In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations.

To address the problems of weak search ability, easily falling into local optimal solutions and poor path quality of sparrow search algorithm in AGV path planning, a multi-strategy improved sparrow search algorithm (MISSA) is proposed in this paper. MISSA improves the global search ability by improving the discoverer position update operator and introducing the sine cosine algorithm; adopts the adaptive number of vigilantes and adaptive adjustment step size to improve the convergence speed; introduces the Levy flight variation strategy to reduce the probability of falling into any local optimal solution; optimizes the boundary handling mechanism to prevent the loss of population diversity at a later stage; finally, uses the large-scale neighborhood search strategy and path smoothing mechanism for path optimization to further improve the path quality. The superiority-seeking ability of MISSA was verified by 12 standard test functions, and then 30 simulation experiments were conducted in grid maps with two specifications of 20×20 and 30×30. The experimental results showed that, by using MISSA, the path length was reduced by 44.1% and 63.1%, the number of turns was reduced by 68.4% and 78.4%, and the risk degree was reduced by 61.3% and 77.2%, which verifies the superiority of MISSA in path planning. Finally, MISSA was ported to the QBot2e mobile robot for physical verification to prove its feasibility in practical applications.

In the application of deep learning-based fault diagnosis, more often than not, the network model could perform better with a balanced dataset input, whereby the number of fault data is equivalent to that of normal data. However, in the context of real-world applications, the number of fault data is generally insufficient compared to the normal data. In this study, a new approach for fault diagnosis in unbalanced data sets is proposed using the Gramian angular field (GAF) method. Firstly, the GAF method is employed to convert one-dimensional data into two-dimensional data, which enhances the feature extraction process. Secondly, to balance the sample distribution, fault data is generated using Generative Adversarial Networks (GANs). Finally, the Residual neural network (ResNet) with an attention mechanism is utilized to improve the accuracy of fault diagnosis. The proposed method is experimentally validated using open-source bearing datasets that are published by Case Western Reserve University and the University of Ottawa. The experimental results show that the proposed method has greatly improved fault diagnosis performance in cases of data distribution imbalance, surpassing that of the compared methods.

In this paper, we firstly extend C. C. chang’s distance functions from MV-algebras into residuated lattices. But in general, the functions may not be a distance function on residuated lattices. We introduce weak involutory residuated lattices, in which Chang’s function is a pseduo distance function. Moreover we prove that the functions become distance functions on involutory residuated lattices. Secondly by use of the function and a lattice filter, we define F-ball on residuated lattices, and we prove that the set of all F-balls forms a base of a topology τLF on an involutory residuated lattice. Moreover we show that the topology τLF on an involutory residuated lattice L makes L to be a topological residuated lattice. At last, we use filters instead of lattice filters to set up filter topologies on an involutory residuated lattice, and study the properties of the filter topologies.

This article proposes an expert-driven consensus and decision-making model that comprehensively considers expert behavior in Multi-criteria decision-making (MCDM) scenarios. Under the premise that experts are willing to adjust their viewpoints, the framework strives to reach group consensus to the utmost degree feasible. To tackle experts’ uncertainty during the evaluation process, this article employs the rejection degree in the picture fuzzy sets (PFS) to signify the level of ignorance while they deliver their evaluation opinions. Due to the diversity of expert views, reaching a group consensus is difficult in reality. Therefore, this article additionally presents a strategy for adjusting the weights of experts who did not reach consensus. This approach upholds data integrity and guarantees the precision of the ultimate decision. Finally, this article confirms the efficiency of the aforementioned model by means of a case study on selecting the optimal carbon reduction alternative for Chinese power plants.

BACKGROUND: In recent years, Despite the proven economic growth brought by AI technology globally, the adoption of AI in the construction industry faces obstacles. To better promote the adoption of AI technology in the construction domain, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, delves into the key factors influencing the adoption of AI technology in the construction industry. By introducing personal-level influencing factors such as AI anxiety and personal innovativeness, the UTAUT model is extended to comprehensively understand users’ attitudes and adoption behaviors towards AI technology. METHODOLOGY: The research framework is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with the added constructs of artificial intelligence anxiety and individual Innovativeness. These data were collected through a combination of online and offline surveys, with a total of 258 valid data collected and analyzed using structural equation modeling. RESULTS: The study found that Usage Behavior (UB) in adopting Artificial Intelligence (AI) is positively influenced by several factors. Specifically, Performance Expectancy (PE) (β= 0.266, 95%), Effort Expectancy (EE) (β= 0.262, 95%), and Social Influence (SI) (β= 0.131, 95%) were identified as significant predictors of UB. Additionally, Facilitating Conditions (FC) (β= 0.168, 95%) also positively influenced UB.Moreover, the study explored the moderating effects of Artificial Intelligence Anxiety and Individual Innovativeness on the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) with the Usage Behavior of AI technology. PRACTICAL IMPLICATIONS: This study lie in informing industry stakeholders about the multifaceted dynamics influencing AI adoption. Armed with this knowledge, organizations can make informed decisions, implement effective interventions, and navigate the challenges associated with integrating AI technology into the construction sector.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

This paper makes a significant contribution to the field of conflict analysis by introducing a novel Interval-Valued Intuitionistic Fuzzy Three-Way Conflict Analysis (IVIFTWCA) method, which is anchored in cumulative prospect theory. The method’s key innovation lies in its use of interval-valued intuitionistic fuzzy numbers to represent an agent’s stance, addressing the psychological dimensions and risk tendencies of decision-makers that have been largely overlooked in previous studies. The IVIFTWCA method categorizes conflict situations into affirmative, impartial, and adverse coalitions, leveraging the evaluation of the closeness function and predefined thresholds. It incorporates a reference point, value functions and cumulative weight functions to assess risk preferences, leading to the formulation of precise decision rules and thresholds. The method’s efficacy and applicability are demonstrated through detailed examples and comparative analysis, and its exceptional performance is confirmed through a series of experiments, offering a robust framework for real-world decision-making in conflict situations.

This study utilized several coupled approaches to create powerful algorithms for forecasting the compressive strength (Cs) of concretes that include metakaolin (MK) and fly ash (FA). For this purpose, three various methods were considered, named random forests (RF), Categorical boosting model (CB), and extreme gradient boosting (XGB) by considering the seven most influential input variables. It was tried to divide the concrete components to binder value (B) to achieve the non-dimensional input variables. Herein, the cutting-edge Tasmanian devil Optimization (TDO) algorithm was linked with RF, XGB, and CB for the purpose of determining the optimal values of hyperparameters (named TD - CB, TD - RF, and TD - XG). It is worth mentioning that developing the mentioned algorithms optimized with TD to estimate the mechanical properties of the concrete containing several important admixtures can be recognized as this study’s contribution to practical applications. The findings indicate that the algorithms possess a notable capacity to precisely forecast the Cs of concrete, which includes MK and FA, with R2 bigger than roughly 0.97. The lower value of OBJ comprehensive index belonged to the TD - CB at 1.5762, followed by TD - XG at 1.9943 and then 2.3317 related to TD - RF with almost 70% reduction. The sensitivity analysis demonstrated that the prediction of Cs is highly influenced by all input parameters, which are higher than 0.8659, but a higher influence from MK/B at 0.9548.