scholarly journals Research on Modern Book Packaging Design under Aesthetic Evaluation Based on Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuanyuan Li
Keyword(s):  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Electronic Information ◽  
Aesthetic Evaluation ◽  
Packaging Design ◽  
Evaluation Indexes ◽  
Adaboost Algorithm ◽  
Continuous Progress ◽  
Specific Implementation ◽  
Deep Learning Model

The development of information technology has led to the rapid development of modern book packaging design. Book packaging design is different from painting. It is a kind of design process that combines artistry and practicality and has double characteristics. With the continuous progress of society, people’s requirements for book packaging design have become higher and higher, and modern book packaging design has become an important topic in the field of art design. To this end, this paper introduces the machine learning algorithms used in this paper, including the AdaBoost algorithm and the SVR algorithm. Specifically, it includes the principles and specific implementation steps for AdaBoost classification algorithm and SVR regression algorithm, as well as evaluation indexes of AdaBoost classification and SVR regression analysis. Realization of physical books reflects artistry, creativity, professionalism, popularity, and vitality of books in the packaging and design of books in the electronic information era. The stimulation effect of this paper starts from packaging design, inspection mechanism, and brand psychology to get the superior design in modern book packaging design.

Dynamic Performance of High-Speed Electric Multiple Unit within Multi-Body System Simulation

2019 ◽  
Vol 12 (4) ◽  
pp. 339-349
Author(s):  
Junguo Wang ◽  
Daoping Gong ◽  
Rui Sun ◽  
Yongxiang Zhao
Keyword(s):  
High Speed ◽  
Rapid Development ◽  
Dynamic Performance ◽  
Body System ◽  
High Speed Railway ◽  
Evaluation Indexes ◽  
Actual Operation ◽  
Multiple Unit ◽  
The Stability ◽  
Multi Body

Background: With the rapid development of the high-speed railway, the dynamic performance such as running stability and safety of the high-speed train is increasingly important. This paper focuses on the dynamic performance of high-speed Electric Multiple Unit (EMU), especially the dynamic characteristics of the bogie frame and car body. Various patents have been discussed in this article. Objective: To develop the Multi-Body System (MBS) model of EMU, verify whether the dynamic performance meets the actual operation requirements, and provide some useful information for dynamics and structural design of the proposed EMU. Methods: According to the technical characteristics of a typical EMU, a MBS model is established via SIMPACK, and the measured data of China high-speed railway is taken as the excitation of track random irregularity. To test the dynamic performance of the EMU, including the stability and safety, some evaluation indexes such as wheel-axle lateral forces, wheel-axle lateral vertical forces, derailment coefficients and wheel unloading rates are also calculated and analyzed in detail. Results: The MBS model of EMU has better dynamic performance especially curving performance, and some evaluation indexes of the stability and safety have also reached China’s high-speed railway standards. Conclusion: The effectiveness of the proposed MBS model is verified, and the dynamic performance of the MBS model can meet the design requirements of high-speed EMU.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Algorithmic management in a work context

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110203
Author(s):  
Mohammad Hossein Jarrahi ◽  
Gemma Newlands ◽  
Min Kyung Lee ◽  
Christine T. Wolf ◽  
Eliscia Kinder ◽  
...  
Keyword(s):  
Information Exchange ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Organizational Level ◽  
Future Research ◽  
Power Dynamics ◽  
Work Processes ◽  
Work Settings ◽  
Standard Work ◽  
Management Functions

The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.

Deep Learning for the Automated Classification of Functional Brain Networks in fMRI (Preprint)

2021 ◽  
Author(s):  
Lukman Ismael ◽  
Pejman Rasti ◽  
Florian Bernard ◽  
Philippe Menei ◽  
Aram Ter Minassian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Brain Networks ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Functional Networks ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Deep Learning Model

BACKGROUND The functional MRI (fMRI) is an essential tool for the presurgical planning of brain tumor removal, allowing the identification of functional brain networks in order to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rsfMRI). However, this technique is not routinely used because of the necessity to have a expert reviewer to identify manually each functional networks. OBJECTIVE We aimed to automatize the detection of brain functional networks in rsfMRI data using deep learning and machine learning algorithms METHODS We used the rsfMRI data of 82 healthy patients to test the diagnostic performance of our proposed end-to-end deep learning model to the reference functional networks identified manually by 2 expert reviewers. RESULTS Experiment results show the best performance of 86% correct recognition rate obtained from the proposed deep learning architecture which shows its superiority over other machine learning algorithms that were equally tested for this classification task. CONCLUSIONS The proposed end-to-end deep learning model was the most performant machine learning algorithm. The use of this model to automatize the functional networks detection in rsfMRI may allow to broaden the use of the rsfMRI, allowing the presurgical identification of these networks and thus help to preserve the patient’s neurological status. CLINICALTRIAL Comité de protection des personnes Ouest II, decision reference CPP 2012-25)

