Ambiguity Problem Research Articles

Identification of Physical Dynamical Processes via Linear Structure Models (Part 1)

The article discusses methods for identifying parameters of partial differential equations. Identification problems are often called poorly conditioned. However, the reason is the ambiguity of the solution, even at the point of minimality of the criterion. In particular, the article discusses: (1) An analysis of singular values for identify the unambiguous solution. The basis of these methods is a singular value decomposition of the matrix of experimental data, what makes it possible to abandon the inversion of matrices and, as a consequence, translate the problem of ill-conditioned problems into the problem of ambiguity of the solution. (2) The issues of anomalous measurements and combination of various experiments. (3) A universal optimization method for identifying parameters by their complete simple enumeration. The method is based on fast calculation of points on a multidimensional sphere. (4) The issues of identifiability of linear structure models and construction of experiments guaranteeing identification. (5) A method for identifying parameters via projecting linear structure model elements onto the plane of guarantors. (6) An approach to constructing histograms of unknown parameters of dynamic systems before calculating them using any algorithm. The approach is based on linear structure models with parameters on a sphere and a rather unexpected application of singular value decomposition. (7) The methods are accompanied by examples of heat equations. The Appendix containsalgorithmsintheMATLABlanguageforallexamples. (8)The presented optimization, projection and statistical methods based on the concept of linear structure models allow solving the same identification problem in fundamentally different ways, which significantly increases the reliability of the results obtained.

Application of the Hilbert-Huang transform to evaluate signals in chromatic confocal spectral interferometry

Chromatic confocal spectral interferometry (CCSI) is a hybrid measurement technique that integrates the principles of spectral interferometry and chromatic confocal microscopy. This innovative approach enables scanning-free acquisition of axial dimensions while leveraging interferometric methods to enhance depth accuracy. This feature allows the CCSI signal to be processed using both peak extraction and evaluation of the interferometric optical path length difference. The phase information offers a decreased measurement uncertainty, making it a commonly used approach in existing studies. Methods such as Fourier transform (FT) and wavelet transform (WT) are frequently employed for this purpose. In this paper, we present a signal processing approach based on the Hilbert-Huang transform (HHT). Through simulations and experiments, we compare HHT with Fourier transform (FT) and wavelet transform (WT), demonstrating its stability, noise resistance, and effectiveness in phase extraction for CCSI measurement signals. Additionally, we leverage the characteristics of CCSI signals and the time-frequency analysis capabilities of HHT to address the direction ambiguity problem in white light interferometry.

Semantic Scene Completion in Autonomous Driving: A Two-Stream Multi-Vehicle Collaboration Approach.

Vehicle-to-vehicle communication enables capturing sensor information from diverse perspectives, greatly aiding in semantic scene completion in autonomous driving. However, the misalignment of features between ego vehicle and cooperative vehicles leads to ambiguity problems, affecting accuracy and semantic information. In this paper, we propose a Two-Stream Multi-Vehicle collaboration approach (TSMV), which divides the features of collaborative vehicles into two streams and regresses interactively. To overcome the problems caused by feature misalignment, the Neighborhood Self-Cross Attention Transformer (NSCAT) module is designed to enable the ego vehicle to query the most similar local features from collaborative vehicles through cross-attention, rather than assuming spatial-temporal synchronization. A 3D occupancy map is finally generated from the features of collaborative vehicle aggregation. Experimental results on both V2VSSC and SemanticOPV2V datasets demonstrate TSMV outpace state-of-the-art collaborative semantic scene completion techniques.

