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Self-adaptive Creating of Truss Ground Structure Based on ANFIS

Li Ying,Hu Yunchang,Cao Hongduo

Strategic Study of CAE 2004, Volume 6, Issue 10, Pages 24-27

Abstract:

The ground structure's intelligent and automatical creating is researched. The ground structures of truss structures are intelligently and automatically created by using collateral ANFIS (Adaptive-Network-Based Fuzzy Inference Systems). The essentials of this method is to form a mapping between configurations of the trusses existed and those to be found. The ground structure is inferred by the mechanism contained in sample trusses. To produce appropriate data format of ANFIS, the topology configuration of trusses is described as a series of decimal fraction. The simulation shows the efficiency of this system.

Keywords： truss ANFIS intelligent self-adaptive

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Automatic image enhancement by learning adaptive patch selection None

Na LI, Jian ZHAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2, Pages 206-221 doi: 10.1631/FITEE.1700125

Abstract:

Today, digital cameras are widely used in taking photos. However, some photos lack detail and need enhancement. Many existing image enhancement algorithms are patch based and the patch size is always fixed throughout the image. Users must tune the patch size to obtain the appropriate enhancement. In this study, we propose an automatic image enhancement method based on adaptive patch selection using both dark and bright channels. The double channels enhance images with various exposure problems. The patch size used for channel extraction is selected automatically by thresholding a contrast feature, which is learned systematically from a set of natural images crawled from the web. Our proposed method can automatically enhance foggy or under-exposed/backlit images without any user interaction. Experimental results demonstrate that our method can provide a significant improvement in existing patch-based image enhancement algorithms.

Keywords： Image enhancement Contrast enhancement Dark channel Bright channel Adaptive patch based processing

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An Adaptive Demodulation Method for BPSK Signals

Li Yanxin,Hu Aiqun,Song Yubo

Strategic Study of CAE 2006, Volume 8, Issue 5, Pages 49-51

Abstract:

The paper presents a novel method for demodulating the binary phase shift keying (BPSK) signals basing on adaptive filtering. The commonly used least mean square (LMS) error adaptive filtering algorithm is employed for studying the demodulating procedure and the performance of the novel adaptive BPSK demodulation. The novel adaptive BPSK demodulation does not need the adaptive filter completing convergence. The performance of the method in theory is compared with computer simulating results. It shows that the error rates in simulation agree well with that in theory. Also, it is indicated that the demodulation method has many advantages over conventional ones, such as the powerful anti-noise ability, the small transfer delay, and the convenient implementation with DSP technology, and has lower error rates than correlation modulation at the same sample rate.

Keywords： digital communications adaptive signal processing demodulation BPSK

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An ANFIS-based Approach for Predicting MiningInduced Surface Subsidence

Ding Dexin,Zhang Zhijun,Bi Zhongwei

Strategic Study of CAE 2007, Volume 9, Issue 1, Pages 33-39

Abstract:

Current approaches for predicting mining induced surface subsidence have a drawback in common that they predict the subsidence only on the basis of a physical or mechanical approach irrespective of the practical examples in engineering practice in mining induced surface subsidence.However,these experiences created in engineering practice are of great value and full use should be made of them to establish an approach for predicting mining induced surface subsidence.Therefore,this paper accumulated a lot of practical examples of mining induced surface subsidence,integrated these examples by using adaptive neuro-fuzzy inference system (ANFIS)and established an ANFIS-based approach for predicting mining induced surface subsidence.The approach was further tested by using practical examples of mining induced surface subsidence.The results show that the approach can converge quickly,fit the data in very good agreement and make generalization prediction with high accuracy.

Keywords： underground mining mining induced surface subsidence adaptive neuro唱fuzzy inferencesystem

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Pricing Based Adaptive Call Admission Control Algorithm for Wireless Networks

Zhang Xue

Strategic Study of CAE 2006, Volume 8, Issue 4, Pages 32-38

Abstract:

In order to efficiently and effectively control the use of wireless network resources, in this paper, according to the characteristic of adaptive multimedia applications in which bandwidths can be adjusted dynamically, and the influence of pricing on the users' behavior, an adaptive admission control algorithm integrated with pricing is proposed. The algorithm, in with the price is adjusted dynamically based on the current network conditions, is fit for the multi-priorilies services. Attempt is tried to make best balance between the efficiency and simplicity for the pricing scheme. Comparison of the performance of the proposed approach is made with the corresponding results of conventional systems where pricing is not taken into consideration in CAC process. The performance results verify the considerable improvement achieved by the integration of pricing with CAC in wireless networks.

