As an exemplary batch process control strategy, iterative learning model predictive control (ILMPC) progressively refines tracking performance through repeated trials. However, the learning-based control method ILMPC generally requires a strict matching of trial lengths to enable the execution of 2-D receding horizon optimization. Randomly varying trial lengths, commonly encountered in practice, can lead to an insufficient grasp of prior information, and even result in a halt to the control update procedure. Regarding the stated issue, this article develops a novel predictive adjustment method integrated into the ILMPC framework. This method adjusts the process data from each trial to a uniform length by inserting predicted sequences to cover any missing running phases at the end of each trial. The proposed modification scheme guarantees the convergence of the classical iterative learning model predictive control (ILMPC) based on an inequality condition, which relates to the probability distribution of trial durations. For prediction-based modifications in practical batch processes with intricate nonlinearities, a two-dimensional neural network predictive model, featuring parameter adaptation across trials, is created to generate highly accurate compensation data. An event-driven learning structure, proposed for ILMPC, aims to dynamically adjust learning sequences according to the likelihood of trial duration alterations, thereby balancing the value of recent and historical trial information. Considering two situations based on the switching condition, the theoretical convergence analysis of the nonlinear event-based switching ILMPC system is conducted. The numerical example simulations, coupled with the injection molding process, confirm the superiority of the proposed control methods.
Capacitive micromachined ultrasound transducers (CMUTs) have been the subject of extensive study for more than 25 years, their advantages lying in the potential for large-scale manufacturing and electronic circuit integration. Prior to recent advancements, CMUTs were built by assembling numerous tiny membranes into a single transducer element. Despite this, suboptimal electromechanical efficiency and transmission performance were exhibited, making the resulting devices not necessarily competitive with piezoelectric transducers. Moreover, dielectric charging and operational hysteresis were common issues in previous iterations of CMUT devices, impeding their long-term operational reliability. A novel CMUT architecture was recently showcased, featuring a single, elongated rectangular membrane per transducer element and unique electrode post structures. This architecture's performance advantages, in addition to its long-term reliability, significantly outperform previously published CMUT and piezoelectric arrays. This research paper seeks to highlight the improvements in performance and provide a comprehensive account of the fabrication process, including recommendations to prevent common errors. A key objective is to furnish comprehensive information, thereby stimulating innovative microfabricated transducer development, and thus leading to performance improvements in the next generation of ultrasound systems.
We introduce a novel approach in this study to elevate cognitive attentiveness and lessen the burden of mental stress in the occupational setting. To induce stress, we implemented an experiment employing the Stroop Color-Word Task (SCWT) with participants subjected to time constraints and negative feedback. Following this, a 10-minute application of 16 Hz binaural beats auditory stimulation (BBs) was used to improve cognitive vigilance and reduce stress levels. To gauge the degree of stress, Functional Near-Infrared Spectroscopy (fNIRS), salivary alpha-amylase, and behavioral responses were employed. Employing reaction time to stimuli (RT), target identification precision, directed functional connectivity calculated by partial directed coherence, graph theory analysis, and the laterality index (LI), the stress level was ascertained. Exposure to 16 Hz BBs resulted in a noteworthy 2183% enhancement in target detection accuracy (p < 0.0001) and a substantial 3028% decrease in salivary alpha amylase levels (p < 0.001), effectively alleviating mental stress. From the partial directed coherence, graph theory analysis, and LI results, it was evident that mental stress reduced information flow from the left to right prefrontal cortex. In contrast, 16 Hz brainwaves (BBs) had a substantial impact on boosting vigilance and alleviating stress by strengthening the connectivity network within the dorsolateral and left ventrolateral prefrontal cortex.
