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8th World Conference on Photovoltaic Energy Conversion; 1094-1098, In this paper, a novel embedded software package, called RoboPV, is introduced for autonomous aerial monitoring of large-scale photovoltaic (PV) plants. RoboPV enables aerial robots to automatically perform aerial monitoring of PV plants, from optimal trajectory planning to real-time fault detection using deep neural network models. This software package is made of four systematically integrated parts: boundary detection, path planning, dynamic processing, and fault detection. The first requirement of efficient path planning is to identify the PV plant boundary closed curve. This requirement is prepared by an encoder-decoder deep learning architecture which is trained by labeled real flight aerial images of PV plants. Subsequently, a novel path planning algorithm is conducted by RoboPV to design an optimal flight path with full coverage of whole regions of the PV plant. Another deep neural network model has been also trained for real-time fault detection in PV plants through PV faults labeled dataset. Using this highly precise trained neural network, aerial images are analyzed in real-time during the flight and when a probable fault is detected, RoboPV commands the aerial robot to maneuver and concentrate more on the detected fault. In this study, several decision-making and maneuver algorithms are developed for various real-world flight conditions to improve the performance of RoboPV during an autonomous aerial inspection. RoboPV is a modular processing library that can be installed on any micro-computer processor with a low computational burden. Moreover, supporting the MAVLink communication protocol enables RoboPV to connect with an intelligent Pixhawk flight autopilot and navigate a wide range of multi-rotors. To demonstrate the performance of RoboPV, a six degrees of freedom dynamic model of a multi-rotor has been developed in a Simulink environment, and processor in the loop (PIL) tests have been performed for aerial monitoring of two different real megawatt-scale PV plants.

38th European Photovoltaic Solar Energy Conference and Exhibition; 86-88, Luminescent solar concentrator (LSC) devices have been in development for nearly fifty years, usually aiming at enhanced combinations of luminophores and photovoltaic solar cells. In this research, the focus is on the development of new geometric designs of LSC PV devices to improve the performance. The research has been executed by LSC device ray-tracing simulation using software LightTools for LSC PV configurations with a PMMA lightguide doped with Lumogen Red 305 dye. The set of LSC models evaluated have different sizes, different dye concentrations and silicon PV cells on the backside and along all four edges. The simulation results show that with changes in size from 10 to 1,000 mm and dye concentration from 5 to 100 PPM, a 5 mm thick LSC planar light guide can have a total optical efficiency in the range of 77% to 90%. Moreover, a curved LSC lightguide with a dye concentration of 100 PPM also provides a good performance with provided 81% optical efficiency at the backside.

38th European Photovoltaic Solar Energy Conference and Exhibition; 32-36, This study presents a novel configuration for luminescent solar concentrator photovoltaic (LSC-PV) devices using a mixture of multiple organic luminophores namely, Perylene Green, Lumogen Red 305, and Lumogen Orange 240. This LSC-PV device includes a cubical lightguide with a dimension of 10× 10×10 mm3 and made of poly (methyl methacrylate) (PMMA), containing a mixture of two or three perylene dyes. The mono-crystalline silicon bifacial solar cells (mono c-Si) are mounted on the edges and bottom sides of this device. This study evaluates the optical efficiency (OE) and power conversion efficiency (PCE) of four different configurations of LSC-PV devices with variations of mixed luminescent dyes in order to obtain the highest performance and optimal structure for the combination of dyes. For this purpose, a Monte Carlo ray tracing simulation is conducted in LightTools software to explore the effect of multiple luminescent dyes on the optical performance of LSC-PV devices and analyse the incident irradiance on the front and rear sides of edges- and bottom-mounted bifacial solar cells. The comparison results show that the highest theoretical optical efficiency is 88.2% under standard test conditions (STC) for an LSC-PV device containing a mixture of Lumogen Orange 240 and Perylene Green dye at 10 ppm. The theoretical power conversion efficiency is then 17.7%.

37th European Photovoltaic Solar Energy Conference and Exhibition; 1580-1583, Aerial infrared thermography (aIRT) has already proven to be a fast and effective method for detecting and classifying faults in photovoltaic (PV) systems. This paper aims at summarizing the key findings of a flight campaign carried out in Brazil over three multicrystalline silicon PV power plants. It reports common defects found in the various plants showing UAV-captured image data and correlation between visible and IR sensors. The most common defect detected were disconnected substrings, indicating module fabrication defects. The second most common fault was disconnected strings, followed by modules with broken front glass and other types of hot-spots. The results indicate the need for a more detailed quality analysis of the PV modules that are being installed at power plants in Brazil, and also demonstrate the viability of using aIRT as a method for inspection and monitoring of PV power plants.

