Time-Optimal Control for High-Order Chain-of-Integrators Systems with Full State Constraints and Arbitrary Terminal States

Time-optimal control for high-order chain-of-integrators systems with full state constraints and arbitrary given terminal states remains a challenging problem in the optimal control theory domain, yet to be resolved. To enhance further comprehension of the problem, this paper establishes a novel notation system and theoretical framework, successfully providing the switching manifold for high-order problems in the form of switching law. Through deriving properties of switching laws on signs and dimension, this paper proposes a definite condition for time-optimal control. Guided by the developed theory, a trajectory planning method named the manifold-intercept method (MIM) is developed. The proposed MIM can plan time-optimal jerk-limited trajectories with full state constraints, and can also plan near-optimal higher-order trajectories with negligible extra motion time. Numerical results indicate that the proposed MIM outperforms all baselines in computational time, computational accuracy, and trajectory quality by a large gap

Electrochemical reforming of ethanol with acetate Co-Production on nickel cobalt selenide nanoparticles

The energy efficiency of water electrolysis is limited by the sluggish reaction kinetics of the anodic oxygen evolution reaction (OER). To overcome this limitation, OER can be replaced by a less demanding oxidation reaction, which in the ideal scenario could be even used to generate additional valuable chemicals. Herein, we focus on the electrochemical reforming of ethanol in alkaline media to generate hydrogen at a Pt cathode and acetate as a co-product at a NiCoSe anode. We first detail the solution synthesis of a series of NiCoSe electrocatalysts. By adjusting the Ni/Co ratio, the electrocatalytic activity and selectivity for the production of acetate from ethanol are optimized. Best performances are obtained at low substitutions of Ni by Co in the cubic NiSe phase. Density function theory reveals that the Co substitution can effectively enhance the ethanol adsorption and decrease the energy barrier for its first step dehydrogenation during its conversion to acetate. However, we experimentally observe that too large amounts of Co decrease the ethanol-to-acetate Faradaic efficiency from values above 90% to just 50 %. At the optimized composition, the NiCoSe electrode delivers a stable chronoamperometry current density of up to 45 mA cm, corresponding to 1.2 A g, in a 1 M KOH + 1 M ethanol solution, with a high ethanol-to-acetate Faradaic efficiency of 82.2% at a relatively low potential, 1.50 V vs. RHE, and with an acetate production rate of 0.34 mmol cm h.This work was supported by the start-up funding at Chengdu University. It was also supported by the European Regional Development Funds and by the Spanish Ministerio de Economía y Competitividad through the project SEHTOP (ENE2016-77798-C4-3-R), MCIN/ AEI/10.13039/501100011033/ project, and NANOGEN (PID2020-116093RB-C43). X. Wang, C. Xing, X. Han, R. He, Z. Liang, and Y. Zhang are grateful for the scholarship from China Scholarship Council (CSC). X. Han and J. Arbiol acknowledge funding from Generalitat de Catalunya 2017 SGR 327. ICN2 acknowledges support from the Severo Ochoa Programme (MINECO, Grant no. SEV-2013-0295). IREC and ICN2 are funded by the CERCA Programme / Generalitat de Catalunya

Positive response to trastuzumab deruxtecan in a patient with HER2-mutant NSCLC after multiple lines therapy, including T-DM1: a case report

Human epidermal growth factor 2 (HER2) mutations are uncommon in non-small cell lung cancer (NSCLC), and the lack of established, effective, targeted drugs has resulted in a persistently poor prognosis. Herein, we report the case of a non-smoking, 58-year-old man diagnosed with lung adenocarcinoma (cT3N0M1c, stage IVB) harboring a HER2 mutation (Y772_A775dupYVMA) and PD-L1 (-). The patient’s Eastern Cooperative Oncology Group performance status (PS) score was assessed as 1. He commenced first-line treatment with chemotherapy, followed by immuno-chemotherapy, and with disease progression, he received HER2-targeted therapy and chemotherapy with an anti-angiogenic agent. However, HER2-targeted therapy, including pan-HER tyrosine kinase inhibitors (afatinib, pyrotinib, and pozitinib) and antibody–drug conjugate (T-DM1), produced only stable disease (SD) as the best response. After the previously described treatment, primary tumor recurrence and multiple brain metastases were observed. Despite the patient’s compromised overall physical condition with a PS score of 3-4, he was administered T-DXd in addition to whole-brain radiotherapy (WBRT). Remarkably, both intracranial metastases and primary lesions were significantly reduced, he achieved a partial response (PR), and his PS score increased from 3-4 to 1. He was then treated with T-DXd for almost 9 months until the disease again progressed, and he did not discontinue the drug despite the occurrence of myelosuppression during this period. This is a critical case as it exerted an effective response to T-DXd despite multiple lines therapy, including T-DM1. Simultaneously, despite the occurrence of myelosuppression in the patient during T-DXd, it was controlled after aggressive treatment

