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US Army Aviation air movement operations assignment, utilization and routing

Journal of Defense Analytics and Logistics 2023年第1期7卷 2-28页

作者： Nelson, Russell King, Russell McConnell, Brandon M. Thoney-Barletta, Kristin Operations Research Graduate Program North Carolina State University Raleigh NC United States Department of Industrial and Systems Engineering North Carolina State University Raleigh NC United States Center for Additive Manufacturing and Logistics North Carolina State University Raleigh NC United States Department of Textile and Apparel Technology and Management North Carolina State University at Raleigh Raleigh NC United States

Purpose: The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost. Design/methodology/approach: In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period. Findings: The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time. research limitations/implications: Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight. Originality/value: This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits. © 2022, In accordance with section 105 of the US Copyright Act, this work has been produced by a US government employee and shall be considered a public domain work, as copyright protection is not available.

关键词： Aircraft Demand priority Dial-a-ride problem Helicopter routing Heuristic Military aviation Multiple refuel nodes Vehicle utilization

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One-Pot Preparation of One-Dimensional Hierarchically Porous Carbon@Sno2 Nanocomposites for Flexible Fabric-Basedsupercapacitor Electrodes

SSRN

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SSRN 2024年

作者： Wang, He Cheng, Yaling Yao, Lan Shi, Hu Ruan, Fangtao Feng, Quan Xu, Zhenzhen Wang, Hongjie School of Textile and Garment Anhui Polytechnic University Wuhu241000 China China National Textile and Apparel Council Key Laboratory of Flexible Devices for Intelligent Textile and Apparel Soochow University Suzhou215123 China Key Laboratory of Pulp and Paper Science & Technology of Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Advanced Fiber Materials Engineering Research Center of Anhui Province Anhui Polytechnic University Wuhu241000 China

The design of functional energy storage materials with superior performances has become the focus topic with the growing energy crisis in recent years. Herein, porous carbon nanofibers@tin dioxide (PCNFs@SnO2) composites are prepared by one-pot electrospinning and followed by carbonization. The PCNFs@SnO2 possesses a hierarchically porous structure containing mesopores and micropores. The specific surface area is as high as ~ 559.4 m2 g-1, and the total pore volume can reach 0.28 cm3 g-1. Meanwhile, the nonwoven fabric surface is coated with a silver layer through the screen printing technique to obtain silver-coated nonwoven fabric (SNF). Importantly, PCNFs@SnO2 and SNF are combined to obtain flexible fabric-based functional materials. As a primary energy application, the prepared PCNFs@SnO2/SNF electrode has a high mass specific capacitance of 583.1 F g-1 at 1 A g-1, a high area specific capacitance of 933 mF cm-2 at 1 mA cm-2, and a good rate capability (70% at 10 A g-1). The supercapacitor device with two PCNFs@SnO2/SNF electrodes also shows outstanding cycling durability, capacitor retention is as high as ~ 91.52%, after 10000 charging/discharging cycles. In brief, our study may provide an innovative route for designing hierarchically porous one-dimensional carbon nanocomposites for supercapacitors with high performances. © 2024, The Authors. All rights reserved.

关键词： Nanocomposites

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Neural Network-Based Exponential Stability of Affine Nonlinear Systems by Event-Triggered Approach

Neural Network-Based Exponential Stability of Affine Nonline...

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Chinese Intelligent Systems Conference, CISC 2020

作者： Liu, Fan Gan, Yiming Yang, Xueqing Zhou, Wuneng Donghua University Shanghai201620 China Guangdong Polytechnic College Foshan528041 China Educational Technology Center Donghua University Shanghai200051 China Engineering Research Center of Digitized Textile and Fashion Technology Donghua University Shanghai China

ISBN: (纸本)9789811584497

In this paper, the event-triggered approach is considered as the implementation of exponential stability for a class of nonlinear systems with an asymptotic control input which is generated by an neural network-based feedback network. Under additional mild assumptions, we first rigorously conduct discussion on the transformation between affine nonlinear systems and linear systems via some existing results. Subsequently, we present a novel weight update law for the feedback neural network (NN) so as to achieve the process of finding the optimal weight matrix. Afterward, we derive an appropriate condition using the Lyapunov-Krasovskii method, that guarantees the practical convergence of the closed-loop system toward an equilibrium point (zero point) and is used to design relevant coefficients for the event-triggering scheme. Finally, a numerical example substantiates the achievement of exponential stability and the reduction of loss of the closed-loop system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Closed loop systems