Keywords： wireless networks adaptive call admission control microeconomic theory pricing connection level QoS

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Adaptive Wavelet Thresholding Denoising Used in Gravitational Signal Processing

Zhao Liye,Zhou Bailing,Li Kunyu

Strategic Study of CAE 2006, Volume 8, Issue 3, Pages 49-52

Abstract:

The theory of wavelet thresholding denoising is analyzed and applied to process the data measured by gravimeter in order to effectively alleviate the effect of different noise in high precise gravitational system. The signal to noise ration (SNR) is used as the index for evaluating the performance of the data processing methods. Theoretical analysis and emulation experiments indicate that wavelet thresholding denoising, adaptive wavelet thresholding denoising and adaptive Kalman filtering are all effective in alleviating the effects of different noise, but the performance of adaptive wavelet thresholding denoising is best.

Keywords： gravimeter signal processing wavelet transform adaptive wavelet threshold adaptive Kalman filering

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The Adaptive Robust Controller of the Centrifuge

Li Guo,Zhang Peichang,Hu Jianfei,Yu Dafei

Strategic Study of CAE 2006, Volume 8, Issue 9, Pages 30-34

Abstract:

This paper investigates the use of the adaptive robust controller for improving control performance and stability of the centrifuge. Based on its structural merit that the electric motor is connected to the centrifuge, the implementation of a control system is expected to achieve satisfactory control performance. An adaptive robust control algorithm of the centrifuge is presented in the paper, and the adaptive robust controller is designed according to the centrifuge model. The effectiveness of the algorithm is verified by the experimental results. It is clarified that the control performance and stability of the centrifuge is improved and the control system still maintains satisfactory control performance despite the change of environment conditions.

Keywords： centrifuge adaptive control robust control

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High Precision Adaptive Predictive Control for Cruise Missile

Sun Mingwei,Chen Zengqiang,Yuan Zhuzhi,Ren Qiang,Yang Ming

Strategic Study of CAE 2005, Volume 7, Issue 10, Pages 23-27

Abstract:

Cruise missile achieves good flight performance by means of stabilization and regulation of its attitudes. Based on analysis of the perturbation model of missile*s dynamic characteristics, series control structures are constructed for attitude control loop, and their discrete models are served as controlled plant for recursive least square (RLS) based adaptive predictive control, thus the mass center control with slow response transforms into trajectory angle control with fast response and high precision. On the basis of missile’s characteristics, generalized predictive control (GPC) is used in inner attitude loop, and an integral form of predictive control is adopted in outter trajectory loop. Effective transformation from mass center command to trajectory reference has achieved to realize high precision tracking. This method realizes the integration of attitude reference signal with guidance command, and that of attitude control with mass center control. It can reduce precision requirements on aerodynamic data and the control parameters can be easily selected. The numerical simulations demonstrate its effectiveness. Finally, some further academic directions are presented.

Keywords： cruise missile adaptive control model based predictive control robustness

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Improving entity linking with two adaptive features Research Article

Hongbin ZHANG, Quan CHEN, Weiwen ZHANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 11, Pages 1620-1630 doi: 10.1631/FITEE.2100495

Abstract:

(EL) is a fundamental task in natural language processing. Based on neural networks, existing systems pay more attention to the construction of the , but ignore latent semantic information in the and the acquisition of effective information. In this paper, we propose two , in which the first adaptive feature enables the local and s to capture latent information, and the second adaptive feature describes effective information for embeddings. These can work together naturally to handle some uncertain information for EL. Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets, and the best average performance on out-domain datasets. These results indicate that the proposed , which are based on their own diverse contexts, can capture information that is conducive for EL.

Keywords： Entity linking Local model Global model Adaptive features Entity type

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Adaptive Extension Controller Design for Nonlinear Systems

Wong Qingchang,Chen Zhenyuan

Strategic Study of CAE 2001, Volume 3, Issue 7, Pages 54-58

Abstract:

A design method for the extension controller is developed in this paper. The proposed adaptive extension control resulting from the direct adaptive approach is employed to directly adapt the gain parameter of the extension controller. Then the constructed controller can be best approximated to a given optimal control. Unlike the fuzzy controller, only one linguistic — like level is needed in the extension controller. The merits of the proposed controller are that (a) the number of adaptation parameter is small; (b) the design algorithm is easily to be implemented. In addition, a maximum control is established to guarantee the system robust stability. The derivation shows that the proposed extension controller is stable in the sense of the Lyapunov. Finally, a nonlinear system simulation example is applied to verifying the effectiveness and the ability of the proposed adaptive extension controller.

