Autonomous weeding robots offer significant potential for reducing labor and environmental costs in precision agriculture. However, most systems decouple chassis locomotion from manipulator control, limiting efficiency in unstructured environments. We present LSTM-PPO-Weeding, an end-to-end framework unifying mobility and manipulation via memory-augmented reinforcement learning (RL). A high-speed region of interest (ROI) filter abstracts camera input into three-dimensional weed/crop poses, enabling the system to focus solely on task-relevant features. To mitigate challenges arising from occlusion and partial observability, we integrate long short-term memory (LSTM) into the proximal policy optimization (PPO) algorithm, establishing the proposed LSTM-PPO framework. The agent learns coordinated actions through a custom reward function balancing speed and precision. Experiments in simulation and real-world greenhouse settings show that LSTM-PPO-Weeding improves throughput while maintaining high accuracy. Compared to baseline methods, our approach reduces average weeding time by up to 74% with minimal loss in success rate, demonstrating its robustness for agricultural mobile manipulation.

High-dimensional feature spaces in Scientific and Technical Service Resources (STSR) classification present significant challenges, including increased computational costs and diminished accuracy. Identifying an optimal subset of features from raw text vectors is thus critical for effective data classification . This paper introduces a novel metaheuristic algorithm called Binary Banyan Tree Growth Optimization (BBTGO), specifically designed for high-dimensional feature selection (FS). Inspired by the unique growth patterns of the banyan tree , BBTGO leverages a combination of innovative Boolean vectors, including rooting, multi-trunk, and adjustment operator, along with a perturbation phase to enhance the search efficiency and reduce feature dimensionality. These operators enhance the search for promising regions and reduce features by utilizing the optimal solutions clustered within subgroups. Furthermore, BBTGO incorporates a dynamic adjustment mechanism that periodically activates different growth operators to meet the search demands of high-dimensional space. We rigorously evaluate the exploration and exploitation capabilities of BBTGO through comprehensive statistical analyses of various performance metrics. The proposed method demonstrates superior results on 12 high-dimensional benchmark datasets and is successfully applied to feature selection in STSR text classification tasks . Experimental results show that BBTGO significantly outperforms existing methods in terms of classification accuracy , selected features, convergence speed, and processing time. These results underscore the potential of BBTGO as a robust and versatile solution for high-dimensional FS, with broad applicability to real-world classification challenges.

Microgrid systems have now seen many integrations with energy storage systems (ESS) and renewable energy sources (RES) to supply cleaner and cheaper energy. A pressing challenge is how to optimally meet the requirements ranging from reducing operational costs and carbon footprints to relieving the grid constraints, alongside the consideration of uncertainties in the supply and demand. To embark on this challenge, this paper proposes a deep reinforcement learning (DRL) approach with direct control responses to optimize multi-objective microgrid operations. First, a new objective function is derived to build a direct response between the control action and the optimization objectives, aiming to improve the learning efficiency. Second, a unified control scheme is designed to study the combined use of past observations and predicted data for microgrid controls. Third, a realistic microgrid model is created to incorporate battery charging and discharging processes with dynamic efficiency and nonlinear battery degradation. Finally, the effectiveness of the proposed approach is validated through various simulations conducted on a US case study, with an additional Norwegian microgrid presented in the supplementary material. The results suggest that the annual reward in the US microgrid can be improved by 139.33% over the baseline (vanilla DQN with a conventional scheme) under perfect predictions, and by 125.45% under noisy predictions. • A direct-response reward function is derived to improve the learning efficiency. • A unified control scheme is designed to organize the state space. • A realistic microgrid model is created for power optimization.

Low light very likely leads to the degradation of an image’s quality and even causes visual task failures. Existing image enhancement technologies are prone to overenhancement, color distortion or time consumption, and their adaptability is fairly limited. Therefore, we propose a new single low-light image lightness enhancement method. First, an energy model is presented based on the analysis of membrane vibrations induced by photon stimulations. Then, based on the unique mathematical properties of the energy model and combined with the gamma correction model, a new global lightness enhancement model is proposed. Furthermore, a special relationship between image lightness and gamma intensity is found. Finally, a local fusion strategy, including segmentation, filtering and fusion, is proposed to optimize the local details of the global lightness enhancement images. Experimental results show that the proposed algorithm is superior to nine state-of-the-art methods in avoiding color distortion, restoring the textures of dark areas, reproducing natural colors and reducing time cost.

Multimodal sentiment analysis has a wide range of applications due to its information complementarity in multimodal interactions. Previous works focus more on investigating efficient joint representations, but they rarely consider the insufficient unimodal features extraction and data redundancy of multimodal fusion. In this paper, a Video-based Cross-modal Auxiliary Network (VCAN) is proposed, which is comprised of an audio features map module and a cross-modal selection module. The first module is designed to substantially increase feature diversity in audio feature extraction, aiming to improve classification accuracy by providing more comprehensive acoustic representations. To empower the model to handle redundant visual features, the second module is addressed to efficiently filter the redundant visual frames during integrating audiovisual data. Moreover, a classifier group consisting of several image classification networks is introduced to predict sentiment polarities and emotion categories. Extensive experimental results on RAVDESS, CMU-MOSI, and CMU-MOSEI benchmarks indicate that VCAN is significantly superior to the state-of-the-art methods for improving the classification accuracy of multimodal sentiment analysis.

This article investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event trigger is developed to handle network-induced packet dropouts, as well as packet disorders resulting from random transmission delays, where the${H_{2}}/{H_{\infty } }$performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complex network-induced problem, which may deteriorate system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.

Tuning performance of scientific applications is a challenging problem since performance can be a complicated nonlinear function with respect to application parameters. Empirical performance modeling is a useful approach to approximate the function and enable efficient heuristic methods to find sub-optimal parameter configurations. However, empirical performance modeling requires a large number of samples from the parameter space, which is resource and time-consuming. To address this issue, existing work based on active learning techniques proposed PBU Sampling method considering performance before uncertainty, which iteratively performs performance biased sampling to model the high-performance subspace instead of the entire space before evaluating the most uncertain samples to reduce redundancy. Compared with uniformly random sampling, this approach can reduce the number of samples, but it still involves redundant sampling that potentially can be improved.We propose a novel active learning based method to exploit the information of evaluated samples and explore possible high-performance parameter configurations. Specifically, we adopt a Performance Weighted Uncertainty (PWU) sampling strategy to identify the configurations with either high performance or high uncertainty and determine which ones are selected for evaluation. To evaluate the effectiveness of our proposed method, we construct random forest to predict the execution time of kernels from SPAPT suite and two typical scientific parallel applications kripke, hypre. Experimental results show that compared with existing methods, our proposed method can reduce the cost of modeling by up to 21x and 3x on average meanwhile hold the same prediction accuracy.