Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)最新文献

Targeted mass spectrometry (MS) holds promise for precise protein and protein-representative peptide identification and quantification, enhancing disease diagnosis. However, its clinical application is hindered by complex data analysis and expert review requirements. It is hypothesized that machine learning (ML) models can automate data analysis to accelerate the clinical application of MS. The approach involves an ML-driven pipeline that extracts statistical and morphological features from an MS target region and feeds these features into ML algorithms to generate and assess predictive models. The findings demonstrate ML prediction models exhibit superior performance when trained on extracted features versus raw spectra intensity data and that random forest models exhibit robust classification performance in both internal and external validation datasets. These models remain effective across varying training dataset sizes and positive sample rates and are enhanced by a nested active learning approach. This approach can thus revolutionize clinical MS applications.

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)-based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29 060 publications is employed. The concept that there are light-absorbing materials, electron-transporting materials, and hole-transporting materials in PSCs is successfully learned by the NLP-based machine learning model without a time-consuming human expert training process. The NLP model highlights a hole-transporting material that receives insufficient attention in the literature, which is then elaborated via density functional theory calculations to provide an atomistic view of the perovskite/hole-transporting layer heterostructures and their optoelectronic properties. Finally, the above results are confirmed by device experiments. The present study demonstrates the viability of NLP as a universal machine learning tool to extract useful information from existing publications.

Collaborative multitarget search and navigation (CMTSN) is highly demanded in complex missions such as rescue and warehouse management. Traditional centralized and decentralized approaches fall short in terms of scalability and adaptability to real-world complexities such as unknown targets and large-scale missions. This article addresses this challenging CMTSN problem in three-dimensional spaces, specifically for agents with local visual observation operating in obstacle-rich environments. To overcome these challenges, this work presents the POsthumous Mix-credit assignment with Attention (POMA) framework. POMA integrates adaptive curriculum learning and mixed individual-group credit assignments to efficiently balance individual and group contributions in a sparse reward environment. It also leverages an attention mechanism to manage variable local observations, enhancing the framework's scalability. Extensive simulations demonstrate that POMA outperforms a variety of baseline methods. Furthermore, the trained model is deployed over a physical visual drone swarm, demonstrating the effectiveness and generalization of our approach in real-world autonomous flight.

Early childhood education is critical in shaping children's intellectual and motor skills as it provides a solid foundation for cognitive, social, and emotional development, which highly depends on spatial thinking. Haptic feedback can be effectively used for educational and training purposes, particularly in fields such as physics, math, and arts, offering a more interactive learning media and supporting kinesthetic learners by its nature. Herein, different ways of implementing haptic feedback on different educational scenarios from the perspective of technological development and their impact on children's learned skills and outcomes (e.g., their motivation, their analytical or spatial thinking abilities, or fine motor skills) will be examined. This article provides an overview of how haptic feedback has been implemented in different learning scenarios for children. Particularly, it is indicated that haptics can potentially improve early childhood learning outcomes and spatial reasoning skills as it can increase children's interest, participation, performance in educational activities, and analytical ability. The major drawbacks of the current studies, such as variance in participants’ learning challenges and small sample numbers are also highlighted.

Autonomous machines must self-maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting symptoms rather than root causes. However, tackling root causes requires identifying the noise sources, considering the limitations of mobile platforms. In this work, a real-time, memory-efficient, and reliable noise source estimator that combines data-based and physically based models is investigated. To this end, a deep neural network that examines an image with camera metadata for major camera noise sources is built and trained. In addition, it quantifies unexpected factors that impact image noise or metadata. This study investigates seven different estimators on six datasets that include synthetic noise, real-world noise from two camera systems, and real-field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. This method outperforms total image noise estimators and can be plug-and-play deployed. It also serves as a basis to include more advanced noise sources, or as part of an automatic countermeasure feedback loop to approach fully reliable machines.