Keywords： extension theory adaptive control extension controller robust stability

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Dynamic parameterized learning for unsupervised domain adaptation Research Article

Runhua JIANG, Yahong HAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11, Pages 1616-1632 doi: 10.1631/FITEE.2200631

Abstract: enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning domain-invariant representations. Recent approaches achieve this by directly matching the marginal distributions of these two domains. Most of them, however, ignore exploration of the dynamic trade-off between and learning, thus rendering them susceptible to the problems of negative transfer and outlier samples. To address these issues, we introduce the dynamic parameterized learning framework. First, by exploring domain-level semantic knowledge, the dynamic alignment parameter is proposed, to adaptively adjust the of and learning. Besides, for obtaining semantic-discriminative and domain-invariant representations, we propose to align training trajectories on both source and target domains. Comprehensive experiments are conducted to validate the effectiveness of the proposed methods, and extensive comparisons are conducted on seven datasets of three visual tasks to demonstrate their practicability.

Keywords： Unsupervised domain adaptation Optimization steps Domain alignment Semantic discrimination

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Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters Research

Fan XU, Jin WANG, Guo-dong LU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11, Pages 1316-1327 doi: 10.1631/FITEE.1601707

Abstract:

The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate translational base frame parameters, internal force, modeling uncertainties, joint friction, and external disturbances. A radial basis function neural network is adopted for all kinds of dynamical estimation, including undesired internal force. To validate the effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this approach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Specialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the face of inaccurate base frame parameters and the aforementioned uncertainties.

Keywords： Cooperative manipulators Neural networks Inaccurate translational base frame Adaptive control Robust control

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Layer-wise domain correction for unsupervised domain adaptation Article

Shuang LI, Shi-ji SONG, Cheng WU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 91-103 doi: 10.1631/FITEE.1700774

Abstract: Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities. However, conventional deep networks assume that the training and test data are sampled from the same distribution, and this assumption is often violated in real-world scenarios. To address the domain shift or data bias problems, we introduce layer-wise domain correction (LDC), a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network. Through the additive layers, the representations of source and target domains can be perfectly aligned. The corrections that are trained via maximum mean discrepancy, adapt to the target domain while increasing the representational capacity of the network. LDC requires no target labels, achieves state-of-the-art performance across several adaptation benchmarks, and requires significantly less training time than existing adaptation methods.

Keywords： Unsupervised domain adaptation Maximum mean discrepancy Residual network Deep learning

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High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction Research Article

Kaili QI, Minqing ZHANG, Fuqiang DI, Yongjun KONG,1804480181@qq.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8, Pages 1156-1168 doi: 10.1631/FITEE.2200501

Abstract: To improve the embedding capacity of , a new RDH-EI scheme is proposed based on and most significant bit (MSB) prediction. First, according to the smoothness of the image, the image is partitioned into blocks based on , and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images. In the data embedding stage, the adaptive MSB prediction method proposed by Wang and He (2022) is improved by taking the upper-left pixel in the block as the target pixel, to predict other pixels to free up more embedding space. To the best of our knowledge, quadtree partitioning is first applied to RDH-EI. Simulation results show that the proposed method is reversible and separable, and that its average embedding capacity is improved. For gray images with a size of 512×512, the average embedding capacity is increased by 25 565 bits. For all smooth images with improved embedding capacity, the average embedding capacity is increased by about 35 530 bits.

Keywords： Adaptive quadtree partitioning Adaptive most significant bit (MSB) prediction Reversible data hiding in encrypted images (RDH-EI) High embedding capacity

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Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance Research Articles

Xuerao Wang, Qingling Wang, Changyin Sun,wangxuerao@seu.edu.cn,qlwang@seu.edu.cn,cysun@seu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7, Pages 986-1001 doi: 10.1631/FITEE.2000145

Abstract: In this paper, an observer-based adaptive tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without . A novel finite-time is constructed to estimate the system uncertainties and external disturbances. To guarantee the , an error transformation is applied to transfer the time-varying constraints into a constant constraint. Then, by employing a barrier Lyapunov function and the backstepping technique, an observer-based tracking control strategy is presented. It is proven that using the proposed algorithm, all the closed-loop signals are bounded, and the tracking errors satisfy the predefined time-varying performance requirements. Finally, simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

Keywords：自适应跟踪控制；预设性能；输入饱和；干扰观测器；神经网络

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Title Author Date Type Operation

Self-adaptive Creating of Truss Ground Structure Based on ANFIS

Li Ying,Hu Yunchang,Cao Hongduo

Journal Article

Automatic image enhancement by learning adaptive patch selection

Na LI, Jian ZHAN

Journal Article

An Adaptive Demodulation Method for BPSK Signals

Li Yanxin,Hu Aiqun,Song Yubo

Journal Article