Springer Science and Business Media LLC

Although Yager has presented a prioritized operator for fuzzy subsets, called the non-monotonic operator, it can not be used to deal with multi-criteria fuzzy decision-making problems when generalized fuzzy numbers are used to represent the evaluating values of criteria. In this paper, we present a prioritized information fusion algorithm based on the similarity measure of generalized fuzzy numbers. The proposed prioritized information fusion algorithm has the following advantages: (1) It can handle prioritized multi-criteria fuzzy decision-making problems in a more flexible manner due to the fact that it allows the evaluating values of criteria to be represented by generalized fuzzy numbers or crisp values between zero and one, and (2) it can deal with prioritized information filtering problems based on generalized fuzzy numbers.

This paper reports on the status of The University of Texas at Arlington student effort to design, build and fly an Autonomous Aerial Vehicle. Both the 1991 entry into the First International Aerial Robotics Competition as well as refinements being made for 1992 are described. Significant technical highlights include a real-time vision system for target objective tracking, a real-time ultrasonic locator system for position sensing, a novel mechanism for gradually moving from human to computer control, and a hierarchical control structure implemented on a 32-bit microcontroller. Detailed discussion about the design of multivariable automatic controls for stability augmentation is included. Position and attitude control loops are optimized according to a combined ℋ2 and ℋ∞ criteria. We present a modification of a recently published procedure for recovering a desired open-loop transfer function shape within the framework of the mixed ℋ2/ℋ∞ problem. This work has led to a new result that frees a design parameter related to imposing the ℋ∞ constraint. The additional freedom can be used to improve upon the performance and robustness characteristics of the system.

Learning from visual observations is a significant yet challenging problem in Reinforcement Learning (RL). Two respective problems, representation learning and task learning, need to solve to infer an optimal policy. Some methods have been proposed to utilize data augmentation in reinforcement learning to directly learn from images. Although these methods can improve generation in RL, they are often found to make the task learning unsteady and can even lead to divergence. We investigate the causes of instability and find it is usually rooted in high-variance of Q-functions. In this paper, we propose an easy-to-implement and unified method to solve above-mentioned problems, Data-augmented Reinforcement Learning with Ensemble Exploration and Exploitation (DAR-EEE). Bootstrap ensembles are incorporated into data augmented reinforcement learning and provide uncertainty estimation of both original and augmented states, which can be utilized to stabilize and accelerate the task learning. Specially, a novel strategy called uncertainty-weighted exploitation is designed for rational utilization of transition tuples. Moreover, an efficient exploration method using the highest upper confidence is used to balance exploration and exploitation. Our experimental evaluation demonstrates the improved sample efficiency and final performance of our method on a range of difficult image-based control tasks. Especially, our method has achieved the new state-of-the-art performance on Reacher-easy and Cheetah-run tasks.

With the increasing scarcity of global energy, the rapid development of science and technology, and the growing demand for environmental protection, wind energy is receiving increasing attention as the cleanest source of energy. Due to its pollution-free nature and widespread availability, it has become a preferred source of electricity generation in many countries. However, wind speed prediction plays a vital role in wind power generation. Traditional prediction models, due to randomness and uncertainty, often produce unstable and inaccurate results, leading to power and economic losses. Therefore, this study proposes a hybrid prediction system based on an information processing strategy and a multi-objective optimization algorithm. By preprocessing the data and optimizing the combination of five individual models, the singularity of a single model is overcome, a Pareto-optimal solution is obtained, and accurate and stable prediction results are provided. To verify the effectiveness of the proposed combined model in predicting wind speed, various experiments on a wind speed series were conducted based on a wind power station located in Penglai, China. The results show that the combined model proposed in this study has better prediction performance than conventional models.

Remote sensing spatiotemporal fusion is currently a popular research field in remote sensing that can cost-effectively generate remote sensing images with high spatiotemporal resolution. The deep neural network-based approach has strong feature extraction capability and has achieved great success in the fields of signal and image processing, but the deep network lacks interpretability. In this paper, a new interpretable deep network-based spatiotemporal fusion (UNSTF) method is proposed. The method proposes a new network model by first establishing a concise a priori formulation using sparse representation, constructing the proposed network by unfolding the proximal gradient algorithm for solving the model, and carefully designing each basic network module in the model to have a reasonable physical meaning, making the whole network interpretable. In addition, the UNSTF network method proposes a new network iteration loss function, where the predicted images of each iteration stage of the network are constrained by the real images, and the final stage and the intermediate network stages conform well to their inherent prior structures, effectively improving the accuracy and reliability of model prediction. Through extensive experiments, it is shown that the proposed method outperforms existing fusion methods in terms of both subjective and objective metrics.

A specification language for performance indicators and their relations and requirements is presented and illustrated for a case study in logistics. The language can be used in different forms, varying from informal, semiformal, graphical to formal. A software environment has been developed that supports the specification process and can be used to automatically check whether performance indicators or relations between them or certain requirements over them are satisfied in a given organisational process.

Scene understanding is a core problem for autonomous navigation. However, its implementation is frustrated by a variety of unsettled issues, such as understanding winding pathways in unknown, dynamic, and field environments. Traditional three-dimensional (3D) estimation from 3D point clouds or fused data is memory intensive and energy-consuming, which makes these approaches less reliable in a resource-constrained field robot system with limited computation, memory, and energy resources. In this study, we present a methodology to understand winding field pathways and reconstruct them in a 3D environment, using a low-cost monocular camera without prior training. Winding angle projections are assigned to clusters. By composing subclusters, candidate surfaces are shaped. Based on geometric inferences of integrity and orientation, a field pathway can be approximately understood and reconstructed using straight and winding surfaces in a 3D scene. With the use of geometric inference, no prior training is needed, and the approach is robust to colour and illumination. The percentage of incorrectly classified pixels was compared to the ground truth. Experimental results demonstrated that the method can successfully understand winding pathways, meeting the requirements for robot navigation in an unstructured environment.