检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Zhang, Yubo Pan, Yuchen Gong, Chen Liu, Beiyuan Xu, Zhengyuan Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences University of Science and Technology of China Anhui Hefei230027 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Shaanxi Xi’an710072 China

We design a receiver assembling several photomultipliers (PMTs) as an array to increase the field of view (FOV) of the receiver and adapt to multiuser situation over None-line-of-sight (NLOS) ultraviolet (UV) channels. Channel estimation and signal detection have been investigated according to the space division characteristics of the structure. Firstly, we adopt the balanced structure on the pilot matrix, analyze the channel estimation mean square error (MSE), and optimize the structure parameters. Then, with the estimated parameters, an analytical threshold detection rule is proposed as a preliminary work of multiuser detection. The detection rule can be optimized by analyzing the separability of two users based on the Gaussian approximation of Poisson weighted sum. To assess the effect of imperfect estimation, the sensitivity analysis of channel estimation error on two-user signal detection is performed. Moreover, we propose a successive elimination method for on-off keying (OOK) modulated multiuser symbol detection based on the previous threshold detection rule. A closed-form upper bound on the detection error rate is calculated, which turns out to be a good approximation of that of multiuser maximum-likelihood (ML) detection. The proposed successive elimination method is twenty times faster than the ML detection with negligible detection error rate degradation. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel estimation

来源：评论

学校读者我要写书评

暂无评论

A Power-System Economic Dispatching Based on Enhanced Group Search Optimizer 1

引用

18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Nguyen, Trong-The Nguyen, Trinh-Dong Nguyen, Vinh-Tiep Dao, Thi-Kien College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Economic dispatch (ED) is one of the critical tasks in a power system’s optimization issues, but its characteristics with non-convex, high-dimensional, non-linear, and non-differentiable, so that caused solutions are more challenging in reality. This paper suggests an enhanced group search optimizer (EGSO) for solving the ED model with the valve-point effect and the different fuels. The EGSO adopts the original group search optimizer (GSO) by changing polar to Cartesian coordinate’s conversion and adding cyclic translation operators to the search behavior of the finder and walker to avoid falling into local optimal and slow convergence of the GSO. In the experiment section, the testing power systems of the ED model are used to validate the EGSO performance. The experimental results reveal that EGSO outperformed the other algorithms regarding convergence speed, the time required to complete calculations, and impacts with lower fuel cost. Additionally, with excellent outcomes in optimization issues involving multiple local optimal values show the EGSO is practical, quick, and efficient in addressing the challenging ED model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoting Wang Juan Wang Haodong Bian Maozu Guo School of Computer Science Inner Mongolia University and Ecological Big Data Engineering Research Center of the Ministry of Education Hohhot China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing P.R. China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing P.R. China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins.

关键词： Proteins Location awareness Support vector machines Deep learning Pathology Predictive models Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Transmitter IQ Mismatch Estimation by Number-Theoretic Net-Based Gaussian Particle Filter for Coherent Optical Communications

Transmitter IQ Mismatch Estimation by Number-Theoretic Net-B...

引用

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shuai Liu Yangfan Xu Xinwei Du Qian Wang Changyuan Yu Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China Department of Electronic and Information Engineering Photonics Research Institute The Hong Kong Polytechnic University Hung Hom Hong Kong

In this paper, we propose a novel approach for estimating in-phase/quadrature-phase (IQ) mismatch at the transmitter using a number-theoretic net (NT-net)-based Gaussian particle filter (NT-GPF). The proposed method generates particles on an ellipse of the given bivariate Gaussian distribution, which helps locate the correct region of the actual state parameters more efficiently. Simulation results demonstrate that the NT-GPF-based approach offers higher estimation accuracy, with the mean squared error (MSE) consistently lower than that of traditional GPF across different particle numbers. Moreover, the proposed NT-GPF-based algorithm significantly improves estimation efficiency and noise tolerance, requiring only 21 particles under low signal-to-noise ratio (SNR).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust Weather Recognition in Images via Interactive CNN-Transformer

Robust Weather Recognition in Images via Interactive CNN-Tra...

