Systems Science & Control Engineering最新文献_第4页

Study on egg sorting model based on visible-near infrared spectroscopy 基于可见-近红外光谱的鸡蛋分选模型研究

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-08-30 DOI: 10.1080/21642583.2022.2112317

Xiaoping Han, Yan-Hong Liu, Xuyuan Zhang, Zhiyong Zhang, Hua Yang

{"title":"Study on egg sorting model based on visible-near infrared spectroscopy","authors":"Xiaoping Han, Yan-Hong Liu, Xuyuan Zhang, Zhiyong Zhang, Hua Yang","doi":"10.1080/21642583.2022.2112317","DOIUrl":"https://doi.org/10.1080/21642583.2022.2112317","url":null,"abstract":"To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate ( ) and the rejection rate ( ) are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44140736","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: 4

Segmentation and weight prediction of grape ear based on SFNet-ResNet18 基于SFNet-ResNet18的葡萄果穗分割与重量预测

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-08-19 DOI: 10.1080/21642583.2022.2110541

C. Liang, Yanwen Li, Yan-Hong Liu, Pengchen Wen, Hua Yang

{"title":"Segmentation and weight prediction of grape ear based on SFNet-ResNet18","authors":"C. Liang, Yanwen Li, Yan-Hong Liu, Pengchen Wen, Hua Yang","doi":"10.1080/21642583.2022.2110541","DOIUrl":"https://doi.org/10.1080/21642583.2022.2110541","url":null,"abstract":"ABSTRACT In this paper, the segment and weight prediction problems are investigated for ear of grape based on deep learning technologies. The image datum is collected from ZaoHeiBao grape in a greenhouse by camera. The grape ear target segmentation model is constructed by cross combining three backbone networks (ResNet18, ResNet50, and ResNet101) and four deep learning semantic segmentation networks (SFNet, GCNet, EMANet, and Deeplabv3). The experimental results show that for the SFNet-ResNet18 model, whose structural size is 52.68MB, the mean Intersection over Union (mIoU) is , the mean Pixel Accuracy (mPA) is , and the average segmentation speed of the image ( ) is 0.217s. Therefore, the performance of the SFNet-ResNet18 model outperforms other combined network models and is selected to segment grape ears. Furthermore, on the basis of the segmentation results of grape ears by using the SFNet-ResNet18 model, the grape ear weight is predicted by adopting fractional regression model. The value of is 0.8903, which means that the selected model could accurately predict the weight of grape ears. The proposed method can not only segment the grape ears and accurately predict the weight of the grape ears, but also provide theoretical and technical support for grape yield prediction.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44151483","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: 15

The concept of improving the security of IT systems supporting the storage of knowledge in organizations 提高支持组织中知识存储的IT系统安全性的概念

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-08-06 DOI: 10.1080/21642583.2022.2102550

Paweł Moszczyński, Maciej Moszczyński, Patrycja Bryczek-Wróbel, M. Kapalka, Tomasz Drozdowski, Jarosław Wróbel

{"title":"The concept of improving the security of IT systems supporting the storage of knowledge in organizations","authors":"Paweł Moszczyński, Maciej Moszczyński, Patrycja Bryczek-Wróbel, M. Kapalka, Tomasz Drozdowski, Jarosław Wróbel","doi":"10.1080/21642583.2022.2102550","DOIUrl":"https://doi.org/10.1080/21642583.2022.2102550","url":null,"abstract":"This article presents the concept of improving the security of information systems intended for storing knowledge in organizations. It is designed to protect knowledge from leakage, theft or destruction, but without denying access to its resources to employees. It is possible thanks to the introduction of the so-called security and sharing groups (S&S groups) that have two attributes at the same time – the level of knowledge security and the level of knowledge sharing. The proposed concept also assumes assigning knowledge categories to data and enabling the organization to manage the allocation of operations on data from individual knowledge categories to S&S groups. As a result, the system enables the protection of the most valuable knowledge resources, without restricting access to its other categories. The developed concept makes it easier to find a compromise between the strength of security and the free flow of knowledge at the time of system implementation. In addition, the dynamic ability to choose the level of security and the level of access to knowledge in the system allows adaptation to changes taking place in the environment of the organization and increases its resilience.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42301054","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