A non-functional requirements classification model based on cooperative attention mechanism fused with label embedding

Intelligent classification of software requirements is a hot research issue in the field of requirements engineering. Complete and accurate identification of functional requirements (FRs) and non-functional requirements (NFRs) is the primary task of requirements engineering. However, in real software projects, NFRs are easily neglected and may become a potential risk of project failure. Text is the main source of information about software requirements. With the increasing scale of software projects, a large number of complex types of text materials are used for software requirements analysis. Manual identification of NFRs of software projects has the problems of easy omission, ambiguity, vagueness, and high time-consuming cost. Based on the above existing defects, a deep neural network model named CAFLE is designed in this paper to solve it. CAFLE is composed of two parts, Text-label cooperative attention encoder (TLCAE) and Label decoder (LD). TLCAE adopts a Bi-directional long short-term memory network (Bi-LSTM) and multi-head cooperative attention mechanism to generate an encoded representation of the mutual involvement of requirement classification labels and requirement text. LD is an LSTM decoder with an attention mechanism constructed for the multi-class classification task of requirement text. LD utilizes the representation generated by TLCAE for prediction. Experimental results on the PROMISE benchmark dataset show that CAFLE outperforms existing NFRs classification methods with an F1 score of 95%.

Communicative consciousness and principles of environmental design in the experimental project "Stone. Inversion. Vessel"

The article is based on the analysis and summary of the design process of the work of the Chinese author "Stone. Inversion. Vessel". It examines three key aspects of the development of environmental design. Firstly, it is a combination of modern technologies with aesthetic representations of traditional Chinese culture. Through the study of the project, it is demonstrated how traditional Chinese art can be combined with modern technologies when creating a design object. Secondly, the project highlights the features of the development of design in the modern era. It is characterized by problems of volatility, uncertainty, complexity and ambiguity, which can be mitigated through interdisciplinary integration with the use of the latest technologies. Environmental design, thanks to the multifaceted concept of "environment", meets the conditions of development of the modern era. Thirdly, the article focuses on the fact that the communication of the subjects of the creative process is based on the images embodied in a specific project. Environmental design as an interdisciplinary field is based on the methods of different scientific fields, since the environment that changes under the influence of human activity is not the same. In the proposed article, the author uses the art criticism method when describing blue-and-green landscape painting, and also assigns a large role to the communicative method of sociology developed by the American scientist Harold Dwight Lasswell. The novelty of the research lies in the fact that the author was able to show the possibility of a harmonious combination of modern 3D modeling technologies with the traditional Chinese art of blue-and-green landscape painting (青绿山水 qīng lǜ shān shuǐ). In other words, a planar image turns into a design object, with the help of which can be realized various spatial combinations. Through artistic thinking, one can enjoy not only the functional, but also the aesthetic possibilities of the design object. So, in the described project, the author focuses on the imagination of the observer, forcing him to climb a mountain peak, but inside the building. This is how communication takes place between the author, the "viewer" and the project itself. The image is the basis of communication between various objects of the environment involved in the design process. The creative process combines the material (functional) and spiritual (image-induced) needs of a person. Environmental design, through the synthesis of spiritual and material, establishing communication between design objects and integrating disciplines through the prism of an image, explores how design affects human activity.

Applications of T-spherical fuzzy aczel-alsina power muirhead mean operators in identifying the most effective water purification process for commercial purpose

One of the key areas of research in ambiguous and inconsistent problems with decision-making is T-spherical multiple-attribute decision making (TSP-FMADM). The TSP-F number (TSP-FN), which is an extension of the fuzzy number, an intuitionistic fuzzy number, and other fuzzy structures, may deal with problems involving a significant amount of incorrect, incomplete, and inconsistent data. Being an extension of various fuzzy structures, the TSP-F sets (TSP-FSs) provide decision-makers greater freedom to voice their actual opinions and offer a broader range of acceptable membership grades. The Muirhead mean (MM) operator and power aggregation (PA) operators are illustrations of standard aggregation operators. They are better because they can reproduce the relationships amongst input qualities and eliminate the negative influence of obstinate data. Since its publication in 1982, Aczel and Alsina's t-norms have proven to be a highly effective and popular technique for generating aggregation operators of any kind. Furthermore, the parameter belongs to (0,+∞) converts the Aczel-Alsina t-norms into a specific instance of the algebraic t-norms. In this article, novel aggregation operators (AGOs) are suggested, considering the advantages of the TSP-FN, to handle the multi-criteria decision-making problems. These new AGOs consider how various input data are related to one another and can mitigate the impact of inaccurate data at the same time. To strengthen the adaptability of these new AGOs, this article proposes the T-spherical fuzzy Aczel-Alsina power Muirhead mean (TsP-FAAPMM) operator, T-spherical fuzzy Aczel-Alsina power dual Muirhead mean (TsP-FAAPDMM) operator which combines the Aczel-Asina operational rules with the power average/geometric operator and the Muirhead/dual Muirhead mean operators. A variety of fundamental characteristics and special cases with respect to the parameters are explored and it is retrieved that various existing AGOs are special cases of these newly initiated AGOs. Further, weighted forms of these AGOs are established. Then, we set up the multiple attribute decision making (MADM) technique using these AGOs that are suggested to solve MADM problems. We then give a numerical example about industrial water purification selection and compare it to other related MADM techniques that are already in use in the TsP-FN information to demonstrate the effectiveness and appropriateness of the anticipated technique.

