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Copyright of Journal of Atomic & Molecular Physics (1000-0364) is the property of Journal of Atomic & Molecular Physics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Noise, as an unavoidable stress (pressure) source in the modern life, affects animals in many ways, both behaviorally and physiologically. Behavioral changes may be driven by changes in hormone secretion in animals. When animals face with noise stress, the neuroendocrine systems, mainly the hypothalamic-pituitary-adrenal (HPA) axis, are activated, which promotes the secretion and release of stress hormones, and then leads to a series of behavioral changes. The behavioral changes can be easily observed, but the changes in physiological indicators such as hormone levels need to be accurately measured. Currently, many studies have measured the variations of stress hormone levels in animals under different noise conditions. Taking glucocorticoid as an example, this paper summarizes the different measurement methods of stress hormones, especially the non-invasive measurement methods, and compares the advantages and shortcomings of them. It provides a variety of measurement choices for the study of related issues, and also helps us to further understand the sources of animal stress, in order to provide a better habitat for animals.

Chinese medical Q&A is easily affected by the noise of medical-specific terminology, making it more challenging than open-domain Q&A. Previous studies on Chinese medical Q&A mainly relied on character-level fine-grained information, neglecting word-level coarse-grained information that carries more semantic information. In addition, introducing external medical knowledge graph can further enrich the fine-grained information in Q&A sentences, but most existing studies usually adopt a simple way of joint representation of sentences and external knowledge. Therefore, this paper proposes a Chinese medical Q&A matching model based on multi-granularity semantic information and knowledge graph (CMQA-MGSI). The model employs a Lattice network to select the most relevant character-level and word-level sequences from the Q&A sentences, and leverages Word2Vec and BERT to enhance the semantic information; to better exploit the external domain knowledge, a dual-channel attention mechanism is devised to capture the multi-angle knowledge representations between the Q&A sentences and the entity embeddings and relation embeddings in the knowledge graph. Experiments on the cMedQA1.0 and cMedQA2.0 datasets demonstrate that the proposed model outperforms existing Chinese medical Q&A matching models. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ram pressure is lower than the total pressure of the cooling fan in stationary or low-speed vehicles, and the heat is typically managed by the speed of the cooling fan or opening the enclosure. However, the long-term noise exposure to the working space can be a problem due to stationary or low speed operation. In this paper, the opening search technique for the enclosure using the adjoint method has been proposed to solve the dilemma between heat management and noise control. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, anomaly detection for multivariate time series has gradually highlighted its importance in various fields. However, due to the spatiotemporal correlation of the multivariate time series distribution and the inevitable noise in the multivariate time series collected, there exists a non-negligible deviation between the distribution learned by the model and the real distribution, which will affect the detection performance. To tackle the above problems, this paper proposed a new robust anomaly detection model (RADAM) for multivariate time series. The new model used contrastive learning to maximize the mutual information of global and local information of multivariate time series to learn the spatiotemporal correlation among time series. It also used the adaptive weights and a sample filter module to reduce the interference of abnormal samples on the training process, which enabled the model to have high noise resistance ability during training. Finally, through comparing the new model with the-state-of-the-art anomaly detection models on five real-world multivariate time series datasets, the experimental results show that RADAM performs better than other baseline models, which demonstrate that RADAM can significantly improve the performance of anomaly detection on polluted multivariate time series datasets. [ABSTRACT FROM AUTHOR]

Noise is one of the most common environmental hazards to which people are exposed,and the exposure to noise can cause not only hearing but also non-hearing damage.Although noise under safety limits may not affect the auditory system,it can cause changes in stress hormone levels,which is harmful to health.However,the current studies about the impact of noise on health mainly focus on the auditory system,and little is known about the relationship between noise and stress hormone levels.Therefore,this paper reviews the studies involving noise exposure and stress hormone levels,aiming to provide ideas for strengthening the prevention and control of noise hazards.