Adaptive synchronization control for stochastic complex networks with derivative coupling 具有导数耦合的随机复杂网络的自适应同步控制

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-07-27 DOI: 10.1080/21642583.2022.2102551

Yujing Shi, Lulu Yao, Shanqiang Li

引用次数: 0

PID controller enhanced with artificial bee colony algorithm for active magnetic bearing 基于人工蜂群算法的主动磁轴承PID控制器

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-07-26 DOI: 10.1080/21642583.2022.2102552

Hualong Du, Qiuyue Cui, Peng Liu, Xinyu Ma, He Wang

引用次数: 3

SSMA: simplified slime mould algorithm for optimization wireless sensor network coverage problem SSMA：用于优化无线传感器网络覆盖问题的简化黏菌算法

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-07-13 DOI: 10.1080/21642583.2022.2084650

Yuanye Wei, Xiuxi Wei, Huajuan Huang, Jian Bi, Yongquan Zhou, Yanlian Du

引用次数: 6

An optimal portfolio method based on real time prediction of gold and bitcoin prices 基于黄金和比特币价格实时预测的最优投资组合方法

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-07-06 DOI: 10.1080/21642583.2022.2096149

Zhongqi Miao, Wenxuan Huang

引用次数: 0

Improving accuracy of indoor localization system using ensemble learning 利用集成学习提高室内定位系统的精度

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-06-30 DOI: 10.1080/21642583.2022.2092782

H. Tran, T. Nguyen, T. V. Huynh, Nhiem Quoc Tran

引用次数: 2

Finite-state super transducers for compact language resource representation in edge voice-AI 边缘语音AI中用于紧凑语言资源表示的有限状态超转换器

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-06-23 DOI: 10.1080/21642583.2022.2089930

S. Dobrišek, Ziga Golob, Jerneja Žganec Gros

{"title":"Finite-state super transducers for compact language resource representation in edge voice-AI","authors":"S. Dobrišek, Ziga Golob, Jerneja Žganec Gros","doi":"10.1080/21642583.2022.2089930","DOIUrl":"https://doi.org/10.1080/21642583.2022.2089930","url":null,"abstract":"Finite-state transducers have been proven to yield compact representations of pronunciation dictionaries used for grapheme-to-phoneme conversion in speech engines running on low-resource embedded platforms. However, for highly inflected languages even more efficient language resource reduction methods are needed. In the paper, we demonstrate that the size of finite-state transducers tends to decrease when the number of word forms in the modelled pronunciation dictionary reaches a certain threshold. Motivated by this finding, we propose and evaluate a new type of finite-state transducers, called ‘finite-state super transducers’, which allow for the representation of pronunciation dictionaries by a smaller number of states and transitions, thereby significantly reducing the size of the language resource representation in comparison to minimal deterministic final-state transducers by up to 25%. Further, we demonstrate that finite-state super transducers exhibit a generalization capability as they may accept and thereby phonetically transform even inflected word forms that had not been initially represented in the original pronunciation dictionary used for building the finite-state super transducer. This method is suitable for speech engines operating on platforms at the edge of an AI system with restricted memory capabilities and processing power, where efficient speech processing methods based on compact language resources must be implemented.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48941475","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

Surface microseismic data denoising based on sparse autoencoder and Kalman filter 基于稀疏自动编码器和卡尔曼滤波器的地表微震数据去噪

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-06-23 DOI: 10.1080/21642583.2022.2087786

Xuegui Li, Shuo Feng, Nan Hou, Ruyi Wang, Hanyang Li, Ming Gao, Siyuan Li

{"title":"Surface microseismic data denoising based on sparse autoencoder and Kalman filter","authors":"Xuegui Li, Shuo Feng, Nan Hou, Ruyi Wang, Hanyang Li, Ming Gao, Siyuan Li","doi":"10.1080/21642583.2022.2087786","DOIUrl":"https://doi.org/10.1080/21642583.2022.2087786","url":null,"abstract":"Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. Sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. Sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44308083","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: 14