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CAHEROS: Constraint-Aware HEuristic Approach for RObust Sensor Placement 20

CAHEROS: Constraint-Aware HEuristic Approach for RObust Sens...

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20th IEEE Sensors conference

作者： Gungor, Onat Rosing, Tajana Aksanli, Baris Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92103 USA Univ Calif San Diego Dept Comp Sci & Engn San Diego CA USA San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA

ISBN: (纸本)9781728195018

Coverage-only sensor placement lacks two important aspects: uncertainty in sensor measurements and possible sensor break-down. To incorporate these components into a sensor placement framework, we first propose our robust sensor placement model. In order to solve this model for large instances efficiently, we adapt different heuristics where we integrate constraints into the heuristic framework. To the best of our knowledge, we are the first ones adapting constraint-aware heuristics for robust sensor placement. Experimentally, by using a factory shop floor, we show that the greedy algorithm outperforms other heuristics while requiring more computational overhead. Compared to the coverage-only approach, our robust heuristics improves system robustness by up to 48%. Our heuristics can find a solution 400x faster than conventional solvers while only being 5.5% less optimal.

关键词： sensor placement mathematical optimization heuristics wireless sensor networks internet of things

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optimization of Prosumer's Flexibility Taking Network Constraints into Account

Optimization of Prosumer's Flexibility Taking Network Constr...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Gough, Matthew Ashraf, Paul Santos, Sergio F. Javadi, Mohammad Lotfi, Mohamed Osorio, Gerardo J. Catalao, Joao P. S. Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal German Univ Cairo New Cairo Egypt C MAST UBI Covilha Portugal

ISBN: (纸本)9781728174556

the integration of new technologies at the residential level such as energy storage systems, electric vehicles, solar photovoltaic generation and mini wind turbines triggered the appearance of a new agent in the power systems called prosumers. this agent has the potential to provide new forms of flexibility and cost-effective solutions. However, associated with these new solutions there are also a number of problems that affect these solutions, particularly network constraints. this work presents an analysis not only on the benefits of utilizing the prosumer's flexibility but also to the problems associated with the operation and optimization of the network. A new model is presented that considers energy transactions between prosumers in the neighborhood and between them and the network using on a stochastic framework, in order to account for a set of uncertainties in the form of scenarios associated with the availability of various resources and technologies. the results show the economic benefit of energy transactions between prosumers resulting in more flexibility for the system while highlighting the effect of network restrictions and potential problems associated with them.

关键词： Prosumers flexibility neighborhood power losses network constraints stochastic optimization

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14th International conference, NooJ 2020

14th International Conference, NooJ 2020

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14th International conference, NooJ 2020

ISBN: (纸本)9783030706289

the proceedings contain 20 papers. the special focus in this conference is on Applications to Natural Language Processing and Digital Humanities. the topics include: the Use of Figurative Language in an Italian Dream Description Corpus: Exploiting NooJ for Stylometric Purposes;paraphrasing Emotions in Portuguese;preparing the NooJ German Module for the Analysis of a Learner Spoken Corpus;automatic Treatment of Causal, Consecutive, and Counterargumentative Discourse Connectors in Spanish: A Pedagogical Application of NooJ;nooJ for Artificial Intelligence: An Anthropic Approach;answering Arabic Complex Questions;the optimization of Portuguese Named-Entity Recognition and Classification by Combining Local Grammars and Conditional Random Fields Trained with a Parsed Corpus;the Automatic Recognition and Translation of Tunisian Dialect Named Entities into Modern Standard Arabic;a Legal Question Answering Ontology-Based system;the Lexical Complexity and Basic Vocabulary of the Italian Language;a Bottom-Up Approach for Moroccan Legal Ontology Learning from Arabic Texts;formalizing Latin: An Example of Medieval Latin Wills;the Morphological Annotation of Reduplication-Circumfix Intersection in Indonesian;multiword Expressions in the Medical Domain: Who Carries the Domain-Specific Meaning;transformations and Paraphrases for Quechua Sentiment Predicates;arabic Psychological Verb Recognition through NooJ Transformational Grammars;grammatical modeling of a Nominal Ellipsis Grammar for Spanish;where the Dickens Are Melville’s Phrasal Verbs?.