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Global navigation satellite system (GNSS) coordinate time series provide important data support for the study of crustal movement and deformation, and plate tectonics. Due to the noise caused by various external factors, the GNSS coordinate time series cannot reflect the real motion information of the station well. To effectively reduce the noise in the GNSS time series, we adopted a noise-reduction method combining genetic algorithm (GA) and variational mode decomposition (VMD-GA-VMD). Methods: First, the genetic algorithm was used to optimize VMD parameters, and the envelope entropy of the input signal was used as the fitness function of the genetic algorithm to find the optimal VMD parameter combination suitable for the signal. According to the optimized parameters, the signal was decomposed by VMD to obtain a series of modal components. Then we calculated the multi-scale permutation entropy (MPE) of each component and then regarded the MPE as the criterion of the noise component. Finally, according to the MPE, the noise components were identified and removed, and the remaining components were reconstructed to obtain the noise-reduced signal. In this paper, the noise reduction effect of GAVMD was analyzed through the example of noise reduction of analog signal and observation data, and compared with wavelet denoising (WD) and empirical mode decomposition (EMD) methods. Results: The experiment results show that: (1) The noise reduction results from the analog signals show that WD and EMD have the incomplete and excessive troubles on the noise reduction, respectively. However, GA-VMD can effectively eliminate noise and retain effective signals. From the evaluation index, compared with WD and EMD, the signal-to-noise ratio were increased by 5.18 dB and 2.91 dB, the correlation coefficient by 0.05 and 0.02, respectively, when using GA-VMD. (2) For the complex observation, we used the noise and velocity uncertainty as accuracy indicators to evaluate the noise reduction effects of the three methods. The results show that WD can only extract a part of the white noise, while EMD and GA-VMD can completely remove the white noise. GA-VMD can reduce the flicker noise to the range of 0 to 6 mm/a0.25. For the velocity uncertainty, the average gain rates of GA-VMD relative to the WD and EMD are 69% and 15.33%, respectively. GA-VMD has an average correction rate of 79.89% and 84.46% for the velocity uncertainty and flicker noise of GNSS coordinate time series. Conclusions: Therefore, GA-VMD is the most effective one among the three noise reduction methods, which can effectively reduce the noise in the GNSS time series and improve its accuracy. However, in this paper, we only discussed the effect of GA on VMD parameter optimization without comparing it with other methods. Hence, it will be the key for studying the advantages and shortcomings of those optimization algorithms in the selection of VMD, and improving the accuracy on the GNSS time series in the future. [ABSTRACT FROM AUTHOR]

The existing one-class support vector machine algorithms are based on the idea of "minimum margin". They consider only the noise close to the space origin and are sensitive to noise. The purpose of this paper is to improve the anti-noise ability of the one-class support vector machine through the idea of "optimal margin distribution", which considers both of the noise close to the space origin and that far from the space origin. This paper proposes a novel One-Class Optimal Margin Distribution Machine (OCODM) method. This method maximizes the margin and minimizes the margin variance to optimize the margin distribution. The experimental results have shown that compared to the existing one-class support vector machine algorithms, this method has better robustness. The conclusion is that this method is a supplement of the existing one-class support vector machine algorithms and can enhance the anti-noise ability of existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

How to effectively mine unimodal representations and achieve full fusion of multimodal information is one of the key points in multimodal sentiment analysis. To address the issues of inter-modal noise and inadequate fusion of multimodal features in multimodal sentiment analysis, this paper proposed a multimodal sentiment analysis model based on cross-modal gating mechanism and improved fusion method. Firstly, this paper utilized cross-modal gating mechanism to remove inter-modal noise and extract complementary information, enhancing the modal representation. Then, the model used weight and similarity constraint to focus the difference of emotional contribution and the consistency of emotional expression among modalities, respectively. Finally, the model combined multi-level representation of modalities to obtain results of sentiment analysis. Experimental results on three public datasets show that the proposed model is effective and achieves better performances than some existing models. [ABSTRACT FROM AUTHOR]

It is difficult to label the label proportions data contaminated by noise in practical applications. This paper applied multi-view learning to uncertain learning with label proportions to improve classifier both performance and stability. Firstly, the proposed method introduced kernel function and super instance label for the representation of multi-view knowledge and label proportions information respectively, and considered the limitation of uncertain label proportions data to construct the model of uncertain label proportions information-based multi-view learning. Then, it transformed the model to its dual forms by the Lagrange method for solving the problem and introduced an iterative framework to obtain the classifiers of multiple views. Finally, this paper applied the proposed method to datasets of text categorization. Extensive experiments show that the proposed method outperforms the existing single-view learning with label proportions methods in accuracy and noise sensitivity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

