Low-Rank Optimal Transport for Robust Domain Adaptation

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.

Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

Robust Adaptive Attitude Control for Non-rigid Spacecraft With Quantized Control Input

In this paper,an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization,inertial uncertainty and external disturbance.TThe control signal for each actuator is quantized by sector-bounded quantizers,including the logarithmic quantizer and the hysteresis quantizer.By describing the impact of quantization in a new affine model and introducing a smooth function and a novel form of the control signal,the influence caused by input quantization and external disturbance is properly compensated for.Moreover,with the aid of the adaptive control technique,our approach can achieve attitude tracking without the explicit knowledge of inertial parameters.Unlike existing attitude control schemes for spacecraft,in this paper,the quantization parameters can be unknown,and the bounds of inertial parameters and disturbance are also not needed.In addition to proving the stability of the closed-loop system,the relationship between the control performance and design parameters is analyzed.Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

For a class of high-order nonlinear multi-agent systems with input hysteresis,an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated.The major properties of the proposed control scheme are:1)According to the different hysteresis input characteristics of each agent in the multi-agent system,a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value.2)A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system.By constructing state constraint control strategy for the hysteretic multi-agent system,it ensures that all the states of the system are always maintained within a predetermined range.3)The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance,and ensures that the input signal transmitted between agents is the quantization value,and the introduced quantizer is implemented under the condition that only its sector bound property is required.The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded.The Star Sim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.

Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization for Hyperspectral Image Classification

Low-Rank and Sparse Representation(LRSR)method has gained popularity in Hyperspectral Image(HSI)processing.However,existing LRSR models rarely exploited spectral-spatial classification of HSI.In this paper,we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization(LRSR-ANR)method for HSI classification.In the proposed method,we first represent the hyperspectral data via LRSR since it combines both sparsity and low-rankness to maintain global and local data structures simultaneously.The LRSR is optimized by using a mixed Gauss-Seidel and Jacobian Alternating Direction Method of Multipliers(M-ADMM),which converges faster than ADMM.Then to incorporate the spatial information,an ANR scheme is designed by combining Euclidean and Cosine distance metrics to reduce the mixed pixels within a neighborhood.Lastly,the predicted labels are determined by jointly considering the homogeneous pixels in the classification rule of the minimum reconstruction error.Experimental results based on three popular hyperspectral images demonstrate that the proposed method outperforms other related methods in terms of classification accuracy and generalization performance.

MODIFIED QUANTIZATION INDEX MODULATION WATERMARKING ADAPTIVE TO CONTRAST MASKING THRESHOLDS

In this paper,we suggest an adaptive watermarking method to improve both transparence and robustness of quantization index modulation(QIM) scheme. Instead of a fixed quantization step-size,we apply a step-size adapted to image content in each 8×8 block to make a balance of robust extraction and transparent embedding.The modified step-size is determined by contrast masking thresholds of Watson’s perceptual model.From a normalized crossed-correlation value between the original watermark and the detected watermark,we could observe that our method is robust to attacks of additive white Gaussian noise(AWGN),Salt and Pepper noise and Joint Photographic Experts Group(JPEG) compression than the original QIM.By taking into account the contrast insensitivity and visible thresholds of human visual system,the suggested improvement achieves a maximum embedding strength and an appropriate quantization step-size which is consistent with local values of a host signal.

An Adaptive Quantization Algorithm for MPEG-2 Video Coding

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IMPLEMENTING THE ADAPTIVE VECTOR QUANTIZER USING CARPENTER/GROSSBERG NET

AVQ(Adaptive Vector Quantizer)overcomes some shortcomings of traditional vectorquantizer with a fixed codebook trained and generated by the LBG or other algorithms by applyinga variab|e codebook.In this paper,we describe an effective and efficient implementation of AVQby modifying the CCN(Carpenter/Grossberg Net).The encoding process of AVQ is very similarto the learning process of the CGN.We study several different encoding schemes,includingwaveform AVQ,analysed parameter AVQ and so on,implemented by the CGN.And we simulatethe encoding performance of each scheme for encoding Gaussian process source,first order Gauss-Markov process source and practical speech signal.Our simulation results show that good qualityboth in subjective and objective tests can be obtained in a low or middle bit rate range.

