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نتیجه جستجو - Open source

تعداد مقالات یافته شده: 71
ردیف عنوان نوع
1 Survey on deep learning based computer vision for sonar imagery
مروری بر بینایی کامپیوتری مبتنی بر یادگیری عمیق برای تصاویر سونار-2022
Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based, approaches for a long time. Over the past 15 years, however, the application of deep learning in this research field has constantly grown. This paper gives a broad overview of past and current research involving deep learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up to four layers outperform conventional methods.
The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods.
keywords: یادگیری عمیق | تصویربرداری سوناری | کامپیوتری | تشخیص خودکار هدف | Statusquoreview | Deeplearning | Sonarimagery | Computervision | Automatictargetrecognition | Statusquoreview
مقاله انگلیسی
2 Design and analysis of gantry robot for pick and place mechanism with Arduino Mega 2560 microcontroller and processed using pythons
طراحی و تجزیه و تحلیل ربات دروازه ای برای مکانیزم انتخاب و مکان با میکروکنترلر آردوینو مگا 2560 و پردازش با استفاده از پایتون-2021
Robots are extensively used in industries for their precision work and amount of work that one can obtain without any defects. In this paper we are using a gantry robot for as it does not occupy the floor space therefore reducing the distance for reachability of the parts and hence reducing unnecessary material for guide way. Robots work in strictly defined path and there is no or very little change in such systems in order to overcome this we are using a vision based control system to make the system dynamic in nature the images are picked by using a USB camera processed images of the object is transmitted via serial communication to the Arduino Mega 2560 microcontroller and processed using pythons open source computer vision (Open CV) image to process the image captured by the USB camera to find the exact col- our and to pick the object and sort it.© 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on Advanced Research in Mechanical, Materials and Manufacturing Engineering-2020.
Keywords: Gantry robot | Machine vision | Image processing | Arduino microcontroller | Open CV | Python
مقاله انگلیسی
3 Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network
به سمت کنترل بهینه واحدهای مدیریت هوا با استفاده از یادگیری تقویتی عمیق و شبکه عصبی بازگشتی -2020
A new generation of smart stormwater systems promises to reduce the need for new construction by enhancing the performance of the existing infrastructure through real-time control. Smart stormwater systems dynamically adapt their response to individual storms by controlling distributed assets, such as valves, gates, and pumps. This paper introduces a real-time control approach based on Reinforcement Learning (RL), which has emerged as a state-of-the-art methodology for autonomous control in the artificial intelligence community. Using a Deep Neu- ral Network, a RL-based controller learns a control strategy by interacting with the system it controls - effectively trying various control strategies until converging on those that achieve a desired objective. This paper formulates and implements a RL algorithm for the real-time control of urban stormwater systems. This algorithm trains a RL agent to control valves in a distributed stormwater system across thousands of simulated storm scenarios, seeking to achieve water level and flow set-points in the system. The algorithm is first evaluated for the control of an individual stormwater basin, after which it is adapted to the control of multiple basins in a larger watershed (4 km 2 ). The results indicate that RL can very effectively control individual sites. Performance is highly sensitive to the reward formulation of the RL agent. Generally, more explicit guidance led to better control performance, and more rapid and stable convergence of the learning process. While the control of multiple distributed sites also shows promise in reducing flooding and peak flows, the complexity of controlling larger systems comes with a number of caveats. The RL controller’s performance is very sensitive to the formulation of the Deep Neural Network and requires a significant amount of computational resource to achieve a reasonable performance en- hancement. Overall, the controlled system significantly outperforms the uncontrolled system, especially across storms of high intensity and duration. A frank discussion is provided, which should allow the benefits and draw- backs of RL to be considered when implementing it for the real-time control of stormwater systems. An open source implementation of the full simulation environment and control algorithms is also provided.