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One-Pot Preparation of One-Dimensional Hierarchically Porous Carbon Nanocomposites for Bifunctional Applications of Flexible Fabric-Based Supercapacitor Electrodes and Electromagnetic Interference Shielding Adsorbents

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SSRN 2024年

The design of multifunctional energy storage and electromagnetic interference shielding materials with superior performances have become the focus topic with the growing energy crisis and electromagnetic pollution recently. Herein, porous carbon nanofibers@tin dioxide (PCNFs@SnO2) composites are prepared by one-pot electrospinning and followed by carbonization. Meanwhile, the nonwoven fabric surface is coated with a silver layer through the screen printing technique to obtain silver-coated nonwoven fabric (SNF). Importantly, PCNFs@SnO2 and SNF are combined to obtain flexible fabric-based functional materials. As a primary energy application, the prepared PCNFs@SnO2/SNF electrode has a high mass specific capacitance of 583.1 F g-1 at 1 A g-1 and a high area specific capacitance of 933 mF cm-2 at 1 mA cm-2. PCNFs@SnO2/SNF electrode also shows outstanding cycling durability, capacitor retention is as high as ~ 91.52%, after 10000 charging/discharging cycles. As a secondary environmental application, PCNFs@SnO2/SNF electromagnetic interference shielding adsorbent has a high shielding effectiveness of ~ 70 dB and adsorbed electromagnetic waves are dominant rather than reflected electromagnetic waves. In brief, our study may provide an innovative route for designing hierarchically porous one-dimensional carbon nanocomposites for supercapacitors and electromagnetic interference shielding applications with high performances. © 2024, The Authors. All rights reserved.

关键词： Nanocomposites

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Fashion Pattern Class Selection and Sensory Words Extraction for Fashion Design

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SSRN 2023年

作者： Zhang, Mengyun Zhang, Jun Tao, Xuyuan Bruniaux, Pascal Zeng, Xianyi School of Fashion Wuhan Textile University Wuhan430073 China 2 Allée Louis et Victor Champier RoubaixF-59100 France Wuhan Textile and Apparel Digital Engineering Technology Research Center Wuhan430073 China

Fashion patterns are important elements for fashion aesthetics. We processed a fashion pattern selection using the literature research method and ranking evaluation method. In this study, 9 classes of fashion patterns were chosen to study the perceptual image of fashion patterns. Term frequency, paragraph frequency, and T/P value were introduced to propose a text mining method for quantitative text analysis and sensory words extraction. 10 pairs of sensory words were extracted using questionnaire investigation, hierarchical cluster analysis, and semantic-based analysis methods for Kansei evaluation. A case study of Kansei evaluation of the "Animal" pattern class was conducted to apply the sensory words, which were proved to be valid for expressing accurate percipient cognitions in the Kansei evaluation of fashion patterns. The results of this study indicated that the cognitions of the same pattern object tend to be unified even for different styles, and fashion pattern with common objects are more effective to connect the designers’ intents and consumers’ percipient cognitions. The findings of this study suggest that fashion designers have better use common pattern objects to effectively transfer their design intents to consumers. © 2023, The Authors. All rights reserved.

关键词： Extraction

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Prescribed Time Cluster Formation for Multi-Agent Systems Under Cooperation-Competition Interaction

Prescribed Time Cluster Formation for Multi-Agent Systems Un...

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Chinese Control Conference (CCC)

作者： Hongliang Li Wuneng Zhou Fei Hu Kaili Liao College of Information Science and Technology Donghua University Shanghai P. R. China Engineering Research Center of Digitized Textile and Fashion Technology Ministry of Education Donghua University Shanghai P. R. China

In this paper, the problem of prescribed time cluster formation of multi-agent systems with cooperation-competition interaction is studied. Firstly, the concept of prescribed time cluster formation of multi-agent systems with cooperation-competition interaction is introduced. Secondly, an state feedback controller is designed to ensure the prescribed time cluster formation by Lyapunov functional method and free-weight matrix technology. Finally, a numerical example is given to illustrate the effective of the the results and method of this paper.