High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.

带有状态/输入量化的无人船自适应模糊跟踪控制

针对海上通讯带宽受限情况下无人船的航迹跟踪控制问题,设计了一种带有状态量化和输入量化的自适应反馈跟踪控制方案。在保证有效跟踪的同时,减少执行器执行频次,降低控制幅度。首先,在不考虑量化情况下基于自适应反步法设计了系统跟踪控制律,并结合动态面技术有效降低了虚拟控制律的计算量膨胀问题。对于控制系统中存在的不确定项,利用模糊逻辑系统进行逼近。其次,采用均匀量化器分别对控制系统中的状态变量和输入变量进行量化,且量化后的状态反馈信息被用于无人船航迹跟踪控制器的设计。根据所得到的量化信息,给出了同时考虑状态量化和输入量化的无人船航迹跟踪控制律。在稳定性分析中,通过Lyapunov稳定性理论证明了在不考虑量化的情况下闭环控制系统的稳定性,并根据递归的方法证明了闭环控制系统中量化变量和非量化变量之间误差的有界性。基于给定的引理,最终证明了在同时考虑状态量化和输入量化的情况下,所设计的带有状态量化和输入量化的模糊自适应反馈跟踪控制系统的稳定性。最后,通过两组仿真实验验证了所提方案的实用性。即在同时考虑状态量化和输入量化的情况下,无人船仍能保持对理想轨迹良好的跟踪性能,并有效减轻了执行器的执行频次,更符合航海工程实践。

自适应量化神经网络滑模无人船编队控制

针对复杂海洋环境下欠驱动水面无人船(unmanned surface vehicle,USV)编队控制存在的模型不确定性、参数摄动、控制输入量化等问题,提出一种自适应量化神经网络滑模控制算法。在USV运动学子系统中,设计基于内外环控制策略的制导律,解决USV欠驱动问题。由于所采用的动力学模型中含有未知项和外界环境干扰,故在USV动力学子系统中通过使用径向基函数神经网络实现对干扰的估计。采用一种线性解析模型来描述输入量化过程。所设计的控制系统不需要量化参数的先验信息。基于输入-状态稳定性理论证明了系统稳定性。通过仿真实验验证了所提算法的有效性。

基于Qball-X4的四旋翼飞行器自适应控制

为实现四旋翼飞行器的精确轨迹跟踪控制,提出了一种基于神经网络逼近器的复合式自适应动态面量化控制方案。该方案主要贡献:①实现了自适应神经网络的量化控制,有效地缓解了量化器引起的强非线性,提高了四旋翼飞行器实时控制系统控制精度;②将自适应神经网络动态面控制技术应用于欠驱动四旋翼飞行器的控制系统中,克服了传统反推方法中的“微分爆炸”问题,并获得了跟踪性能的L∞范数。最后,在Qball-X4四旋翼飞行器运动控制平台上进行了实验,验证了所提控制方案的有效性。

欺骗攻击下Markov跳变系统的不匹配量化自适应安全控制

本文研究了欺骗攻击下Markov跳变系统的不匹配量化自适应安全控制策略.在控制器端设置了一个多通道结构的量化器,且每个通道具有随时间变化的不匹配度,设计了一个自适应更新率来估计网络攻击的未知界限.接着,设计了一个由三部分组成的自适应安全控制器,基于Lyapunov理论和顶点分离技术,闭环系统随机有界稳定并满足所需的H∞性能指标.最后,通过仿真实验验证了所提策略的有效性.