引用

Chinese Control Conference (CCC)

作者： Tianyu Liu Qingwen Hou Jie Zhang Xianzhong Chen Jinghui Cheng School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing China

Weather recognition is a fundamental yet challenging task for the multi -sensor perception system of driverless vehicles. It is difficult for current joint perception framework to autonomously recognize weather scenes and adaptively adjust the weight of camera involved in perception. This paper proposes a weather recognition method with a new interactive CNN - Transformer architecture to learn global and local contextual information from images. The framework starts by deepening and widening the convolution blocks to obtain multi-scale local scene features. We re-weight different feature channels by channel-attention mechanism. Then, a new interactive feature fusion network between convolutional structure and stacked Transformer is developed, which can gradually complement the global feature information lost in CNN. We evaluated the proposed method on a public weather image dataset. The recognition accuracy reaches 95.14%, which improves 5.68% and 6.15% compared with the ResNet-50 and ViT16 and achieves state-of-the-art performance on the experimental dataset. At the same time, the comprehensive evaluation of recall and precision show that the proposed method is more robust when dealing with multi-class weather recognition tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-Aware POI Recommendation 3

A Fuzzy Clustering Based Collaborative Filtering Algorithm f...

引用

2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Yin, Minghao Liu, Yanheng Zhou, Xu Sun, Geng College of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China Center for Computer Fundamental Education Jilin University Changchun Jilin China

Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-Aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users' check-in behaviours. Individual's check-in behaviour can be under the influence of location region and time slots, so user's feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy clustering

来源：评论

学校读者我要写书评

暂无评论

Encoding Global Semantic and Localized Geographic Spatial-Temporal Relations for Traffic Accident Risk Prediction

SSRN

引用

SSRN 2024年

作者： Alhaek, Fares Li, Tianrui Rajeh, Taha M. Javed, Muhammad Hafeez Liang, Weichao School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

The proliferation of vehicles and the intricate layout of road systems have contributed to a significant rise in traffic accidents, posing a pressing concern globally. Despite the advancements facilitated by deep learning, several challenges persist in the domain of traffic accident prediction. Firstly, the sparsity of accident data in certain city regions presents a significant obstacle, particularly when attempting fine-grained predictions at a local level. Secondly, the intricate spatial-temporal relations inherent in traffic accident data pose a challenge for existing prediction models. In response to these formidable challenges, this paper presents GLST-TARP, a novel model for predicting traffic accident risk in urban environments by leveraging both global semantic and localized geographic spatial-temporal relations. Specifically, we construct multi-graphs to encode global static and dynamic spatial relations, incorporating attention mechanisms to adaptively focus on relevant information and temporal relations. Additionally, we employ channel-wise convolutional neural network blocks to extract localized geographic features and enhance predictive accuracy. The proposed model is trained using a Huber loss function tailored for regression tasks, relieving the impact of zero values during optimization. Experimental results demonstrate that GLST-TARP outperforms state-of-the-art methods in predicting traffic accident risks, showcasing its potential for enhancing urban safety and transportation management. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on Production Condition Identification Method of Pumping Well Based on an Improved Pattern Recognition Algorithm

Research on Production Condition Identification Method of Pu...

引用

International Conference on Network and Information Systems for Computers (ICNISC)

作者： Haili Wu Xiangyang Zhang Dan Wu Qun Li Gang Yao Key Laboratory of Internet of Things CNPC Petroleum and Petrochemical Research Center of National Engineering Laboratory of Big Data Collaborative Security CNPC Research Institute of Petroleum Exploration and Development-Northwest PetroChina Lanzhou China Research Institute of Petroleum Exploration and Development-Northwest PetroChina Lanzhou China