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A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes

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textile research Journal 2019年第17期89卷 3539-3555页

作者： Wei, Bing Hao, Kuangrong Tang, Xue-Song Ding, Yongsheng Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education College of Information Sciences and Technology Donghua University China

The convolutional neural network (CNN) has recently achieved great breakthroughs in many computer vision tasks. However, its application in fabric texture defects classification has not been thoroughly researched. To this end, this paper carries out a research on its application based on the CNN model. Meanwhile, since the CNN cannot achieve good classification accuracy in small sample sizes, a new method combining compressive sensing and the convolutional neural network (CS-CNN) is proposed. Specifically, this paper uses the compressive sampling theorem to compress and augment the data in small sample sizes;then the CNN can be employed to classify the data features directly from compressive sampling;finally, we use the test data to verify the classification performance of the method. The explanatory experimental results demonstrate that, in comparison with the state-of-the-art methods for running time, our CS-CNN approach can effectively improve the classification accuracy in fabric defect samples, even with a small number of defect samples. © The Author(s) 2018.

关键词： Compressed sensing

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A Music Generation Model Based on Generative Adversarial Networks with Bayesian Optimization

A Music Generation Model Based on Generative Adversarial Net...

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Chinese Intelligent Systems Conference, CISC 2020

作者： Xu, Yijie Yang, Xueqing Gan, Yiming Zhou, Wuneng Cheng, Hangyang He, Xuehui College of Information Science and Technology Donghua University Shanghai201620 China Educational Technology Center Donghua University Shanghai200051 China Guangdong Polytechnic College Foshan528041 China Engineering Research Center of Digitized Textile and Fashion Technology Donghua University Shanghai China

ISBN: (纸本)9789811584497

In recent years, a huge number of neural networks have been applied in music generation, many of which use generative adversarial networks (GAN). In this paper, a novel melody generation framework is proposed to create motivation for composers, which contains a generator made by bidirectional long short-term memory (Bi-LSTM) and a discriminator made by long short-term memory (LSTM). We change the traditional optimization policy of GAN by bringing Bayesian optimization in our model. In last, we conduct a user study that show better preference of our generated melodies over that produced by several recent other music generation models. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Interactive Attention and Position-Aware Mechanism for Aspect-Level Sentiment Analysis

Interactive Attention and Position-Aware Mechanism for Aspec...

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Chinese Intelligent Systems Conference, CISC 2020

作者： He, Xuehui Gan, Yiming Yang, Xueqing Zhou, Wuneng Ren, Yuanhong College of Information Science and Technology Donghua University Shanghai201620 China Guangdong Polytechnic College Foshan528041 China Educational Technology Center Donghua University Shanghai200051 China Engineering Research Center of Digitized Textile and Fashion Technology Donghua University Shanghai China

ISBN: (纸本)9789811584497

Aspect-level sentiment classification is a fine-grained work in sentiment analysis with the goal of predicting the sentiment categories of target words in a given text. In this paper, a framework of an interactive attention based on bidirectional LSTM networks and position-aware mechanism for aspect-level sentiment classification has been proposed, in which a fine-grained attention is introduced to capture the intersection between context and aspect words. Meanwhile, the position awareness mechanism is introduced to make the weight distribution of cross attention more reasonable. Experiments on the SemEval-2014 datasets show that our proposed model is always superior to the state-of-the-art methods of all datasets. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Solder Joint Defect Detection Based on Depth Image CNN for 3D Shape Classification

Solder Joint Defect Detection Based on Depth Image CNN for 3...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Zifan Shao Kuangrong Hao Bing Wei Xue-Song Tang College of Information Science and Technology. Engineering Research Center of Digitized Textile &Apparel Technology Ministry of Education Donghua University Shanghai P. R. China

ISBN: (纸本)9781665401166

In recent years, 3D classification has attracted a lot of attention, and many deep learning models have achieved good performance. In this paper, we propose a point cloud classification model for solder joint defect detection. Different from existing point clouds classification models such as MVCNN and PointNet, our model consumes six depth images. We present a standard CNN architecture learned to extract a global embedding of the 3D object from six depth images. We find that using depth images as input has great benefits. Because depth image is regular, we can simply use CNN to extract local features without any other measures. However, PointNet++ has to introduce KNN and farthest point sampling to abstract local features, which leads to expensive computation costs. So it will face a dilemma of either reducing the point cloud size in exchange for efficiency or increasing the number of the input points to achieve higher accuracy. Besides, depth images will not introduce geometry loss like rendered images used in MVCNN, and it is not necessary to deal with the uneven distribution problem of point clouds. Experiments show that our algorithm can achieve outstanding performance on fine-grained classification tasks such as solder joint defect detection.

关键词： Point cloud compression Geometry Solid modeling Three-dimensional displays Shape Feature extraction Safety

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