Hasil untuk "Control engineering systems. Automatic machinery (General)"

Qun Niu, Chuanlin Zhang, Tianyu Zhang et al.

In open‐world robotic manipulation tasks, language‐guided model‐free grasping has garnered increasing attention. However, existing approaches often overlook the geometric structure of target objects, which limits the effectiveness of subsequent tasks such as manipulation and placement. To address this limitation, a novel method called Language‐Guided Grasping via Primitive Fitting is proposed. This approach integrates language instructions with multimodal perception to enhance the semantic interpretability and downstream usability of the grasp through structured geometric modeling. Specifically, the user‐specified object using 2D images and depth data via multimodal understanding is first localized. Then, primitive fitting on the object's point cloud using basic geometric shapes (e.g., cuboids, ellipsoids, truncated cones) to extract approximate size and structural features is performed. Based on the geometric information, a grasp pose generation strategy guided by semantic geometry is defined, and modules for grasp feasibility filtering and task‐oriented optimization to select the optimal grasp pose are introduced. This method is validated in real‐world complex environments and achieved grasp success rates of 95% in structured and 90% in cluttered scenes. Geometric fitting enhances post‐grasp predictability and semantic consistency, enabling better generalization and planning.

Qingsen Cai, Luochang Wu, Chunyang Gao

Energy hub (EH) is a complex system integrating multiple energy sources, playing a crucial role in the Energy Internet (EI). Conventional modelling methods often treat energy sources separately, failing to capture the full dynamic interactions and operational complexities. This paper proposes a novel multi-layer network structure (MNS) for modelling EH, which synchronizes energy flows and optimizes control parameters for energy consumption reduction. The method integrates equipment performance curves into the network, providing a dynamic model that is computationally feasible for real-world applications. In project implementation, the dynamic control method is applied hourly between 8:00 and 17:00, with specific case studies for winter and summer days. The results show that the optimized control strategy can achieve up to 70% energy cost savings in summer and 20% savings in winter while maintaining equipment efficiency above 65% in summer and 60% in winter. The energy consumption costs before and after optimization are significantly reduced, as demonstrated by the comparative analysis. The proposed approach not only enhances system performance but also provides practical implications for optimizing energy hubs in diverse operational conditions.

Xile Kang, Hideaki Ishii

This paper focuses on cyber-security issues of networked control systems in closed-loop forms from the perspective of quantized sampled-data systems. Quantization of control inputs adds quantization error to the plant input, resulting in certain variation in the plant output. On the other hand, sampling can introduce non-minimum phase zeros in discretized systems. We consider zero-dynamics attacks, which is a class of false data injection attacks utilizing such unstable zeros. Although non-quantized zero-dynamics attacks are undetectable from the plant output side, quantized attacks may be revealed by larger output variation. Our setting is that the attack signal is applied with the same uniform quantizer used for the control input. We evaluate the attack stealthiness in the closed-loop system setting by quantifying the output variation. Specifically, we characterize the cases for static and dynamic quantization in the attack signal, while keeping the control input statically quantized. Then we demonstrate that the attacker can reduce such output variation with a modified approach, by compensating the quantization error of the attack signal inside the attack dynamics. We provide numerical examples to illustrate the effectiveness of the proposed approaches. We show that observing the quantized control input value by a mirroring model can reveal the zero-dynamics attacks.

Po‐Yuan Chen, Tai‐Ming Ko

Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored to accurately annotate the oxidative stress state of innate immune cells at the single‐cell level, is introduced. Compared to the traditional gene‐set‐variation‐analysis‐based enrichment method, OxSpred demonstrates superior accuracy with an area under the receiver operating characteristic curve of 0.89 and offers interpretable embeddings with significant biological relevance. Using the predicted ROS states, precise elucidation and interpretation of the roles of novel innate immune cell subtypes can be achieved. Overall, OxSpred enhances the utility of single‐cell transcriptomic datasets by providing a robust in silico method for determining intracellular oxidative stress states, thereby enriching the understanding of innate immune cell functions during inflammation.

Youssef Abdalla, Martin Ferianc, Haya Alfassam et al.

3D printing offers a promising approach to creating personalized medicines. However, costly, expertise‐dependent trial‐and‐error methods hinder efficient drug formulation, posing challenges for tailoring treatments to individual patients. To address this, a novel pipeline is developed for 3D printing using selective laser sintering (SLS), replacing laborious steps with advanced computational methods. A differential evolution‐based optimizer generates formulations for the desired drugs, while a deep learning ensemble predicts the optimal printing parameters along with associated confidence intervals. Manual handling is only required for the final formulation preparation and printing processes. The pipeline successfully generates diverse formulations, composed of a wide variety of materials and with high printability probabilities. This was validated by successfully printing 80% of the generated drug formulations and achieving 92% accuracy in predicting printing parameters. Notably, the time required to develop and print a new drug formulation is decreased to a single day. This study is the first to demonstrate a semiautomated, 3D printing drug formulation design and printing parameter selection pipeline. Furthermore, the pipeline is not limited to SLS printing but can also be adapted for the optimization of other 3D printing technologies or formulation platforms.

Rihab Abdelkrim

Abigail Machaj, Kinga Sobczyk, Wiktoria Wojnarowska et al.

