Part qualification is crucial in additive manufacturing (AM) because it ensures that additively manufactured parts can be consistently produced and reliably used in critical applications. Part qualification aims at verifying that an additively manufactured part meets performance requirements; therefore, predicting the complex stress-strain behaviors of additively manufactured parts is critical. We develop a dynamic time warping (DTW)-transfer learning (TL) framework for additive manufacturing part qualification by transferring knowledge of the stress-strain behaviors of additively manufactured low-cost polymers to metals. Specifically, the framework employs DTW to select a polymer dataset as the source domain that is the most relevant to the target metal dataset. Using a long short-term memory (LSTM) model, four source polymers (i.e., Nylon, PLA, CF-ABS, and Resin) and three target metals (i.e., AlSi10Mg, Ti6Al4V, and carbon steel) that are fabricated by different AM techniques are utilized to demonstrate the effectiveness of the DTW-TL framework. Experimental results show that the DTW-TL framework identifies the closest match between polymers and metals to select one single polymer dataset as the source domain. The DTW-TL model achieves the lowest mean absolute percentage error of 12.41% and highest coefficient of determination of 0.96 when three metals are used as the target domain, respectively, outperforming the vanilla LSTM model without TL as well as the TL model pre-trained on four polymer datasets as the source domain.

Do the main claims made in the abstract and introduction accurately reflect the paper's Did you describe the limitations of your work? If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Y es] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? Did you include the total amount of compute and the type of resources used (e.g., type Did you include any new assets either in the supplemental material or as a URL? [N/A] Did you discuss whether and how consent was obtained from people whose data you're If you used crowdsourcing or conducted research with human subjects... (a) Therefore, an additional tuning process is needed to find an appropriate λ for different tasks. For better visualization, the scores are smoothed by a window with length 20. For better visualization, the scores are smoothed by a window with length 20.

Do the main claims made in the abstract and introduction accurately reflect the paper's Did you describe the limitations of your work? If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Y es] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? Did you include the total amount of compute and the type of resources used (e.g., type Did you include any new assets either in the supplemental material or as a URL? [N/A] Did you discuss whether and how consent was obtained from people whose data you're If you used crowdsourcing or conducted research with human subjects... (a) Therefore, an additional tuning process is needed to find an appropriate λ for different tasks. For better visualization, the scores are smoothed by a window with length 20. For better visualization, the scores are smoothed by a window with length 20.

In the trajectory planning of automated driving, data-driven statistical artificial intelligence (AI) methods are increasingly established for predicting the emergent behavior of other road users. While these methods achieve exceptional performance in defined datasets, they usually rely on the independent and identically distributed (i.i.d.) assumption and thus tend to be vulnerable to distribution shifts that occur in the real world. In addition, these methods lack explainability due to their black box nature, which poses further challenges in terms of the approval process and social trustworthiness. Therefore, in order to use the capabilities of data-driven statistical AI methods in a reliable and trustworthy manner, the concept of TrustMHE is introduced and investigated in this paper. TrustMHE represents a complementary approach, independent of the underlying AI systems, that combines AI-driven out-of-distribution detection with control-driven moving horizon estimation (MHE) to enable not only detection and monitoring, but also intervention. The effectiveness of the proposed TrustMHE is evaluated and proven in three simulation scenarios.

Large vision-language models (LVLMs) have demonstrated remarkable image understanding and dialogue capabilities, allowing them to handle a variety of visual question answering tasks. However, their widespread availability raises concerns about unauthorized usage and copyright infringement, where users or individuals can develop their own LVLMs by fine-tuning published models. In this paper, we propose a novel method called Parameter Learning Attack (PLA) for tracking the copyright of LVLMs without modifying the original model. Specifically, we construct adversarial images through targeted attacks against the original model, enabling it to generate specific outputs. To ensure these attacks remain effective on potential fine-tuned models to trigger copyright tracking, we allow the original model to learn the trigger images by updating parameters in the opposite direction during the adversarial attack process. Notably, the proposed method can be applied after the release of the original model, thus not affecting the model's performance and behavior. To simulate real-world applications, we fine-tune the original model using various strategies across diverse datasets, creating a range of models for copyright verification. Extensive experiments demonstrate that our method can more effectively identify the original copyright of fine-tuned models compared to baseline methods. Therefore, this work provides a powerful tool for tracking copyrights and detecting unlicensed usage of LVLMs.