Multi-Modal LiDAR Point Cloud Semantic Segmentation with Salience Refinement and Boundary Perception

Point cloud segmentation is essential for scene understanding, which provides advanced information for many applications, such as autonomous driving, robots, and virtual reality. To improve the accuracy and robustness of point cloud segmentation, many researchers have attempted to fuze camera images to complement the color and texture information. The common fusion strategy is the combination of convolutional operations with concatenation, element-wise addition or element-wise multiplication. However, conventional convolutional operators tend to confine the fusion of modal features within their receptive fields, which can be incomplete and limited. In addition, the inability of encoder–decoder segmentation networks to explicitly perceive segmentation boundary information results in semantic ambiguity and classification errors at object edges. These errors are further amplified in point cloud segmentation tasks, significantly affecting the accuracy of point cloud segmentation. To address the above issues, we propose a novel self-attention multi-modal fusion semantic segmentation network for point cloud semantic segmentation. Firstly, to effectively fuze different modal features, we propose a self-cross fusion module (SCF), which models long-range modality dependencies and transfers complementary image information to the point cloud to fully leverage the modality-specific advantages. Secondly, we design the salience refinement module (SR), which calculates the importance of channels in the feature maps and global descriptors to enhance the representation capability of salient modal features. Finally, we propose the local-aware anisotropy loss measure the element-level importance in the data and explicitly provide boundary information for the model, which alleviates the inherent semantic ambiguity problem in segmentation networks. Extensive experiments on two benchmark datasets demonstrate that our proposed method surpasses current state-of-the-art methods.

Molecular dynamics and small-angle x-ray scattering: a comparison computational and experimental approaches to studying the structure of biological complexes

The results of studying DNA-protein complexes using two independent structural methods – molecular dynamics (MD) and small-angle X-ray scattering (SAXS) – are compared. MD is a computational method that allows visualization of macromolecule behavior in real environmental conditions based on the laws of physics but suffers from numerous simplifications. SAXS is an X-ray method that allows the reconstruction of the three-dimensional structure of an object in solution based on the one-dimensional profile of small-angle scattering, which presents the problem of ambiguity in solving inverse problems. The use of structural characteristics of complexes obtained by the SAXS method for validating 3D structural models obtained in MD experiments has significantly reduced the ambivalence of theoretical predictions and demonstrated the effectiveness of combining MD and SAXS methods for solving structural biology problems.

Deep fuzzy physics-informed neural networks for forward and inverse PDE problems

As a grid-independent approach for solving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have garnered significant attention due to their unique capability to simultaneously learn from both data and the governing physical equations. Existing PINNs methods always assume that the data is stable and reliable, but data obtained from commercial simulation software often inevitably have ambiguous and inaccurate problems. Obviously, this will have a negative impact on the use of PINNs to solve forward and inverse PDE problems. To overcome the above problems, this paper proposes a Deep Fuzzy Physics-Informed Neural Networks (FPINNs) that explores the uncertainty in data. Specifically, to capture the uncertainty behind the data, FPINNs learns fuzzy representation through the fuzzy membership function layer and fuzzy rule layer. Afterward, we use deep neural networks to learn neural representation. Subsequently, the fuzzy representation is integrated with the neural representation. Finally, the residual of the physical equation and the data error are considered as the two components of the loss function, guiding the network to optimize towards adherence to the physical laws for accurate prediction of the physical field. Extensive experiment results show that FPINNs outperforms these comparative methods in solving forward and inverse PDE problems on four widely used datasets. The demo code will be released at https://github.com/siyuancncd/FPINNs.

