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Frontiers in Terahertz Imaging Applications beyond AbsorptionCross-Section and Diffraction Limits

ACS PHOTONICS 2022年第5期9卷 1500-1512页

作者： Lee, Geon Lee, Jinwoo Park, Q-Han Seo, Minah Korea Inst Sci & Technol Sensor Syst Res Ctr Seoul 02792 South Korea Seoul Natl Univ Dept Phys & Astron Seoul 08826 South Korea Korea Univ KU KIST Grad Sch Converging Sci & Technol Seoul 02841 South Korea Korea Univ Dept Phys Seoul 02841 South Korea

Label-free imaging technology is highly desirable for various bioengineering,medicine, and chemistry applications. Most existing optical imaging techniques requirelabeling of the biospecimens and have limitations that may affect the intrinsic properties ofthe target species. The electromagnetic waves in the terahertz (THz) spectral range can be anexcellent alternative to visible light, which provides plentiful vibrational signatures of manymolecules with very low photon energy (1 THz is equivalent to 4 meV), completely free fromdamage to the biomaterials. For this reason, THz waves possessing a broadband spectrumhave emerged as a critical technology for fundamental research in bio/chemical detection andmedical imaging, as well as in solid-state physics, chemistry, material science, and highlyanticipated 6G next-generation telecommunications. The limited performance of THz waves as a sensing or imaging tool has been aconsiderable drawback resulting from low sensitivity or diffraction-limited low spatial resolution. Nevertheless, many successes inobtaining increased sensitivity with additional nanostructures and improving spatial resolution with geometric beam shaping openedup a way for highly efficient real-time THz imaging. From this Perspective, recent trends in innovative THz sensing and imagingresearch deserve an introduction regarding the level of reliability and sensitivity that can evolve into an actual medical device andother applications. It can also be expected to enable progress in analysis algorithms (compressive phase retrieval, reconstruction, ormachine/deep learning), enabling better data sampling, denoising, deblurring, and efficient computing cost, thusfinally providing aleap forward in the THz imaging area beyond the absorption cross-section and diffraction limits.

关键词： terahertz imaging molecular-specific sensing optimization algorithm metasurfaces single-pixel imaging compressive sensing

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Synthesis Method of an Orthogonal Beamforming Network With Arbitrary Ports for Shaped Beams

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第6期70卷 4794-4802页

作者： Sun, Li Zhou, Shi-Gang Zhang, Guan-Xi Northwestern Polytech Univ Sch Microelect Xian 710072 Peoples R China Huawei Technol Co Ltd Shanghai 201200 Peoples R China

This article proposes a synthesis method of orthogonal beamforming networks (BFNs) with arbitrary ports. The proposed method is based on matrix decomposition theory and breakthrough the synthesized BFN number to arbitrary. It has been theoretically demonstrated that the BFN with cascading 180 degrees hybrid couplers can synthesize shaped beams. Therefore, a BFN synthesis method that combines the QR decomposition theory and the optimization algorithm is introduced. With the help of an optimization program, a specific power ratio of each 180 degrees hybrid coupler in the BFN is determined. The proposed synthesis method is verified by experiments with an orthogonal 6 x 2 BFN with flat-topped radiation patterns. Through optimization, five 180 degrees hybrid couplers with different power ratios synthesize the proposed BFN. Results show that two flat-topped beams with a beamwidth of (32 degrees +/- 0.3 degrees), gain ripples less than 2.5 dB, and a sidelobe level less than 12 dB are achieved. The proposed method has the advantages of high accuracy and efficiency and a simple structure, which makes it convenient for multi-beam antenna arrays in multiple-input-multiple-output (MIMO) systems.

关键词： Arbitrary port number matrix decomposition optimization algorithm orthogonal beamforming network (BFN) shaped beam

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Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine

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ENERGY REPORTS 2021年 7卷 5446-5454页

作者： Yang, Rui Liu, Yongbao Yu, Youhong He, Xing Li, Hongsong Naval Univ Engn Coll Power Engn Wuhan 430033 Peoples R China

In order to improve the control performance of the micro gas turbine in the full range of operating conditions, this paper proposes a new control technique based on the hybridization of improved particle swarm optimization algorithm and cuckoo search algorithm (HIPSO_CS) to tune the fuzzy PID controller parameters. Firstly, the traditional particle swarm optimization algorithm is improved by linearly decreasing the number of particles and the value of the inertia weight. Secondly, the cuckoo algorithm's local random walk strategy is introduced into the particle swarm optimization algorithm to enhance particles' diversity. Through comparing with traditional optimization algorithms, the proposed HIPSO_CS algorithm is verified to have high convergence accuracy and fast iteration speed. To improve the dynamic response performance of the micro gas turbine, controllers with different structures are designed, and a comparative study of HIPSO_CS optimized Fuzzy PID/PID/PI is presented. The simulation results show that the micro gas turbine controlled by the fuzzy PID controller has a rapid response to fuel flow, minor speed overshoot, and shorter stabilization time during load increase or decrease. In addition, the designed control method can also achieve significant control effects under load disturbance, model parameter changes, and extreme operating condition. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Micro gas turbine optimization algorithm Fuzzy PID controller Robustness