scholarly journals STUDY ON THE WATER BODY EXTRACTION USING GF-1 DATA BASED ON ADABOOST INTEGRATED LEARNING ALGORITHM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 641-648
Author(s):  
J. Y. Sun ◽  
G. Z. Wang ◽  
G. J. He ◽  
D. C. Pu ◽  
W. Jiang ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Rapid Development ◽  
Kappa Coefficient ◽  
Water Bodies ◽  
Integrated Learning ◽  
Visual Interpretation ◽  
Adaboost Algorithm ◽  
Classification Time ◽  
Better Than

Abstract. Surface water system is an important part of global ecosystem, and the changes in surface water may lead to disasters, such as drought, waterlog, and water-borne diseases. The rapid development of remote sensing technology has supplied better strategies for water bodies extraction and further monitoring. In this study, AdaBoost and Random Forest (RF), two typical algorithms in integrated learning, were applied to extract water bodies in Chaozhou area (mainly located in Guangzhou Province, China) based on GF-1 data, and the Decision Tree (DT) was used for comparative tests to comprehensively evaluate the performance of classification algorithms listed above for surface water body extraction. The results showed that: (1) Compared with visual interpretation, AdaBoost performed better than RF in the extraction of several typical water bodies, such as rivers, lakes and ponds Moreover, the water extraction results of the strong classifiers using AdaBoost or RF were better than the weak basic classifiers. (2) For the quantitative accuracy statistics, the overall accuracy (96.5%) and kappa coefficient (93%) using AdaBoost exceeded those using RF (5.3% and 10.6%), respectively. The classification time of AdaBoost increased by 403 seconds and 918 seconds relative to RF and DT methods. However, in terms of visual interpretation, quantitative statistical accuracy and classification time, AdaBoost algorithm was more suitable for the water body extraction. (3) For the sample proportion comparison experiment of AdaBoost, four sampling proportions (0.1%, 0.2%, 1% and 2%) were chosen and 0.1% sampling proportion reached the optimum classification accuracy (93.9%) and kappa coefficient (87.8%).

scholarly journals IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2556
Author(s):  
Liyang Wang ◽  
Yao Mu ◽  
Jing Zhao ◽  
Xiaoya Wang ◽  
Huilian Che
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Test Set ◽  
Non Invasive ◽  
Deep Learning Model

The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model—referred to as IGRNet—is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.

scholarly journals Developing a Task-Based Dialogue System for English Language Learning

2020 ◽  
Vol 10 (11) ◽  
pp. 306
Author(s):  
Kuo-Chen Li ◽  
Maiga Chang ◽  
Kuan-Hsing Wu
Keyword(s):  
Language Learning ◽  
Learning Style ◽  
English Language ◽  
Rapid Development ◽  
Past Research ◽  
Machine Learning Algorithms ◽  
Computer Assisted ◽  
Human Communication ◽  
Dialogue System ◽  
English Speaking

This research involved the design of a task-based dialogue system and evaluation of its learning effectiveness. Dialogue training still heavily depends on human communication with instant feedback or correction. However, it is not possible to provide a personal tutor for every English learner. With the rapid development of information technology, digitized learning and voice communication is a possible solution. The goal of this research was to develop an innovative model to refine the task-based dialogue system, including natural language understanding, disassembly intention, and dialogue state tracking. To enable the dialogue system to find the corresponding sentence accurately, the dialogue system was designed with machine learning algorithms to allow users to communicate in a task-based fashion. Past research has pointed out that computer-assisted instruction has achieved remarkable results in language reading, writing, and listening. Therefore, the direction of the discussion is to use the task-oriented dialogue system as a speaking teaching assistant. To train the speaking ability, the proposed system provides a simulation environment with goal-oriented characteristics, allowing learners to continuously improve their language fluency in terms of speaking ability by simulating conversational situational exercises. To evaluate the possibility of replacing the traditional English speaking practice with the proposed system, a small English speaking class experiment was carried out to validate the effectiveness of the proposed system. Data of 28 students with three assigned tasks were collected and analyzed. The promising results of the collected students’ feedback confirm the positive perceptions toward the system regarding user interface, learning style, and the system’s effectiveness.