A Study of Difference and Identification between Homonymy and Polysemy

Lexical ambiguity is an essential problem in practical and applied language processing, but a few relatively information is available about what causes and effect the problem of lexical ambiguity. After many linguistic and professional researched lexical relations for a long time, people found that homonymy and polysemy are two well-known semantic problems. This paper mainly discusses the homonymy and polysemy from the definition, cause of formation, the principles of distinction and semantic ambiguity. At the same time, some methods of distinguishing homonymous and polysemous words are offered in one part of thesis. It is valuable reference for English learning, English teaching and cross-cultural communication.

Main lobe deceptive jamming suppression based on blind source separation and energy detection for monopulse radar

AbstractMain lobe deceptive jamming always causes the serious degradation of signal detection ability and angle measurement precision of monopulse radar. In recent years, the Blind Source Separation (BSS) method has been adopted to suppress the main lobe jamming. However, the separation results of BSS have the problem of amplitude ambiguity, which will cause the radar using monopulse angle measurement fail to measure the angle parameters after jamming suppression. To tackle this problem, a novel main lobe jamming suppression method is proposed based on BSS and energy detection. Firstly, the models of target echoes and jamming in the sum and difference beam receiving channels are derived. Secondly, the target echoes and interferences are separated by the Joint Approximate Diagonalisation of Eigenmatrices (JADE) algorithm, and then the unperturbed signal segments in the mixed signal are extracted by energy detection, thereby obtaining the precise ratio of the sum and difference channels to complete the angle measurement. Performance of the method was verified by numerical simulation. The results show that the proposed method can achieve interference suppression while accurately estimating the angle parameter of the target.

Classical Logic, Some of its Limits, and the Psychological Normality

This article identifies some situations of linguistic ambiguities and how general logic tries to solve them and it analyzes the influence it can have in particular situations. Since the topic is vast, we stopped at the ambiguities of language caused by identity. First, the attempt to solve the problem was the enunciation of the laws of logic, namely the principle of identity, and we followed its limits. There are analyzed three aspects emphasizing the degree of remaining ambiguity: the relationship between intension and extension, vague terms, and symbolical-metaphorical thinking. Each of the examples given accentuated the limits of classical logic in the face of the natural language ambiguity problems. Ambiguous situations do not only have effects on logical or communicative levels; we also showed the impact on the fields of psycho-social interventions: therapy, counseling, and education.

Long‐term Motion In‐betweening via Keyframe Prediction

AbstractMotion in‐betweening has emerged as a promising approach to enhance the efficiency of motion creation due to its flexibility and time performance. However, previous in‐betweening methods are limited to generating short transitions due to growing pose ambiguity when the number of missing frames increases. This length‐related constraint makes the optimization hard and it further causes another constraint on the target pose, limiting the degrees of freedom for artists to use. In this paper, we introduce a keyframe‐driven approach that effectively solves the pose ambiguity problem, allowing robust in‐betweening performance on various lengths of missing frames. To incorporate keyframe‐driven motion synthesis, we introduce a keyframe score that measures the likelihood of a frame being used as a keyframe as well as an adaptive keyframe selection method that maintains appropriate temporal distances between resulting keyframes. Additionally, we employ phase manifolds to further resolve the pose ambiguity and incorporate trajectory conditions to guide the approximate movement of the character. Comprehensive evaluations, encompassing both quantitative and qualitative analyses, were conducted to compare our method with state‐of‐the‐art in‐betweening approaches across various transition lengths. The code for the paper is available at https://github.com/seokhyeonhong/long-mib

An Improved Unfolded Coprime Linear Array Design for DOA Estimation with No Phase Ambiguity

