Electronic Research Archive最新文献_第2页

EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits optimization and wireless sensor networks 基于人工兔子优化和无线传感器网络的畜牧业节能簇头选择机制

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023158

Rajakumar Ramalingam, S. B., Shakila Basheer, Prakash Balasubramanian, Mamoon Rashid, Gitanjali Jayaraman

{"title":"EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits optimization and wireless sensor networks","authors":"Rajakumar Ramalingam, S. B., Shakila Basheer, Prakash Balasubramanian, Mamoon Rashid, Gitanjali Jayaraman","doi":"10.3934/era.2023158","DOIUrl":"https://doi.org/10.3934/era.2023158","url":null,"abstract":"In the livestock industry, wireless sensor networks (WSNs) play a significant role in monitoring many fauna health statuses and behaviors. Energy preservation in WSNs is considered one of the critical, complicated tasks since the sensors are coupled to constrained resources. Therefore, the clustering approach has proved its efficacy in preserving energy in WSNs. In recent studies, various clustering approaches have been introduced that use optimization techniques to improve the network lifespan by decreasing energy depletion. Yet, they take longer to converge and choose the optimal cluster heads in the network. In addition, the energy is exhausted quickly in the network. This paper introduces a novel optimization technique, i.e., an artificial rabbits optimization algorithm-based energy efficient cluster formation (EECHS-ARO) approach in a WSN, to extend the network lifetime by minimizing the energy consumption rate. The EECHS-ARO technique balances the search process in terms of enriched exploration and exploitation while selecting the optimal cluster heads. The experimentation was carried out on a MATLAB 2021a platform with varying sensor nodes. The obtained results of EECHS-ARO are contrasted with other existing approaches via teaching–learning based optimization algorithm (TLBO), ant lion optimizer (ALO) and quasi oppositional butterfly optimization algorithm (QOBOA). The proposed EECHS-ARO enriches the network lifespan by ~15% and improves the packet delivery ratio by ~5%.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70245319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Barycentric rational interpolation method for solving KPP equation 求解KPP方程的质心有理插值法

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023152

Jin Li, Yongling Cheng

引用次数: 3

A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest 基于位置关系和随机森林的汽车与电动自行车碰撞现场伤害严重程度评估模型

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023173

Ye Yu, Zhiyuan Liu

{"title":"A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest","authors":"Ye Yu, Zhiyuan Liu","doi":"10.3934/era.2023173","DOIUrl":"https://doi.org/10.3934/era.2023173","url":null,"abstract":"Vulnerable road users (VRUs) are usually more susceptible to fatal injuries. Accurate and rapid assessment of VRU injury severity at the accident scene can provide timely support for decision-making in emergency response. However, evaluating VRU injury severity at the accident scene usually requires medical knowledge and medical devices. Few studies have explored the possibility of using on-site positional relationship to assess injury severity, which could provide a new perspective for on-site transportation professionals to assess accident severity. This study proposes a data-driven on-site injury severity assessment model for car-to-electric-bicycle accidents based on the relationship between the final resting positions of the car, electric bicycle and cyclist at the accident scene. Random forest is employed to learn the accident features from the at-scene positional relationship among accident participants, by which injury severity of the cyclist is assessed. Conditional permutation importance, which can account for correlation among predictor variables, is adopted to reflect the importance of predictor variables more accurately. The proposed model is demonstrated using simulated car-to-electric-bicycle collision data. The results show that the proposed model has good performance in terms of overall accuracy and is balanced in recognizing both fatal and non-fatal accidents. Model performance under partial information confirms that the position information of the electric bicycle is more important than the position information of the cyclist in assessing injury severity.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70245719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentence opinion mining model for fusing target entities in official government documents 面向政府公文目标实体融合的句子意见挖掘模型

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023177

Xiao Ma, Teng Yang, Feng Bai, Yunmei Shi

{"title":"Sentence opinion mining model for fusing target entities in official government documents","authors":"Xiao Ma, Teng Yang, Feng Bai, Yunmei Shi","doi":"10.3934/era.2023177","DOIUrl":"https://doi.org/10.3934/era.2023177","url":null,"abstract":"When drafting official government documents, it is necessary to firmly grasp the main idea and ensure that any positions stated within the text are consistent with those in previous documents. In combination with the field's demands, By taking advantage of suitable text-mining techniques to harvest opinions from sentences in official government documents, the efficiency of official government document writers can be significantly increased. Most existing opinion mining approaches employ text classification methods to directly mine the sentential text of official government documents while disregarding the influence of the objects described within the documents (i.e., the target entities) on the sentence opinion categories. To address these issues, this study proposes a sentence opinion mining model that fuses the target entities within documents. Based on the Bi-directional long short-term (BiLSTM) and attention mechanisms, the model fully considers the attention given by a official government document's target entity to different words within the corresponding sentence text, as well as the dependency between words of the sentence. The model subsequently fuses two by using feature vector fusion to obtain the final semantic representation of the text, which is then classified using a fully connected network and softmax function. Experimental results based on a dataset of official government documents show that the model significantly outperforms baseline models such as Text-convolutional neural network (TextCNN), recurrent neural network (RNN), and BiLSTM.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":"83 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70245873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Barycentric rational interpolation method for solving time-dependent fractional convection-diffusion equation 求解时变分数阶对流扩散方程的质心有理插值方法