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An Artificial Intelligence-Powered Optimisation algorithm to Identify Writers' Handwritten Digits 5

An Artificial Intelligence-Powered Optimisation Algorithm to...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Correa, Frederick Sidney Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350383522

The proliferation of technology has been credited with making processes that used to take a lot of time more easier to do. Machine learning (ML) enables software products to grow increasingly accurate at predicting outcomes without the need to be explicitly programmed to do so. This is possible because ML allows software to learn from past experiences. Machine learning algorithms make predictions about future output values by using data from the past as input. In the course of this investigation, we are going to investigate an artificial intelligence (AI)-based optimization method with the goal of recognizing the digits that are written by hand. Research in the fields of machine learning, image processing, and computer vision are making significant progress in the area of handwritten digit recognition. The style, shape, orientation, and size of a digit written by one person may be different from those written by another person. An algorithm for optimization is a procedure that repeatedly evaluates a number of potential solutions in order to locate the one that is most suitable. The phase of the design process known as computer-aided design has always included optimization ever since computers were first developed. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Hand Written Digit Recognition optimization algorithm

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A Novel Data-Driven Method to Estimate Invisible Solar Power Generation: A Case Study in Taiwan

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第6期58卷 7057-7067页

作者： Nguyen, Thi Bich Phuong Wu, Yuan-Kang Pham, Manh-Hai Natl Chung Cheng Univ Chiayi 62102 Taiwan Elect Power Univ Hanoi 10000 Vietnam

As the penetration of photovoltaic (PV) solar generation has increased, a significant number of residential and commercial solar PV systems have been installed without meters. The majority of these systems, however, are also not monitored by power system operators. Therefore, the uncertainty of the net load owing to this "invisible " solar power generation has raised additional challenges for power system operation. To reduce the impact of this, we propose a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that could assist in the identification of the representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating aggregated power generation compared to other existing methods.

关键词： Behind-the-meter (BTM) data dimension reduction hybrid method invisible solar power optimization algorithm photovoltaic (PV)

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Iterative Pose Estimation for a Planar Object Using Virtual Sphere

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第4期58卷 3650-3657页

作者： Jiang, Cuicui Hu, Qinglei Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Hangzhou Innovat Inst Hangzhou 310023 Peoples R China

This article proposed an iterative pose estimation for the planar object to deal with the pose ambiguity in the Perspective n Points (PnP) problem. Specifically, by utilizing the unit virtual sphere, the PnP pose estimation problem can be performed as a minimization of the error function with three independent transitional parameters, referred to as the coupling position. Then, Levenberg-Marquardt (LM) optimization algorithm is applied to acquire the coupling position of the first local minimum. Furthermore, the coupling position is represented as the two Euler angles and the vector length, which are combined with the planar points to compute the second local minimum approximation as the initialization of the second LM algorithm. Consequently, once the global coupling position in the two local minimum is decided by the lower error, the orientation and position are directly decoupled by using the singular value decomposition. It is shown that the designed pose estimation is able to achieve prescribed performance for locating and distinguishing two local minimum, and meanwhile guarantee the superior computation behavior under the pose ambiguity for the planar object. Finally, numerical simulation and physical experiment are conducted to validate the effectiveness of the proposed method, compared with the state-of-the-art PnP methods.

关键词： Couplings Pose estimation Transmission line matrix methods Cameras Three-dimensional displays Radio frequency Navigation Local minimum optimization algorithm planar object pose ambiguity singular value decomposition (SVD)

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Online machine learning algorithms to optimize performances of complex wireless communication systems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第2期19卷 2056-2094页

作者： Oshima, Koji Yamamoto, Daisuke Yumoto, Atsuhiro Kim, Song-Ju Ito, Yusuke Hasegawa, Mikio Natl Inst Informat & Commun Technol Innovat Design Initiat Koganei Tokyo Japan Tokyo Univ Sci Dept Elect Engn Tokyo Japan SOBIN Inst LLC Kawanishi Hyogo Japan

Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.

