scholarly journals Research on Emotion Analysis of Chinese Literati Painting Images Based on Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Jie Zhang ◽  
Yingjing Duan ◽  
Xiaoqing Gu
Keyword(s):  
Deep Learning ◽  
New Technology ◽  
Salient Feature ◽  
Suitable Model ◽  
Emotion Analysis ◽  
Literati Painting ◽  
Cultural Research ◽  
New Ideas ◽  
Improved Model ◽  
Picture Content

Starting from a pure-image perspective, using machine learning in emotion analysis methods to study artwork is a new cross-cutting approach in the field of literati painting and is an effective supplement to research conducted from the perspectives of aesthetics, philosophy, and history. This study constructed a literati painting emotion dataset. Five classic deep learning models were used to test the dataset and select the most suitable model, which was then improved upon for literati painting emotion analysis based on accuracy and model characteristics. The final training accuracy rate of the improved model was 54.17%. This process visualizes the salient feature areas of the picture in machine vision, analyzes the visualization results, and summarizes the connection law between the picture content of the Chinese literati painting and the emotion expressed by the painter. This study validates the possibility of combining deep learning with Chinese cultural research, provides new ideas for the combination of new technology and traditional Chinese literati painting research, and provides a better understanding of the Chinese cultural spirit and advanced factors.

scholarly journals A Novel Paradigm to Design Personalized Derived Images of Art Paintings Using an Intelligent Emotional Analysis Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Yingjing Duan ◽  
Jie Zhang ◽  
Xiaoqing Gu
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
New Technology ◽  
New Method ◽  
Facial Emotion ◽  
Design System ◽  
Analysis Model ◽  
Emotion Analysis ◽  
New Ideas ◽  
User Demand

With the development of artificial intelligence (AI), it is imperative to combine design methods with new technologies. From the perspective of the personalized design of derived images of art paintings, this study analyzes the new user demand generated by the current situation and background of personalized design, puts forward a new method of derivative design based on AI emotion analysis, verifies the feasibility of the new method by constructing a personalized design system of derived images of art paintings driven by facial emotion features, and explores the method of combining AI emotion recognition, emotion analysis, and personalized design. This study provides new ideas for the design of art derivatives for the future with massive personalized demand. Thinking and practicing from the perspective of the development of new technology will promote the change of design paradigms in the digital age.

Smart paddy field monitoring system using deep learning and IoT

2021 ◽  
pp. 1063293X2198894
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Nithiyakanthan Kannan ◽  
Sridevi Narayanan ◽  
Chanki Pandey
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Paddy Field ◽  
New Technology ◽  
Support Vector ◽  
Nitrogen Status ◽  
Deep Feature ◽  
Per Capita ◽  
Deep Feature Extraction

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

scholarly journals Influence in Technological Innovation Spaces: A Network Science Approach to Understand Innovation for Sustainability in the Global South

2020 ◽  
Vol 12 (5) ◽  
pp. 1858
Author(s):  
Daniel Schmitt ◽  
Chisenga Muyoya
Keyword(s):  
Urban Areas ◽  
Network Science ◽  
Research Output ◽  
New Technology ◽  
Geographical Location ◽  
Global South ◽  
Production Environment ◽  
Science Approach ◽  
New Ideas ◽  
Sustainability Challenges

The number of scholars working on transition concepts in the Global South is rapidly increasing. In this context, a substantial amount of research output particularly focusses on niches and how they affect transition towards sustainability in a wider framework of the multi-level-perspective. At the same time, there is a growing interest in digital technology and its effect on sustainability challenges. In this article, we combine the two fields, and by utilizing social media data, we create an innovative network science approach to analyze the production environment of digital innovations in Africa. We focus on three innovation hubs that we conceptualize as niches and innovation intermediaries that not only create communities to develop, test and implement new technology but also function as networks to discuss and form new ideas around innovations. Our key findings show how local communities are embedded in larger innovation structures. The connections between local stakeholders and global actors are predominantly created through bridge actors, who hold key positions in their communities. With tools from network science, we demonstrate that these linking elements can regulate and steer discussions and therefore, strongly influence digital niche environments. Utilizing geographical location data, we can also see that the online space of technological innovations in Africa is heavily cantered in urban areas.