Research on Oil Separator Control and Simulation

2012 ◽  
Vol 580 ◽  
pp. 365-368
Author(s):  
Xiao Li ◽  
Jin Hai Zhang
Keyword(s):  
Computer Simulation ◽  
Computer Technology ◽  
Rapid Development ◽  
Simulation Technology ◽  
Timing Control ◽  
Electronic Information ◽  
Typical Application ◽  
Training Assessment ◽  
Marine Oil ◽  
Important Means

With the rapid development of computer technology and electronic information technology is widely used, and application of computer simulation technology is increasingly popular. Engine room Simulator is a typical application of computer simulation technology in the field of maritime, has become an important means of training, assessment of the majority of the crew. Among them, marine oil separator system is an important part of marine Simulator. Simulation program for marine oil separator system timing control simulation for oil separator, and simulation for common failures.

scholarly journals Research on the Integration of Media Literacy Innovative Concept and Entrepreneurship Education and Digital Dynamic Creative Expression Talents

2021 ◽  
Vol 12 ◽  
Author(s):  
Jie Zhang ◽  
Mingming Zhang ◽  
Yaqian Liu ◽  
Ruimin Lyu ◽  
Rongrong Cui
Keyword(s):  
Digital Media ◽  
Media Literacy ◽  
Design Thinking ◽  
Rapid Development ◽  
Dynamic Performance ◽  
Entrepreneurship Education ◽  
Digital Design ◽  
Dynamic Expression ◽  
Specific Implementation ◽  
Innovative Concept

The rapid development of digital technology has created a variety of forms of digital media. In these emerging media, with the support of high-performance computers, increasingly dynamic performance has become possible, and the public has cultivated a preference for dynamic content cognition. This study, based on the basic characteristics of visual perception to the cognition of motion form, aims to cultivate the cognitive literacy of pan-digital media with innovative concepts and entrepreneurship education and to explore the cognition and innovative expression methods of dynamic language in digital design. The research leads the static oriented morphological exploration and expression to the dynamic expression and thinking of the same concept object. The basic thinking steps for students from “static” to “dynamic” are established, and students are encouraged to use “Synesthesia,” “metaphor” and other methods to carry out a “dynamic expression” level of emotional association. In the experiment, two different ways of design expression, static and dynamic, are required to design and evolve graphics. In this study, 50 freshmen were selected as the training objects for the planning and training of design thinking and performance means. In the visual elaboration and expression of the inner emotion of the same content with innovative concept and entrepreneurship education, not only should the changes and combinations of the graphics be innovated, but the emotional characteristics of the more abstract graphics should be explored as well. The feedback data of students’ thinking and cognition differences in the two stages of expression were obtained through a questionnaire and analyzed and compared. The experimental results show that after the training, students’ ability to develop innovative concepts and entrepreneurship education through dynamic expression, consciousness and perception were significantly improved. This research also provides a new vision and specific implementation method for the future training of digital dynamic innovation expression ability and the cultivation of innovative concepts of digital media literacy and entrepreneurship education.

scholarly journals Development Trend of Ecological and Green Visual Communication Design Education in the New Era

2021 ◽  
Author(s):  
Jian Rao, Jingwen Huang
Keyword(s):  
Visual Communication ◽  
Design Education ◽  
Design Thinking ◽  
Material Selection ◽  
Rapid Development ◽  
Green Design ◽  
Recycling Rate ◽  
Visual Communication Design ◽  
Communication Design ◽  
Specific Implementation

In recent years, green design has experienced rapid development, but also exposed a series of problems such as high cost, lack of humanization, low recycling rate and so on. For these drawbacks, designers have an unavoidable responsibility. This paper analyzes the existing problems in green design. From the change of design thinking, the eco-friendly design method is explored. In this paper, the design from material selection to production, and then to the recovery of the whole process of overall consideration, follow the principle of reduction, reuse, recycling. The content of visual communication design can be roughly classified. According to their own characteristics, the best design scheme is selected. Through the application of different media, replace the high pollution, high consumption of paper printing. It is proposed that the large-scale promotion of environmental protection materials can be realized by simplifying the quantity and types of materials. At the same time, this paper expounds the implementation method of green design education in art design education in Colleges and universities. This paper fully analyzes the purpose and value of green design education from the curriculum and content, teaching methods, the specific implementation of teaching effect evaluation and evaluation. The results show that promoting sustainable development of green education can cultivate students' creativity, innovation and creative ability. A perfect green design education system and a correct concept of ecological civilization can be established.

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