检索结果-内蒙古大学图书馆

Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition

引用

IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Pan, Linqiang Lin, Jianqing Wang, Handing He, Cheng Tan, Kay Chen Jin, Yaochu School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence Xidian University Xi’an China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Engineering Trustworth and General Artificial Intelligence Laboratory Westlake University Hangzhou China

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables. IEEE

关键词： Iron

来源：评论

学校读者我要写书评

暂无评论

Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

Memory-Based Contrastive Learning with Optimized Sampling fo...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

来源：评论

学校读者我要写书评

暂无评论

Location IoU: A New Evaluation and Loss for Bounding Box Regression in Object Detection

Location IoU: A New Evaluation and Loss for Bounding Box Reg...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Lu Yang Kai Zhang Jiaqi Liu Chongke Bi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Autobrain (Tianjin) Technology LTD Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the field of object detection bounding box regression, IoU (Intersection over Union) is a commonly used evaluation metric for measuring the overlap between predicted and ground truth bounding boxes. One limitation of IoU, however, is that the gradient goes to zero when there is no intersection between bounding boxes. Recent evaluation metrics have been developed to overcome this problem by incorporating penalty terms and other techniques for improving IoU. Despite these advances, existing methods still suffer to some degree from the problems of small gradients and slow rate of convergence. In this paper, a new evaluation method named LIoU (Location IoU) is proposed to address these issues. It can maximize the shared area of the intersection when used as the loss function and incorporate a variable gradient parameter to fit to datasets of different sizes. The experimental results indicate that we achieve 0.27% and 3.48% accuracy improvements respectively in SSD and YOLOv5 network on the VOC dataset, and 2.23% in the YOLOv5 network on the COCO dataset. The results of this study highlight the effectiveness of LIoU in improving the performance of object detection models.

关键词： YOLO Measurement Location awareness Accuracy Shape Neural networks Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Residual network and embedding usage: New tricks of node classification with graph convolutional networks

arXiv

引用

arXiv 2021年

作者： Chi, Huixuan Wang, Yuying Hao, Qinfen Xia, Hong Institute of Computing Technology Chinese Academy of Sciences School of Control and Computer Engineering North China Electric Power University

Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks. In the literature, however, most tricks or techniques are either briefly mentioned as implementation details or only visible in source code. In this paper, we first summarize some existing effective tricks used in GCNs mini-batch training. Based on this, two novel tricks named GCN res Framework and Embedding Usage are proposed by leveraging residual network and pre-trained embedding to improve baseline’s test accuracy in different datasets. Experiments on Open Graph Benchmark (OGB) show that, by combining these techniques, the test accuracy of various GCNs increases by 1.21%∼2.84%. We open source our implementation at https://***/ytchx1999/PyG-OGB-Tricks. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Distributed Charging Coordination for Electric Trucks under Limited Facilities and Travel Uncertainties

arXiv

引用

arXiv 2024年

作者： Bai, Ting Li, Yuchao Malikopoulos, Andreas A. Johansson, Karl Henrik Mårtensson, Jonas Integrated Transport Research Lab The Division of Decision and Control Systems The Digital Futures KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Computing and Augmented Intelligence Arizona State University TempeAZ-85281 United States School of Civil and Environmental Engineering Cornell University IthacaNY-14853 United States

In this work, we address the problem of charging coordination between electric trucks and charging stations. The problem arises from the tension between the trucks’ nontrivial charging times and the stations’ limited charging facilities. Our goal is to reduce the trucks’ waiting times at the stations while minimizing individual trucks’ operational costs. We propose a distributed coordination framework that relies on computation and communication between the stations and the trucks, and handles uncertainties in travel times and energy consumption. Within the framework, the stations assign a limited number of charging ports to trucks according to the first-come, first-served rule. In addition, each station constructs a waiting time forecast model based on its historical data and provides its estimated waiting times to trucks upon request. When approaching a station, a truck sends its arrival time and estimated arrival-time windows to the nearby station and the distant stations, respectively. The truck then receives the estimated waiting times from these stations in response, and updates its charging plan accordingly while accounting for travel uncertainties. We performed simulation studies for 1, 000 trucks traversing the Swedish road network for 40 days, using realistic traffic data with travel uncertainties. The results show that our method reduces the average waiting time of the trucks by 46.1% compared to offline charging plans computed by the trucks without coordination and update, and by 33.8% compared to the coordination scheme assuming zero waiting times at distant stations. Copyright © 2024, The Authors. All rights reserved.