关键词： wireless communication systems machine learning cross layer optimization optimization algorithm cognitive radio complex systems multi-armed bandit problem reinforcement learning

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Grinding Wheel Parametric Design for Machining Arbitrary Grooves on the Helical Rake Face of the Tool

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2022年第4期9卷 997-1008页

作者： Shen, Changqing Xiao, Yuminghao Xiong, Liangshan Huazhong Univ Sci & Technol Natl Engn Res Ctr Mfg Equipment Digitizat Sch Mech Sci & Engn Wuhan 430074 Peoples R China Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

In the present study, a novel grinding wheel design method is proposed to design a wheel for grinding different small helical grooves on the existing helical rake faces of the tool. The proposed method can be applied to manufacture tools with uneven helical rake face like zigzag-edge twist drills. In the proposed method, the processing of the non-wedge groove is initially converted into a superposition of processing steps of several wedge grooves. Then, based on the simplified non-thickness grinding wheel parametric design and envelop graphic design, the grinding wheel capable of processing small wedge grooves with various specified shapes can be obtained. Moreover, through establishing quantitative evaluation criteria, the determination of design parameters concerning the non-thickness grinding wheel is transmitted into an optimization problem. Finally, helical grooves with trapezoid truncation and wedge truncations are simulated on a virtual five-axis machine tool to verify the effectiveness of the grinding wheel design method.

关键词： Grinding wheel optimization algorithm Profile optimization Helical groove

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Research on a Bearing Fault Enhancement Diagnosis Method with Convolutional Neural Network Based on Adaptive Stochastic Resonance

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SENSORS 2022年第22期22卷 8730页

作者： Wang, Chen Qiao, Zijian Huang, Zhangjun Xu, Junchen Fang, Shitong Zhang, Cailiang Liu, Jinjun Zhu, Ronghua Lai, Zhihui Shenzhen Univ Coll Mechatron & Control Engn Shenzhen Key Lab High Performance Nontradit Mfg Shenzhen 518060 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Guangdong Key Lab Electromagnet Control & Intelli Shenzhen 518060 Peoples R China Ningbo Univ Sch Mech Engn & Mech Ningbo 315211 Peoples R China Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China

As a powerful feature extraction tool, a convolutional neural network (CNN) has strong adaptability for big data applications such as bearing fault diagnosis, whereas the classification performance is limited when the quality of raw signals is poor. In this paper, stochastic resonance (SR), which provides an advanced feature enhancement approach for weak signals with strong background noise, is introduced as a data pre-processing method for the CNN to improve its classification performance. First, a multiparameter adjusting bistable Duffing system that can achieve SR under large-parameter weak signals is introduced. A hybrid optimization algorithm (HOA) combining the genetic algorithm (GA) and the simulated annealing (SA) is proposed to adaptively obtain the optimized parameters and output signal-to-noise ratio (SNR) of the Duffing system. Therefore, the data optimization based on the multiparameter-adjusting SR of Duffing system can be realized. An SR-based mapping method is further proposed to convert the outputs of the Duffing system into grey images, which can be further processed by a normal CNN with batch normalization (BN) layers and dropout layers. After verifying the feasibility of the HOA in multiparameter optimization of the Duffing system, the bearing fault data set from the CWRU bearing data center was processed by the proposed fault enhancement classification and identification method. The research showed that the weak features of the bearing signals could be enhanced significantly through the adaptive multiparameter optimization of SR, and classification accuracies for 10 categories of bearing signals could achieve 100% and those for 20 categories could achieve more than 96.9%, which is better than other methods. The influences of the population number on the classification accuracies and calculation time were further studied, and the feature map and network visualization are presented. It was demonstrated that the proposed method can realize high-per

关键词： bearing fault diagnosis stochastic resonance optimization algorithm convolutional neural network

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A Monthly Hydropower Scheduling Model of Cascaded Reservoirs with the Zoutendijk Method

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WATER 2022年第23期14卷 3978页

作者： Zhou, Binbin Feng, Suzhen Xu, Zifan Jiang, Yan Wang, Youxiang Chen, Kai Wang, Jinwen Yunnan Elect Power Dispatch Control Ctr Kunming 650041 Peoples R China Huazhong Univ Sci & Technol Inst Water Resources & Hydropower 1037 Luoyu Rd Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital River Basin Sci & Technol 1037 Luoyu Rd Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Peoples R China

A monthly hydropower scheduling determines the monthly flows, storage, and power generation of each reservoir/hydropower plant over a planning horizon to maximize the total revenue or minimize the total operational cost. The problem is typically a complex and nonlinear optimization that involves equality and inequality constraints including the water balance, hydraulic coupling between cascaded hydropower plants, bounds on the reservoir storage, etc. This work applied the Zoutendijk algorithm for the first time to a medium/long-term hydropower scheduling of cascaded reservoirs, where the generating discharge capacity is handled with an iterative procedure, while the other head-related nonlinear constraints are represented with exponential functions fitting to discrete points. The procedure starts at an initial feasible solution, from which it finds a feasible improving direction, along which a better feasible solution is sought with a one-dimensional search. The results demonstrate that the Zoutendijk algorithm, when applied to six cascaded hydropower reservoirs on the Lancang River, worked very well in maximizing the hydropower production while ensuring the highest firm power output to be secured.

关键词： hydropower scheduling Zoutendijk method optimization algorithm large scale power system

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