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Optimized Dynamic Voltage Restorer (DVR) for Stabilizing Unbalanced Loads in Power system

Optimized Dynamic Voltage Restorer (DVR) for Stabilizing Unb...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Mohammadpour, Hamid Adelmanesh, Behin Shirdare, Erfan Kermani, Mostafa Kermani, Morteza IAU Univ Birjand Dept Elect Engn Birjand Iran Sapienza Univ Rome Dept Astronaut Elect & Energy Engn DIAEE Rome Italy IAU Univ Neyshaboor Dept Elect Engn Neyshaboor Iran

ISBN: (数字)9781728174556

ISBN: (纸本)9781728174556

Various approaches presented so far, to overcome load imbalances, mainly in two groups of traditional methods based on power demand information, and new methods based on power electronic devices, such as Static Synchronous Compensator (STATCOM) and, Dynamic Voltage Restorer (DVR). this paper optimizes DVR coefficients based on the Genetic Algorithm (GA) to improve DVR performance in stabilizing the unbalanced systems. After modeling the various unbalanced effects of the power system, in the real case study, DVR has been used to improve the load balance. Finally, by applying the genetic algorithm proposed in this paper, the performance of DVR in load balancing has been significantly improved. To verification of the accuracy of the GA method, the results of the obtained coefficients are compared with the Simulated Annealing algorithm (SA) and the experimental values.

关键词： Dynamic Voltage Restorer Unbalanced Load Genetic Algorithm Simulated Annealing algorithm

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A Combined Model to Jointly Optimize the Left-Turn Trajectory and Approaching Speed for the Connected and Autonomous Vehicles 21

A Combined Model to Jointly Optimize the Left-Turn Trajector...

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20th and 21st Joint COTA International conference of Transportation Professionals - Advanced Transportation, Enhanced Connection

作者： Liu, Xiaogao Li, Keping Zhang, Runhan Chen, Can Tongji Univ Dept Comprehens Traff Informat Shanghai Peoples R China Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai Peoples R China Tongji Univ Dept Traff Engn Shanghai Peoples R China

ISBN: (纸本)9780784483565

Currently, with the development of connected and autonomous vehicles (CAVs), rich traffic flow information could be obtained easily to optimize trajectories. Existing studies that generally simplify trajectories as ideal curves or design a passing strategy for all vehicles at a constant speed fail to coordinate global trajectory and speed fluctuation. therefore, a combined model aiming at optimizing the left-turn trajectory and approaching speed jointly for CAVs is proposed in this study. First, a rule-based classification method is proposed based on relevant indicators and three categories are subdivided. A changeable speed adjustment strategy, considering speed fluctuation is designed to generate the optimal strategy with minimum total time. Finally, the combined model is tested in three scenarios. Results show that delay of left-turn vehicles is optimized by 19.8%, and the proposed model could improve the operation efficiency of intersections.

关键词： CAVs speed adjustment trajectory optimization operation efficiency

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optimization of PV Energy Conversion system Using Reinforcement Learning Algorithm 20

Optimization of PV Energy Conversion System Using Reinforcem...

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20th International conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2020

作者： Zeddini, Mohamed Ali Turki, Mourad Mimoun, Mohamed Faouzi Monastir5019 Tunisia

ISBN: (纸本)9781728188157

this paper proposes a novel MPPT algorithm using a reinforcement learning (RL) to track the Global Maximum Power Point (GMPP) for photovoltaic (PV) applications. the RL MPPT algorithm was validated by simulation studies under Matlab-simulink for a 2.5 kW PV conversion system based on 5*4 PV modules, a DC/DC converter and a resistive Load. In order to enhance the searching ability of proposed MPPT algorithm, a load and irradiation variations are introduced on simulations tests. In particular, a changing of partial shading condition (PSC) is undertaken to change the position and the value of the GMPP a lot of time for improving the efficiency of the algorithm. © 2020 IEEE.

关键词： Reinforcement learning

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optimization of diode bridge rectifier output voltage in Train Suspension Energy Harvesters 20

Optimization of diode bridge rectifier output voltage in Tra...

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20th IEEE Mediterranean Eletrotechnical conference (IEEE MELECON)

作者： Costanzo, Luigi Lo Schiavo, Alessandro Vitelli, Massimo Zuo, Lei Univ Campania Luigi Vanvitelli Dept Engn Aversa CE Italy Virginia Tech Dept Mech Engn Blacksburg VA USA

ISBN: (纸本)9781728152004

this paper is focused on energy harvesting from railcar suspensions. In particular, the power extracted from a train suspension energy harvester based on a mechanical motion rectifier system is evaluated in presence of a passive AC/DC converter. through a proper theoretical analysis, experimentally validated, it is shown that the extracted power is strictly dependent on the DC side voltage and that the value of the DC voltage at the output of the diode bridge rectifier maximizing the average extracted power is proportional to the generator speed. the above analysis is essential for the proper design of maximum power point tracking techniques.