37th European Photovoltaic Solar Energy Conference and Exhibition; 1374-1377, This paper presents a novel monitoring system called Autonomous Monitoring and Analysis which aims to facilitate the procedures of data acquisition, pre-processing, post-processing in order to provide accurate and real-time information about the operational status and performance of PV systems. The main objective of this research is to integrate various monitoring systems and platforms to diagnose the failures as fast as possible and also predict the service life of PV systems. This system consists of four main parts namely, Unmanned Aerial Vehicle (UAV)-based monitoring system, smart monitoring system, Internet of Thing (IoT) and Big Data Analytics (BDA) platforms. Preliminary results demonstrate this Autonomous Monitoring and Analysis system is able to detect the Line-Line faults in PV arrays with an accuracy of 97% and also predict the output power of PV systems with an accuracy of 88%.

Maryam Hamzeloo-Moghadam,1 Mahmoud Aghaei,2 Mohammad Hossein Abdolmohammadi,3 Amir Khalaj,1,4 Faranak Fallahian3 1Traditional Medicine and Materia Medica Research Center and Department of Traditional Pharmacy, School of Traditional Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; 2Department of Clinical Biochemistry, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran; 3Cellular and Molecular Research Center, Qom University of Medical Sciences, Qom, Iran; 4Food and Drug Laboratory Research Center, Food and Drug Organization, Ministry of Health and Medical Education, Tehran, Iran Background: Drimia maritima (D.maritima) is a plant belonging to the family Asparagaceae, which has been used for the treatment of several ailments including cancer around the world. Toour knowledge, there is no comprehensive study about the molecular mechanisms of anticancer activity of this plant, yet.Materials and methods: In the current study, cell viability, apoptosis induction, ROS production, mitochondrial apoptotic pathway, and ER stress mediators have been evaluated in breast cancer cells, MCF7, and MDA-MB-468 treated with D.maritima.Results: Significant cytotoxic effects were observed in MCF-7 and MDA-MB-468 cells after exposure to D.maritima. Apoptosis induction was determined using Annexin-V-FITC and propidium iodide staining. Furthermore, an increase of ROS, loss of mitochondrial membrane potential, the release of cytochrome c, activation of caspases, and elevation in the Bax/Bcl-2 ratio was determined. D.maritima dose-dependently increased the mRNA expression of ER stress markers such as CHOP, ATF-4, GADD34, and TRIB3 in MCF-7, and MDA-MB-468 cells.Conclusion: These data suggest that D.maritima induces apoptosis in human breast cancer cells via the mitochondrial-mediated pathway. In addition, endoplasmic reticulum stress seems to be involved in D.maritima-induced cell death. Keywords: Drimia maritima, ROS, apoptosis, mitochondria, breast cancer, ER stress&nbsp

Background: In many of past studies, the role of God as an attachment figure in reducing psychopathological symptoms has strongly been confirmed. In an effort to account for the effectiveness of attachment to God in mitigating psychopathological symptoms in healthy people we came upon self-compassion as a potential mediating variable in this process. Hence, in the current research, we studied this relation in Iranian Muslim women diagnosed with breast cancer. Methods: A total of 360 Muslim women diagnosed with breast cancer were asked to fill the Attachment to God Inventory, Self-Compassion Scale, and Depression, Anxiety, and Stress Scale. Data were analyzed using path analysis method with AMOS 22. Results: The anxiety dimension of attachment to God was significantly correlated with the severity of anxiety, but the avoidance dimension had no direct effect on any of the symptoms. Also, the anxiety dimension was found to be positively correlated with depression and stress indirectly via self-compassion. However, in the case of the avoidance dimension, no such relationship was observed. As a result, attachment anxiety causes a decrease in self-compassion in this group of women, and this, in turn, results in more severe psychopathological symptoms like anxiety, stress, and depression. Conclusions: Considering the results of this study, we conclude that improvements in the mental health of Muslim women diagnosed with breast cancer are not exclusively achieved by attachment to material symbols. Rather, attachment to God as a spiritual symbol can have a great impact on the mental health of these women. In fact, secure attachment to God can help improve mental health through positive effects on self-compassion and should be considered as a treatment in psychological interventions.

36th European Photovoltaic Solar Energy Conference and Exhibition; 1302-1307, Aerial Infrared Thermography (IRT) is a non-destructive and cost-effective method for detecting faults in large-scale photovoltaic (PV) power plants. However, the visual assessment of the images captured by aerial IRT, and the analysis of a large number of image frames is very time-consuming. This paper proposes a method for detecting and classifying faults on PV modules, through aerial IRT images, combining Digital Image Processing (DIP) and Convolutional Neural Networks (CNNs) algorithms. With the results obtained so far, the IR images acquired were successfully processed with DIP techniques to detect the faults of PV modules in the power plant that are used as samples for training the CNN. The developed neural network algorithm can detect faults on the aIRT images and classify them in three categories: disconnected substrings, hot spots, and disconnected strings. The results have demonstrated that the method is effective in detecting and classifying faults, and it is an important step for the full automation of aIRT inspection.

