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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Mingyuan Li Bowen Ma Hao Wang Yingbo Li Dongyue Chen Tong Jia College of Information Science and Engineering Northeastern-University Shenyang China Foshan Graduate School of Innovation Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University China

X-ray prohibited items detection is an effective and crucial measure in various security inspection scenarios. However, the overlapping phenomenon in X-ray images exacerbates the foreground-background class imbalance, and the imaging principle of X-ray images results in missing texture features. To address these challenges, we propose an end-to-end X-ray Prohibited Items Detector (PID-YOLOX) based on YOLOX-Tiny, which offers fast detection speed and high accuracy. Specifically, we introduce the Generalized Label Assignment (GLA) scheme to tackle the foreground-background class imbalance problem, and the Multi-Cardinality Attention (MCA) mechanism to alleviate the issue of missing texture features. Our experimental results show that PID-YOLOX achieves 54.9% average precision (AP) on the PIXray dataset, surpassing YOLOX-Tiny by 2.2% AP. Furthermore, extensive experiments demonstrate that PID-YOLOX is superior to the state-of-the-art methods, indicating its potential applications in the prohibited items detection field.

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The Burning Loss Behavior of Titanium Element in Electroslag Remelting Process and the Prediction of Titanium Content at the End Point Were Studied

SSRN

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SSRN 2024年

作者： Chen, Xi Dong, Yanwu Jiang, Zhouhua Wang, Jia Liu, Yuxiao School of Metallurgy Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang110000 China Key Laboratory of Ecological Metallurgy of Multimetallic Mineral Northeastern University Shenyang110819 China

In this study, we investigate the burning behavior of titanium during the electroslag remelting (ESR) process and its impact on the titanium content at the endpoint using machine learning. Initially, a comprehensive database was established by collecting data from literature and experiments, encompassing slag system composition, smelting temperature, and material composition content. Subsequently, six machine learning algorithms, including random forest and Bayesian regression, were employed to model the burning loss behavior of titanium. The random forest model, which exhibited optimal mean square error (MSE) performance, was utilized to generate partial dependence plots. These plots, in conjunction with experimental observations and existing studies, facilitated the analysis of key factors influencing titanium burn loss. Furthermore, the same six machine learning models were applied to predict the endpoint titanium content. The Bayesian regression model demonstrated superior performance in terms of R2 and MSE, leading to the derivation of an empirical formula for predicting endpoint titanium content. This empirical formula was subsequently validated, refined, and optimized using a thermodynamic model based on the theory of molecular ion coexistence. The final prediction formula achieved an error margin of 0.123%. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Model-based rolling matching strategy for crowdsourced drivers and delivery tasks considering uncertain transportation duration

Model-based rolling matching strategy for crowdsourced drive...

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作者： Zhang, Qi Liu, Yang Fan, Zhi-Ping Li, Zong-Lin Department of Information Management and Decision Sciences School of Business Administration Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

The matching of crowdsourced drivers and delivery tasks is an important decision problem for the crowdsourced delivery platform. Although the existence of uncertainty of transportation duration in logistics delivery has been verified, uncertain transportation duration has not been considered in previous studies on the matching of crowdsourced drivers and delivery tasks. This would lead to the limitation that the results of the existing methods cannot meet the time requirements of senders. In this case, the profit and customer satisfaction of the crowdsourced delivery platform would decrease. In this paper, a model-based rolling matching strategy to match crowdsourced drivers and delivery tasks considering uncertain transportation duration is proposed. In addition, it is assumed that the crowdsourced delivery platform also has some dedicated drivers to implement the delivery tasks that cannot be implemented by crowdsourced drivers. First, a simpler problem is described, which is to match crowdsourced drivers and delivery tasks considering uncertain transportation duration in a static data environment. Then, a model is proposed to solve the above problem. Based on the proposed model, this paper further proposes a rolling procedure to solve the problem in a data refreshing environment. Moreover, a heuristic algorithm is presented for combining multiple delivery tasks to solve the one-to-many matching. Finally, a case study and comparison are given to illustrate the validity and the contribution of the proposed matching strategy. The results show that the proposed matching strategy has a distinct advantage of cost savings. © National Academy of Sciences: Transportation Research Board 2020.

关键词： Customer satisfaction

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OSN Rumor Control Model Based on Community Immunization

OSN Rumor Control Model Based on Community Immunization

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Ming Dong Zhiqiang Sun Zhenhua Tan Bin Zhang School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper proposes a rumor control model based on community immunization. Based on the community division and the trust network inference algorithm, the model redefines the standard to measure the importance of nodes in the network. First, the model uses the Louvain clustering algorithm based on the Ochiai coefficient to discover the network community and then presents the trust network inference algorithm. By analyzing the key factors that affect trust transfer between nodes, the trust evaluation between unfamiliar nodes is inferred, and important nodes with a high degree of trust in the network community are calculated. Finally, combined with the characteristics of inner degree and outer degree centrality of nodes in the network community, five types of important nodes in the network are screened out. To avoid repeated selection of nodes, this paper identifies a group of key nodes in the network community for local immunization by means of deduplication and taking intersection, so as to realize effective control of rumors in the network.

