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

Real-time missile identification using artificial intelligence (AI) is becoming a crucial element in modern warfare that can significantly affect the national air defense. In this study, a real-time missile target identification (MTI) AI model is developed using step-weighted long–short-term memory networks based on a bit quantization scheme of the fabricated 1 kbit TiO_x memristor array to classify five missile types: nonthreat (Non), field gun (FG), mortar (Mt), rocket (Rk), and rocket-assisted projectile (RAP). To enhance accuracy and address dataset imbalance during training, data augmentation techniques are employed, including random trajectory rotation and Gaussian noise into the radar cross-section, as well as introducing a custom loss function and dynamic learning rate (LR) to enhance early-stage prediction and accelerate learning. Employing these strategies, the proposed MTI AI model achieves a 94.4% accuracy at 3.2 s in identifying Non class, while average accuracy for five classes is 94.4% at 12.8 s. The model exhibits ≈43.6% greater accuracy at 3.2 s than that of the conventional model, and the estimated false-negative rate can be kept less than 2.5%. This MTI AI model can reduce the uncertainty of premature alerts for unidentified targets and exhibit superior detection capabilities for identifying and targeting missiles.

Since ancient times, oracles (e.g., Delphi) has the ability to provide useful visions of where the society is headed, based on key event correlations and educated guesses. Currently, foundation models are able to distill and analyze enormous text-based data that can be used to understand where societal components are headed in the future. This work investigates the use of three large language models (LLM) and their ability to aid the research of nuclear materials. Using a large dataset of Journal of Nuclear Materials papers spanning from 2001 to 2021, models are evaluated and compared with perplexity, similarity of output, and knowledge graph metrics such as shortest path length. Models are compared to the highest performer, OpenAI's GPT-3.5. LLM-generated knowledge graphs with more than 2 × 10⁵ nodes and 3.3 × 10⁵ links are analyzed per publication year, and temporal tracking leads to the identification of criteria for publication innovation, controversy, influence, and future research trends.

Conventional systems based on traditional design strategies typically excel at single-task performance but lack adaptability when operating conditions change. Reconfiguration offers a promising alternative, enabling systems to adopt multiple configurations tailored to varying requirements. Natural biological organisms regularly modify their morphology to overcome environmental challenges, inspiring engineering applications that seek similar adaptability. However, the real potential of reconfiguration in engineering is often bounded by traditional design strategies and rigid materials. In this case, shape-changing structures can provide new insights. This review focuses on the structural foundations of reconfigurable design, emphasizing key principles across origami, bistable structures, and laminate structures, and examines how these shape-changing structures can enhance the multifunctionality in soft robotics, soft manipulators, and metamaterials. Finally, the review discusses the primary challenges faced by achieving the multifunctionality in practical applications. In conclusion, combining advanced materials with innovative structural designs enables systems to achieve diverse working modes and adaptive properties, paving the way for more versatile and resilient applications across various fields.

This article presents a computational method for generating virtual 3D morphologies of functional materials using low-parametric stochastic geometry models, that is, digital twins, calibrated with 2D microscopy images. These digital twins allow systematic parameter variations to simulate various morphologies, which can be deployed for virtual materials testing by means of spatially resolved numerical simulations of macroscopic properties. Generative adversarial networks (GANs) have gained popularity for calibrating models to generate realistic 3D morphologies. However, GANs often comprise numerous uninterpretable parameters, making systematic variation of morphologies for virtual materials testing challenging. In contrast, low-parametric stochastic geometry models (e.g., based on Gaussian random fields) enable targeted variation but may struggle to mimic complex morphologies. Combining GANs with advanced stochastic geometry models (e.g., excursion sets of more general random fields) addresses these limitations, allowing model calibration solely from 2D image data. This approach is demonstrated by generating digital twins for the morphology of microstructures in all-solid-state battery (ASSB) cathodes. Since the digital twins are parametric, they support systematic exploration of structural scenarios and their macroscopic properties. The proposed method facilitates simulation studies for optimizing 3D morphologies, benefiting not only ASSB cathodes but also other materials with similar structures.

Multisensory integration (MSI) is a core brain function underlying perception, learning, and behavior. Understanding the computational mechanisms of MSI is key to advancing AI and brain-inspired systems. While earlier models relied on probabilistic frameworks, recurrent neural networks (RNNs) offer advantages in capturing temporal dynamics and neural computations. This review presents a critical examination of computational models of MSI, focusing on the evolution from probabilistic integration to modern RNN-based methods. Biological evidence for temporal coordination in multisensory areas is analyzed and explored how different RNN architectures (e.g., vanilla, long short-term memory, and gated recurrent unit) simulate these dynamics. Comparative analyses show RNNs’ superiority in robustness and learning efficiency, with up to 46.9% improvement in classification tasks involving sensory fusion. We introduce a taxonomy of MSI tasks and a novel evaluation framework for model benchmarking. Real-world case studies—from speech recognition to prosthetic control—highlight practical applications. Challenges in interpretability, data efficiency, and generalization are also discussed. The review provides actionable insights for future research in both computational neuroscience and artificial intelligence. By bridging neurobiological principles and machine learning, RNN-based models pave the way for intelligent systems capable of flexible, context-aware multisensory processing.