PGO is a high-dimensional non-convex optimization algorithm commonly used in the back-end optimization of SLAM, which is usually modeled as maximum likelihood estimation. Since the current PGO algorithm faces difficulties in improving speed while ensuring accuracy when optimizing large-scale noise datasets, this paper proposed a hierarchical pose graph optimization algorithm based on nested dissection algorithm for large noise datasets. The algorithm firstly established a χ² test model with different distance measures, and then removed outlier points. Secondly, it used nested dissection method to split the original pose graph into a set of subgraphs and extracted a skeleton graph from these subgraphs. The skeleton represented the abstract topology of the original SLAM problem. Then, the algorithm optimized the skeleton graph and completed the initialization. Finally, experimental evaluation on simulated and real pose graph datasets show that the proposed algorithm can improve the calculation speed and scalability of the algorithm without affecting the accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To protect user privacy, social network graph data is usually anonymized before publishing. However, the existing anonymous technologies can not protect user privacy well and affect the availability of social network data due to too much change. This paper proposed a data publishing model (DPPM) for social network graph satisfying differential privacy. This model extracted the structure information of a graph into dK degree correlation statistics, introduced noise into the dataset, and generated a social network graph. It was proved theoretically that the method satisfies differential privacy. This paper used three real social network datasets to evaluate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Atomic & Molecular Physics (1000-0364) is the property of Journal of Atomic & Molecular Physics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of the low accuracy of fine retinal vessel segmentation, this paper proposed improved U-Net methods combining scalable cascade modules, Transformer and self-calibrated attention modules to improve the accuracy of fine retinal vessel segmentation. Firstly, this paper used scalable cascaded modules in the encoding stage to enable the learning of complex and variable retinal vessel structures. Secondly, in the decoding stage, this paper proposed a self-calibration attention mechanism, which used the multi-scale compression excitation module to adaptively recalibrate the blood vessel from channel and spatial of features, it could enhance the feature response of the target area, and suppressed the background noise. Finally, the Transformer feature extraction block improved the capability of feature space mapping. This proposed method tested on public datasets, i. e. the DRIVE and CHASEDB1. The experimental results of proposed method show that the accuracy of the retinal vessel segmentation from the two datasets reach 96. 49%/96. 67%, the sensitivity reach 83. 75%/83. 30%, the specificity reach 98. 28%/98. 01% and the AUC reach 0. 987 1/0. 987 2, respectively. The performance of proposed method is better than most of existing methods, and each modules can improve the ability of fine retinal vessel segmentation. [ABSTRACT FROM AUTHOR]

Copyright of Nonferrous Metals (Mining Section) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to reduce the noise ratio of pseudo labels in unsupervised deep hash learning, this paper proposed a novel two-stage unsupervised distillation hashing method based on equivalent constraint clustering（UDH-ECC）. The main idea of this method was to utilize a robust clustering algorithm to annotate unlabeled images, which can get better soft pseudo labels for hash learning. To be specific, in the first stage, select a pre-trained teacher network to extract deep image features. Then, cluster the deep image features by the proposed equivalent constraint clustering algorithm, than assign hard pseudo labels to unlabeled images by the clustering results. Benefiting from the high accuracy of the hard pseudo labels, this method fine-tune the teacher network to further adjust the unlabeled dataset. In the second stage, utilize the predictive probability distribution produced from the fine-tuned teacher network as the soft pseudo labels to train the student hashing network. To further reduce the noise in soft pseudo labels, this paper proposed a distillation hashing method to convert noisy labels into clean hash codes. This paper compare our method with other twelve stateof-the-art methods on three public datasets. The proposed method can outperform other state-of-the-art methods by a large margin, which is 12.7% higher than the TBH method on CIFAR-10, 1.0% higher than the DistillHash method on FLICKR25 K, and 16.9% higher than the ETE-GAN method on EuroSAT. Comprehensive experimental results show that our method not only has high performance, but also has good adaptability to variety datasets. [ABSTRACT FROM AUTHOR]