关键词： Distributed processing Clustering algorithms Inference algorithms Standards Open data

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XTM-GAN: Generative Adversarial Networks for Tone Mapping of Dual-Energy X-ray Security Images

XTM-GAN: Generative Adversarial Networks for Tone Mapping of...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Da Cai Tong Jia Hao Wang Dongyue Chen College of Information Science and Engineering Northeastern University Shenyang Liaoning China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang Liaoning China Foshan Graduate School of Innovation Northeastern University Foshan Guangdong China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

X-ray imaging technology is vital in national public security. However, compressing 16-bit high dynamic range X-ray images into 8-bit displayable images often leads to loss of crucial details, hampering effective security inspection. To address this, we propose XTM-GAN, a dual-energy X-ray tone mapping generative adversarial network, to enhance X-ray imaging quality. By leveraging the properties of dual-energy X-rays, we construct a dedicated dataset called DEXray. We design a dual-branch generation network and a discrimination network that combines global and local image information. Through end-to-end generative adversarial training using pairs of dual-energy HDR and LDR X-ray images, which can generate high-quality X-ray security images. We train and compare our algorithm using the DEXray dataset against other state-of-the-art tone mapping algorithms. The results demonstrate the superior performance of our approach. The proposed method has significant practical applications and holds research significance in the fields of security and transportation.

关键词： Training Image quality Visualization Image coding Heuristic algorithms Transportation Generative adversarial networks High dynamic range Security X-ray imaging

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Binocular active stereo matching based on multi-scale random forest

Binocular active stereo matching based on multi-scale random...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Jiatong Liu Mingqi Pu Ghutian Wang Dongyue Chen Tang Jia College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University. Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China

With the rapid development of 3D vision technology, the existing passive binocular cameras can no longer meet the practical needs of depth perception. Therefore, this paper proposes a binocular active stereo matching method based on multi-scale random forest. Firstly, a binocular active vision system consists of a near-infrared random speckle projector and a binocular camera is constructed, and the system was calibrated using Zhang's calibration method. Secondly, gamma image enhancement and image difference method are used for processing to reduce the impact of ambient light on the measurement results. Next, extract the points of interest and use the multi-scale random forest algorithm to match the window where the points of interest are located to generate a sparse structured light anchor disparity map globally. Finally, using the Census transforms as the matching cost, iterate continuously, refine the disparity and get a dense disparity map. The experimental results show that this system can achieve good depth perception accuracy and robustness under complex indoor lighting conditions.

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Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-enabled Multi-Agent Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Shengbo Lin, Chuan Han, Guangjie Zhu, Shengchao Li, Zhixian Wang, Zhenyu Ma, Yunpeng Software College Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China Department of Internet of Things Engineering Hohai University Changzhou213022 China Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Hohai University China

In recent years, autonomous underwater vehicle (AUV) swarms are gradually becoming popular and have been widely promoted in ocean exploration or underwater tracking, etc. In this paper, we propose a multi-AUV cooperative underwater multi-target tracking algorithm especially when the real underwater factors are taken into account. We first give normally modelling approach for the underwater sonar-based detection and the ocean current interference on the target tracking process. Then, based on software-defined networking (SDN), we regard the AUV swarm as a underwater ad-hoc network and propose a hierarchical software-defined multi-AUV reinforcement learning (HSARL) architecture. Based on the proposed HSARL architecture, we propose the"Dynamic-Switching" mechanism, it includes"Dynamic-Switching Attention" and"Dynamic-Switching Resampling" mechanisms which accelerate the HSARL algorithm’s convergence speed and effectively prevents it from getting stuck in a local optimum state. Additionally, we introduce the reward reshaping mechanism for further accelerating the convergence speed of the proposed HSARL algorithm in early phase. Finally, based on a proposed AUV classification method, we propose a cooperative tracking algorithm called Dynamic-Switching-Based MARL (DSBM)driven tracking algorithm. Evaluation results demonstrate that our proposed DSBM tracking algorithm can perform precise underwater multi-target tracking, comparing with many of recent research products in terms of various important metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Al_xCoCrCuFeNi (X = 0, 0.8) high entropy alloys fabricated by laser powder bed fusion

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Heat Treatment and Surface Engineering 2024年第1期6卷

作者： Feng, Zhengjie Gao, Jingbo Zhang, Deliang School of Materials Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University Shenyang China

