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Hydrogen Ion Implantation Induced Cutting Behavior Variation in Plunge Cutting of the Monocrystalline Silicon

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Nanomanufacturing and Metrology 2021年第4期4卷 209-215页

作者： Zejia Zhao E.V.Jelenkovic Gaobo Xiao Zhuoxuan Zhuang Suet To Institute of Semiconductor Manufacturing Research College of Mechatronics and Control EngineeringShenzhen UniversityNan-hai Ave 3688Shenzhen 518060GuangdongPeople’s Republic of China State Key Laboratory of Ultra-precision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHung HornKowloonHong Kong SARPeople’s Republic of China Zhuhai Bojay Electronics Co. Ltd10 Futian RdXiangzhou DistrictZhuhai.GuangdongPeople’s Republic of China

In this study,surface modification of monocrystalline silicon with two doses of hydrogen ion implantation and the plunge cutting process were conducted to explore the influence of hydrogen ions on the cutting behavior of *** results show that ion implantation is capable of deteriorating or improving the machinability of silicon,depending on the implantation *** cleavages and a reduction of critical depth of cut(CDoC)were observed for the silicon with a low implantation dose in the cutting direction of<100>in comparison to bare silicon,while no cleavage and an increase of CDoC were achieved after implantation with a high dose in the same cutting ***,the ductile cutting and thrust forces of the silicon with the low dose are larger than the bare silicon,but the forces are significantly reduced for the silicon after the high dose of *** variation of the cutting forces is due to the different required stresses to overcome ductile and fracture deformation of silicon.

关键词： Hydrogen ion implantation Plunge cutting Silicon Critical depth of cut Cutting forces

Modified Carrier-Based SPWM Technique for Zero Sequence Circulating Current Elimination in Parallel Inverters

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Modified Carrier-Based SPWM Technique for Zero Sequence Circ...

Advanced technology in Electronic and Electrical Engineering (ICATEEE), International Conference of

作者： Ala Addin Mohammed Al-Dwa Chebabhi Ali Choudar Adel Barkat Said Fellah Mohammed Karim Department of Electrical Engineering Electrical Engineering Laboratory University of M’sila M’sila Algeria LPMRN Laboratory Faculty of Sciences and Technology University of Bordj Bou Arreridj Bordj Bou Arreridj Algeria Intelligent Control & Electrical Power Systems Laboratory University of Djillali Liabes Sidi Bel-Abbes Algeria

For medium and high-power renewable energy applications, parallel four-leg inverters are highly recommended to increase the power capability, power rating, and the efficiency of the system. However, a zero-sequence circulating current (ZSCC) will be generated by the difference between the zero sequence voltages (ZSVs) due to unequal output currents and filter inductors, as well as other factors such as different switching frequencies and dead times when the inverters are connected in parallel via common DC and AC buses. The ZSCC will seriously distort the output currents and degrade the control performance and the dynamic response of the overall parallel system. This paper suggests a modified carrier-based SPWM to suppress the ZSCC by eliminating the difference between ZSVs through the adjustment of modulation voltages of the modified carrier-based SPWM. The proposed ZSCC suppression method can successfully achieve the ZSCC suppression even when the parallel four-leg inverters system has unbalanced output current sharing and filter inductances. The simulation results based on the parallel four-leg inverters distinctly confirm the effectiveness of the suggested ZSCC suppression method.

关键词： Electrical engineering Renewable energy sources Simulation Switching frequency Power quality Modulation Inverters

Hard hat wearing detection based on head keypoint localization

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arXiv 2021年

作者： Wójcik, Bartosz Zarski, Mateusz Ksiązek, Kamil Miszczak, Jaroslaw A. Skibniewski, Miroslaw J. Faculty of Civil Engineering Silesian University of Technology Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland Department of Civil & Environmental Engineering University of Maryland College Park United States

In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems. However, still there is no reliable way to establish the relationship between supervised construction workers and their basic personal protective equipment, like hard hats. To address this problem, a novel deep learning method combining object detection, keypoint localization, and simple rule-based reasoning is proposed in this article. In tests, this solution surpassed the previous methods based on the relative bounding box position of different instances, as well as direct detection of hard hat wearers and non-wearers. Achieving MS COCO style overall AP of 67.5% compared to 66.4% and 66.3% achieved by the above-mentioned approaches, with class specific AP for hard hat non-wearers of 64.1% compared to 63.0% and 60.3%. The results show that the conjunction of deep learning methods with humanly-interpretable rule-based algorithm is better suited for detection of hard hat non-wearers. Code is available at barwojcik/hard-hats. © 2021, CC BY.

