Journal of Advanced Computational Intelligence and Intelligent Informatics最新文献_第5页

Lightweight Bilateral Network for Real-Time Semantic Segmentation 用于实时语义分割的轻量级双边网络

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0673

Pengtao Wang, Lihong Li, Feiyang Pan, L. Wang

{"title":"Lightweight Bilateral Network for Real-Time Semantic Segmentation","authors":"Pengtao Wang, Lihong Li, Feiyang Pan, L. Wang","doi":"10.20965/jaciii.2023.p0673","DOIUrl":"https://doi.org/10.20965/jaciii.2023.p0673","url":null,"abstract":"Herein, a dual-branch semantic segmentation model based on depth-separable convolution and attention mechanism is proposed for the real-time and accuracy requirement of semantic segmentation. The proposed approach overcomes the problems of poor segmentation effect and over-simplification of feature fusion arising from the constant downsample operations in semantic segmentation. The network is divided into spatial detail and semantic information paths. The spatial detail path utilizes a smaller downsample multiplier to maintain resolution and efficiently extract spatial information. The semantic information path is constructed by a non-bottleneck residual unit with dilated convolution; it extracts semantic features. For the feature aggregation problem, the feature-guided fusion module is designed to assign different weights to the parts of the two paths and fuse them to obtain the final output. The proposed algorithm achieves a segmentation accuracy of 69.6% and speed of 70 fps on the Cityscapes dataset, with a model parameter count of only 0.76 M, thus indicating some advantages over recent real-time semantic segmentation algorithms. The proposed method with depth separable convolution and attention mechanism can effectively extract features and compensate for the loss of accuracy caused by downsampling. The experiments demonstrate that the proposed fusion module outperforms other methods in fusing different features.","PeriodicalId":45921,"journal":{"name":"Journal of Advanced Computational Intelligence and Intelligent Informatics","volume":"15 1","pages":"673-682"},"PeriodicalIF":0.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90222657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloud-Edge Cooperative Control System in Continuous Annealing Processes 连续退火过程中的云边缘协同控制系统

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0638

Wenshuo Song, Weihua Cao, Wenkai Hu, Min Wu

引用次数: 0

Researcher Network Visualization Using Matrix Researcher2vec 基于矩阵的研究者网络可视化

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0603

Enna Hirata, Takahiro Yamashita, Seiichi Ozawa

{"title":"Researcher Network Visualization Using Matrix Researcher2vec","authors":"Enna Hirata, Takahiro Yamashita, Seiichi Ozawa","doi":"10.20965/jaciii.2023.p0603","DOIUrl":"https://doi.org/10.20965/jaciii.2023.p0603","url":null,"abstract":"In this study, we introduce a system called Matrix Researcher2vec (MResearcher2vec) which generates researcher embedding vectors from their papers and research projects in researchmap and KAKENHI databases. The system includes data on 276,841 researchers, 6,161,592 papers, and research projects. Utilizing natural language processing techniques, the MResearcher2vec model extracts researcher vectors from the papers and research project summaries of KAKENHI grant recipients. The similarity between reseachers is then computed to visualize inter-researcher relationships. The machine learning results have been integrated into a web service, providing a novel approach for academic relationship mining. It can be applied in the matching of research contents and researchers in evaluation of industry-government-academia collaboration and joint research. It contributes in four aspects: (1) exchanges between researchers, (2) creation of opportunities for researchers and companies to connect, (3) further promotion of interdisciplinary research, and (4) reduction of lost opportunities for research institutions to acquire talents.","PeriodicalId":45921,"journal":{"name":"Journal of Advanced Computational Intelligence and Intelligent Informatics","volume":"16 1","pages":"603-608"},"PeriodicalIF":0.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88607022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trash Detection Algorithm Suitable for Mobile Robots Using Improved YOLO 基于改进YOLO的移动机器人垃圾检测算法

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0622

Ryotaro Harada, T. Oyama, Kenji Fujimoto, T. Shimizu, Masayoshi Ozawa, Julien Samuel Amar, Masahiko Sakai

{"title":"Trash Detection Algorithm Suitable for Mobile Robots Using Improved YOLO","authors":"Ryotaro Harada, T. Oyama, Kenji Fujimoto, T. Shimizu, Masayoshi Ozawa, Julien Samuel Amar, Masahiko Sakai","doi":"10.20965/jaciii.2023.p0622","DOIUrl":"https://doi.org/10.20965/jaciii.2023.p0622","url":null,"abstract":"The illegal dumping of aluminum and plastic into cities and marine areas leads to negative impacts on the ecosystem and contributes to increased environmental pollution. Although volunteer trash pickup activities have increased in recent years, they require significant effort, time, and money. Therefore, we propose automated trash pickup robot, which incorporates autonomous movement and trash pickup arms. Although these functions have been actively developed, relatively little research has focused on trash detection. As such, we have developed a trash detection function by using deep learning models to improve the accuracy. First, we created a new trash dataset that classifies four types of trash with high illegal dumping volumes (cans, plastic bottles, cardboard, and cigarette butts). Next, we developed a new you only look once (YOLO)-based model with low parameters and computations. We trained the model on a created dataset and a dataset consisting of marine trash created during previous research. In consequence, the proposed models achieve the same detection accuracy as the existing models on both datasets, with fewer parameters and computations. Furthermore, the proposed models accelerate the edge device’s frame rate.","PeriodicalId":45921,"journal":{"name":"Journal of Advanced Computational Intelligence and Intelligent Informatics","volume":"12 1","pages":"622-631"},"PeriodicalIF":0.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83565967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Distributed Machine Learning Model for LoRa-ESL LoRa-ESL分布式机器学习模型的评价

