scholarly journals Effect of Environmental Conditions and Training Algorithms on the Efficiency of a NARX Based Approach to Predict PV Panel Power Output

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Natalie V. Klinard ◽  
Edmund A. Halfyard ◽  
Jordan K. Matley ◽  
Aaron T. Fisk ◽  
Timothy B. Johnson

Abstract Background Acoustic telemetry is an increasingly common method used to address ecological questions about the movement, behaviour, and survival of freshwater and marine organisms. The variable performance of acoustic telemetry equipment and ability of receivers to detect signals from transmitters have been well studied in marine and coral reef environments to inform study design and improve data interpretation. Despite the growing use of acoustic telemetry in large, deep, freshwater systems, detection efficiency and range, particularly in relation to environmental variation, are poorly understood. We used an array of 90 69-kHz acoustic receivers and 8 sentinel range transmitters of varying power output deployed at different depths and locations approximately 100–9500 m apart for 215 days to evaluate how the detection efficiency of acoustic receivers varied spatially and temporally in relation to environmental conditions. Results The maximum distance that tags were detected ranged from 5.9 to 9.3 km. Shallow tags consistently had lower detection efficiency than deep tags of the same power output and detection efficiency declined through the winter months (December–February) of the study. In addition to the distance between tag and receiver, thermocline strength, surface water velocity, ice thickness, water temperature, depth range between tag and receiver, and number of fish detections contributed to explaining variation in detection efficiency throughout the study period. Furthermore, the most significant models incorporated interactions between several environmental variables and tag–receiver distance, demonstrating the complex temporal and spatial relationships that exist in heterogeneous environments. Conclusions Relying on individual environmental variables in isolation to interpret receiver performance, and thus animal behaviour, may be erroneous when detection efficiency varies across distances, depths, or tag types. As acoustic telemetry becomes more widely used to study ecology and inform management, it is crucial to understand its limitations in heterogeneous environments, such as freshwater lakes, to improve the quality and interpretation of data. We recommend that in situ range testing and retrospective analysis of detection efficiency be incorporated into study design for telemetry projects. Furthermore, we caution against oversimplifying the dynamic relationship between detection efficiency and environmental conditions for the sake of producing a correction that can be applied directly to detection data of tagged animals when the intended correction may not be justified.


Author(s):  
Mohamad Modrek ◽  
Ali Al-Alili

Photovoltaic thermal collectors (PVT) combines technologies of photovoltaic panels and solar thermal collectors into a hybrid system by attaching an absorber to the back surface of a PV panel. PVT collectors have gained a lot of attention recently due to the high energy output per unit area compared to a standalone system of PV panels and solar thermal collectors. In this study, performance of a liquid cooled flat PVT collector under the climatic conditions of Abu Dhabi, United Arab Emirates was experimentally investigated. The electrical performances of the PVT collector was compared to that of a standalone PV panel. Moreover, effect of sand accumulation on performance of PVT collectors was examined. Additionally, effect of mass flow rate on thermal and electrical output of PVT collector was studied. Electrical power output is slightly affected by changes in mass flow rate. However, thermal energy increased by 22% with increasing flow rate. Electrical power output of a PV panel was found to be 38% lower compared to electrical output of PVT collectors. Dust accumulation on PVT surface reduced electrical power output up to 7% compared with a reference PVT collector.


Data ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 4 ◽  
Author(s):  
Viacheslav Moskalenko ◽  
Alona Moskalenko ◽  
Artem Korobov ◽  
Viktor Semashko

Trainable visual navigation systems based on deep learning demonstrate potential for robustness of onboard camera parameters and challenging environment. However, a deep model requires substantial computational resources and large labelled training sets for successful training. Implementation of the autonomous navigation and training-based fast adaptation to the new environment for a compact drone is a complicated task. The article describes an original model and training algorithms adapted to the limited volume of labelled training set and constrained computational resource. This model consists of a convolutional neural network for visual feature extraction, extreme-learning machine for estimating the position displacement and boosted information-extreme classifier for obstacle prediction. To perform unsupervised training of the convolution filters with a growing sparse-coding neural gas algorithm, supervised learning algorithms to construct the decision rules with simulated annealing search algorithm used for finetuning are proposed. The use of complex criterion for parameter optimization of the feature extractor model is considered. The resulting approach performs better trajectory reconstruction than the well-known ORB-SLAM. In particular, for sequence 7 from the KITTI dataset, the translation error is reduced by nearly 65.6% under the frame rate 10 frame per second. Besides, testing on the independent TUM sequence shot outdoors produces a translation error not exceeding 6% and a rotation error not exceeding 3.68 degrees per 100 m. Testing was carried out on the Raspberry Pi 3+ single-board computer.


Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2761
Author(s):  
Vaios Ampelakiotis ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis ◽  
George Tsihrintzis

In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.


Author(s):  
Mervin Chandrapal ◽  
XiaoQi Chen ◽  
WenHui Wang ◽  
Benjamin Stanke ◽  
Nicolas Le Pape

AbstractAlthough surface electromyography (sEMG) has a high correlation to muscle force, an accurate model that can estimate joint torque from sEMG is still elusive. Artificial neural networks (NN), renowned as universal approximators, have been employed to capture this complex nonlinear relation. This work focuses on investigating possible improvements to the NN methodology and algorithm that would consistently produce reliable sEMG-to-knee-joint torque mapping for any individual. This includes improvements in number of inputs, data normalization techniques, NN architecture and training algorithms. Data (sEMG) from five knee extensor and flexor muscle from one subject were recorded on 10 random days over a period of 3 weeks whilst subject performed both isometric and isokinetic movements. The results indicate that incorporating more muscles into the NN and normalizing the data at each isometric angle prior to NN training improves torque estimation. The mean lowest estimation error achieved for isometric motion was 10.461% (1.792), whereas the lowest estimation errors for isokinetic motion were larger than 20%.