Virtual Clutch Development Model

2007 ◽  
Author(s):  
Lina J. Lundquist ◽  
Franz Eberle ◽  
Mikael B. Mohlin ◽  
Rainer Sponsel
Keyword(s):  
Product Development ◽  
Product Quality ◽  
New Product Development ◽  
New Technology ◽  
Development Model ◽  
Lead Times ◽  
Automotive Company ◽  
Clutch System ◽  
New Ideas ◽  
Development Approach

In a world of constant development and where competition grows stronger for every minute, there is a need to work smart to stay on the market. Product development in the automotive business is not an exception. It is though not enough to adapt new technology and new ideas, one has to apply it to the organization in the smartest way to be able to achieve one of the most wanted goals; shortened lead-time in combination with improved product quality. As well known, virtual prototyping is a mean to achieve the above stated goal. This paper describes how this method has been the basis for a new product development approach in the clutch system area in an automotive company. The new virtual development approach is enabled by creation of the Virtual Clutch Development Model (VCDM). The main benefit of the simulation model is that several clutch performance phenomena can easily be investigated at once to get an overview of the performance of the clutch system, this in an early phase of the development process. This will facilitate trade off decisions and avoid suboptimization and thus shorten lead-times and improve product quality.

scholarly journals Analysis and Best Parameters Selection for Person Recognition based on Gait model using CNN algorithm and Image Augmentation

2020 ◽  
Author(s):  
Abeer Saleh ◽  
Talal Hamoud
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Person Recognition ◽  
Critical Issues ◽  
Dataset Size ◽  
Deep Convolution Neural Network ◽  
Human Pose ◽  
Improved Model ◽  
Pose Variation ◽  
Best Parameters

Abstract Person recognition based on gait model and motion print is indeed a challenging and novel task due to its usages and to the critical issues of human pose variation, human body occlusion, camera view variation, etc. In this project, a deep convolution neural network (CNN) was modified and adapted for person recognition with image augmentation technique. CNN is best algorithm of deep learning algorithms. Adaptation aims to get best values for CNN parameters to get best CNN model. In Addition to the CNN parameters, the design of CNN model itself was adapted to get best model design; number of layers and normalization between them. After choosing best parameters and best design, Image augmentation was used to increase train dataset with many copies of the image to boost the number of different images that will be used to train Deep learning algorithms. The tests were achieved using known dataset (Market dataset). The dataset contains sequential pictures of people in different gait status. The image in CNN model as matrix is extracted to many images or matrices, so dataset size may be bigger by hundred times to make the problem a big data problem, in this project Results show that adaptation has improved the accuracy of person recognition using gait model, that is represented in many successive images for the same person. In addition, dataset contains images of person carrying things. The improved model of CNN is robust to image dimensions (quality and resolution) and to carried things by persons.

scholarly journals Light People: Professor Aydogan Ozcan

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tingting Sun
Keyword(s):  
Deep Learning ◽  
Los Angeles ◽  
Learning Algorithm ◽  
Computational Imaging ◽  
Technological Innovations ◽  
Image Generation ◽  
Sensing Technology ◽  
Reconstruction Methods ◽  
New Ideas ◽  
The University

EditorialIn 2016, the news that Google’s artificial intelligence (AI) robot AlphaGo, based on the principle of deep learning, won the victory over lee Sedol, the former world Go champion and the famous 9th Dan competitor of Korea, caused a sensation in both fields of AI and Go, which brought epoch-making significance to the development of deep learning. Deep learning is a complex machine learning algorithm that uses multiple layers of artificial neural networks to automatically analyze signals or data. At present, deep learning has penetrated into our daily life, such as the applications of face recognition and speech recognition. Scientists have also made many remarkable achievements based on deep learning. Professor Aydogan Ozcan from the University of California, Los Angeles (UCLA) led his team to research deep learning algorithms, which provided new ideas for the exploring of optical computational imaging and sensing technology, and introduced image generation and reconstruction methods which brought major technological innovations to the development of related fields. Optical designs and devices are moving from being physically driven to being data-driven. We are much honored to have Aydogan Ozcan, Fellow of the National Academy of Inventors and Chancellor’s Professor of UCLA, to unscramble his latest scientific research results and foresight for the future development of related fields, and to share his journey of pursuing Optics, his indissoluble relationship with Light: Science & Applications (LSA), and his experience in talent cultivation.