This study investigates the integration of modern engineering techniques, including 3D scanning and additive manufacturing, in the design and production of wrist orthoses. The research aims to enhance orthotic devices by proposing three innovative fastening methods - Velcro straps, screws, and magnets - designed for use with 3D-printed orthoses. The study outlines the entire process from patient hand scanning to the final orthosis creation, emphasizing the precision and customization affordedby these advanced technologies. The proposed designs are intended to improve the comfort, effectiveness, and usability of orthoses for patients with musculoskeletal dysfunctions. The findings demonstrate the potential for significant advancements in personalized medical devices, offering new avenues for rehabilitation and patient care.

LYU Shiwei, ZHU Yinggu, LU Nibin et al.

Addressing the challenges that unmanned underwater vehicles (UUVs) face in path planning, including complex constraints, unstable optimization algorithm performance, and unsmooth paths, this paper proposes a path planning method using an improved particle swarm optimization (PSO) algorithm. Firstly, the function simulation method is employed to construct the underwater terrain and obstacle environment. Secondly, in terms of designing optimization objectives, the paper adds to the conventional minimization of path length, optimizing changes in the unmanned underwater vehicle's attitude angle and the uniform distribution of steering node positions, making it more adaptable to actual conditions, all with a focus on reducing energy consumption. Following this, the article analyzes the effect of inertia weight in the PSO algorithm on algorithm performance, introducing improvement measures to enhance the algorithm's optimization performance. Lastly, the calculated initial path is smoothed via B-spline curves to derive the final trajectory for robot motion planning. The simulation results demonstrate that compared to traditional ant colony optimization and standard PSO algorithms, the devised path optimization method reduces overall energy consumption by 56.6% and 19.3%, respectively, presenting superior problem-solving ability and convergence performance.

Penghai Li, Chen Wang, Mingji Li et al.

A flexible silver/carbon nanotube‐graphene oxide‐polydimethylsiloxane (Ag/CNT‐GO‐PDMS) patch electrode for recording electroencephalography (EEG) signals and recognizing words is prepared. These patches record EEG signals under the synergistic sensing mechanism of the noncontact capacitance mode of the CNT‐GO‐PDMS patch and contact current mode of the Ag claws, with low scalp contact resistance of 6.4 kΩ. In the occipital region, the signal‐to‐noise ratios (SNR) are ≈90 dB for α‐waves and 9 dB for visual‐evoked signals; the SNR of auditory‐evoked EEG signals in the temporal region is ≈10 dB. The EEG cap comprises seven Ag/CNT‐GO‐PDMS patches to record EEG signals in steady‐state visual‐evoked potentials (SSVEP) and multiple auditory steady‐state response (MASSR). These patches can recognize nine words (“one” to “nine”) in the SSVEP–MASSR paradigm, with a visual accuracy of 90.4% and auditory accuracy of 54.0%. The statistical analysis also shows that the stimulation frequency and scalp channel are significant influencing factors for the accuracy of word recognition. We developed a standardized process of flexible Ag/CNT‐GO‐PDMS patches and herein propose a new strategy to identify words, which is of great significance for the establishment of the EEG language database and the application of EEG in the field of information transmission.

Jingon Jang, Takgyeong Jeon, Gunuk Wang

A novel methodology in the manner of vector‐matrix multiplication (VMM) architecture is suggested for intelligently determining traffic signal changes to enhance the flow of urban traffic. Unlike the conventional prediction‐based traffic model, a real‐time decision model considering the traffic density at each transport section is established, which simplifies the traffic signal decision process as a convolutional transformation. Compared with a periodically repetitive signal changing system, the suggested VMM system actively optimizes the signal configuration in an irregular shape according to the traffic density distribution, resulting in reduction in the time cost with highly improved decision efficiency. With this system based on particle dynamics, the travel time is reduced by ≈10% at the same pass ratio for different road structures (one‐way, bidirectional, and intersectional transport). The pass ratio and resulting flow dynamics can be controllable using the different transformation matrix selections according to the traffic conditions. In addition, the analog conductance of the memristor device to the transformation matrix elements is applied, maintaining its reduction rate with a deviation tolerance of the VMM process up to ≈50%. It is believed that VMM‐based signal decision platform can lead to great progress for fast and efficient transport in complex urban traffic networks.

Manimaran M., Nagalakshmi S., Vasanthi S. et al.

Biodiesel is touted to be an alternative to the fossil fuels as it is conducive to the environment. This investigation proposes a control framework to produce biodiesel in a reactive distillation column via a transesterification process. To extract quality product, the temperature profile must be maintained along the column as per the requirements. However, constant interactions among the products inside the column disturb the temperature profile and consequently the product quality. Therefore, this investigation treats the process as a single input and single output system, where in the process interactions are modelled as disturbances. A model predictive controller (MPC) is designed for the proposed system to ensure product quality. The MPC parameters must be selected appropriately to ensure optimal performance. In this regard, to tune the MPC parameters optimally, we use two evolutionary algorithms namely, the real coded genetic algorithm (RGA) and the bio-geography based optimization algorithm (BBO). The results indicate the proposed control strategy provides offset free set point tracking when compared to the multivariable control strategy employed using the MPC algorithm. Among the two evolutionary controllers used for tuning the MPC parameters, the RGA MPC controller provides a satisfactory performance.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.