关键词： Train Suspension Energy Harvesters Maximum Power Point Tracking Power Electronics Interface Modelling

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Fuzzy Observer for MPPT Control of Variable Wind Energy Conversion system Associed to AC-DC Converters 20

Fuzzy Observer for MPPT Control of Variable Wind Energy Conv...

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20th International conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2020

作者： Bedoud, Khouloud Merabet, Hichem Bahi, Tahar Drici, Djalel Research Center in Industrial Technologies Crti Ex Csc BP 64 Cheraga Algeria Automatic Laboratory and Signals Badji Mokhtar University Annaba Algeria

ISBN: (纸本)9781728188157

this paper presents the description, modeling, and control of an electrical energy production system based on a wind power system operating at variable speed. Such energy production systems are inevitably called upon for randomly varying wind speeds. Morever, MPPT control using fuzzy logic observer (FLO) for rotational speed estimation without using wind speed data is adopted. To improve and verify this approach's effectiveness, a quantitative and qualitative analysis of the energy generation system with a power of 1.5 MW is investigated by numerical simulation using Matlab/Simulink software. thus, simulation tests validate the technique and improve the dynamic behavior of the system. © 2020 IEEE.

关键词： Wind turbines

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A blind watermarking system based on deep learning model

A blind watermarking system based on deep learning model

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IEEE International conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Lichao Zhang Wangbin Li Hailiang Ye Inossem Software Technology Co. Ltd Shenyang China

ISBN: (纸本)9781665416597

In recent years, as an important means of digital image copyright protection, image invisible watermarking technology has attracted more and more attention. Based on the watermarking system built by deep learning model, this paper proposes an optimization algorithm which can extract watermark and improve robustness. the watermark is embedded and extracted by the encoder and decoder of the model respectively. In order to make the model robust to various image attacks, two noise layers are added to the model. In order to further improve the robustness and concealment of watermark, adversarial training is used. In order to improve the accuracy of model joint training, a two-step training strategy is adopted. the first step is to train the best pre training model of encoder and decoder. the second step is to train different decoders according to different image attack means, so as to improve the robustness of the whole watermarking system. Experimental results show that, compared with other watermarking algorithms based on deep learning model, the proposed method is superior to other algorithms in the concealment and robustness.

关键词： Training Deep learning Privacy Computational modeling Neural networks Transform coding Watermarking

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Intra-array Non-Idealities modeling and Algorithm optimization for RRAM-based Computing-in-Memory Applications 14

Intra-array Non-Idealities Modeling and Algorithm Optimizati...

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14th IEEE International conference on ASIC, ASICON 2021

作者： Zhao, Chenyang Fang, Jinbei Jiang, Jingwen Guo, Zhiwang Xue, Xiaoyong Zeng, Xiaoyang Fudan University State Key Laboratory of ASIC and System School of Microelectronics Shanghai China

ISBN: (纸本)9781665411493

In recent years, Computing-in-Memory (CIM) has shown attractive advantages over CPU/FPGA/ASIC in terms of area, energy efficiency and latency for neural network acceleration in edge applications. Among them, RRAM-based CIM accelerators has gained lots of attentions with fast read access, and low leakage power. However, the inherent non-idealities of RRAM devices, such as read non-linearity and read variation will directly impact the hardware performance. So, based on the mainstream RRAM mathematical model, we first establish 9 models of RRAM cells with different LRS/HRS combinations. then we analyze the circuit simulation results to model the non-idealities mentioned above. A non-ideality-aware algorithm is proposed based on these models, which are brought into the training process of the neural network, to improve the reliability of the accelerator. A multi-layer perceptron (MLP) with one input layer, two hidden layers and one output layer is used to perform recognition of MNIST for evaluation purpose. Given the read voltage ranged from 0 to 1.0V, read variation σ =20%, LRS/HRS = 200KΩ/2MΩ, the proposed algorithm can improve the accuracy by 5.42%. © 2021 IEEE.

关键词： RRAM

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