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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Das, Subhranil Kumari, Rashmi Thakur, Abhishek Singh, Raghwendra Kishore Nigam, Akriti Faculty of Business and Leadership School of Business MIT World Peace University Pune India School of Computer Science and Engineering Bennett University Greater Noida India Department of Electrical and Electronics Engineering BIT Mesra Ranchi India Department of ECE NIT Jamedeshpur Jamedeshpur India Department of CSE BIT Mesra Ranchi India

ISBN: (纸本)9789819731794

Automated Guided Vehicles (AGVs) have found extensive use in industry, particularly in port shipping, logistics, and warehousing. Finding the shortest path is the most crucial problem with the AGV. In this proposed work, a new optimization algorithm, i.e., the Modified Harris Hawks Optimization (MHHO) technique, has been applied for path planning problems. Simulation results show the proposed technique performs much better when compared with four other state-of-the-art techniques. The efficacy of the proposed MHHO technique has been verified with two working environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Motion planning

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Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!

arXiv

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arXiv 2023年

作者： Pal, Avik Edelman, Alan Rackauckas, Christopher CSAIL MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Mathematics MIT United States

Implicit layer deep learning techniques, like Neural Differential Equations, have become an important modeling framework due to their ability to adapt to new problems automatically. Training a neural differential equation is effectively a search over a space of plausible dynamical systems. However, controlling the computational cost for these models is difficult since it relies on the number of steps the adaptive solver takes. Most prior works have used higher-order methods to reduce prediction timings while greatly increasing training time or reducing both training and prediction timings by relying on specific training algorithms, which are harder to use as a drop-in replacement due to strict requirements on automatic differentiation. In this manuscript, we use internal cost heuristics of adaptive differential equation solvers at stochastic time-points to guide the training towards learning a dynamical system that is easier to integrate. We "close the blackbox" and allow the use of our method with any adjoint technique for gradient calculations of the differential equation solution. We perform experimental studies to compare our method to global regularization to show that we attain similar performance numbers without compromising on the flexibility of implementation on ordinary differential equations (ODEs) and stochastic differential equations (SDEs). We develop two sampling strategies to trade-off between performance and training time. Our method reduces the number of function evaluations to 0.556 × −0.733× and accelerates predictions by 1.3 × −2×. © 2023, CC BY.

关键词： Forecasting

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PeRP: Personalized Residual Policies For Congestion mitigation Through Co-operative Advisory Systems

PeRP: Personalized Residual Policies For Congestion Mitigati...

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International Conference on Intelligent Transportation

作者： Aamir Hasan Neeloy Chakraborty Haonan Chen Jung-Hoon Cho Cathy Wu Katherine Driggs-Campbell Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Department of Civil and Environmental Engineering (CEE) MIT Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Department of Civil and Environmental Engineering (CEE) MIT Laboratory for Information & Decision Systems (LIDS) Institute for Data Systems & Society (IDSS) at the Massachusetts Institute of Technology

Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these systems assume precise control over autonomous vehicle fleets, and are hence limited in practice as they fail to account for uncertainty in human behavior. Piecewise Constant (PC) Policies address these issues by structurally modeling the likeness of human driving to reduce traffic congestion in dense scenarios to provide action advice to be followed by human drivers. However, PC policies assume that all drivers behave similarly. To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP. PeRP advises drivers to behave in ways that mitigate traffic congestion. We first infer the driver's intrinsic traits on how they follow instructions in an unsupervised manner with a variational autoencoder. Then, a policy conditioned on the inferred trait adapts the action of the PC policy to provide the driver with a personalized recommendation. Our system is trained in simulation with novel driver modeling of instruction adherence. We show that our approach successfully mitigates congestion while adapting to different driver behaviors, with 4 to 22% improvement in average speed over baselines. 1

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Cyber-Secure and Safe Operation of Solar Photovoltaic Power Distribution Systems

Cyber-Secure and Safe Operation of Solar Photovoltaic Power ...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Jaewon Kim Hasan Ibrahim Shaozhe Wang Akshay Mete Le Xie Prasad Enjeti P. R. Kumar Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology (MIT) Cambridge MA USA Department of Electrical & Computer Engineering Texas A&M University College Station TX USA

Solar photovoltaic (PV)-rich power distribution systems are networked Cyber-Physical Systems (CPS). These are control systems where multiple computing nodes and diverse intelligent agents interact with the physical world in real-time. However, the presence of networked components renders them vulnerable to potential cyber-attacks, cyber-intrusions, and other malicious events. This is because these systems depend on the measurements reported from their heterogeneous sensors. This makes them vulnerable to potential cyber-attacks where malicious agents can compromise the sensors or the communication networks carrying the sensor measurements. This paper proposes a novel methodology for enhancing the cyber-security and cyber-resilient post-attack safe operation of solar PV-rich power distribution systems against potential cyber-attacks through the Dynamic Watermarking (DW), using online system identification. The resiliency of the proposed technique is tested and validated with several attack scenarios on both a lab-scale 3kW grid-connected PV inverter and a Hardware-in-the-Loop (HiL) system. The proposed approach can be applied to other types of power distribution systems to enhance their cyber-secure and cyber-resilient safe operation. This paper thereby contributes to the field of cyber-security of Cyber-Physical Energy Systems (CPES).

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Analysis of a Toothed Consequent-Pole Flux Reversal Motor with Assisting Permanent Magnets

Analysis of a Toothed Consequent-Pole Flux Reversal Motor wi...