Clusters, an aggregation of several to thousands of atoms, molecules, or ions, are the building blocks of novel functional materials by atomic manufacturing and exhibit excellent applications in catalysis, quantum information, and nanomedicine. The evolution of cluster structures has been studied for many years. Many effective structural search methods, such as genetic algorithm, basin-hopping, and so on, have been developed. However, the efficient execution of these methods relies on precise energy calculators, such as density functional theory (DFT) calculations. Up to now, limited by computational methods and capabilities, the researches mainly focus on free-standing clusters, which are different from clusters in practical applications. Recently, the rapid development of big data-driven machine learning is expected to replace DFT for high-precision large-scale computing. In this review, the present cluster search methods and challenges currently faced have been summarized. It is proposed that the development of artificial intelligence has the potential to solve some practical problems including the structural and properties evolution of clusters in complex environment, causing revolutionary developments in the fields of catalysis, quantum information, and nanomedicine based on clusters.

The conservation of hydrological resources involves continuously monitoring their contamination. A multiagent system composed of autonomous surface vehicles is proposed herein to efficiently monitor the water quality. To achieve a safe control of the fleet, the fleet policy should be able to act based on measurements and fleet state. It is proposed to use local Gaussian processes and deep reinforcement learning to jointly obtain effective monitoring policies. Local Gaussian processes, unlike classical global Gaussian processes, can accurately model the information in a dissimilar spatial correlation which captures more accurately the water quality information. A deep convolutional policy is proposed, that bases the decisions on the observation on the mean and variance of this model, by means of an information gain reward. Using a double deep Q-learning algorithm, agents are trained to minimize the estimation error in a safe manner thanks to a Consensus-based heuristic. Simulation results indicate an improvement of up to 24% in terms of the mean absolute error with the proposed models. Also, training results with 1–3 agents indicate that our proposed approach returns 20% and 24% smaller average estimation errors for, respectively, monitoring water quality variables and monitoring algae blooms, as compared to state-of-the-art approaches.

The fabric-based pneumatic exosuit is now a hot research topic because it is lighter and softer than traditional exoskeletons. Existing research focuses more on the mechanical properties of the exosuit (e.g., torque and speed), but less on its wearability (e.g., appearance and comfort). This work presents a new design concept for fabric-based pneumatic exosuits: volume transfer, which means transferring the volume of pneumatic actuators beyond the garment's profile to the inside. This allows for a concealed appearance and a larger stress area while maintaining adequate torques. In order to verify this concept, a fabric-based pneumatic exosuit is developed for knee extension assistance. Its profile is only 26 mm and its stress area wraps around almost half of the leg. A mathematical model and simulation is used to determine the parameters of the exosuit, avoiding multiple iterations of the prototype. Experiment results show that the exosuit can generate a torque of 7.6 Nm at a pressure of 90 kPa and produce a significant reduction in the electromyography activity of the knee extensor muscles. It is believed that volume transfer can be utilized prevalently in future fabric-based pneumatic exosuit designs to achieve a significant improvement in wearability.

Inspired by the kirigami and recombination mechanism, a kirigami flexible gripper is presented. The configuration variation behaviors are realized by recombinant kirigami segments, and thus, with only head segment actuation, the gripper slickly deforms among different configurations. Revolutionizing the conventional deployable systems, the proposed design methodology can realize configuration variation-locking integration, and reusing of multi-stability property. To realize rapid configuration switching with the lowest energy input, a nested origami actuation, called trigger structure, is given. Furthermore, advanced design for the trigger structure is carried out, broadening the bistable state to four-stable geometric configurations for enhanced reachable space of the flexible gripper. By combining the design of the advanced trigger structure, the flexible gripper enables an energy-free switching behavior between deployed and curled configurations in symmetrical and asymmetrical planes. The asymmetrical configurations, induced by the multi-stability property of the advanced trigger structure, make the flexible gripper appropriate for various moving velocities capture. The proposed kirigami multi-stable flexible gripper has significant capture capability for moving targets with different types and motion attitudes. Summarily, the proposed kirigami multi-stable flexible gripper opens a new avenue for flexible robots, with potential applications in space exploration, grippers, and beyond.

Hollow Piezoelectric Miniature Robot

Inspired by the locomotive gait of crocodiles, Binbin Zhu and Yan Qing Wang in article number 2300612 introduce a hollow piezoelectric miniature robot (HPMR). Based on the hollow-center design, the robot realizes multiple motion outputs with single energy input, incorporating two working modes, oscillation and rotation. The HPMR satisfies the needs of miniaturization, rapid and accurate movement, and high adaptability to a certain extent; in particular, its amphibious mobility expands the application scenarios. This design concept gives the robot an enhanced ability to multitask in constrained spaces with equal energy input and fewer external components.