This article discusses the integration of the Artificial Bee Colony (ABC) algorithm with two supervised learning methods, namely Artificial Neural Networks (ANNs) and Adaptive Network-based Fuzzy Inference System (ANFIS), for feature selection from Near-Infrared (NIR) spectra for predicting the molecular weight of medical-grade Polylactic Acid (PLA). During extrusion processing of PLA, in-line NIR spectra were captured along with extrusion process and machine setting data. With a dataset comprising 63 observations and 512 input features, appropriate machine learning tools are essential for interpreting data and selecting features to improve prediction accuracy. Initially, the ABC optimization algorithm is coupled with ANN/ANFIS to forecast PLA molecular weight. The objective functions of the ABC algorithm are to minimize the root mean square error (RMSE) between experimental and predicted PLA molecular weights while also minimizing the number of input features. Results indicate that employing ABC-ANFIS yields the lowest RMSE of 282 Da and identifies four significant parameters (NIR wavenumbers 6158 cm-1, 6310 cm-1, 6349 cm-1, and melt temperature) for prediction. These findings demonstrate the effectiveness of using the ABC algorithm with ANFIS for selecting a minimal set of features to predict PLA molecular weight with high accuracy during processing

We describe a force-controlled robotic gripper with built-in tactile and 3D perception. We also describe a complete autonomous manipulation pipeline consisting of object detection, segmentation, point cloud processing, force-controlled manipulation, and symbolic (re)-planning. The design emphasizes versatility in terms of applications, manufacturability, use of commercial off-the-shelf parts, and open-source software. We validate the design by characterizing force control (achieving up to 32N, controllable in steps of 0.08N), force measurement, and two manipulation demonstrations: assembly of the Siemens gear assembly problem, and a sensor-based stacking task requiring replanning. These demonstrate robust execution of long sequences of sensor-based manipulation tasks, which makes the resulting platform a solid foundation for researchers in task-and-motion planning, educators, and quick prototyping of household, industrial and warehouse automation tasks.

Xi Jinping, at the 20th National Congress of the Chinese Communist Party (CCP) on October 16, stated that more quickly elevating the People's Liberation Army (PLA) to a world-class army is a strategic requirement for building a modern socialist country in all respects. At the 19th Party Congress five years ago, Xi insisted China would build a world-class army by the middle of this century; this time he did not mention a definite deadline but clearly stated that he would achieve the goal more quickly. How is Xi trying to accelerate the construction of a world-class military? The PLA is seeking to capitalize on the introduction of advanced technology, with a particular focus on the use of unmanned weapons and artificial intelligence. In this report, Xi Jinping mentioned the word "intelligent" (智能化) three times. The concept of "intelligent," which refers to the use of weapon systems based on artificial intelligence, has rapidly gained attention since the release of the 2019 National Defense White Paper.

Despite measures to limit U.S. technology exports to the Chinese military, chips designed by U.S. companies still end up in the hands of the People's Liberation Army (PLA), according to a report by the Center for Security and Emerging Technology (CSET) at Georgetown University. For the report, researchers combed through over 66,000 publicly available PLA purchase records during the eight-month period from April to November 2020 and identified 97 unique, high-end artificial intelligence (AI) chips ordered by the PLA. Nearly all of them were designed by U.S. firms Nvidia, Xilinx (now AMD), Intel, and Microsemi. The CSET report, released last month, also noted that the researchers couldn't find any public records of the PLA purchasing the high-end AI chips from any Chinese companies, including HiSilicon (Huawei), Sugon, Sunway, Hygon, and Phytium. These AI chips are critical components to the Chinese Communist Party (CCP) for the "intelligentization" (the addition of artificial intelligence to a system, according to Kaikki.org) of its military and to the regime's goal to gain dominance over the global AI design and manufacturing market.

Fox News national security correspondent Jennifer Griffin discusses a report alleging China is developing'brain control weapons' on'Fox Report.' Reports from India claim that China has started to deploy armed robotic vehicles to handle the altitude and terrain that has proven too difficult for its troops. China and India clashed in Sept. 2020 during a border dispute along the southern coast of Pangong Lake in an area known in China as Shenpaoshan and in India as Chushul, but the armies continued their standoff along the two nations' borders throughout 2021. China has now reportedly deployed unmanned ground vehicles (UGV) to the region of Tibet to strengthen its position. People's Liberation Army (PLA) soldiers march next to the entrance to the Forbidden City during the opening ceremony of the Chinese People's Political Consultative Conference (CPPCC) in Beijing on May 21, 2020.