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electrical Machines and Drives (ICEMD), International Conference on

作者： Mohammad Reza Sarshar Jamshid Kavianpour Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Amir Khorsandi Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

This article studies three new toothed consequent-pole flux-reversal permanent magnet (FRPM) motors, each with specific combined PM arrays. The PM arrays comprise the biased-flux (BF) and flux-reversal (FR) effects. The FR effect is added mainly to boost the output torque production, and applying a magnetic-equivalent circuit (MEC) shows that the BF effect lowers the saturation risk in the stator core. Also, using finite element analysis (FEA), the main proposed motor generates the sinusoidal back-EMF waveform with an RMS value of 11.3V. Additionally, the full-load results show that the proposed motor produces 1.76N.m torque with 3.5% ripple. Furthermore, the flux density distribution of motors shows the importance of the BF effect in overload performances and verifies the analytical MEC results.

关键词： Stator cores Torque Windings Production Motors Permanent magnets Finite element analysis Torque measurement Optimization Saturation magnetization

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Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning(RAPID)

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eLight 2023年第1期3卷 198-210页

作者： Ziling Wu Iksung Kang Yudong Yao Yi Jiang Junjing Deng Jeffrey Klug Stefan Vogt George Barbastathis Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA Argonne National Laboratory LemontIL 60439USA Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 138602Singapore Present Address:Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 138602Singapore Present Address:University of California BerkeleyCA 94720USA

X-ray ptychographic tomography is a nondestructive method for three dimensional(3D)imaging with nanometer-sized resolvable *** size of the volume that can be imaged is almost arbitrary,limited only by the penetration depth and the available scanning *** we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned *** proposed 3D reconstruction method“RAPID”relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground *** is then trained to reproduce equal fidelity from much fewer *** training,it performs with similar fidelity on the hitherto unexamined portions of the object,previously not shown during training,with a limited set of *** our experimental demonstration,the nominal number of angles was 349 and the reduced number of angles was 21,resulting in a×140 aggregate speedup over a volume of 4.48×93.18×3.92μm^(3) and with(14 nm)^(3) feature size,i.e.-10^(8) ***’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic *** found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.

关键词： X-ray ptychographic tomography Deep learning Reduced-angle Rapid imaging Atrous spatial pyramid pooling Anisotropic

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Computationally efficient reductions between some statistical models

arXiv

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arXiv 2024年

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin Schools of Industrial and Systems Engineering Georgia Tech United States Schools of Electrical and Computer Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. Copyright © 2024, The Authors. All rights reserved.

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Study of a Modular Consequent-Pole Flux Reversal Motor with the Synergy of Biased-Flux Effect

Study of a Modular Consequent-Pole Flux Reversal Motor with ...

引用

electrical Machines and Drives (ICEMD), International Conference on

作者： Mohammad Reza Sarshar Mohammad Afrank Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Javad Shokrollahi Moghani Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

This paper studies a novel flux reversal permanent magnet (FRPM) motor in which PMs are positioned in consequent-pole and intermodular pattern. The proposed structure applies the synergies of flux reversal and biased flux effects to improve the torque density. The motor is optimized with multi-objective genetic algorithm (GA) to achieve a high torque with low torque ripple. Also, the FRPM is compared with the conventional intermodular PM motor (IMPM) to outline its superiorities. By means of finite element analysis (FEA), the motors are compared under the no-load and full-load conditions, and the results in terms of back-EMF and its harmonic distribution, air-gap flux density distributions, and full-load torque and ripple are analyzed. The FEA results shows that the proposed structure enhances the output torque by 40% compared to the IMPM while reducing the PM volume.

关键词： Torque Motors Harmonic analysis Permanent magnets Topology Finite element analysis Scattering parameters Torque measurement Optimization Genetic algorithms

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Study of an Improved Flux Reversal Permanent Magnet Outer-Rotor Motor

Study of an Improved Flux Reversal Permanent Magnet Outer-Ro...

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Iranian Conference on electrical engineering (ICEE)

作者： Mohammad Reza Sarshar Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper analyzes two proposed flux reversal permanent magnet (FRPM) motors. The main motor, an improved FRPM (I-FRPM), was tested under no-load and full-load conditions. I-FRPM performs a higher torque density and low torque ripple structure. Using finite element methods (FEM), all curves are illustrated and compared to these two structures under fair conditions. This I-FRPM can be used in electric vehicle applications because it suggests a high torque density with minimal ripple motor structure.

关键词： electrical engineering Torque Motors Harmonic analysis Electric vehicles Permanent magnets Finite element analysis

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ITERATED ENTROPY DERIVATIVES AND BINARY ENTROPY INEQUALITIES

arXiv

引用

arXiv 2023年

作者： Wakhare, Tanay Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States

We embark on a systematic study of the (k + 1)-th derivative of xk−rH(xr), where H(x):= −x log x − (1 − x) log(1 − x) is the binary entropy and k ≥ r ≥ 1 are integers. Our motivation is the conjectural entropy inequality αkH(xk) ≥ xk−1H(x), where 0 k dxdkk+1+1 xk−rH(xr) as a rational function, an infinite series, and a sum over generalized Stirling numbers. This allows us to reduce the proof of the entropy inequality for real k to showing that an associated polynomial has only two real roots in the interval (0, 1), which also allows us to prove the inequality for fractional exponents such as k = 3/2. The proof suggests a new framework for proving tight inequalities for the sum of polynomials times the logarithms of polynomials, which converts the inequality into a statement about the real roots of a simpler associated *** Codes 94A17, 26C10, 11B65, 05A10 Copyright © 2023, The Authors. All rights reserved.

关键词： Rational functions

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