In this paper, the direction of arrival (DOA) estimation problem for the unfolded coprime linear array (UCLA) is researched. Existing common stacking subarray-based methods for the coprime array are invalid in the case of its subarrays, which have the same steering vectors of source angles. To solve the phase ambiguity problem, we reconstruct an improved unfolded coprime linear array (IUCLA) by rearranging the reference element of the prototype UCLA. Specifically, we design the multiple coprime inter pairs by introducing the third coprime integer, which can be pairwise with the other two integers. Then, the phase ambiguity problem can be solved via the multiple coprime property. Furthermore, we employ a spectral peak searching method that can exploit the whole aperture and full DOFs of the IUCLA to detect targets and achieve angle estimation. Meanwhile, the proposed method avoids extra processing in eliminating ambiguous angles, and reduces the computational complexity. Finally, the Cramer–Rao bound (CRB) and numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed method.

DBMHT: A double-branch multi-hypothesis transformer for 3D human pose estimation in video

The estimation of 3D human poses from monocular videos presents a significant challenge. The existing methods face the problems of deep ambiguity and self-occlusion. To overcome these problems, we propose a Double-Branch Multi-Hypothesis Transformer (DBMHT). In detail, we utilize a Double-Branch architecture to capture temporal and spatial information and generate multiple hypotheses. To merge these hypotheses, we adopt a lightweight module to integrate spatial and temporal representations. The DBMHT can not only capture spatial information from each joint in the human body and temporal information from each frame in the video but also merge multiple hypotheses that have different spatio-temporal information. Comprehensive evaluation on two challenging datasets (i.e. Human3.6M and MPI-INF-3DHP) demonstrates the superior performance of DBMHT, marking it as a robust and efficient approach for accurate 3D HPE in dynamic scenarios. The results show that our model surpasses the state-of-the-art approach by 1.9% MPJPE with ground truth 2D keypoints as input.

The Problem of Visual Complexity: Criteria for Analysis and Evaluation

The article analyses the concept of visual complexity, highlighted by different researchers as a significant characteristic of contemporary visual communication and culture. The problem of ambiguity of the concept and its dependence on different objective and subjective factors and perception variables is posed. Formal categories and criteria for assessing visual complexity are defined, its stable patterns in the subject area of corporate identity —logo, are revealed. With the help of the identified categories visual complexity is analyzed through specific formal universal attributes that can be systematized, which makes it possible to answer the question of what makes a logo complex. The concept of complexity of corporate identity is meaningfully revealed from the point of view of designers — its direct developers, who determine the visual-graphic, constructive and plastic character of the brand logo. For the first time in the Russian-speaking scientific space, an empirical study of the position of professional designers regarding the significance of the proposed criteria was conducted. The quantitative survey method allowed objectifying the point of view of the professional audience: the data is characterized by stability in comparison with the data obtained from the general audience. Visual complexity is thus conceptualized and empirically operationalized without a gap between the theory and practice of visual communication. It is concluded that, as an expressive design tool, visual complexity is a positive characteristic in contemporary media communication.

A novel energy-focused slow-time MIMO radar and signal processing scheme

In this paper, we propose a novel energy-focused slow-time MIMO radar and signal processing scheme, aimed at addressing key challenges in slow-time coding and signal processing technology. Conventional slow-time MIMO radar faces issues such as energy waste due to the omnidirectional transmit beampattern of orthogonal coding and the velocity ambiguity problem. To overcome these limitations, the proposed radar system utilizes a method based on Doppler frequency offset diversity (DFOD) for slow-time coding design. This method enables the adjustment of Doppler offset parameters to achieve a rectangular transmit beampattern with any mainlobe width within a single coherent processing interval (CPI), offering the advantage of low computational complexity. Through an analysis of the ambiguity function for DFOD-based coding, we evaluate both Doppler and angular resolution. To further improve Doppler frequency resolution, a slow-time coding design is introduced based on Pulse Random Permutation (PRP). Subsequently, a signal processing scheme based on matched filtering is presented. To tackle the high Doppler sidelobe issue associated with PRP-based coding, we propose a mismatch filter (MMF) design method utilizing convex optimization. Ultimately, the performance enhancement of the proposed slow-time MIMO radar is verified through simulation analysis in comparison to existing technologies.

