Optimizing Operation of Photovoltaic System Using Neural Network and Fuzzy Logic

It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now

The value of prebiopsy FDG-PET/CT in discriminating malignant from benign vertebral bone lesions in a predominantly oncologic population

Purpose: To determine the value of prebiopsy 18F-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET)/computed tomography (CT) in discriminating malignant from benign vertebral bone lesions. Materials and methods: This retrospective study included 53 patients with 55 vertebral bone lesions that underwent FDG-PET/CT before CT-guided biopsy. Pathologic examination of the biopsy sample and a minimum follow-up of 1 year were used as reference standard. Results: Sensitivity, specificity, positive predictive value, and negative predictive value of visual FDG-PET analysis (with lesion FDG uptake higher than liver FDG uptake as threshold for malignancy) in discriminating malignant from benign vertebral bone lesions were 91.3% (42/46), 22.2% (2/9), 85.7% (42/49), and 33.3% (2/6), respectively. The semiquantitative FDG-PET metrics SUVmax and SUVpeak achieved areas under the receiver operating characteristics curve of 0.630 and 0.671, respectively. Malignant lesions demonstrated bone lysis more frequently than benign lesions (60.9% (28/46) vs. 22.2% (2/9)), and this difference was nearly significant (P = 0.064). All other clinical and conventional imaging characteristics (including patient age, gender, previous diagnosis of malignancy, bone pain, weight loss, any CT abnormality, sclerosis, cortical destruction, bone marrow replacement, associated extraosseous soft tissue mass, and accompanying vertebral height loss, multiple bone lesions on FDG-PET/CT, and suspicious extraosseous lesions on FDG-PET/CT) were not significantly different (P = 0.143 to 1.000). Conclusion: FDG-PET/CT may steer the diagnosis (particularly thanks to a relatively high PPV and value of semiquantitative measurements), but cannot always classify vertebral bone lesions as malignant or benign with sufficient certainty. In these cases, biopsy and/or follow-up remain necessary to establish a final diagnosis

Value of 18F-FES-PET to solve clinical dilemmas in breast cancer patients:a retrospective study

Background: Breast cancer (BC) is a heterogeneous disease, in which estrogen receptor (ER) expression plays an important role in the majority of breast tumors. A clinical dilemma may arise when a metastasis biopsy to determine the ER status cannot be performed safely or when ER heterogeneity is suspected between tumor lesions. Whole-body ER imaging, such as 16α-18F-fluoro-17β-estradiol (18F-FES) positron emission tomography (PET), may have added value in these situations. However, the role of this imaging technique in routine clinical practice remains to be further determined. Therefore, we assessed the value of 18F-FES-PET by evaluating if the physician's clinical dilemma that remained after standard workup was solved by the 18F-FES-PET scan. Methods: In this retrospective study, 18F-FES-PET scans, performed in patients with (suspected) ER+ metastatic BC with remaining clinical dilemma after standard workup, at the University Medical Center of Groningen between November 2009 and January 2019, were included. We investigated whether the physician's clinical dilemma was solved, defined as 1) 18F-FES-PET provided a solution for the clinical dilemma, and/or 2) a treatment decision was based directly on the 18F-FES-PET. In addition, category of clinical dilemma, and rate of 18F-FES positive or negative PET scans were reported, and related to frequency of solved dilemmas. Results: One hundred 18F-FES-PET scans were performed in 83 patients. Clinical dilemma categories were: 1) inability to determine extent of (suspected) metastatic disease with standard workup (n = 52), 2) unclear ER status of the tumor (n = 31), and 3) inability to determine which primary tumor caused metastases (n = 17). Dilemmas were solved by 18F-FES-PET in 87/100 cases (87%). In 81/87 cases a treatment decision was made based directly on the 18F-FES-PET (treatment change: n = 51 cases; continuance: n = 30 cases). The frequency of solved dilemmas was not related to the clinical dilemma category (P = 0.334). However, the frequency of solved dilemmas was related to whether scans were 18F-FES positive (n = 63) or negative (n = 37; p<0.001). Conclusion: For various indications, the 18F-FES-PET scan can help to solve the vast majority of clinical dilemmas that may remain after standard workup. Therefore, the 18F-FES-PET scan has added value in BC patients presenting with a clinical dilemma

Analyzing the Estrogen Receptor Status of Liver Metastases with [F-18]-FES-PET in Patients with Breast Cancer

Background: Positron emission tomography (PET) with 16α-[18F]-fluoro-17β-estradiol ([18F]-FES) can visualize estrogen receptor (ER) expression, but it is challenging to determine the ER status of liver metastases, due to high physiological [18F]-FES uptake. We evaluated whether [18F]-FES-PET can be used to determine the ER status of liver metastases, using corresponding liver biopsies as the gold standard. Methods: Patients with metastatic breast cancer (n = 23) were included if they had undergone a [18F]-FES-PET, liver metastasis biopsy, CT-scan, and [18F]-FDG-PET. [18F]-FES-PET scans were assessed by visual and quantitative analysis, tracer uptake was correlated with ER expression measured by immunohistochemical staining and the effects of region-of-interest size and background correction were determined. Results: Visual analysis allowed ER assessment of liver metastases with 100% specificity and 18% sensitivity. Quantitative analysis improved the sensitivity. Reduction of the region-of-interest size did not further improve the results, but background correction improved ER assessment, resulting in 83% specificity and 77% sensitivity. Using separate thresholds for ER+ and ER− metastases, positive and negative predictive values of 100% and 75%, respectively, could be obtained, although 30% of metastases remained inconclusive. Conclusion: In the majority of liver metastases, ER status can be determined with [18F]-FES-PET if background correction and separate thresholds are applied

Optimizing the operation of a photovoltaic generator by a genetically tuned fuzzy controller

This paper presents design and application of advanced control scheme which integrates fuzzy logic concepts and genetic algorithms to track the maximum power point in photovoltaic system. The parameters of adopted fuzzy logic controller are optimized using genetic algorithm with innovative tuning procedures. The synthesized genetic algorithm which optimizes fuzzy logic controller is implemented and tested to achieve a precise control of the maximum power point response of the photovoltaic generator. The performance of the adopted control strategy is examined through a series of simulation experiments which prove good tracking properties and fast response to changes of different meteorological conditions such as isolation or temperature

Optimizing Operation of Photovoltaic System Using Neural Network and Fuzzy Logic

It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now

Optimizing Operation of Photovoltaic System Using Neural Network and Fuzzy Logic

It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now

Optimizing Operation of Photovoltaic System Using Neural Network and Fuzzy Logic

It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now

SISO modelling of a web server to be controlled by a feedback control scheme

Performance modeling is an important topic in overload control for web servers. Several attempts have been made to create performance models for web servers. The paper describes modeling a Web server to be controlled by Feedback control scheme. Feedback command theory was initially used to control of industrial processes. Its use for the control of performance software is recent. It provides a number of mathematical tools which can be used to analyze the stability of the commanded system and find the best adjustment that responds to the performance criteria. Our approach proceeds in two steps: system identification and controller design. In system identification, we construct mathematical models of the target system in forms of discrete transfer function focused on single-input single-output (SISO) systems. The role of controller is to modify the transfer function of the target system with regard to the control error between the reference value and the output valu