In the process of oil and gas production, indicator diagram is an important basis for production management. Diagnosing the working condition of pumping unit based on indicator diagram can quickly grasp the working state of oil well, analyze and judge whether the production parameters of oil well are reasonable. The pattern recognition technology of indicator diagram generally includes two stages: feature extraction and classification. Classification methods mainly use a few common machine learning methods such as artificial neural network and SVM. In recent years, feature extraction methods mainly include geometric feature method, grid feature and moment invariant feature methods. Due to the limitations of these extraction methods, most of them can only extract some primary feature quantities. However, using these primary feature quantities to recognize the indicator diagram often has the technical problems of low recognition efficiency and low recognition accuracy. In this paper, an improved indicator diagram pattern recognition method based on binary gradient direction histogram feature is proposed. Compared with the previous feature extraction methods, this method has good robustness, fast calculation speed and high recognition accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual Information

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutua...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jicun Li Xingjian Li Tianyang Wang Shi Wang Yanan Cao Chengzhong Xu Dejing Dou Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Big Data Lab Baidu Research State Key Lab of IOTSC University of Macau University of Alabama at Birmingham Institute of Information Engineering Chinese Academy of Sciences

Fine-tuning pre-trained language models, such as BERT, has shown enormous success among various NLP tasks. Though simple and effective, the process of fine-tuning has been found unstable, which often leads to unexpected poor performance. To increase stability and generalizability, most existing works resort to maintaining the parameters or representations of pre-trained models during fine-tuning. Nevertheless, very little work explores mining the reliable part of pre-learned information that can help to stabilize fine-tuning. To address this challenge, we introduce a novel solution in which we fine-tune BERT with stabilized cross-layer mutual information. Our method aims to preserve the reliable behaviors of cross-layer information propagation, instead of preserving the information itself, of the pre-trained model. Therefore, our method circumvents the domain conflicts between pre-trained and target tasks. We conduct extensive experiments with popular pre-trained BERT variants on NLP datasets, demonstrating the universal effectiveness and robustness of our method.

关键词： Cross layer design Bit error rate Signal processing Stability analysis Robustness Acoustics Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Optimizing Carbon Emissions in Green Logistics for Time-Dependent Routing

SSRN

引用

SSRN 2024年

作者： Liu, Yiming Yu, Yang Roberto, Baldacci Tang, Jiafu Sun, Wei State Key Laboratory of Synthetic Automation for Process Industries Institute of Intelligent Data and Systems Engineering Northeastern University Shenyang110819 China Dalian University of Technology School of Economics and Management Liaoning Dalian116024 China Division of Engineering Management and Decision Sciences College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation P.O. Box 34110 Doha Qatar College of Management Science and Engineering Dongbei University of Finance and Economics Shahekou Dalian116026 China Business School Liaoning University Shenyang110316 China

This paper considers a green vehicle routing problem termed the time-dependent green vehicle routing problem with time windows (TDGVRPTW). The TDGVRPTW is an extension of the green vehicle routing problem with time windows in green logistics. It considers time-dependent vehicle speed and aims to minimize carbon emissions. Since the travel times and carbon emissions between locations depend on the departure time from the starting location, optimizing carbon emissions requires determining the optimal departure times from both the depot and customers. This paper presents a branch-price-and-cut (BPC) algorithm to solve the TDGVRPTW. The problem is formulated based on a set-partitioning model. To solve the pricing problem associated with the set-partitioning model, we first define backward non-dominated time-dependent arcs and optimal timed routes. We also introduce three types of routes, forward, backward, and patchwork optimal timed routes, and analyze their characteristics. Then, we introduce a method to adjust the times in backward and patchwork optimal timed routes from the latest to the earliest. We prove that this method guarantees correctness and effectively reduces complexity. We propose a bidirectional labeling algorithm to generate routes. The pricing problem employs state-of-the-art techniques, including limited memory subset row cuts (lm-SRCs), ng-route relaxation, and bucket graphs to enhance the algorithm’s efficiency. The BPC algorithm is tested on a set of instances derived from benchmark instances in the literature. The results demonstrate the effectiveness of the proposed algorithm in solving the TDGVRPTW. © 2024, The Authors. All rights reserved.

关键词： Travel time

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：