This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.In daily pipeline inspection, it is significant to ensure good network communication and security. With the development of drone technology, it is possible to apply drones as air routers to collect information from pipeline networks and transmit it to pipeline inspectors. It is also crucial to achieve optimal drone deployment in pipeline networks. This article proposes a two-phase evolution optimal 3-D drone layout algorithm to deploy drones in pipeline networks. First, a 3-D pipeline graph model is designed to represent the possible projection position of drones, and the objective function is proposed for optimal drone deployment. Then, in the first phase, based on the features of the 3-D pipeline graph, the drone flight rules and constraint conditions are presented to calculate the number of drones and the initial layout sequence. https://www.selleckchem.com/products/cc-90001.html In the second phase, according to the objective function and the above results, every drone is continuously moved in a small area to achieve a tradeoff between signal coverage and interference. Moreover, the key parameters of the objective function can be discussed to further optimize drone deployment. Simulation results are presented to illustrate the effectiveness and advantages of the proposed algorithm.Attribute reduction is one of the most important preprocessing steps in machine learning and data mining. As a key step of attribute reduction, attribute evaluation directly affects classification performance, search time, and stopping criterion. The existing evaluation functions are greatly dependent on the relationship between objects, which makes its computational time and space more costly. To solve this problem, we propose a novel separability-based evaluation function and reduction method by using the relationship between objects and decision categories directly. The degree of aggregation (DA) of intraclass objects and the degree of dispersion (DD) of between-class objects are first defined to measure the significance of an attribute subset. Then, the separability of attribute subsets is defined by DA and DD in fuzzy decision systems, and we design a sequentially forward selection based on the separability (SFSS) algorithm to select attributes. Furthermore, a postpruning strategy is introduced to prevent overfitting and determine a termination parameter. Finally, the SFSS algorithm is compared with some typical reduction algorithms using some public datasets from UCI and ELVIRA Biomedical repositories. The interpretability of SFSS is directly presented by the performance on MNIST handwritten digits. The experimental comparisons show that SFSS is fast and robust, which has higher classification accuracy and compression ratio, with extremely low computational time.Uncertainty is inevitable in the decision-making process of real applications. Quantum mechanics has become an interesting and popular topic in predicting and explaining human decision-making behaviors, especially regarding interference effects caused by uncertainty in the process of decision making, due to the limitations of Bayesian reasoning. In addition, complex evidence theory (CET), as a generalized Dempster-Shafer evidence theory, has been proposed to represent and handle uncertainty in the framework of the complex plane, and it is an effective tool in uncertainty reasoning. Particularly, the complex mass function, also known as a complex basic belief assignment in CET, is complex-value modeled, which is superior to the classical mass function in expressing uncertain information. CET is considered to have certain inherent connections with quantum mechanics since both are complex-value modeled and can be applied in handling uncertainty in decision-making problems. In this article, therefore, by bridging CET and quantum mechanics, we propose a new complex evidential quantum dynamical (CEQD) model to predict interference effects on human decision-making behaviors. In addition, uniform and weighted complex Pignistic belief transformation functions are proposed, which can be used effectively in the CEQD model to help explain interference effects. The experimental results and comparisons demonstrate the effectiveness of the proposed method. In summary, the proposed CEQD method provides a new perspective to study and explain the interference effects involved in human decision-making behaviors, which is significant for decision theory.Domain adaptation aims to facilitate the learning task in an unlabeled target domain by leveraging the auxiliary knowledge in a well-labeled source domain from a different distribution. Almost existing autoencoder-based domain adaptation approaches focus on learning domain-invariant representations to reduce the distribution discrepancy between source and target domains. However, there is still a weakness existing in these approaches the class-discriminative information of the two domains may be damaged while aligning the distributions of the source and target domains, which makes the samples with different classes close to each other, leading to performance degradation. To tackle this issue, we propose a novel dual-representation autoencoder (DRAE) to learn dual-domain-invariant representations for domain adaptation. Specifically, DRAE consists of three learning phases. First, DRAE learns global representations of all source and target data to maximize the interclass distance in each domain and minimize the marginal distribution and conditional distribution of both domains simultaneously.