دانلود و نمایش مقالات مرتبط با deep learning::صفحه 1
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نتیجه جستجو - deep learning

تعداد مقالات یافته شده: 438
ردیف عنوان نوع
1 DEGAN : شبکه های مولد متخاصم غیر متمرکز
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 23
در این مطالعه، یک چارچوب توزیع شده و غیرمتمرکز از شبکه های مولد متخاصم (GAN) بدون تبادل داده های آموزشی پیشنهاد شد. هر گره شامل مجموعه ی از داده محلی ، یک تفکیک کننده کننده و یک مولد است که فقط گرادیان ژنراتور آن با سایر گره ها به اشتراک گذاشته می شوند. در این مقاله ، تکنیک توزیع جدید معرفی می شود که در آن کارکنان مستقیماً با یکدیگر ارتباط برقرار می کنند و هیچ گره مرکزی وجود ندارد. نتایج تجربی ما در مجموعه داده های معیار ، عملکرد و دقت تقریباً یکسانی را در مقایسه با چارچوب های GAN متمرکز موجود نشان می دهد. چارچوب پیشنهادی به عدم یادگیری غیرمتمرکز برای GAN ها می پردازد.
کلمات کلیدی: یادگیری عمیق | شبکه های مولد متخاصم | یادگیری ماشین توزیع شده | معماری غیرمتمرکز
مقاله ترجمه شده
2 Optimal carbon storage reservoir management through deep reinforcement learning
مدیریت بهینه ذخیره مخزن کربن از طریق یادگیری تقویتی عمیق-2020
Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decisionmaking problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.
Keywords: Reinforcement learning | Multistage decision-making | Deep autoregressive model | Deep Q network | Surrogate modeling | Markov decision process | Geological carbon sequestration
مقاله انگلیسی
3 سرمایه گذاری مالی بلاکچین برمبنای الگوریتم شبکه یادگیری عمیق
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 30
به منظور مطالعه استفاده از یادگیری عمیق برای پردازش داده های مالی، پیشنهاد می شود که می توان از فناوری مرتبط با شبکه عصبی و یادگیری عمیق برای داده های مالی استفاده کرد و از شاخص واقعی سهام و داده های آتی برای بررسی تاثیر کاربرد شبکه عصبی و یادگیری عمیق استفاده کرد. درابتدا نظریه و مدل یادگیری عمیق و شبکه عصبی با جزئیات معرفی می شوند. سپس از یک شبکه عصبی ساده و مدل یادگیری عمیق در شاخص سهام و پیش بینی قیمت آتی استفاده می شود. داده های استفاده شده در داده های ورودی به مدل شامل قیمت یک سهام در معامله جاری و برخی شاخص های داده ای و قیمت بسته شدن یک سهام در زمان بعدی می شود. کاهش قیمت در خروجی منعکس خواهد شد. اگر خروجی برای 1 بالا و برای صفر پایین باشد، داده های جدید پس از راه اندازی مدل وارد خواهند شد. نهایتا" جهت قضاوت روی تاثیر کاربرد مدل، می توان پس از مقایسه e و تحلیل تاثیر کاربرد مدل، داده های خروجی را با داده های واقعی مقایسه کرد. نتایج نشان می دهند که تحقیق انجام شده در این مطالعه می تواند به سرمایه گذاران کمک کند تا یک مدل سرمایه گذاری خودکار و راهبرد سرمایه گذاری در بازار سهام بسازند. از این سازه می توان برای ارجاع جهت بهبود راهبرد سرمایه گذاری سرمایه گذاران و نرخ بازگشت استفاده کرد.
کلیدواژه ها: یادگیری عمیق | سرمایه گذاری در بازار بورس | شبکه عصبی | سرمایه گذاری مالی
مقاله ترجمه شده
4 Modified deep learning and reinforcement learning for an incentive-based demand response model
یادگیری عمیق اصلاح شده و یادگیری تقویتی برای یک مدل پاسخ تقاضای مبتنی بر انگیزه-2020
Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand during peak period through rewards. In this study, an incentive-based DR program with modified deep learning and reinforcement learning is proposed. A modified deep learning model based on recurrent neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then, reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the proposed modified deep learning model can achieve more accurate forecasting results compared with some other methods. It can support the development of incentive-based DR programs under uncertain environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs while reducing the peak electricity demand. A short-term DR program was developed for peak electricity demand period, and the experimental results show that peak electricity demand can be reduced by 17%. This contributes to mitigating the supply-demand imbalance and enhancing power system security.
