检索结果-南通市图书馆

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第7期72卷 1017-1033页

作者： noman Shabbir Lauri Kütt Muhammad Jawad Oleksandr Husev Ateeq Ur Rehman Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Department of Electrical Power Engineering&Mechatronics Tallinn University of TechnologyTallinn19086Estonia Department of Electrical and Computer Engineering COMSATS University Islamabad(Lahore Campus)54000Pakistan Department of Electrical Engineering Government College UniversityLahore54000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Wind energy is featured by instability due to a number of factors,such as weather,season,time of the day,climatic area and so on.Furthermore,instability in the generation of wind energy brings new challenges to electric power grids,such as reliability,flexibility,and power quality.This transition requires a plethora of advanced techniques for accurate forecasting of wind energy.In this context,wind energy forecasting is closely tied to machine learning(ML)and deep learning(DL)as emerging technologies to create an intelligent energy management paradigm.This article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data set.Moreover,we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead forecast.For the selection of model parameters,a detailed exploratory data analysis is conducted.all models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 h.The main objective of the study is to foster an efficient forecasting technique for Estonia.The comparative analysis of the results indicates that Support Vector Machine(SVM),Non-linear Autoregressive Neural Networks(NAR),and Recurrent Neural Network-Long-Term Short-Term Memory(RNNLSTM)are respectively 10%,25%,and 32%more efficient compared to TSO’s forecasting algorithm.Therefore,RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution.

关键词： Wind energy production energy forecast machine learning

Hyperspectral anomaly detection:a performance comparison of existing techniques

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2022年第1期15卷 2078-2125页

作者： noman Raza Shah Abdur Rahman M.Maud Farrukh Aziz Bhatti Muhammad Khizer ali Khurram Khurshid Moazam Maqsood Muhammad Amin iVISION Lab Electrical Engineering DepartmentInstitute of Space TechnologyIslamabadPakistan AI Enabling Technologies Lab Center of Excellence in Artificial IntelligenceBahria UniversityIslamabadPakistan Department of Electrical and Computer Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripurPakistan Avionics Engineering Department Institute of Space TechnologyIslamabadPakistan

Anomaly detection in Hyperspectral Imagery(HSI)has received considerable attention because of its potential application in several areas.Numerous anomaly detection algorithms for HSI have been proposed in the literature;however,due to the use of different datasets in previous studies,an extensive performance comparison of these algorithms is missing.In this paper,an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different categories.In addition,this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available datasets.Results indicate that attribute and edge-preserving filtering-based detection(AED),local summation anomaly detection based on collaborative representation and inverse distance weight(LSAD-CR-IDW)and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector(LSUNRSORAD)perform better as indicated by the mean and median values of area under the receiver operating characteristic(ROC)curves.Finally,this paper studies the effect of various dimensionality reduction techniques on anomaly detection.Results indicate that reducing the number of components to around 20 improves the performance;however,any further decrease deteriorates the performance.

关键词： Anomaly detection algorithms hyperspectral imagery deep learning dimensionality reduction

Identification and Visualization of Spatial and Temporal Trends in Textile Industry

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text corpora.Due to these enormous text corpora,we are drowning in data and starving for information.Therefore,recent research employed different text mining approaches to extract information from this text corpus.These proposed approaches extract meaningful and precise phrases that effectively describe the text’s information.These extracted phrases are commonly termed keyphrases.Further,these key phrases are employed to determine the different fields of study trends.Moreover,these key phrases can also be used to determine the spatiotemporal trends in the various research fields.In this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend analysis.Therefore,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific period.This study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research trend.This data was collected from various open access journals.Further,this research determined the spatiotemporal trends using quality phrasemining.This research also focused on finding the research collaboration of different countries in a particular research subject.The research collaborations of other countries’researchers show the impact on import and export of those countries.The visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

Mechanism and technological evaluation of biopeptidal-based emulsions

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Food Bioscience 2022年第3期47卷 81-90页

作者： Tolulope Joshua Ashaolu Ramy M.Khoder Mohammed Saeed alkaltham Asad Nawaz noman Walayat Muhammad Umair Ibrahim Khalifa Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam Faculty of Environmental and Chemical Engineering Duy Tan UniversityDa Nang550000Viet Nam College of Food Science and Technology Huazhong Agricultural UniversityWuhanHubei Province430070PR China Department of Food Science & Nutrition College of Food and Agricultural SciencesKing Saud UniversityRiyadhSaudi Arabia Shenzhen Key Laboratory of Marine Microbiome Engineering Institute for Advanced StudyShenzhen UniversityShenzhen518060China College of Food Science and Technology Zhejiang University of TechnologyHangzhou310014China Department of Food Science and Engineering College of Chemistry and Environmental EngineeringShenzhen UniversityShenzhen518060GuangdongPR China Food Technology Department Faculty of Agriculture13736MoshtohorBenha UniversityEgypt

Both natural and synthetic peptides are not only biologically important,continuous research and comprehension of these short protein fragments show that they also have multifarious potentials in food manufacturing,namely emulsion-based products.The emulsions may basically be oil-in-water or water-in-oil,as well as temporary,semi-permanent,or permanent in a continuous phase that suspends the droplets of the other elements used in food and pharmaceutical industries to formulate products such as gels,creams,ointments,and vaccines.In this review,the classification,preparation,and identification of peptides were described,followed by peptide-based emulsion properties and their relationship with regards to stability,structure,solubility,hydrophobicity,foaming,and water holding capacity.This then dovetailed into the release rate and gastric stability of encapsulated peptides in the human digestive system,before exploring further needs and recommendations.It is desired that more studies be conducted to define the impact of carrier ingredients selection,encapsulation and processing techniques,environment,and peptides chemistry on peptide-based emulsions to attain high efficacy.

关键词： Peptides Emulsions Physicochemical properties Bioactivity Gastrointestinal stability

Diversity of bacteria in different life stages and their impact on the development and reproduction of Zeugodacus tau(Diptera:Tephritidae)

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Insect Science 2021年第2期28卷 363-376页

作者： MdShibly noman Ge Shi Li-Jun Liu Zhi-Hong Li Department of Entomology MOA Key Lab of Pest Monitoring and Green ManagementCollege of Plant ProtectionChina Agricultural UniversityBeijingChina

Fruit flies usually harbor diverse communities of bacteria in their digestive systems,which are known to play a significant role in their fitness.However,little information is available on Zeugodacus tau,a polyphagous pest worldwide.This study reports the first extensive analysis of bacterial communities in different life stages and their effect on the development and reproduction of laboratory-reared Z tan.Cultured bacteria were identified using the conventional method and all bacteria were identified by highthroughput technologies(16S ribosomal RNA gene sequencing of V3-V4 region).A total of six bacterial phyla were identified in larvae,pupae,and male and female adult flies,which were distributed into 14 classes,32 orders,58 families and 96 genera.Proteobacteria was the most represented phylum in all the stages except larvae.Enterobacter,Klebsiella,Providencia,and Pseudomonas were identified by conventional and next-generation sequencing analysis in both male and female adult flies,and Enterobacter was found to be the main genus.After being fed with antibiotics from the first instar larvae,bacterial diversity changed markedly in the adult stage.Untreated flies laid eggs and needed 20 days before oviposition while the treated flies showed ovary development inhibited and were not able to lay eggs,probably due to the alteration of the microbiota.These findings provide the cornerstone for unexplored research on bacterial function in Z tau,which will help to develop an environmentally friendly management technique for this kind of harmful insect.

关键词： antibiotics bacterial diversity fitness oviposition symbiotic bacteria Zeugodacus tau

Physicochemical,rheological and antioxidant profiling of yogurt prepared from non-enzymatically and enzymatically hydrolyzed potato powder under refrigeration

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Food Science and Human Wellness 2023年第1期12卷 69-78页

作者： Ishtiaq Ahmad Zhouyi Xiong Hanguo Xiong Rana Muhammad Aadil Nauman Khalid allah Bakash Jvaid Lakhoo Zia-ud-din Asad Nawaz noman Walayat Rao Sanaullah Khan College of Food Science and Technology Huazhong Agricultural UniversityWuhan 430070China Fisheries Research Institute Wuhan Academy of Agricultural SciencesWuhan 430207China National Institute of Food Science and Technology University of AgricultureFaisalabad 38000Pakistan Schools of Food and Agricultural Sciences University of Management and TechnologyLahore 54000Pakistan College of Food Science and Technology Zhejiang University of TechnologyHangzhou 310014China

Evidences show that the storage period greatly affects the quality of yogurt.In this study,three types of yogurt:control yogurt(CY),non-hydrolyzed potato powder yogurt(PPY)and enzymatically hydrolyzed potato powder yogurt(EHPPY)were prepared at 42℃ for 5 h and stored for 28 days at 4℃.The yogurts were evaluated for quality characteristics at different storage periods.Negligible differences in pH values,titratable acidities and viable counts were detected in all three types of yogurt during storage.However,compared to other yogurts,EHPPY exhibited desirable water holding capacity,throughout the storage period.Apart from this,sensory properties and antioxidant activities(2-diphenyl-1-picryl-hydrazyl(DPPH)free radical scavenging activity and ferric reducing antioxidant power(FRAP))of EHPPY were also significantly improved during the storage period.Furthermore,the storage(G’)and loss(G”)modulus of PPY,EHPPY were lower than CY at 4℃ while a hysteresis loop was shown by all yogurts at the temperature range of 4-50℃ indicating higher G’(elasticity)than G”(viscosity).Based on our findings,EHPP could be an important functional ingredient in improving the quality and storage stability of yogurt for its production at an industrial level.

关键词： Storage stability Potato powder Yogurt Rheology Microbiology Antioxidant activity

Kuroshio intrusion drives the Trichodesmium assemblage and shapes the phytoplankton community during spring in the East China Sea

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Oceanology and Limnology 2021年第2期39卷 536-549页

作者： Jiaqi YUE Md Abu noman Jun SUN Tianjin Key Laboratory of Marine Resources and Chemistry Tianjin University of Science and TechnologyTianjin 300457China Research Centre for Indian Ocean Ecosystem Tianjin University of Science and TechnologyTianjin 300457China

To understand the influence of Kuroshio intrusion on the phytoplankton community,a field investigation was conducted in spring 2017 in the East China Sea(ECS),and 130 seawater samples were collected and analyzed.Trichodesmium comprised the highest cell abundance contributing about 66%of the total phytoplankton followed by diatoms(17%)and dinoflagellates(16%).The dominance of the Kuroshio Waters(KW)and the Taiwan Warm Currents(TWC)were higher than the Coastal Waters(CW).The vertical distribution of physicochemical parameters depicted the intrusion of KW at the bottom layer,but it failed to reach the surface as strong upwelling was not initiated.Therefore,the dissolved inorganic phosphate(DIP)concentrations and P/N ratios were the lowest in the CW and the upper water layers,which limited the diatom growth in this area.Besides,the dinoflagellates cell abundance was also lower except in the surface and CW,though they comprised the maximum richness of species among the phytoplankton community.However,the unique characteristics such as diazotrophy and gas vacuoles of Trichodesmium made the situation advantageous,and they comprised the maximum cell abundance in this area especially in KW and the TWC.Temperature,DIP and P/N ratios appeared to be the major environmental drivers for Trichodesmium proliferation in the ECS during the study period.

关键词： Kuroshio waters phytoplankton Trichodesmium community shift

CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第11期47卷 1751-1773页

作者： Muhammad Munsif Fath U Min Ullah Samee Ullah Khan noman Khan Sung Wook Baik Department of Software Convergence Sejong UniversitySeoul143-747Korea Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul143-747Korea

Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid.

关键词： Solar energy forecasting renewable energy systems photovoltaic generation forecasting time series data transformer models deep learning machine learning

Electrical control strategy for an ocean energy conversion system

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Protection and Control of Modern Power Systems 2021年第1期6卷 160-177页

作者： Muhammad noman Guojie Li Keyou Wang Bei Han Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina

Globally abundant wave energy for power generation attracts ever increasing attention. Because of non-linear dynamics and potential uncertainties in ocean energy conversion systems, generation productivity needs to be increased by applying robust control algorithms. This paper focuses on control strategies for a small ocean energy conversion system based on a direct driven permanent magnet synchronous generator (PMSG). It evaluates the performance of two kinds of control strategies, i.e., traditional field-oriented control (FOC) and robust adaptive control. The proposed adaptive control successfully achieves maximum velocity and stable power production, with reduced speed tracking error and system response time. The adaptive control also guarantees global system stability and its superiority over FOC by using a non-linear back-stepping control technique offering a better optimization solution. The robustness of the ocean energy conversion system is further enhanced by investigating the Lyapunov method and the use of a DC-DC boost converter. To overcome system complexity, turbine-generator based power take-off (PTO) is considered. A Matlab/Simulink study verifies the advantages of a non-linear control strategy for an Oscillating Water Column (OWC) based power generation system.

关键词： OWC Ocean wave power generation Robust adaptive control FOC PMSG Wells turbine