Stroke frequently leaves patients with motor and sensory impairments, which in turn lead to difficulties in walking. learn more Analyzing muscle control mechanisms during walking can provide clues about neurological changes after a stroke; however, how stroke influences individual muscle actions and the synchronization of muscles across different phases of gait requires additional study. This study's aim is to thoroughly examine ankle muscle activity and intermuscular coupling patterns in patients who have had a stroke, paying close attention to the influence of different phases of movement. Infant gut microbiota Ten post-stroke patients, ten young healthy individuals, and ten elderly healthy subjects participated in this experiment. While walking at their preferred speeds on the ground, all subjects had their surface electromyography (sEMG) and marker trajectory data collected concurrently. Each subject's gait cycle was subdivided into four substages, in accordance with the labeling present in the trajectory data. continuing medical education Using fuzzy approximate entropy (fApEn), the complexity of ankle muscle activity during gait was assessed. Employing transfer entropy (TE), the directed information transmission between ankle muscles was evaluated. Similar patterns in the complexity of ankle muscle activity were observed in both stroke patients and healthy subjects, according to the research findings. In contrast to healthy individuals, the intricacy of ankle muscle activity during gait phases is frequently amplified in stroke patients. A consistent decrease in TE values of ankle muscles is observed in stroke patients as the gait cycle progresses, with a significant drop occurring during the second double support phase. In contrast to age-matched healthy individuals, patients exhibit increased motor unit recruitment during their gait, alongside enhanced muscle coupling, to accomplish the act of walking. For a more complete insight into phase-dependent muscle modulation in post-stroke patients, the application of fApEn and TE is essential.
The process of sleep staging is essential for assessing sleep quality and diagnosing sleep-related medical conditions. Existing methods in automatic sleep staging primarily leverage time-domain characteristics, yet frequently disregard the inherent transformation patterns between sleep stages. For the purpose of automated sleep staging using a single-channel EEG, we present the Temporal-Spectral fused and Attention-based deep neural network model, TSA-Net, to tackle the preceding challenges. A two-stream feature extractor, feature context learning, and conditional random field (CRF) constitute the TSA-Net. For sleep staging, the two-stream feature extractor module automatically extracts and fuses EEG features from time and frequency domains, noting that the temporal and spectral features hold abundant differentiating information. Employing the multi-head self-attention mechanism, the feature context learning module subsequently determines the interdependencies among features, resulting in a tentative sleep stage classification. The CRF module, in its final step, employs transition rules for a more precise classification. For the purpose of evaluating our model, we leverage two public datasets, namely Sleep-EDF-20 and Sleep-EDF-78. In terms of accuracy metrics, the TSA-Net achieved 8664% and 8221% on the Fpz-Cz channel, respectively. The results of our experiments indicate that TSA-Net can effectively refine sleep staging, achieving a higher level of performance than prevailing methodologies.
As quality of life enhances, individuals exhibit heightened concern regarding sleep quality. Electroencephalogram (EEG)-derived sleep stage classification is a useful tool for understanding sleep quality and recognizing various sleep disorders. Human specialists are responsible for the design of the vast majority of automatic staging neural networks at this point in time, making the process both protracted and demanding. We present a novel NAS framework, employing bilevel optimization approximation, for the task of sleep stage classification using EEG signals. The architectural search process of the proposed NAS architecture hinges primarily on a bilevel optimization approximation. Simultaneously, model optimization is attained by strategically approximating and regularizing the search space with parameters shared uniformly among cells. The performance of the model, selected by NAS, was evaluated on the Sleep-EDF-20, Sleep-EDF-78, and SHHS datasets, showing an average accuracy of 827%, 800%, and 819%, respectively. The proposed NAS algorithm's impact on automatic network design for sleep classification is substantiated by the experimental results obtained.
Image understanding and its integration with natural language comprehension continue to pose a significant problem within the realm of computer vision. Conventional methods of deep supervision are focused on finding answers to questions within datasets containing a limited number of images and specific textual ground-truth. In the face of limited labeled data for learning, the prospect of building a vast dataset of several million visuals, meticulously annotated with texts, is enticing; unfortunately, this approach is exceedingly time-consuming and fraught with significant challenges. Knowledge-based applications often conceptualize knowledge graphs (KGs) as static, searchable tables, overlooking the dynamic evolution of the graph through updates. To address these limitations, we suggest a knowledge-embedded, Webly-supervised model for visual reasoning tasks. Leveraging the tremendous success of Webly supervised learning, we extensively employ easily available web images and their loosely annotated textual data to develop a robust representational framework.