关键词： Trucks

来源：评论

学校读者我要写书评

暂无评论

Elucidation of Mechanisms, Pathways, and Toxicity of Fabricated Z-Scheme Knbo3/Znin2s4 Hollow Core-Shell Composites for Enhanced Ciprofloxacin Photodegradation

SSRN

引用

SSRN 2023年

作者： He, Zuming Lin, Kai Wong, Ngie Hing Sunarso, Jaka Xia, Yongmei Fu, Xiaofei Su, Jiangbin Huang, Zhengyi Wang, Yong Tang, Bin School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China Huaide School Changzhou University Jingjiang214500 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia School of Materials and Engineering Jiangsu University of Technology Changzhou213001 China School of Pharmaceutical and Materials Engineering Taizhou University Jiaojiang318000 China

Semiconductor photocatalysis technology for environmental remediation faces primary challenges, such as charge-carrier recombination and interfacial charge transfer inhibition, which affect its degradation efficiency in removing organic contaminants. In this work, we successfully synthesized a unique Z-scheme KNbO3/ZnIn2S4 hollow core-shell microsphere via combined hard template, thermal treatment, and hydrothermal processes as the photocatalyst for ciprofloxacin (CIP) remediation under sunlight irradiation. Compared to the pure KNbO3 and ZnIn2S4, our optimized 15-KNbO3/ZnIn2S4 composite exhibited enhanced photodegradation performance with up to 97.8% CIP removal within 1.5 h. At the KNbO3/ZnIn2S4 heterojunction interface, the composite developed its internal electric field and enhanced its photoabsorption under visible light region, thus boosting its charge separation. Subsequently, we systematically investigated the Z-scheme charge migration process within the heterojunction with a series of experiments. Our results indicated that the hydroxyl and superoxide radicals were the dominant active species during photocatalysis. The composite also could achieve excellent stability and adaptability. We also managed to identify the CIP-degraded intermediates and deduce their possible degradation pathways. Lastly, our toxicity analysis revealed that CIP toxicity could decrease gradually during photocatalysis. In this work, we presented a unique method for preparing inexpensive and highly efficient Z-scheme hollow core-shell structure photocatalysts with excellent antibiotic remediation in water under simulated sunlight illumination. © 2023, The Authors. All rights reserved.

关键词： Electric fields

来源：评论

学校读者我要写书评

暂无评论

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

arXiv

引用

arXiv 2021年

作者： Barišić, Antonella Petric, Frano Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb Croatia

This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder's trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target's 3D position. Obtained 3D measurements of target's position are used to calculate the position, orientation, and size of a figure-eight shaped trajectory, which we approximate using a Bernoulli lemniscate. Once the approximation is deemed sufficiently precise, as measured by the distance between observations and estimate, we calculate an interception point to position the interceptor UAV directly on the intruder's path. Our method, which we have significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. Our results confirm that we have developed an efficient, visual-perception module that can extract information describing the intruder UAV's motion with precision sufficient to support interception planning. In a majority of our simulated encounters, we can track and intercept a target that moves 30% faster than the interceptor. Corresponding tests in an unstructured environment yielded 9 out of 12 successful results. © 2021, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Generating 3d Fish Skeleton Via Optimization Iterative Method and Fishskeletonnet

SSRN

引用

SSRN 2023年

作者： Shi, Min Zhao, Guo-Liang Sun, Bi-Lian Zhuo, Xin-Ru Zhu, Deng-Ming Li, Zhao-Xin School of Control and Computer Engineering North China Electric Power University Beijing102206 China Prospective Research Laboratory Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing100081 China