Keywords: Demand response | Modified deep learning | Reinforcement learning | Smart grid
مقاله انگلیسی
5 Recent advances in digital image manipulation detection techniques: A brief review
پیشرفت های اخیر در تکنیک های تشخیص دستکاری تصویر دیجیتال: مروری کوتاه-2020
A large number of digital photos are being generated and with the help of advanced image editing software and image altering tools, it is very easy to manipulate a digital image nowadays. These manipulated or tampered images can be used to delude the public, defame a persons personality and business as well, change political views or affect the criminal investigation. The raw image can be mutilated in parts or as a whole image so there is a need for detection of what type of image tampering is performed and then localize the tampered region. Initially, single handcrafted manipulated images were used to detect the only image tampering present in the image but in a real-world scenario, a single image can be mutilated by numerous image manipulation techniques. Nowadays, multiple tampering operations are performed on the image and post-processing is done to erase the traces left behind by the tampering operation, making it more difficult for the detector to detect the tampering. It is seen that the recent techniques that are used to detect image manipulation are based on deep learning methods. In this paper, more focus is on the study of various recent image manipulation detection techniques. We have examined various image forgeries that can be performed on the image and various image manipulation detection and localization methods.
Keywords: Image manipulation | Image tampering | Convolutional neural network | Deep learning
مقاله انگلیسی
6 Looking in the Right Place for Anomalies: Explainable Ai Through Automatic Location Learning
جستجوی مکان مناسب برای ناهنجاری ها: هوش مصنوعی قابل توضیح از طریق یادگیری خودکار مکان-2020
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their ’black box’ way of classifying medical images into anomaly labels poses problems for their acceptance, particularly with clinicians. Current explainable AI methods offer justifications through visualizations such as heat maps but cannot guarantee that the network is focusing on the relevant image region fully containing the anomaly. In this paper we develop an approach to explainable AI in which the anomaly is assured to be overlapping the expected location when present. This is made possible by automatically extracting location-specific labels from textual reports and learning the association of expected locations to labels using a hybrid combination of Bi-Directional Long Short-Term Memory Recurrent Neural Networks (Bi- LSTM) and DenseNet-121. Use of this expected location to bias the subsequent attention-guided inference network based on ResNet101 results in the isolation of the anomaly at the expected location when present. The method is evaluated on a large chest X-ray dataset.
مقاله انگلیسی
7 Study on deep reinforcement learning techniques for building energy consumption forecasting
مطالعه تکنیک های یادگیری تقویتی عمیق برای پیش بینی مصرف انرژی در ساخت-2020
Reliable and accurate building energy consumption prediction is becoming increasingly pivotal in build- ing energy management. Currently, data-driven approach has shown promising performances and gained lots of research attention due to its efficiency and flexibility. As a combination of reinforcement learning and deep learning, deep reinforcement learning (DRL) techniques are expected to solve nonlinear and complex issues. However, very little is known about DRL techniques in forecasting building energy con- sumption. Therefore, this paper presents a case study of an office building using three commonly-used DRL techniques to forecast building energy consumption, namely Asynchronous Advantage Actor-Critic (A3C), Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG). The objective is to investigate the potential of DRL techniques in building energy consumption predic- tion field. A comprehensive comparison between DRL models and common supervised models is also provided. The results demonstrate that the proposed DDPG and RDPG models have obvious advantages in forecast- ing building energy consumption compared to common supervised models, while accounting for more computation time for model training. Their prediction performances measured by mean absolute error (MAE) can be improved by 16%-24% for single-step ahead prediction, and 19%-32% for multi-step ahead prediction. The results also indicate that A3C performs poor prediction accuracy and shows much slower convergence speed than DDPG and RDPG. However, A3C is still the most efficient technique among these three DRL methods. The findings are enlightening and the proposed DRL methodologies can be positively extended to other prediction problems, e.g., wind speed prediction and electricity load prediction.