36th European Photovoltaic Solar Energy Conference and Exhibition; 1627-1631, Precision forecasting of Photovoltaic (PV) output power is very required for the safe performance of electricity grid and also it leads to reducing the operating expenses of PV systems. The main objective of this paper is to predict simultaneously the output power of PV systems for the next days by using previous performance data of PV systems. For this purpose, an LSTM ensemble application is proposed for prediction of the output power of PV for the next days. For validation, Australian solar PV data are used for one year in order to compare the results with advanced prediction and classification modelling. The results demonstrate that the proposed LSTM ensemble is very reliable and the accuracy of forecasting performance is higher than other single neural networks and batch models for output power prediction.

36th European Photovoltaic Solar Energy Conference and Exhibition; 1220-1223, Photovoltaic (PV) Emulators with the highest precision and fast response plays a crucial role to test and monitor PV systems performance as real-time. However, designing a precise Photovoltaic Emulator is difficult due to PV parameters are very dependent to weather and environment conditions. In addition, PV has nonlinear characteristics, which makes the PV emulator design more complicate. Moreover, converter based PV Emulators are designed with conventional controller, which has nonlinear behaviour and variation parameters in converters and it, makes PV systems instable. The main purpose of this paper is to propose a PV Emulator based on a DC-DC Buck converter using Artificial Neural Network (ANN) algorithm combined with Proportional Integral (PI) controller in order to overcome afore mentioned challenges. In addition, a mathematical model is used for emulating PV module characteristics in order to simplicity and easy implementation. This PV Emulator evaluates PV systems in various conditions including solar irradiance, temperature and load. However, results demonstrate that the proposed method is very accurate, reliable and fast for any condition.

36th European Photovoltaic Solar Energy Conference and Exhibition; 1660-1663, One of the most important issues that reduces the performance of photovoltaic (PV) modules which is due to irradiance degradation caused by dust deposition on the top surface of PV modules. It is a well-known fact that this phenomenon would decrease irradiance and the question of how much loss would happen due to dust has been a major subject among researchers. This study was conducted to determine the soiling loss for photovoltaic modules installed in Shiraz, Iran. For measurement, a set of PV modules installed with a tilt angle of 30° and short-circuited current of each module, wind speed, ambient humidity, ambient temperature, module temperature as well as solar radiation are measured continuously during the period of experiment. Results from the measurement field indicate that average daily soiling loss for eight weeks of exposure which is started since 11 December 2016 is – 4.45 %.

Design by Contract (DbC), also referred as Programming by Contract is a programming paradigm for software verification proposed by Bertrand Meyer. The idea is to put obligations for code elements such as methods, interfaces and classes to satisfy the specification of the source code. Indeed, DbC enforces a piece of code to satisfy some conditions before execution (Precondition), and to ensure some conditions after execution (Postcondition) with holding some conditions unchanged (Invariant). This settlement is called Contract which must be valid for that piece of code. According to Meyer’s paradigm, a program can work correctly if it fulfills DbC principles for each method. To empower programmers with DbC, various libraries are made to make DbC possible in coding phase each of which is applicable in a specific programming language and has some features and functionalities. However, choosing the most suitable tool for coding upon a particular purpose is considerably important for development teams with software validation deployment. This thesis aimed to experimentally evaluate and compare DbC instrumentors specially for Java and figure out that which tool had better performance. In order to accomplish such a task, a simple model system had to be designed and implemented with regard to using constraining principles of mentioned tools. The scrutiny of the extensions revealed that Open JML as a powerful framework has generated better results rather than other tools. However, the results of this research is viable for small projects deploying constraining tools.

In deep learning, visualization techniques extract the salient patterns exploited by deep networks for image classification, focusing on single images; no effort has been spent in investigating whether these patterns are systematically related to precise semantic entities over multiple images belonging to a same class, thus failing to capture the very understanding of the image class the network has realized. This paper goes in this direction, presenting a visualization framework which produces a group of clusters or summaries, each one formed by crisp salient image regions focusing on a particular part that the network has exploited with high regularity to decide for a given class. The approach is based on a sparse optimization step providing sharp image saliency masks that are clustered together by means of a semantic flow similarity measure. The summaries communicate clearly what a network has exploited of a particular image class, and this is proved through automatic image tagging and with a user study. Beyond the deep network understanding, summaries are also useful for many quantitative reasons: their number is correlated with ability of a network to classify (more summaries, better performances), and they can be used to improve the classification accuracy of a network through summary-driven specializations., Comment: Project page and code available at http://marcocarletti.altervista.org/publications/understanding-visual-summaries/