Keywords: Real-time control | Reinforcement learning | Smart stormwater systems
مقاله انگلیسی
4 Leading successful government-academia collaborations using FLOSS and agile values
پیشرو همکاریهای موفق دولت و آکادمی با استفاده از FLOSS و مقادیر چابک-2020
Government and academia share concerns for efficiently and effectively servicing societal demands, which includes the development of e-government software. Government-academia partnerships can be a valu- able approach for improving productivity in achieving these goals. However, governmental and academic institutions tend to have very different agendas and organizational and managerial structures, which can hinder the success of such collaborative projects. In order to identify effective approaches to overcome collaboration barriers, we systematically studied the case of the Brazilian Public Software portal project, a 30-month government-academia collaboration that, using Free/Libre/Open Source Software practices and agile methods for project management, developed an unprecedented platform in the context of the Brazil- ian government. We gathered information from experience reports and data collection from repositories and interviews to derive a collection of practices that contributed to the success of the collaboration. In this paper, we describe how the data analysis led to the identification of a set of three high-level decisions supported by the adoption of nine best practices that improved the project performance and enabled professional training of the whole team.
Keywords: Project management | Government-Academia collaboration | Free software | Open source software | Agile methodologies | e-Government
مقاله انگلیسی
5 Distributed Bayesian optimization of deep reinforcement learning algorithms
توزیع بهینه سازی بیزی الگوریتم های یادگیری تقویتی عمیق-2020
Significant strides have been made in supervised learning settings thanks to the successful application of deep learning. Now, recent work has brought the techniques of deep learning to bear on sequential decision processes in the area of deep reinforcement learning (DRL). Currently, little is known regarding hyperparameter optimization for DRL algorithms. Given that DRL algorithms are computationally intensive to train, and are known to be sample inefficient, optimizing model hyperparameters for DRL presents significant challenges to established techniques. We provide an open source, distributed Bayesian model-based optimization algorithm, HyperSpace, and show that it consistently outperforms standard hyperparameter optimization techniques across three DRL algorithms.
Keywords: Bayesian optimization | Deep reinforcement learning
مقاله انگلیسی
6 Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids
رویکرد یادگیری تقویتی عمیق برای کنترل MPPT سیستم های PV نیمه سایه دار در شبکه های هوشمند-2020
Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available energy output of the arrays and in turn make the MPPT task extremely difficult. For these reasons, under partial shading conditions, it is necessary to have algorithms that are able to learn and adapt online to the changing state of the system. In this work we propose the use of deep reinforcement learning (DRL) techniques to address the MPPT problem of a PV array under partial shading conditions. We develop a model free RL algorithm to maximize the efficiency in MPPT control. The agent’s policy is parameterized by neural networks, which take the sensory information as input and directly output the control signal. Furthermore, a PV environment under shading conditions was developed in the open source OpenAI Gym platform and is made available in an open repository. Several tests are performed, using the developed simulated environment, to test the robustness of the proposed control strategies to different climate conditions. The obtained results show the feasibility of our proposal with a successful performance with fast responses and stable behaviors. The best results for the presented methodology show that the maximum operating power point achieved has a deviation less than 1% compared to the theoretical maximum power point.