Comparing the similarity of different enhancements of the same image is the key to achieving remarkable results in contrastive learning. The traditional contrastive learning method uses two different views of the image. In order to learn more information of the image to improve the classification accuracy, based on MoCo, this paper presented multi-view momentum contrast learning algorithms. In each iteration, this paper used a query encoder and multiple momentum encoders to extract feature for multiple data enhancements of the image, so that it could use more data enhancements and negative samples in this iteration. And it used the optimized noise contrast estimation(InfoNCE) to calculate the loss, so that the query encoder could obtain feature representations which was more beneficial to downstream tasks. The query encoder network updated with gradient backhaul, and each momentum encoder updated with an improved momentum update formula to improve the generalization ability of the model. Experimental results show that using multi-view momentum contrastive learning can effectively improve the classification accuracy of the model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposed an improved unscented Kalman filter(UKF) mobile robot localization algorithm based on Sage-Husa filter to solve the problem of low localization accuracy caused by unknown or inaccurate noise statistical characteristics in multi-sensor fusion localization process. Firstly, it established the mobile robot localization model. Then, according to the time-varying characteristics of noise statistics, this article adaptively estimated the state noise and measurement noise to reduce the error of position calculation caused by perturbation noise. The paper also introduced the convergence factor in the state update to accelerate the convergence speed of the algorithm. Finally, the UKF algorithm and the improved UKF algorithm are applied to the laboratory mobile robot for simulation experiments. The experimental results show that the proposed algorithm has strong resistance to state disturbance, and has remarkable effect on improving the accuracy and stability of robot positioning. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyzes the shortcomings of the existing pure tone audiometers, and proposes a system to realize pure tone audiometry and speech audiometry with a new DSP processor. The pure tone test signal produced by the system has accurate frequency, high signal-to-noise ratio, and small harmonic distortion. The noise generator that comes with DSP adds a band-pass filter to realize the generation of narrow-band noise. At the same time, due to the modular structure of software design, the system has good ease of use and scalability. The test results show that the hearing test system has excellent performance and can be better used in hearing medical diagnosis.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposed a new network framework for RGB-D salient object detection (SR-Net). In order to effectively integrate the complementarity of multi-model features, this paper took the depth feature extraction as an independent branch, use the Convolutional Block Attention Module (CBAM) to enhance the depth feature, and integrate the complementary information of the enhanced depth feature and RGB feature. Then, in order to remove feature redundancy and reduce the interference of background noise on the prediction results, it proposed a sequential refining network in the up-sampling network, that is, first, the primary global features are obtained by integrating the complementarity of multi-level and multiscale features, and used the Primary Global Feature Weight Matrix Acquisition Module (PFW) which based on the channel weight to obtains the weight matrix of the primary global feature, and then uses the obtained weight matrix to refine the features of each level to suppress the interference which caused by background noise. Finally, in order to better optimize the whole network, it proposed a new loss function. The experimental results on four public datasets show that the model is superior to nine advanced methods in different model evaluation indexes, and achieves more advanced performance. [ABSTRACT FROM AUTHOR]

Recommendation systems are widely used everywhere and have a great influence on daily life. Aiming to train an ideal recommendation system, a massive of ‘use-item’ interactive pairs should be provided. However, the dataset obtained is usually sparse, which might result in an over-fitting model and be hard to obtain the satisfying performance. In order to address this problem, the cross-domain recommendation is raised. Most of the existing methods on cross-domain recommendation systems borrow the ideas of the conventional unsupervised domain adaptation, which employ the feature alignment or adversarial training methods. Hence they can transfer the domain-invariant interests of users from the source to the target domains, e. g., from the Douban Movies to the Douban Books. However, since the network structures vary with different recommendation platforms, the existing methods on cross-domain recommendation systems straightforwardly extract the domain-invariant representation may entangle the structure information, which may result in the false alignment phenomenon. Furthermore, the previous efforts ignore the noise information behind the graph data, which further degenerate the experimental performance. In order to address the aforementioned problems, this paper brings the causal generation process of graph data into the cross-domain recommendation systems, this paper use the semantic latent variables of each node to calculate the relationships between users and items via disentangling the semantic latent variables, domain latent variables and noise latent variables. Experiments studies testify that the proposed method yields state-of-the-art performance on several cross-domain recommendation system benchmark datasets. [ABSTRACT FROM AUTHOR]