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023205

Jin Li, Yongling Cheng

引用次数: 1

Uniqueness criteria for initial value problem of conformable fractional differential equation 符合分数阶微分方程初值问题的唯一性准则

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023207

Y. Zou, Yujun Cui

引用次数: 0

Satellite road extraction method based on RFDNet neural network 基于RFDNet神经网络的卫星道路提取方法

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023223

Weichi Liu, Gaifang Dong, Mingxin Zou

{"title":"Satellite road extraction method based on RFDNet neural network","authors":"Weichi Liu, Gaifang Dong, Mingxin Zou","doi":"10.3934/era.2023223","DOIUrl":"https://doi.org/10.3934/era.2023223","url":null,"abstract":"The road network system is the core foundation of a city. Extracting road information from remote sensing images has become an important research direction in the current traffic information industry. The efficient residual factorized convolutional neural network (ERFNet) is a residual convolutional neural network with good application value in the field of biological information, but it has a weak effect on urban road network extraction. To solve this problem, we developed a road network extraction method for remote sensing images by using an improved ERFNet network. First, the design of the network structure is based on an ERFNet; we added the DoubleConv module and increased the number of dilated convolution operations to build the road network extraction model. Second, in the training process, the strategy of dynamically setting the learning rate is adopted and combined with batch normalization and dropout methods to avoid overfitting and enhance the generalization ability of the model. Finally, the morphological filtering method is used to eliminate the image noise, and the ultimate extraction result of the road network is obtained. The experimental results show that the method proposed in this paper has an average F1 score of 93.37% for five test images, which is superior to the ERFNet (91.31%) and U-net (87.34%). The average value of IoU is 77.35%, which is also better than ERFNet (71.08%) and U-net (65.64%).","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70246690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Derivations of finite-dimensional modular Lie superalgebras $ overline{K}(n, m) $ 有限维模李超代数的导数$ overline{K}(n, m) $

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023217

Dan Mao, Keli Zheng

引用次数: 0

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain 基于图自编码器和分类链的泛癌RAS通路激活解码与识别

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023253

Jianting Gong, Yingwei Zhao, Xiantao Heng, Yongbing Chen, Pingping Sun, Fei He, Zhiqiang Ma, Zilin Ren

{"title":"Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain","authors":"Jianting Gong, Yingwei Zhao, Xiantao Heng, Yongbing Chen, Pingping Sun, Fei He, Zhiqiang Ma, Zilin Ren","doi":"10.3934/era.2023253","DOIUrl":"https://doi.org/10.3934/era.2023253","url":null,"abstract":"<abstract> <p>The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://github.com/zhaoyw456/DIPRAS\">https://github.com/zhaoyw456/DIPRAS</ext-link>.</p> </abstract>","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70247114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical simulation analysis for the effect of water content on the intelligent compaction quality of roadbed 含水量对路基智能压实质量影响的数值模拟分析

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023254

Yuan Ma, Y. Luan, Shuangquan Jiang, Jianming Zhang, Chuan Wang

{"title":"Numerical simulation analysis for the effect of water content on the intelligent compaction quality of roadbed","authors":"Yuan Ma, Y. Luan, Shuangquan Jiang, Jianming Zhang, Chuan Wang","doi":"10.3934/era.2023254","DOIUrl":"https://doi.org/10.3934/era.2023254","url":null,"abstract":"In the process of intelligent compaction of roadbeds, the water content of the roadbed is one of the important influencing factors of compaction quality. In order to analyze the effect of water content on the compaction quality of roadbeds, this paper is developed by secondary development of Abaqus finite element numerical simulation software. At the same time, the artificial viscous boundary was set to eliminate the influence of boundary conditions on the results in the finite element modeling process, so that the numerical simulation can be refined to model. On this basis, the dynamic response analysis of the roadbed compaction process is performed on the finite element numerical simulation results. This paper established the correlation between compaction degree and intelligent compaction index CMV (Compaction Meter Value) and then analyzed the effect of water content on the compaction quality for the roadbed. The results of this paper show that the amplitude of the vertical acceleration is almost independent of the moisture content, and the vertical displacement mainly occurs in the static compaction stage. The vertical displacement changes sharply in the first 0.5 s when the vibrating wheel is in contact with the roadbed. The main stage of roadbed compaction quality increase is before the end of the first compaction. At the end of the first compaction, the roadbed compaction degree increased rapidly from 80% to 91.68%, 95.34% and 97.41%, respectively. With the increase in water content, the CMV gradually increased. At the end of the second compaction, CMV increased slightly compared with that at the end of the first compaction and stabilized at the end of the second compaction. The water content of the roadbed should be considered to be set slightly higher than the optimal water content of the roadbed by about 1% during the construction of the roadbed within the assumptions of this paper.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70247121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1