关键词： Object detection

A Sample-Based Algorithm for Approximately Testing r-Robustness of a Digraph

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arXiv 2022年

作者： Yi, Yuhao Wang, Yuan He, Xingkang Patterson, Stacy Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-100 44 Sweden The Department of Electrical Engineering University of Notre Dame Notre Dame IN46556 United States The Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States

One of the intensely studied concepts of network robustness is r-robustness, which is a network topology property quantified by an integer r. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining r-robustness is intractable for large networks. In this paper, we propose a sample-based algorithm to approximately test r-robustness of a digraph with n vertices and m edges. For a digraph with a moderate assumption on the minimum in-degree, and an error parameter 0 Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

A New Locally Active Memristive Synapse Coupled Neuron Model

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Research Square

Research Square 2021年

作者： Li, Ronghao Wang, Zenghui Dong, Enzeng Tianjin Key Laboratory For Control Theory & Applications in Complicated Systems Tianjin University of Technology Tianjin300384 China Department of Electrical and Mining Engineering University of South Africa Florida1710 South Africa

In this paper, a new type of non-volatile locally active memristor with bi-stability is proposed by injecting appropriate voltage pulses to realize a switching mechanism between two stable states. It is found that the memristive parameters of the new memristor can affect the local activity, which has been rarely reported, and this phenomenon is explained based on mathematical analyses and numerical simulations. Then, a locally active memristive coupled neuron model is constructed using the proposed memristor as a connecting synapse. The parameter-associated dynamical behaviors are revealed by bifurcation plots, phase plane portraits and dynamical evolution maps. Moreover, the bi-stability phenomenon of the new coupled neuron model is disclosed by local attraction basins, and the periodic burster and multi-scroll chaotic burster are found if a multi-level pulse current is used to imitate a periodical external stimulus on the neurons. The Hamiltonian energy function is calculated and analyzed with or without external excitation. Finally, the neuronal circuit is designed and implemented, which can mimic electrical activity of the neurons and is useful for physical applications. The experimental results captured from the analog circuit are consistent well with the numerical simulation results. © 2021, CC BY.

关键词： Memristors

Leveraging classification metrics for quantitative system-level analysis with temporal logic specifications

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arXiv 2021年

作者： Badithela, Apurva Wongpiromsarn, Tichakorn Murray, Richard M. Control and Dynamical Systems Computing and Mathematical Sciences California Institute of Technology PasadenaCA91106 United States Department of Computer Science Iowa State University AmesIA50011 United States

In many autonomy applications, performance of perception algorithms is important for effective planning and control. In this paper, we introduce a framework for computing the probability of satisfaction of formal system specifications given a confusion matrix, a statistical average performance measure for multi-class classification. We define the probability of satisfaction of a linear temporal logic formula given a specific initial state of the agent and true state of the environment. Then, we present an algorithm to construct a Markov chain that represents the system behavior under the composition of the perception and control components such that the probability of the temporal logic formula computed over the Markov chain is consistent with the probability that the temporal logic formula is satisfied by our system. We illustrate this approach on a simple example of a car with pedestrian on the sidewalk environment, and compute the probability of satisfaction of safety requirements for varying parameters of the vehicle. We also illustrate how satisfaction probability changes with varied precision and recall derived from the confusion matrix. Based on our results, we identify several opportunities for future work in developing quantitative system-level analysis that incorporates perception models. Copyright © 2021, The Authors. All rights reserved.