Nickel iron diselenide for highly efficient and selective electrocatalytic conversion of methanol to formate

The electro-oxidation of methanol to formate is an interesting example of the potential use of renewable energies to add value to a biosourced chemical commodity. Additionally, methanol electro-oxidation can replace the sluggish oxygen evolution reaction when coupled to hydrogen evolution or to the electroreduction of other biomass-derived intermediates. But the cost-effective realization of these reaction schemes requires the development of efficient and low-cost electrocatalysts. Here, a noble metal-free catalyst, Ni1−xFexSe2 nanorods, with a high potential for an efficient and selective methanol conversion to formate is demonstrated. At its optimum composition, Ni0.75Fe0.25Se2, this diselenide is able to produce 0.47 mmol cm−2 h−1 of formate at 50 mA cm−2 with a Faradaic conversion efficiency of 99%. Additionally, this noble-metal-free catalyst is able to continuously work for over 50 000 s with a minimal loss of efficiency, delivering initial current densities above 50 mA cm−2 and 2.2 A mg−1 in a 1.0 m KOH electrolyte with 1.0 m methanol at 1.5 V versus reversible hydrogen electrode. This work demonstrates the highly efficient and selective methanol-to-formate conversion on Ni-based noble-metal-free catalysts, and more importantly it shows a very promising example to exploit the electrocatalytic conversion of biomass-derived chemicals.J.L. obtained International Postdoctoral Exchange Fellowship Program (Talent-Introduction program No. YJ20190126) in 2019 and is grateful for the project (2019M663468) funded by the China Postdoctoral Science Foundation. This work was supported from the UESTC start-up funding, the Recruitment Program of Thousand Youth Talents, and the Natural Science Foundation of China (22072013). It was also supported by the European Regional Development Funds and by the Spanish Ministerio de Economía y Competitividad through the project SEHTOP (ENE2016-77798-C4-3-R) and VALPEC (ENE2017-85087-C3). C.X., Y.Z., and T.Z. are grateful for the China Scholarship Council (CSC) for scholarship support. T.Z., and J.A. acknowledge funding from Generalitat de Catalunya 2017 SGR 327. ICN2 acknowledges support from the Severo Ochoa Programme (MINECO, Grant no. SEV-2013-0295). T.Z. has received funding from the CSC-UAB PhD scholarship program. M.C.S. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 754510 (PROBIST) and the Severo Ochoa programme. IREC and ICN2 are funded by the CERCA Programme/Generalitat de Catalunya. Part of the present work has been performed in the framework of Universitat Autònoma de Barcelona Materials Science PhD program. J.L. is a Serra Húnter Fellow and is grateful to MICINN/FEDER RTI2018-093996-B-C31, GC 2017 SGR 128 and to ICREA Academia program.Peer reviewe

Cognitive System for the Control of Myoelectric Hands

学位論文(Thesis

Unraveling Convolution Neural Networks: A Topological Exploration of Kernel Evolution

Convolutional Neural Networks (CNNs) have become essential in deep learning applications, especially in computer vision, yet their complex internal mechanisms pose significant challenges to interpretability, crucial for ethical applications. Addressing this, our paper explores CNNs by examining their topological changes throughout the learning process, specifically employing persistent homology, a core method within Topological Data Analysis (TDA), to observe the dynamic evolution of their structure. This approach allows us to identify consistent patterns in the topological features of CNN kernels, particularly through shifts in Betti curves, which is a key concept in TDA. Our analysis of these Betti curves, initially focusing on the zeroth and first Betti numbers (respectively referred to as Betti-0 and Betti-1, which denote the number of connected components and loops), reveals insights into the learning dynamics of CNNs and potentially indicates the effectiveness of the learning process. We also discover notable differences in topological structures when CNNs are trained on grayscale versus color datasets, indicating the need for more extensive parameter space adjustments in color image processing. This study not only enhances the understanding of the intricate workings of CNNs but also contributes to bridging the gap between their complex operations and practical, interpretable applications

Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network

(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling

Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder

Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions