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نتیجه جستجو - عادی سازی

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 Application of AI for Frequency Normalization of Solar PV-Thermal Electrical Power System
کاربرد هوش مصنوعی برای عادی سازی فرکانس سیستم برق الکتریکی حرارتی خورشیدی PV-2020
Grid-connected solar-PV schemes have become a significant part of the energy balance in the power system to satisfy the growing request for clean, affordable energy. This study attempts to link solar-PV generation with conventional thermal power plants and to integrate the control zone resulting in a hybrid solar PV-thermal electric power system using an AC tie line. An analysis of the frequency dynamics for varying load conditions of the interconnected system is studied. Diverse approaches of proportional, integral, and proportional-integral fuzzy logic built controllers are design and tested in order to match the electric power with variable loads of the system and hence to normalize the frequency ofthe system in shortest possible time. A comparative analysis of the design topologies is conducted out for the PV-Thermal scheme. Results obtain from the implementation are shown to justify the performance of proposed control efforts, using MATLAB software tool.
Keywords: Solar PV-Thermal electrical power system | frequency dynamics | Proportional| Integral | FLPI control
مقاله انگلیسی
2 Detection of flood disaster system based on IoT, big data and convolutional deep neural network
تشخیص سیستم بحرانی سیل بر اساس اینترنت اشیا، داده های بزرگ و شبکه عصبی عمیق پیچشی-2020
Natural disasters could be defined as a blend of natural risks and vulnerabilities. Each year, natural as well as human-instigated disasters, bring about infrastructural damages, distresses, revenue losses, injuries in addition to huge death roll. Researchers around the globe are trying to find a unique solution to gather, store and analyse Big Data (BD) in order to predict results related to flood based prediction system. This paper has proposed the ideas and methods for the detection of flood disaster based on IoT, BD, and convolutional deep neural network (CDNN) to overcome such difficulties. First, the input data is taken from the flood BD. Next, the repeated data are reduced by using HDFS map-reduce (). After removal of repeated data, the data are pre-processed using missing value imputation and normalization function. Then, centred on the pre-processed data, the rule is generated by using a combination of attributes method. At the last stage, the generated rules are provided as the input to the CDNN classifier which classifies them as a) chances for the occurrence of flood and b) no chances for the occurrence of a flood. The outcomes obtained from the proposed CDNN method is compared parameters like Sensitivity, Specificity, Accuracy, Precision, Recall and F-score. Moreover, when the outcomes is compared other existing algorithms like Artificial Neural Network (ANN) & Deep Learning Neural Network (DNN), the proposed system gives is very accurate result than other methods.
Keywords: Hadoop distributed file system (HDFS) | Convolutional deep neural network (CDNN) | Normalization | Rule generation | Missing value imputation
مقاله انگلیسی
3 A framework for distributed data mining heterogeneous classifier
چارچوبی برای طبقه بندی ناهمگن داده کاوی توزیع شده-2019
Distributed Data Mining (DDM) emerged as a huge area by the tremendous growth of geographically distributed data and powerful computational capability of computing. In this, ENcryption, NORMalization, MApping (ENORMA), a privacy preserving heterogeneous classifier framework for universal DDM is proposed. Three algorithms are proposed for maintaining data privacy, retrieval and integration on DDM. For data privacy, privacy-preserving algorithm is designed for protection of data in both the levels; for data retrieval, an algorithm is developed for value normalization and for integration, Mapping algorithm is developed to map the data with schema in global level. Experimental implementation on Electronic Health Records (EHRs), Job Recruitment Records (JRRs) and Agriculture Weather Forecast Records (AWFRs) datasets shows an improved result compared to conventional frameworks.
Keywords: Distributed data mining framework | Heterogeneous datasites | Privacy-preserving | Data normalization | Data integration
مقاله انگلیسی
4 A new machine learning technique for an accurate diagnosis of coronary artery disease
یک روش جدید یادگیری ماشین برای تشخیص دقیق بیماری عروق کرونر-2019
Background and objective: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients. Methods: We first tested ten traditional machine learning algorithms, and then the three-best perform- ing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features. Results: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field. Conclusions: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.
Keywords: Coronary artery disease (CAD) | Machine learning | Normalization | Genetic algorithm | Particle swarm optimization | Feature selection | Classification
مقاله انگلیسی
5 Unsupervised extraction of patterns and trends within highway systems condition attributes data
استخراج بدون نظارت الگوها و روندها در داده های ویژگی های شرایط سیستم های بزرگراه -2019
Highway agencies combine expert opinions and basic regression modeling techniques to process vast amounts of time series condition attributes data to define highway network health. The health rating exhibit high variability and lack adequate detail for executive-level maintenance planning and resource allocation. This paper presents a new methodology for data abstraction, analysis, and clustering for pattern recognition of highway network health. The methodology describes mathematical and statistical data abstraction algorithms for data preprocessing (smoothening (unweighted moving average), scaling (normalization), and weights derivation (entropy) to compute a composite health index (CHI)), and salient features extraction. Data analysis involved cluster analysis to identify patterns in asset current health and future outlook. The outcome is a characterization of highway network health for executive-level decision making. The algorithms included in this methodology have been successfully applied in the fields of biology, finance, econometrics, bioinformatics, marketing, and social science for pattern recognition. The accuracy of the new methodology is illustrated with an experiment using 463 in-service pavement assets and internal/external metrics (including the degree to which methodology performance classification outcomes conform to national expert opinion). The results from the experiment confirm an accurate and computationally inexpensive methodology, which provides outcomes that compare to real-world pavement condition rating metrics.
Keywords: Highway | Composite health | Future outlook | Data abstraction | Cluster analysis | Normalization | Entropy | Time series
مقاله انگلیسی
6 Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study
افزایش شبکه های نرم افزاری بصورت جمع شده با استفاده از واحدهای خطی یکسو کننده جابجا شده: یک مطالعه مقایسه ای سیستماتیک-2019
A substantial number of expert and intelligent systems rely on deep learning methods to solve problems in areas such as economics, physics, and medicine. Improving the accuracy of the activation functions used by such methods can directly and positively impact the overall performance and quality of the mentioned systems at no cost whatsoever. In this sense, enhancing the design of such theoretical fun- damental blocks is of great significance as it immediately impacts a broad range of current and future real-world deep learning based applications. Therefore, in this paper, we turn our attention to the inter- working between the activation functions and the batch normalization, which is practically a mandatory technique to train deep networks currently. We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of us- ing distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models. These Convo- lutional Neural Networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learn- ing computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments ( p < 0.05) showed DReLU enhanced the test accu- racy presented by ReLU in all scenarios. Furthermore, DReLU showed better test accuracy than any other tested activation function in all experiments with one exception, in which case it presented the second best performance. Therefore, this work demonstrates that it is possible to increase performance replacing ReLU by an enhanced activation function.
Keywords: DReLU | Activation function | Batch normalization | Comparative study | Convolutional Neural Networks | Deep learning
مقاله انگلیسی
7 Using multivariate statistics to assess ecotourism potential of water-bodies: A case-study in Mauritania
استفاده از آمارهای چند متغیره برای بررسی پتانسیل گردشگری بومی بدنه های آبی: یک مطالعه موردی در موریتانی-2018
Evaluating the ecotourism potential of sites is a key issue in tourism management. Multiple methodologies have been developed to assess the ecotourism potential of sites. However, there are many constraints affecting their quality. Methodologies independent of subjective criteria and weights are lacking, compromising following interpretations on where to allocate efforts for ecotourism development. We propose a new approach to circumvent these issues that combines independent statistical procedures to assess ecotourism potential. By combining multi-criteria with ordination and clustering algorithms, this two-stage statistical approach allowed identifying suitable water-bodies for ecotourism development in Mauritania and independently assessed which features are related with ecotourism potential. The method was able to group sites for different ecotourist demands, which has implications for policy makers and tourism planners trying to optimize investments while protecting biodiversity and supporting communities. We provide a framework that is scalable and applicable by stakeholders operating in ecotourism planning and management worldwide.
keywords: Tourism planning |Deserts |Multi-criteria approach |Ordination methods |Clustering algorithms |Low-income countries |Wetlands
مقاله انگلیسی
8 Mining and municipal finance in Kathu, an open mining town in South Africa
معدنکاری و امور مالی شهرداری در Kathu، معدنکاری باز در جنوب آفریقا-2018
Worldwide, normalising mining towns or making them into open towns has become conventional wisdom. The local governments of these towns consequently have to take over the management of municipal finance from the mining companies and cope with decentralised planning. Kathu, in the Gamagara Local Municipality in South Africa, serves as one example of this. This municipality has found it difficult to plan for the risky economic conditions arising from China’s demand for iron ore. Local planning and municipal finance have been put under pressure and the mining companies continue to shoulder these responsibilities − albeit indirectly. The benefits associated with the “new natural resources agenda” do not accrue automatically, and often, as our case study shows, municipalities plan on the basis of inaccurately predicted municipal income. The mines actively support normalisation because it minimises their own long-term risks. We argue that the South African government does not do enough to assist mining towns.
Keywords: Mining policy ، Mining town ، Municipal finance ، Normalisation ، Open town
مقاله انگلیسی
9 تحلیل عملکرد پروتکل مسیریابی AODV با و بدون حملات مخرب در شبکه های ادهاک سیار
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 15
شبکه Adhoc سیار مجموعه ای از گره های مختلف سیار است که آن را تغییر می دهد و خود آن را بر روی شبکه تنظیم میکند. در MANET، بردار مسافت در تقاضا (AODV) Adhoc بسته ها را برای پیدا کردن مسیر بصورت فزاینده ای به کار میبرد. در پروتکل مسیریابی (AODV) بردار در تقاضای Adhoc برای MANET (شبکه های Adhoc سیار)، گره های مخرب به دلیل محدودیت های ذاتی به آسانی ارتباطات را مختل می کنند. در این مقاله، عملکرد پروتکل مسیریابی AODV با ملات مخرب و بدون حملات مخرب آنالیز می شود. یک گره مخرب موجب اختلال در محدوده می شود و شبکه با بسته های کنترلی اشتباه از هم گسیخته می شود. گره مخرب بر کل شبکه اثرگذار است چرا که پهنای باند بیشتری را مصرف می کند و بسته-ها رها می شوند که به نوبه خود موجب کاهش عملکرد پروتکل مسیریابی AODV می شوند. عملکرد تحت پارامترهای مختلفی همچون توان عملیاتی، نسبت تحویل بسته، بسته های رها شده و بار مسیریابی عادی سازی شده انجام می شود.
کلمات کلیدی: MANET | AODV | توان عملیاتی | نسبت تحویل بسته | بسته های رها شده | بار مسیریابی عادی سازی شده | NS2.
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