Simplified fish skeleton can help 3D fish modeling, and can quickly simulate the poses and motions of fish of different shapes, which is important for virtual reality in agriculture. Compared with the same characteristics of the human skeleton, the fish skeleton structure is not always consistent. Therefore, it is challenging to generate a realistic and simple fish skeleton. We present an iterative optimization method for generating a 3D fish skeleton model that accurately captures the complex structure of the fish skeleton. Our method differs from conventional methods that generate the skeleton based solely on the geometric shape of the 3D mesh. We analyze the biological features of the fish's actual motion to extract the skeleton of the main part and define the fish skeleton model. The model parameters, including the number and position of joints, are iteratively optimized using a sample dataset with different shapes and five common postures. Finally, we propose FishSkeletonNet to generate fish skeletons of different shapes and poses based on the above data as the dataset. We conduct experiments on the grass carp dataset to quantitatively and qualitatively evaluate the number and location of generated skeleton joints. The experimental results demonstrate that our method generates fish skeletons that are closer to the true structure of the fish skeleton and exhibit better biological plausibility compared to those generated by existing methods. © 2023, The Authors. All rights reserved.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

A Modified P&O-MPPT Technique Using Fuzzy Logic controller for PV Systems

A Modified P&O-MPPT Technique Using Fuzzy Logic Controller f...

引用

Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Rafah Ibraheem Jabbar Karam Khairullah Mohammed Saad Mekhilef Marizan Mubin Mehdi Seyedmahmoudian Alex Stojcevski Ben Horan Mohamed Becherif Department of Electrical Engineering Universiti Malaya Kuala Lumpur Malaysia Mechatronics Engineering Department Universiti Teknologi Malaysia Control Johor Bahru Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia School of Engineering Deakin University Geelong VIC Australia FEMTO-ST Institute Univ. Bourgogne Franche- Comté UTBM CNRS Rue Ernest Thierry Mieg Belfort France

The perturb and observe (P&O) algorithm is the most popular of the numerous maximum power point tracking (MPPT) algorithms due to its simplicity and ease of implementation. However, the method has limitations regarding convergence speed, steady-state oscillation, and drift problem under sudden irradiance variations. This paper presents an improved P&O-MPPT algorithm to enhance the tracking convergence speed using adaptive duty cycle perturbation step fuzzy logic (FL-∆D) size. Moreover, a new strategy is added to the algorithm to stop the steady-state oscillation once MPP is tracked. The proposed algorithm was verified using MATLAB/Simulink under constant and varied irradiance levels. The results showed that the proposed algorithm with an adaptive FL-∆D size could efficiently track an accurate MPP with zero-state oscillation at different irradiance levels. Moreover, the algorithm achieves fast tracking under a rapid change in irradiance. The proposed algorithm was compared with the traditional P&O algorithm and the modified P&O algorithms that have recently been published. The comparison results validated the effectiveness of the proposed algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation

arXiv

引用

arXiv 2023年

作者： Islam, Mobarakol Seenivasan, Lalithkumar Sharan, S.P. Viekash, V.K. Gupta, Bhavesh Glocker, Ben Ren, Hongliang Dept. of Computing Imperial College London United Kingdom Dept. of Biomedical Engineering National University of Singapore Singapore Dept. of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India Dept. of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India Dept. of Mechanical Engineering Indian Institute of Technology Delhi India Dept. of Electrical Engineering Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong

Purpose: In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress. While both methods heavily rely on the ability to score the difficulty of data samples, an optimal scoring function is still under exploration. Methodology: Distillation is a knowledge transfer approach where a teacher network guides a student network by feeding a sequence of random samples. We argue that guiding student networks with an efficient curriculum strategy can improve model generalization and robustness. For this purpose, we design an uncertainty-based paced curriculum learning in self-distillation for medical image segmentation. We fuse the prediction uncertainty and annotation boundary uncertainty to develop a novel paced-curriculum distillation (PCD). We utilize the teacher model to obtain prediction uncertainty and spatially varying label smoothing with Gaussian kernel to generate segmentation boundary uncertainty from the annotation. We also investigate the robustness of our method by applying various types and severity of image perturbation and corruption. Results: The proposed technique is validated on two medical datasets of breast ultrasound image segmentation and robotassisted surgical scene segmentation and achieved significantly better performance in terms of segmentation and robustness. Conclusion: P-CD improves the performance and obtains better generalization and robustness over the dataset shift. While curriculum learning requires extensive tuning of hyper-parameters for pacing function, the level of performance improvement suppresses this limitation. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：