The treatment plan for brain tumors varies depending on the type and stage of the tumor. Early diagnosis plays a vital role in determining appropriate treatment. In addition to clinical routines, artificial intelligence-based systems that produce automated, quantitative, and objective results can assist clinicians and scientists in making early diagnoses. For this motivation, this study proposes a deep learning-based system that classifies brain tumors obtained by magnetic resonance imaging. In the proposed approach, several wavelet transform approaches are applied to the raw dataset images. Thus, in addition to automated feature extraction in deep learning, it aimed to detect more detailed features. Therefore, four types of datasets have been obtained. Then, using the transfer learning approach, some popular convolutional neural network and vision transformer models are trained separately with the four-type datasets, and the test results are compared. The networks that produced the highest results are used to make the final decision with the ensemble technique. In the first analysis, the best performance was obtained using original data with an 83.50% accuracy value, and the second highest performance is obtained 81.72% accuracy value using the Daubhecies wavelet before deep learning. The third and fourth high performances are 81.47% and 81.22% accuracy, respectively, using original data. In the ensemble analysis, the highest result is achieved at 85.03% accuracy value using the bagging-ensemble approach of the networks, namely MobileNet-v3, vision transformer, ResNeXt, and DenseNet-201. This study demonstrates that using a hybrid wavelet transform and deep learning approach improves classification performance. This may inspire the use of the same method to solve different classification problems.

Mimicking invertebrates, soft robots tend to control each of their body joints to achieve the desired shape changes and movements to accomplish different tasks. Shape-memory alloy (SMA) is a common actuation material but needs to be designed into specific shapes to disperse local strains. In this article, a dedicated configuration of SMA strips is introduced for soft robotic body joint manipulation and morphing. Inspired by the Möbius strip, the proposed SMA torsion strip (STS) can meet the requirements of both large deformation and large force output desirable for robotic applications. Compared to conventional morphing methods, the STSs can supply considerable torque output over a wide range of bending angles to freely chosen body joints. Therefore, both pattern-to-pattern extreme shape morphing, such as tendrils curling, and programmable shape morphing can be achieved. A mathematical model is established to describe how geometry and temperature affect the STS properties to optimize performance. Due to the extensibility of the STS, it can be used for a large variety of robotic applications that are partially illustrated in this research as artificial muscles, grippers, jumping robots, and soft proportional valves.

The growth and characterization of materials using empirical optimization typically requires a significant amount of expert time, experience, and resources. Several complementary characterization methods are routinely performed to determine the quality and properties of a grown sample. Machine learning (ML) can support the conventional approaches by using historical data to guide and provide speed and efficiency to the growth and characterization of materials. Specifically, ML can provide quantitative information from characterization data that is typically obtained from a different modality. In this study, the feasibility of projecting quantitative metrics from microscopy measurements, such as atomic force microscopy (AFM), using data obtained from spectroscopy measurements, like Raman spectroscopy is investigated. Generative models are also trained to generate the full and specific features of the Raman and photoluminescence spectra from each other and the AFM images of the thin-film MoS₂. The results are promising and have provided a foundational guide for the use of ML for the cross-modal characterization of materials for their accelerated, efficient, and cost-effective discovery.

This study introduces a minimally invasive robotic probe inspired by plant root growth, designed for subsoil exploration and future ecosystem monitoring and intervention. The bio-inspired probe advances in soil by mimicking plant root apical growth, creating and consolidating a borehole through the injection of a bio-based, biodegradable binder at its tip. This innovative process confines penetration resistance to the tip while generating a hollow tubular structure by harnessing in situ local soil. The probe's penetration is facilitated by a linear actuator, which can be retracted upon reaching a desired depth, thereby minimizing the environmental dispersion of mechatronic components. This approach not only enhances the efficiency of subsoil exploration (whether on-Earth or in outer space) by reducing penetration force requirements and reliance on exogenous material but also ensures environmental sustainability by employing biodegradable materials and lowering mechanical footprints. The robotic probe's design and functionality highlight the potential of bio-inspired technologies to address complex environmental challenges, paving the way for future innovations in ecological research and conservation efforts. This study underscores the importance of integrating biological principles into engineering solutions to develop tools that are both effective and environmentally responsible.

This article presents a novel, versatile wearable force myography (FMG) system based on optical fiber technology, designed for high sensitivity and mechanical robustness. Unlike conventional FMG systems, which are susceptible to environmental interference, the proposed system utilizes light loss through controlled fiber–polymer contact to achieve stable and noise-free signal transmission. Its compact and flexible form factor allows seamless integration into wearable devices, facilitating muscle-activity monitoring under diverse real-world conditions, including biologically challenging scenarios such as sweating. Experimental evaluations highlight the system's ability to detect even micronewton-scale forces and accurately recognize multiple gestures. Furthermore, the system can estimate joint angles, including those of individual fingers, which underscores its potential for precise motion capturing and continuous tracking. Overall, the proposed FMG system represents a promising solution for a wide range of practical human–robot interaction applications.