Frontiers of Information Technology & Electronic Engineering最新文献_第10页

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking 用于高机动性多目标跟踪的混合驱动高斯过程在线学习

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2300348

Qiang Guo, Long Teng, Tianxiang Yin, Yunfei Guo, Xinliang Wu, Wenming Song

引用次数: 0

A high-isolation coupled-fed building block for metal-rimmed 5G smartphones 用于金属边框 5G 智能手机的高隔离耦合馈电构件

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2300203

Aidi Ren, Chengwei Yu, Lixia Yang, Wei Cui, Zhixiang Huang, Ying Liu

引用次数: 0

A novel topology with controllable wideband baseband impedance for power amplifiers 功率放大器宽带基带阻抗可控的新型拓扑结构

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2300074

Yao Yao, Zhijiang Dai, Mingyu Li

引用次数: 0

Magnetically driven microrobots moving in a flow: a review 磁力驱动微型机器人在流动中移动：综述

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2300054

Jiamiao Miao, Xiaopu Wang, Yan Zhou, Min Ye, Hongyu Zhao, Ruoyu Xu, Huihuan Qian

引用次数: 0

A multimodal dense convolution network for blind image quality assessment 用于盲图像质量评估的多模态密集卷积网络

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2200534

Nandhini Chockalingam, Brindha Murugan

{"title":"A multimodal dense convolution network for blind image quality assessment","authors":"Nandhini Chockalingam, Brindha Murugan","doi":"10.1631/fitee.2200534","DOIUrl":"https://doi.org/10.1631/fitee.2200534","url":null,"abstract":"Technological advancements continue to expand the communications industry’s potential. Images, which are an important component in strengthening communication, are widely available. Therefore, image quality assessment (IQA) is critical in improving content delivered to end users. Convolutional neural networks (CNNs) used in IQA face two common challenges. One issue is that these methods fail to provide the best representation of the image. The other issue is that the models have a large number of parameters, which easily leads to overfitting. To address these issues, the dense convolution network (DSC-Net), a deep learning model with fewer parameters, is proposed for no-reference image quality assessment (NR-IQA). Moreover, it is obvious that the use of multimodal data for deep learning has improved the performance of applications. As a result, multimodal dense convolution network (MDSC-Net) fuses the texture features extracted using the gray-level co-occurrence matrix (GLCM) method and spatial features extracted using DSC-Net and predicts the image quality. The performance of the proposed framework on the benchmark synthetic datasets LIVE, TID2013, and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic parameterized learning for unsupervised domain adaptation 用于无监督领域适应的动态参数化学习

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2200631

Runhua Jiang, Yahong Han

{"title":"Dynamic parameterized learning for unsupervised domain adaptation","authors":"Runhua Jiang, Yahong Han","doi":"10.1631/fitee.2200631","DOIUrl":"https://doi.org/10.1631/fitee.2200631","url":null,"abstract":"Unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning domain-invariant representations. Recent approaches achieve this by directly matching the marginal distributions of these two domains. Most of them, however, ignore exploration of the dynamic trade-off between domain alignment and semantic discrimination learning, thus rendering them susceptible to the problems of negative transfer and outlier samples. To address these issues, we introduce the dynamic parameterized learning framework. First, by exploring domain-level semantic knowledge, the dynamic alignment parameter is proposed, to adaptively adjust the optimization steps of domain alignment and semantic discrimination learning. Besides, for obtaining semantic-discriminative and domain-invariant representations, we propose to align training trajectories on both source and target domains. Comprehensive experiments are conducted to validate the effectiveness of the proposed methods, and extensive comparisons are conducted on seven datasets of three visual tasks to demonstrate their practicability.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction 改进的和谐搜索算法及其在加权模糊生产规则提取中的应用

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2200334

Shaoqiang Ye, Kaiqing Zhou, Azlan Mohd Zain, Fangling Wang, Yusliza Yusoff

{"title":"A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction","authors":"Shaoqiang Ye, Kaiqing Zhou, Azlan Mohd Zain, Fangling Wang, Yusliza Yusoff","doi":"10.1631/fitee.2200334","DOIUrl":"https://doi.org/10.1631/fitee.2200334","url":null,"abstract":"Harmony search (HS) is a form of stochastic meta-heuristic inspired by the improvisation process of musicians. In this study, a modified HS with a hybrid cuckoo search (CS) operator, HS-CS, is proposed to enhance global search ability while avoiding falling into local optima. First, the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization. This is to improve the efficiency and accuracy of HS algorithm optimization. Second, the CS operator is introduced to expand the scope of the solution space and improve the density of the population, which can quickly jump out of the local optimum in the randomly generated harmony and update stage. Finally, a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization. Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic algorithm. In addition, 12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS. The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness, high convergence speed, and high convergence accuracy. For further verification, HS-CS is used to optimize the back propagation neural network (BPNN) to extract weighted fuzzy production rules. Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules. Therefore, the proposed HS-CS is proved to be effective.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning 将专家示范嵌入聚类缓冲区，实现有效的深度强化学习

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2300084

Shihmin Wang, Binqi Zhao, Zhengfeng Zhang, Junping Zhang, Jian Pu

{"title":"Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning","authors":"Shihmin Wang, Binqi Zhao, Zhengfeng Zhang, Junping Zhang, Jian Pu","doi":"10.1631/fitee.2300084","DOIUrl":"https://doi.org/10.1631/fitee.2300084","url":null,"abstract":"As one of the most fundamental topics in reinforcement learning (RL), sample efficiency is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the environment. Specifically, we first employ clustering methods in the policy sampling process to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policy data, off-policy data, and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way, our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective sampling. In particular, on the LGSVL task, our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore, our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software development in the age of intelligence: embracing large language models with the right approach 智能时代的软件开发：用正确的方法拥抱大型语言模型

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2300537

Xin Peng

引用次数: 0

High-emitter identification for heavy-duty vehicles by temporal optimization LSTM and an adaptive dynamic threshold 通过时间优化 LSTM 和自适应动态阈值识别重型车辆的高发射器

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI: 10.1631/fitee.2300005

Zhenyi Xu, Renjun Wang, Yang Cao, Yu Kang

{"title":"High-emitter identification for heavy-duty vehicles by temporal optimization LSTM and an adaptive dynamic threshold","authors":"Zhenyi Xu, Renjun Wang, Yang Cao, Yu Kang","doi":"10.1631/fitee.2300005","DOIUrl":"https://doi.org/10.1631/fitee.2300005","url":null,"abstract":"Heavy-duty diesel vehicles are important sources of urban nitrogen oxides (NOx) in actual applications for environmental compliance, emitting more than 80% of NOx and more than 90% of particulate matter (PM) in total vehicle emissions. The detection and control of heavy-duty diesel emissions are critical for protecting public health. Currently, vehicles on the road must be regularly tested, every six months or once a year, to filter out high-emission mobile sources at vehicle inspection stations. However, it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections, and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions. An on-board diagnostic device (OBD) is installed inside the vehicle and can record the vehicle’s emission data in real time. In this paper, we propose a temporal optimization long short-term memory (LSTM) and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data, which can continuously track and record the emission status in real time. First, a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice. Then, the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria, calculated by an adaptive dynamic threshold with changing driving conditions. Finally, a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results. Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1