Keywords: Energy consumption prediction | Ground source heat pump | Deep reinforcement learning | Asynchronous advantage Actor-Critic | Deep deterministic Policy gradient | Recurrent deterministic Policy gradient
مقاله انگلیسی
8 A Novel Multi-Modal Framework for Migrants Integration Based on AI Tools and Digital Companion
یک چارچوب چند حالته جدید برای ادغام مهاجران بر اساس ابزارهای هوش مصنوعی و همراه دیجیتال-2020
ICT have proven to provide significant aid for appropriate integration of migrants. These tools can support the inclusion by providing guidance, education opportunities, job seeking, culture immersion and facilitating access to primary services. In this paper, a complete framework for migrants (with special focus on refugees) guidance and inclusion is presented. This framework comprises a set of novel AI tools aimed at enabling mentioned services from diverse perspectives: a) users’ profiling; b) skills matching c) recommendations; d) user profiling and e) digital companion. Consideration about data collection, data flow, architecture and interactions are provided.
Index Terms: AI users’ profiling | Deep Learning
مقاله انگلیسی
9 Automatic human identification from panoramic dental radiographs using the convolutional neural network
شناسایی خودکار انسان از رادیوگرافی دندانپزشکی پانوراما با استفاده از شبکه عصبی کانولوشن-2020
Human identification is an important task in mass disaster and criminal investigations. Although several automatic dental identification systems have been proposed, accurate and fast identification from panoramic dental radiographs (PDRs) remains a challenging issue. In this study, an automatic human identification system (DENT-net) was developed using the customized convolutional neural network (CNN). The DENT-net was trained on 15,369 PDRs from 6300 individuals. The PDRs were preprocessed by affine transformation and histogram equalization. The DENT-net took 128  128  7 square patches as input, including the whole PDR and six details extracted from the PDR. Using the DENT-net, the feature extraction took around 10 milliseconds per image and the running time for retrieval was 33.03 milliseconds in a 2000-individual database, promising an application on larger databases. The visualization of CNN showed that the teeth, maxilla, and mandible all contributed to human identification. The DENT-net achieved Rank-1 accuracy of 85.16% and Rank-5 accuracy of 97.74% for human identification. The present results demonstrated that human identification can be achieved from PDRs by CNN with high accuracy and speed. The present system can be used without any special equipment or knowledge to generate the candidate images. While the final decision should be made by human specialists in practice. It is expected to aid human identification in mass disaster and criminal investigation
Keywords: Forensic odontology | Human identification | Panoramic dental radiographs | Deep learning | Convolutional neural network
مقاله انگلیسی
10 AI-based Framework for Deep Learning Applications in Grinding
چارچوبی مبتنی بر هوش مصنوعی برای کاربردهای یادگیری عمیق در شبکه سازی-2020
Rejection costs for a finish-machined gearwheel with grinding burn can rise to the order of 10,000 euros each. A reduction in costs by reducing rejection rate by only 5-10 pieces per year already amortizes costs for data-acquisition hardware for online process monitoring. The grinding wheel wear, one of the major influencing factors responsible for the grinding burn, depends on a large number of influencing variables like cooling lubricant, feed rate, circumferential wheel speed and wheel topography. In the past, machine learning algorithms such as Support Vector Machines (SVM), Hidden Markov Models (HMM) and Artificial Neural Networks (ANN) have proven effective for the predictive analysis of process quality. In addition to predictive analysis, AI-based applications for process control may raise the resilience of machining processes. Using machine learning methods may also lead to a heavy reduction of cost amassed due to a physical inspection of each workpiece. With this contribution, information from previous works is leveraged and an AI-based framework for adaptive process control of a cylindrical grinding process is introduced. For the development of such a framework, three research objectives have been derived: First, the dynamic wheel wear needs to be modelled and measured, because of its strong impact on the resulting workpiece quality. Second, models to predict the quality features of the produced workpieces depending on process setup parameters and materials used have to be established. Here, special focus is set on deriving models that are independent of a specific wheel-workpiece-pair. The opportunity to use such a model in a variety of grinding configurations gives the production line consistent process support. Third, the resilience of analytical models regarding graceful degradation of sensors needs to be tackled, since the stability of such systems has to be guaranteed to be used in productive environments. Process resilience against human errors and sensor failures leads to a minimization of rejection costs in production. To do so, a framework is presented, where virtual sensors, upon the failure or detection of an erroneous signal from physical sensors, will be activated and provide signals to the downstream smart systems until the process is completed or the physical sensor is changed.
Keywords: Cylindrical Grinding | Wheel Wear | Virtual Sensors | Process Resilience | Artificial Intelligence
مقاله انگلیسی
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