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0700

Malak Abid Ali Khan, Hongbin Ma, Z. Rehman, Ying Jin, A. Rehman

引用次数: 1

Development and Application of a Heat-Transfer Experimental System for the Mechanical Engineering Applied Experiment 机械工程应用实验换热实验系统的研制与应用

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0632

Jinseok Woo, Minoru Hara, Y. Ohyama

引用次数: 0

Mixed Dissipativity Control and Disturbance Rejection for Singular Systems 广义系统的混合耗散控制与扰动抑制

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0720

Fang Gao, Wenbin Chen

引用次数: 0

Two-Direction Prediction Method of Drilling Fluid Based on OS-ELM for Water Well Drilling 基于OS-ELM的水井钻井钻井液双向预测方法

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0594

Yuan Xu, Di Zhang, Tianlang Xian, Zhizhang Ma, Hui Gao, Yuanyuan Ma

引用次数: 0

New Spatial Value Estimation Method for Curved Characteristic Line 曲线特征线空间值估计新方法

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0616

T. Ohkubo, E. Matsunaga

{"title":"New Spatial Value Estimation Method for Curved Characteristic Line","authors":"T. Ohkubo, E. Matsunaga","doi":"10.20965/jaciii.2023.p0616","DOIUrl":"https://doi.org/10.20965/jaciii.2023.p0616","url":null,"abstract":"Numerical calculations are used in various situations. However, to achieve accurate numerical calculations, accuracy in the calculation method and initial values with high spatial resolution are necessary. Therefore, we propose a new method for estimating spatial values that considers characteristic theory but does not use interpolation. We consider the treatment of the curved characteristic line, which implies that the characteristic speed is altered locally. In the new method named averaging inverse characteristics method (AICM), the locally changing characteristic speed is averaged with the characteristic speed of the previous steps. We calculated the spatial values of the shock tube problem, described by the Euler equation, and examined the accuracy of the AICM by comparing the results of the inverse characteristics method (ICM) proposed in the previous study and the traditional interpolating methods. Compared to other methods, AICM reduced the error to less than 1/10 for all parameters. We determined from these results that the AICM accurately estimates the spatial distribution of problems where characteristic speed has significantly changed.","PeriodicalId":45921,"journal":{"name":"Journal of Advanced Computational Intelligence and Intelligent Informatics","volume":"102 1","pages":"616-621"},"PeriodicalIF":0.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75796113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Elastic Adaptively Parametric Compounded Units for Convolutional Neural Network 卷积神经网络的弹性自适应参数复合单元

IF 0.7

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI: 10.20965/jaciii.2023.p0576

Changfan Zhang, Yifu Xu, Zhenwen Sheng

{"title":"Elastic Adaptively Parametric Compounded Units for Convolutional Neural Network","authors":"Changfan Zhang, Yifu Xu, Zhenwen Sheng","doi":"10.20965/jaciii.2023.p0576","DOIUrl":"https://doi.org/10.20965/jaciii.2023.p0576","url":null,"abstract":"The activation function introduces nonlinearity into convolutional neural network, which greatly promotes the development of computer vision tasks. This paper proposes elastic adaptively parametric compounded units to improve the performance of convolutional neural networks for image recognition. The activation function takes the structural advantages of two mainstream functions as the function’s fundamental architecture. The SENet model is embedded in the proposed activation function to adaptively recalibrate the feature mapping weight in each channel, thereby enhancing the fitting capability of the activation function. In addition, the function has an elastic slope in the positive input region by simulating random noise to improve the generalization capability of neural networks. To prevent the generated noise from producing overly large variations during training, a special protection mechanism is adopted. In order to verify the effectiveness of the activation function, this paper uses CIFAR-10 and CIFAR-100 image datasets to conduct comparative experiments of the activation function under the exact same model. Experimental results show that the proposed activation function showed superior performance beyond other functions.","PeriodicalId":45921,"journal":{"name":"Journal of Advanced Computational Intelligence and Intelligent Informatics","volume":"37 1","pages":"576-584"},"PeriodicalIF":0.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79239479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0