We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electricalengineering. We focused on two leading pose detection technologies: YO...
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ISBN:
(数字)9798350360721
ISBN:
(纸本)9798350360738
We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electricalengineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.
A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches...
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A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy.
Severe intensity inhomogeneity (InH) and complex real-world textures cause great difficulties and become two important issues in image segmentation and object extraction applications. Plenty of methods are proposed to...
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DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorit...
DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorithm, state representation, and training procedure. In this paper we explore various cutting-edge DRL algorithms, such as policy-, value-, and actor-critic-based approaches. Our results demonstrate the effectiveness of the ranging sensor approach, which achieves robust navigation policies capable of generalizing to unseen virtual environments with a high success rate. We combine Behavior Cloning with Imitation Learning to expedite the training process, leveraging expert demonstrations and reinforcement learning. Our methodology enables faster training while enhancing the learning efficiency and performance of the robot, obtaining better results in terms of crash and success rate, and being able to reach a cumulative reward of approximately 12000.
Electronic technologies are growing very rapidly, especially those related to automation and robotics. Robotics technology is currently being developed and implemented in various fields including military, search and ...
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Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterat...
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Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context...
Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context of unmanned autonomous vehicles. In many applications, the agent may have to react to environment shifting. Algorithms such as geometric and dynamic programming as well as techniques such as artificial potential fields have been employed to tackle this local planning problem. In recent years, machine learning has gained more evidence in many research fields due to its flexibility and generalization capabilities. In this study, we propose a Q-learning-based approach to local planning, which weighs three crucial factors- path length, safety, and energy consumption- that can be freely adjusted by the user to suit its application’s needs. The performance of the proposed method was tested in simulated static and dynamic scenarios as well as benchmarked with a baseline approach. The results show that it can perform well in both kinds of environments without struggling with the commom pitfalls of other local planning algorithms.
The immunohistochemical(IHC)staining of the human epidermal growth factor receptor 2(HER2)biomarker is widely practiced in breast tissue analysis,preclinical studies,and diagnostic decisions,guiding cancer treatment a...
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The immunohistochemical(IHC)staining of the human epidermal growth factor receptor 2(HER2)biomarker is widely practiced in breast tissue analysis,preclinical studies,and diagnostic decisions,guiding cancer treatment and investigation of ***2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist,which typically takes one day to prepare in a laboratory,increasing analysis time and associated ***,we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images,matching the standard HER2 IHC staining that is chemically performed on the same tissue *** efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis,in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images(WSIs)to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts.A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail,membrane clearness,and absence of staining artifacts with respect to their immunohistochemically stained *** virtual HER2 staining framework bypasses the costly,laborious,and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.
In this work, we report the second-order nonlinear optical susceptibility χ(2) for epsilon phase Gallium Oxide (ϵ-Ga2O3) thin film on sapphire. ϵ-Ga2O3 exhibits hexagonal P63mc space group symmetry, which is a non-ce...
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The demand for electricity has increased rapidly and, for this reason, there is a need to efficiently use it. In this way, the identification of residential appliances enables such use for consumers and is crucial for...
The demand for electricity has increased rapidly and, for this reason, there is a need to efficiently use it. In this way, the identification of residential appliances enables such use for consumers and is crucial for demand response programs. Due to the variety of appliances in homes and their dynamic behavior, the search for patterns that explain and allow the correct labeling of temporal windows becomes a challenging task, since a window may contain more than one appliance. In this sense, the present paper proposes the transformation of time-series into images, using Gramian angular field and recurrence plots. The dataset composed of images was submitted to the labeling process, considering the use of convolutional neural networks. A comparative analysis was performed using the UK-DALE dataset. The results demonstrated the effectiveness of the proposed feature engineering stage, since the labeling task reached F1-scores until 94 %.
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