2019 ◽  
Vol 2 (2) ◽  
pp. 101-106
Author(s):  
Annisa Nur Ichniarsyah ◽  
Heny Agustin ◽  
Maulidian Maulidian

Abstract: urban farming means to cultivate and nurture animals in a city or within its rural area. There are variety of plants that could be grown namely vegetables and fruits. Urban agriculture is expeted to help improve the economy of the people in an area because the yields produced can provide economic benefits. The Asofa Foundation tried to capture this opportunity in the context of developing a masjid-based economy through hydroponic training for the surrounding community. Therefore, a series of training was conducted to improve the capacity of the community. The training included training on seeding vegetables in rockwool, training on transplanting, and training in preparing and mixing hydroponic fertilizers. The results of the training were that residents were able to cultivate plants using hydroponics. However, the boundary was the environmental conditions (in this case water) in Bekasi which were not good enough so that the seedlings died after being transplanted into the hydroponic kit. Further training needed can be in the form of training fruit plants using hydroponics which can withstand inadequate water conditions. Another training that can be carried out is training in vegetable cultivation with planting methods other than hydroponics that are able to accommodate environmental conditions Keywords: urban farming, economic development, masjid-based economy, trainings  Abstrak: Pertanian perkotaan adalah menanam dan memelihara binatang ternak di dalam atau sekitar kota. Beragam jenis tanaman dapat dibudidayakan terutama tanaman sayuran dan buah. Pertanian perkotaan mampu membantu peningkatan ekonomi rakyat di suatu daerah karena hasil panen yang dihasilkan dapat memberikan keuntungan ekonomis. Peluang inilah yang berusaha ditangkap oleh Yayasan Asofa dalam rangka pengembangan ekonomi berbasis masjid lewat pelatihan hidroponik untuk masyarakat sekitar. Oleh karena itu, dilakukanlah serangkaian pelatihan untuk meningkatkan kemampuan masyarakatnya. Pelatihan yang dilakukan antara lain pelatihan penyemaian tanaman sayuran di rockwool, pelatihan pindah tanam, dan pelatihan meracik pupuk hidroponik. Hasil dari pelatihan tersebut adalah warga mampu melakukan budidaya tanaman dengan menggunakan hidroponik hanya saja kondisi lingkungan (dalam hal ini air) di daerah Bekasi kurang baik sehingga tanaman semaian mati setelah dipindahkan ke dalam kit hidroponik. Pelatihan lanjutan yang diperlukan dapat berupa pelatihan tanaman buah dengan menggunakan hidroponik yang tahan kondisi air yang kurang memadai. Pelatihan lain yang dapat dilakukan adalah pelatihan budidaya tanaman sayuran dengan metode tanam selain hidroponik yang mampu mengakomodasi kondisi lingkungan. Kata kunci: pertanian perkotaan, pengembangan ekonomi, ekonomi berbasis masjid, pelatihan


2021 ◽  
Vol 2 (1a) ◽  
pp. C20A02-1-C20A02-6
Author(s):  
Diatta Sène ◽  
◽  
Adama Sarr ◽  
Mouhamadou Falilou Ndiaye

Avec le coût élevé de l'électricité, les centrales électriques ne sont plus rentables et les énergies renouvelables deviennent un domaine d'étude privilégié. Cela justifie qu'au Sénégal, on constate de plus en plus la création de centrales photovoltaïques et de petites installations domestiques afin de satisfaire les besoins en électricité. Cependant, nous constatons une limitation des terrains pour répondre à nos besoins pour l'installation de ces centrales solaires PV. La question est de voir avec le peu d'espace dont nous disposons comment optimiser au maximum la quantité d'énergie pour répondre à la demande ? Comme l'inclinaison des panneaux photovoltaïques est souvent fixe et que cela ne donne pas toujours une énergie optimale, nous avons pensé aux suiveurs solaires. L'objectif de ce travail est de comparer la puissance de sortie des deux panneaux PV, avec les mêmes caractéristiques et dans les mêmes conditions environnementales. Pour ce faire, nous procédons en calculant la puissance maximale disponible à la sortie d'un panneau photovoltaïque. Cela consiste à rechercher les modèles mathématiques qui permettent de calculer cette puissance. Pour choisir le modèle le plus proche de la puissance caractéristique donnée par le fabricant du panneau PV dans les conditions de test, nous avons écrit un programme sous l'environnement Matlab, les résultats de ce script distinguent ce modèle. Les caractéristiques du panneau PV choisi sont appliquées dans chaque cas d'installation photovoltaïque. Ces résultats ont montré que la production du panneau PV mobile est plus importante que celle du panneau PV fixe. Nous avons constaté que la puissance électrique du panneau PV mobile produit plus de 10 à 38% supérieure à celle du panneau PV fixe selon le jour du mois considéré.


2016 ◽  
Vol 10 (03) ◽  
pp. 417-439 ◽  
Author(s):  
Xing Hao ◽  
Guigang Zhang ◽  
Shang Ma

Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.


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