can better predict unbalanced data. Aiming at the premature convergence problem in the particle swarm optimization algorithm, the population is adaptively divided according to the fitness of the population, and an adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum. The main steps of the model are as follows: Normalize the data set, initialize the model on the training set, and then use the particle swarm optimization algorithm to optimize the modeling process to establish a classification model. Experimental results show that our proposed algorithm is better than traditional algorithms, especially in terms of F1-Measure and ACC evaluation standards. The results of the six-keel imbalanced data set demonstrate the advantages of our proposed algorithm.
基金Sponsored by the Beijing Municipal Natural Science Foundation(4082027)
文摘To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior probability estimator for visual concepts is provided. The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models, as well as in improved robustness with respect to high-level visual semantic concept classification.
基金Supported by the Natural Science Foundation of Jiangsu Province （ BK2010411 ） and the National International Cooperation Project of China-Korea （2011DFA11310）.
文摘This paper presents an innovative switched-mode auto gain control （AGC） circuit with internally created reset module for DC-10Mb/s burst-mode unbalanced （BMU） optical data transmission. Conventional AGC circuit is inappropriate for BMU data transmission because it is based on average level detection and requires considerable time to settle on a predefined gain. Therefore, we adopt a fast switched-mode AGC based on peak level detection. After the gain is adjusted, the peak level detectors need to re-detect the peak level of the input signal. Thus, we develop an internally created reset module. This AGC with reset module exhibits a fast operation and achieves an adjusted stable gain within one-bit, avoiding any bit loss up to 10Mb/s data rate. During power-up, the peak level detectors possibly hold an uncertain level resulting in the bit-errors. We propose a power-up reset circuit to solve this problem. Designed in a 0.5μm CMOS technology, the circuit achieves an optical sensitivity of better than -30dBm and a wide dynamic range of over 30dB with a power dissipation of only 30 mW from a 5V supply.
基金the Natural Science Foundation of Liaoning Province,China(20180550067)Liaoning Province Ministry of Education Scientific Study Project(2020LNZD06 and 2017LNQN11)University of Science and Technology Liaoning Talent Project Grants(601011507-20 and 601013360-17).
文摘Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LSSVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LSSVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability.
文摘The expected mean squares for unbalanced mixed effect interactive model were derived using Brute Force Method. From the expected mean squares, there are no obvious denominators for testing for the main effects when the factors are mixed. An expression for F-test for testing for the main effects was derived which was proved to be unbiased.
文摘针对滚动轴承实际运行中的故障数据远少于正常数据,从而影响故障诊断模型诊断率的问题,提出了一种数据不平衡情况下的基于改进生成对抗网络(generative adversarial networks,GAN)的滚动轴承故障诊断方法——基于梯度惩罚的Wasserstein生成对抗网络(Wassserstein generative adversarial networks based on gradient penalty,WGAN-GP)。首先,采用连续小波变换(continuewavelettransform,CWT)将振动信号集转化为二维图像数据集。然后,用Wasserstein距离替代GAN的Jensen-Shannon(JS)散度,再使用梯度惩罚策略在WGAN权值裁剪过程中优化模型,使生成器损失函数的权值在区间中取得均衡,实现故障数据的自动生成,扩充故障数据集。最后,设置了不平衡数据集和数据增强对比实验,结果表明,WGAN-GP在所设置的不同不平衡比例实验下的模型诊断率分别提高了2.29%、1%、2.85%,在数据增强对比实验中的诊断率也高于几何变换增强后的数据和原始数据。