A Synchronization Algorithm for MBOC Signal Based on Reconstructed Correlation Function

In order to address the ambiguous synchronization problem caused by the multi-peak nature of the autocorrelation function of the modulated signal of the Multiplexed Binary Offset Carrier (MBOC) in the Global Navigation Satellite System (GNSS), a new synchronization algorithm for MBOC signals is presented in this paper, which uses a reconstruction correlation function to effectively handle synchronization ambiguities associated with multi-peak signals. The paper proposes an algorithm for reconstructing the correlation function of the MBOC signal by analyzing its characteristics. The algorithm generates three local auxiliary signals, namely, pseudo-random codes (PRN), BOC(1,1) signals, and MBOC signals, which are correlated with the received signal. By combining the three correlation functions, the algorithm produces a reconstructed correlation function based on reconstruction rules, eliminating side peaks and achieving unambiguous synchronization. Simulation results show that the proposed algorithm in this paper eliminates all side peaks while maintaining a high detection probability, and its deblurring capability is optimal compared to other algorithms. In addition, the discriminant curve shows that the algorithm in this paper successfully eliminates all the mis-locked points, and the slope gain is improved by more than 2.5 dB compared with other algorithms, and the anti-multipath performance of the algorithm in this paper is better than that of other traditional algorithms, such as ASPeCT (Autocorrelation Side-Peak Cancellation Technique).

Learning accurate monocular 3D voxel representation via bilateral voxel transformer

Vision-based methods for 3D scene perception have been widely explored for autonomous vehicles. However, inferring complete 3D semantic scenes from monocular 2D images is still challenging owing to the 2D-to-3D transformation. Specifically, existing methods that use Inverse Perspective Mapping (IPM) to project image features to dense 3D voxels severely suffer from the ambiguous projection problem. In this research, we present Bilateral Voxel Transformer (BVT), a novel and effective Transformer-based approach for monocular 3D semantic scene completion. BVT exploits a bilateral architecture composed of two branches for preserving the high-resolution 3D voxel representation while aggregating contexts through the proposed Tri-Axial Transformer simultaneously. To alleviate the ill-posed 2D-to-3D transformation, we adopt position-aware voxel queries and dynamically update the voxels with image features through weighted geometry-aware sampling. BVT achieves 11.8 mIoU on the challenging Semantic KITTI dataset, considerably outperforming previous works for semantic scene completion with monocular images. The code and models of BVT will be available on GitHub.

Soft Systems Methodology in Standardizing the Method for Applying Dolphin-Assisted Therapies in Neurodivergent Patients: Case Study of Delfiniti Mexico

Dolphin-assisted therapy (DAT) currently lacks a standard for their application, making it difficult to collect the consistent data necessary for comparative studies and the development of new evidence-based therapeutic strategies. Due to their high social component, DAT requires a standardized method that identifies the elements that affect them, understands their complex situations, and proposes solutions to the challenges. This study aims to establish the first steps towards standardizing DAT, using the Soft Systems Methodology (SSM) as the central approach. SSM is suitable for addressing complex and ambiguous problems that involve multiple actors and perspectives. Through SSM, the study seeks to visualize problems, clarify conflict relationships that hinder standardization, and propose effective solutions. To establish an initial standard method, a time and motion study is performed to identify activities that disrupt the sequence of operations and the capture of EEG signals collected before, during, and after DAT. SSM allows for summarizing the current system situation, identifying and analyzing problems, clarifying challenges, and proposing pertinent solutions to achieve the standardization of this therapy. This methodology facilitates the identification of critical points and the development of intervention strategies that could improve the efficiency and effectiveness of the therapeutic process, establishing a more coherent framework for the implementation of DAT. Thus, the contribution of this work is based on systems thinking to strategic management, as it demonstrates the potential role of systems thinking, specifically SSM, in analyzing complex problems, improving strategy mapping, fostering strategic decision making, and planning for the future in the context of strategic management.