关键词： Image resolution

Bayesian optimization assisted meal bolus decision based on gaussian processes learning and risk-sensitive control

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arXiv 2021年

作者： Cai, Deheng Liu, Wei Ji, Linong Shi, Dawei State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Department of Endocrine and Metabolism Peking University People’s Hospital Beijing China

Effective postprandial glucose control is important to glucose management for subjects with diabetes mellitus. In this work, a data-driven meal bolus decision method is proposed without the need of subject-specific glucose management parameters. The postprandial glucose dynamics is learnt using Gaussian process regression. Considering the asymmetric risks of hyper- and hypoglycemia and the uncertainties in the predicted glucose trajectories, an asymmetric risk-sensitive cost function is designed. Bayesian optimization is utilized to solve the optimization problem, since the gradient of the cost function is unavailable. The proposed approach is evaluated using the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the standard insulin bolus calculator. For the case of announced meals, the proposed method achieves satisfactory and similar performance in terms of mean glucose and percentage time in [70, 180] mg/dL without increasing the risk of hypoglycemia. Similar results are observed for the case without the meal information (assuming that the patient follows a consistent diet) and the case of basal rate mismatches. In addition, advisory-mode analysis is performed based on clinical data, which indicates that the method can determine safe and reasonable meal boluses in real clinical settings. The results verify the effectiveness and robustness of the proposed method and indicate the feasibility of achieving improved postprandial glucose regulation through a data-driven optimal control method. © 2021, CC BY.

关键词： Gaussian distribution

Boosting the Performance of Co-Doped Ceo2 Catalyst in the Catalytic Oxidation of Toluene by Modulating Oxygen Vacancies

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SSRN

SSRN 2023年

作者： Liu, Jie Wang, Hua Zhang, Wanying Gao, Jiawu Yu, Qing Ke, Jun Wang, Lidong Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China School of Chemistry and Environmental Engineering Wuhan Institute of Technology Wuhan430205 China

Metal doping is one of the most effective strategies to enhance redox capacity and modulate the oxygen vacancies in Ce-based catalysts. This manuscript reports a Co-doped CeO2 catalyst (Co0.2Ce0.8 (W/E=1/1)) for the catalytic oxidation of toluene fabricated by a facile co-precipitation method. The appropriate ratio of deionized (DI) water/ethanol in the reaction solvent contributed to the mesocrystallization of the CeO2 phase and the highly uniform dispersion of the Co‒O‒Ce mixed bond in the catalyst. The feasible lattice distortion induced the generation of richer oxygen vacancies in both the surface and bulk of the catalyst. Meanwhile, the efficient cycle of Co2+ + Ce4+ ↔ Co3+ + Ce3+ enhanced the redox property of the catalyst. As revealed by in situ DRIFTS studies, the as-formed surface oxygen vacancies promoted the adsorption and partial oxidation of toluene. The oxygen vacancies in the bulk of the catalyst improved the migration of lattice oxygen, accelerating the mineralization of the aromatic ring. Thus, the excellent low-temperature oxidation of toluene by Co0.2Ce0.8 (W/E=1/1), reducing T90 to 192 °C, was attributed to the synergism between the catalyst’s surface and bulk oxygen vacancies. © 2023, The Authors. All rights reserved.

关键词： Toluene

Privacy-Preserving distributed average consensus in finite time using random gossip

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arXiv 2021年

作者： Manitara, Nicolaos E. Rikos, Apostolos I. Hadjicostis, Christoforos N. Department of Electrical and Computer Engineering KIOS Center of Excelence University of Cyprus Nicosia1678 Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In this paper, we develop and analyze a gossip-based average consensus algorithm that enables all of the components of a distributed system, each with some initial value, to reach (approximate) average consensus on their initial values after executing a finite number of iterations, and without having to reveal the specific value they contribute to the average calculation. We consider a fully-connected (undirected) network in which each pair of components (nodes) can be randomly selected to perform pairwise standard gossip averaging of their values, and propose an enhancement that can be followed by each node that does not want to reveal its initial value to other (curious) nodes. We assume that curious nodes try to identify the initial values of other nodes but do not interfere in the computation in any other way;however, as a worst-case assumption, curious nodes are allowed to collaborate arbitrarily and are assumed to know the privacy-preserving strategy (but not the actual parameters chosen by the nodes that want to preserve their privacy). We characterize precisely conditions on the information exchange that guarantee privacy-preservation for a specific node. The protocol also provides a criterion that allows the nodes to determine, in a distributed manner (while running the enhanced gossip protocol), when to terminate their operation because approximate average consensus has been reached, i.e., all nodes have obtained values that are within a small distance from the exact average of their initial values. © 2021, CC BY.

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