Keywords: MPPT | Deep RL | PV systems | OpenAI Gym
مقاله انگلیسی
7 The role of data within coastal resilience assessments: an East Anglia, UK, case study
نقش داده ها در ارزیابی های تاب آوری ساحلی: آنگلیای شرقی ، انگلیس ، مطالعه موردی-2020
Embracing the concept of resilience within coastal management marks a step change in thinking, building on the inputs of more traditional risk assessments, and further accounting for capacities to respond, recover and implement contingency measures. Nevertheless, many past resilience assessments have been theoretical and have failed to address the requirements of practitioners. Assessment methods can also be subjective, relying on opinion-based judgements, and can lack empirical validation. Scope exists to address these challenges through drawing on rapidly emerging sources of data and smart analytics. This, alongside the careful selection of the metrics used in assessment of resilience, can facilitate more robust assessment methods. This work sets out to establish a set of core metrics, and data sources suitable for inclusion within a data-driven coastal resilience assessment. A case study region of East Anglia, UK, is focused on, and data types and sources associated with a set of proven assessment metrics were identified. Virtually all risk-specific metrics could be satisfied using available or derived data sources. However, a high percentage of the resilience-specific metrics would still require human input. This indicates that assessment of resilience is inherently more subjective than assessment of risk. Yet resilience assessments incorporate both risk and resilience specific variables. As such it was possible to link 75% of our selected metrics to empirical sources. Through taking a case study approach and discussing a set of requirements outlined by a coastal authority, this paper reveals scope for the incorporation of rapidly progressing data collection, dissemination, and analytical methods, within dynamic coastal resilience assessments. This could facilitate more sustainable evidence-based management of coastal regions
Keywords: Coastal management | Resilience metrics | Geospatial data | Open source data | Big data
مقاله انگلیسی
8 Advanced cyberinfrastructure for intercomparison and validation of climate models
زیرساخت های پیشرفته سایبر برای مقایسه و اعتبار مدل های آب و هوایی-2020
The current routine of comparison and validation in climate science is frequently static and of low efficiency, which hinders evidence-based decision making and scientific confidence. Due to the aggressively increasing resolution, complexity, and associated data volumes of climate models, objectively comparing multiple models and assessing their accuracy against observations is an ever-increasing challenge. We propose an integrated framework for harmonizing state-of-the-art cyberinfrastructure techniques with the user habits formed by longterm familiarity with existing community-oriented software. An open source prototype named COVALI is implemented and used to compare and validate the results of several widely-used climate models and datasets. Our results show that the proposed cyberinfrastructure-based strategy can significantly automate the comparison and validation processes in climate modeling. More importantly, the new strategy retains the existing user habits in the climate community while making it easier for scientists to adopt new technology in their research routine.
Keywords: Model comparison | Model validation | Climate science | Web service | Cyberinfrastructure | Big data
مقاله انگلیسی
9 GeoVReality: A computational interactive virtual reality visualization framework and workflow for geophysical research
GeoVReality: چارچوب تجسم واقعیت مجازی تعاملی محاسباتی و گردش کار برای تحقیقات ژئوفیزیکی-2020
We present a new interactive computational virtual reality (VR) visualization framework for geophysical Big Data and models for the development of immersive collaborative virtual reality applications with a focus on targeted processing and interaction of Big Data. The framework includes a high-performance scalable persistent storage solution for the spatial analysis of Geospatial Information System (GIS), which uses an engine based on efficient in-memory computing. To more effectively visualize and interact in a VR environment, a machine learning algorithm library is used for compressing and extracting visual data. The framework supports mainstream rendering engines and VR hardware. The framework is extensible, customizable, cross-platform, and it is based only on open source tools. A workflow was introduced, and the geophysical data visualization and interaction effects were demonstrated by taking the abyss data of the Mariana Trench as example.
Keywords: Virtual reality | Geophysical model | Interactive visualization | Unreal engine | Unity 3D | Big data
مقاله انگلیسی
10 Robust influence modeling under structural and parametric uncertainty: An Afghan counternarcotics use case
مدل سازی تأثیرپذیر تحت عدم قطعیت ساختاری و پارامتری: مورد استفاده یک افغانی از مواد مخدر -2020
An entity often seeks to influence the decisions of others in a system. This dynamic is apparent in a variety of settings including criminal justice, environmental regulation, and marketing applications. However, the central task of the influencing entity is confounded by uncertainty regarding their understanding of the structure and/or parameters of the decisions being made. The research herein sets forth a decision support methodology to identify robust influence strategies under such uncertain conditions. Furthermore, the utility of this framework and its proper parameterization are illustrated via an application to the contemporary, global problem of the Afghan opium trade. Utilizing open source data, we demonstrate how counternarcotic policy can be informed using a quantitative analysis that embraces both the bounded rationality of the economys decisionmakers and the governments uncertainty regarding the degree of their deviation from perfect rationality. In this manner, we provide a new framework with which robust influence decisions can be identified under realistic information conditions, and we discuss how it can be used to inform real-world policy.
Keywords: Robust decisionmaking | Persuasion | Behavioral OR | Prospect theory | Behavioral economics | Bounded rationality
مقاله انگلیسی
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