The AlxCoCrCuFeNi (x = 0, 0.8) high entropy alloys (HEAs) were prepared by laser powder bed fusion (LPBF), and the effects of volumetric energy density (VED) and heat treatment on microstructures and mechanical proper... 详细信息

The Al_xCoCrCuFeNi (x = 0, 0.8) high entropy alloys (HEAs) were prepared by laser powder bed fusion (LPBF), and the effects of volumetric energy density (VED) and heat treatment on microstructures and mechanical properties of HEAs were investigated. The results show that the CoCrCuFeNi HEA has a single FCC phase structure and exhibits good ductility. Its yield strength is relatively low, being 520 MPa. After the alloy composition was adjusted from CoCrCuFeNi to Al_0.8CoCrCuFeNi, the HEA changed from FCC single-phase to FCC + BCC dual-phase structure and the hardness and compressive properties were significantly improved. Among them, the Al_0.8CoCrCuFeNi HEA prepared with the VED of 167 J/mm³ has the best mechanical properties, with a compressive yield strength of 652 MPa and good plasticity. After annealing at 600°C and 900°C for 1 h, the mechanical properties of HEAs are significantly improved. In particular, the microhardness and compressive yield strength of the Al_0.8CoCrCuFeNi HEA annealed at 600°C for 1 h are 378 HV and 810 MPa, respectively. The coordinated deformation between FCC and BCC domains, back stress strengthening, solid solution strengthening and precipitation strengthening after heat treatment make the Al_0.8CoCrCuFeNi HEA achieve a balance of high strength and good ductility. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： heat treatment high entropy alloys Laser powder bed fusion mechanical properties microstructures

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Microstructure and mechanical properties of a recycled aluminum alloy fabricated by consolidation of small pieces

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Heat Treatment and Surface Engineering 2024年第1期6卷

作者： Liu, Feifei Zhang, Deliang School of Materials Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University Shenyang China

The microstructure and mechanical properties of recycled AA6063 aluminum alloy rods fabricated by hot extrusion of forging compacts formed by upsetting compacts of small AA6063 aluminum alloy pieces with different Fe contents before and after T6 heat treatment were studied. In the as-extruded state and with the higher extrusion ratio of 25:1, the microstructure consists of fully recrystallized grains, but the high iron content regions exhibit clearly finer equiaxed grains due to the stronger grain boundary pinning and nucleation effects of a higher number density of Fe-rich phase particles. Increasing the extrusion ratio increases both the strength and ductility of the as-extruded material. The reason is that increasing extrusion ratio enhances the bonding strength of the prior boundaries between the strips formed by deformation of the small pieces. In the T6 heat-treated state, increasing the extrusion ratio from 9:1 to 25:1 clearly increases the fraction of fine and equiaxed grains. The enhancement of the bonding strength and the refinement of the microstructure with the increase of extrusion ratio cause the yield strength and ultimate tensile strength to increase from 174 and 221 MPa to 213 and 237 MPa, respectively, and the elongation to fracture to decrease from 22.1 to 15.4%. Solid-state recycled samples all exhibit a ductile fracture behavior despite formation of microcracks at the prior boundaries. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Aluminum alloy hot extrusion mechanical properties microstructure solid state recycling

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Annihilating the Formation of Silicon Carbide:Molten Salt Electrolysis of Carbon-Silica Composite to Prepare the Carbon-Silicon Hybrid for Lithium-lon Battery Anode

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Energy & Environmental Materials 2020年第2期3卷 166-176页

作者： Xianbo Zhou Hongwei Xie Xiao He Zhuqing Zhao Qiang Ma Muya Cai Huayi Yin Key Laboratory for Ecological Metallurgy of Multimetallic Mineral of Ministry of Education School of metallurgyNortheastern University110819Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of Education110819Shenyang China

Silicon(Si)and carbon(C)composites hold the promise for replacing the commercial graphite anode,thus increasing the energy density of lithium-ion batteries(LIBs).To mitigate the formation of SiC,this paper reports a molten salt electrolysis approach to prepare C-Si composite by the electrolysis of C-SiO2 *** the conventional way of making a C coating on Si,C-SiO2 composites were prepared by pyrolyzing the low-cost sucrose and *** electrochemical deoxidation of the C-SiO2 composites not only produces nanostructured Si inside the C matrix but also introduces voids between the C and Si owing to the volume shrinkage from converting SiO2 to *** importantly,the use of Mg ion-containing molten salts precludes the generation of SiC,and the electrolytic Si@C composite anode delivers a capacity of about 1500 mAh g-1 after 100 cycles at a current density of 500 mA ***,the Si@C‖LiNi0.6Co0.2Mn0.2O2 full cell delivers a high energy density of 608 Wh ***,the molten salt approach provides a one-step electrochemical way to convert oxides@C to metals@C functional materials.

关键词： anode material carbon lithium-ion batteries molten salt electrolysis silicon

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