融合在线检索和量化低秩适配器微调范式的新闻文稿生成

现有大语言模型(LLM)由于存在信息滞后性,在特定风格新闻稿件生成任务上存在生成内容捏造、行文不流畅连贯等问题。为了缓解这些问题,提出一套基于实时在线的web_search技术和量化低秩适配器(QLoRA)微调技术的新闻文稿生成系统的解决方案。首先,利用Bing和Google提供的API根据给定的新闻标题,获取最新的新闻素材集合;其次,利用语义相关性模型和摘要模型对初始素材集合进行筛选和文本处理,选取准确的新闻内容;再次,设计动态的prompt模板综合处理检索到的新闻素材,并在prompt中加入新闻风格约束提示词;最后,将完整的prompt提示词指令输入经过QLoRA微调的LLM中,生成新闻文稿。实验结果显示,在人工整理的热点新闻标题数据集上,所提方案生成的新闻在内容正确性、逻辑连贯性等多维人工评估标准上的平均准确率达到90%,满足实际生产应用的需求,有效提高了新闻生产的效率和质量。目前,该系统已在杭州文广集团内部成功部署应用。

基于损失变化的CNN混合精度量化方法

针对卷积神经网络(convolutional neural network,CNN)在存储和计算资源有限的边缘设备中难以部署应用的问题,提出一种基于损失变化的混合精度量化方法,以低位宽定点数代替全精度浮点数进行运算,降低网络所需资源。根据每个量化层的一阶和二阶信息指导位宽分配,采用K-means方法将量化层聚类成块,降低位宽策略搜索空间。提出一种自适应搜索方式,根据历史策略训练结果自行调整搜索状态。重新整合量化训练过程,减少传统量化训练中计算量。实验结果表明,采用所提方法可在CNN模型推理损失精度较小的前提下,有效压缩模型。

具有输入量化和执行器故障的非线性多智能体系统的自适应包容控制

研究了一类具有输入量化、外部扰动及动态领导者的非线性严格反馈多智能体系统的自适应容错包容控制问题.通过建立带有未知估计的阻尼项、正时变积分函数、一阶滤波器和补偿信号的参数更新律,提出了一种自适应反步控制方案.该方案在补偿滤波误差的同时在虚拟控制律中引入新的光滑函数来降低量化的影响,利用有界估计法克服了故障只能发生有限次的限制,保证所有跟随者的输出收敛于领导者的输出所形成的凸包内,且所有的信号都是有界的.仿真结果验证了该方案的有效性.

微波数据压缩及定标测试方法的研究

针对星载合成孔径雷达系统回波数据容量大、难以满足星地链路间传输速率要求的问题,根据max⁃lloyd量化方法获得了回波数据量化压缩的门限,提出星载SAR数据自适应量化编码压缩和解压缩的具体实现方法。同时,以理想调频连续波信号为发射定标信号,对星载合成孔径雷达系统数据压缩及解压缩过程中的误差进行了详细的分析,并且根据雷达系统压缩后的回波数据在数据高速传输过程中的帧格式,对雷达回波数据进行了二进制的解码及解压缩操作,获得了数据的真实大小。最后,通过Matlab/GUI编程设计了能够对雷达回波数据进行定标分析、解码及解压缩的软件演示系统。通过人机交互的设计,将数据处理过程中的实验结果以dat数据、图片数据和word文档数据等形式进行了存储和显示。

基于时序信息的轻量级视频车辆目标检测方法

为实现在低功耗嵌入式设备上部署视频车辆目标检测模型,提出一种基于时序信息的轻量级视频车辆目标检测方法。该方法以SSD网络为基础,使用MobileNetV3-Small替换原主干特征提取网络VGG-16,并在SSD网络中直接注入注意力机制卷积GRU用于融合时序信息,提升车辆检测精度;关键帧检测网络控制的跳跃连接使模型只在关键帧更新GRU状态,非关键帧直接复制上一关键帧GRU状态,提升模型检测速度。为进一步减少计算量,网络中大量使用深度可分离卷积替换标准卷积层,同时使用量化感知训练方法压缩模型。在UA-DETRAC数据集上的实验表明,该方法在Intel Core i7 CPU和树莓派4B上平均每帧检测时间分别为18 ms和134 ms,准确率达到了较高水平,为78.81%。