Application and Research on New Technology of Foam Lightweight Soil in Treatment of Soft Soil Foundation of Railway Engineering

2012 ◽  
Vol 204-208 ◽  
pp. 1622-1625 ◽  
Author(s):  
Zi Lin Li ◽  
Kai Feng Zhang
Keyword(s):  
New Technology ◽  
Soft Soil ◽  
Actual Application ◽  
Foundation Treatment ◽  
Soft Foundation ◽  
Depth Analysis ◽  
Depth Study ◽  
New Ideas ◽  
Soil Foundation ◽  
Soft Soil Foundation

Foam lightweight soil is a new type of material appearing in recent years, whose advantages are paid more and more attention to by engineers and it will have broad application prospect in the engineering. An in-depth study application of foam lightweight soil in the complex of railway soft soil foundation treatment what based on a railway foundation project in Tianjin, this paper discusses the actual application state of foam lightweight soil in handling of special railway soft soil foundation. Through in-depth analysis of the advantages of a foam lightweight soil, optimization of construction replacement thickness and moisture density, discussion on construction of it, we can find large advantages of foam lightweight soil as replacement material in the complex railway soft soil foundation treatment, and provide new ideas about processing complex soft foundation.

DOI Theoretical Framework

2018 ◽  
pp. 144-166
Keyword(s):  
Diffusion Of Innovations ◽  
Fossil Fuels ◽  
New Technologies ◽  
New Technology ◽  
Cost Effective ◽  
Theoretical Framework ◽  
Adaptation To Climate Change ◽  
New Ideas ◽  
The Masses ◽  
Structured Means

Change is not easy! People adhere to old routines and habits tenaciously. Most people are slow to accept new ideas, new products, in short, innovations. When it comes to new technologies that can aid in adaptation to climate change, there is fierce resistance from farmers (to sustainable agriculture), from the fossil fuels industries (to sustainable energy), from developers (to going green), and the list goes on. While a new technology does involve a certain investment of time and money at first, it is cost effective and profitable in the long term. When it comes to sustainability, nothing less than the future of our planet is at stake, so it is incumbent upon us to find a way to “sell” the innovations to the masses. The Diffusion of Innovations (DOI) Theoretical Framework provides an effective, structured means of doing this; its efficacy has been established for hundreds of innovations, and it is particularly suited to technologies.

Internal Branding as Innovation Tenet

2019 ◽  
pp. 156-182
Author(s):  
Maria Matiatou
Keyword(s):  
Diffusion Of Innovation ◽  
Business Strategy ◽  
New Technology ◽  
Vital Role ◽  
Employee Perceptions ◽  
Internal Branding ◽  
New Ideas ◽  
Communication Practices ◽  
Culture Positive

Innovation as a core value for most organizations is not simply the application of new technology to achieve a business goal: it must be directly expressed through brand experience. Brand driven innovation is human centric. New ideas require a welcoming organizational culture, positive mindsets and internal advocacy to grow. Businesses can really innovate when employees become their brand evangelists. In this chapter, we initially explore internal branding values and tactics. We assess its role as critical bridge over vision, culture and image gaps in case studies to bring awareness on success and risk factors. Employee perceptions of communication practices are captured and matched to aspiration, missions and organizational values. From this premise, we establish internal branding as practice that affects the company's ability to innovate effortlessly and organically. The strong liaison between diffusion of innovation and brand endorsement is confirmed, consolidating the vital role of internal branding in the implementation of an organization's business strategy.

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