Submit to Electronics Review for Electronics Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Applications of Deep Learning Techniques

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 14591

Share This Special Issue

Special Issue Editors

Prof. Dr. Junhua Ding

E-Mail Website
Guest Editor

Department of Information Science, University of North Texas, Denton, TX 76203, USA
Interests: machine learning; software engineering; legal intelligence; biomedical computation
Special Issues, Collections and Topics in MDPI journals

Dr. Haihua Chen

E-Mail Website
Guest Editor

Department of Information Science, University of North Texas, Denton, TX 76203, USA
Interests: natural language processing; information retrieval; applied machine learning; data quality in machine learning

Dr. Yunhe Feng

E-Mail Website
Guest Editor

Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA
Interests: responsible AI; data security and privacy; and big data analytics

Dr. Tozammel Hossain

E-Mail Website
Guest Editor

Department of Information Science, University of North Texas, Denton, TX 76203, USA
Interests: machine learning; data science; bioinformatics; health informatics; social media analytics; cybersecurity

Special Issue Information

Dear Colleagues,

Since the concept “Deep Neural Networks (DNNs)” was proposed, a number of subsequent deep learning techniques, including convolutional neural networks, graph neural networks, sequence-to-sequence models, generative models, deep reinforcement learning, and others, have been proposed. Those deep-learning techniques are widely applied to different fields and domains, including self-driving, virtual assistants, healthcare, personalization, automatic game playing, chatbots, etc. Meanwhile, fantastic deep learning applications such as AlphaGo, Alexa, AlphaFold2, ChatGPT, and others have been developed and are changing human life.

The journal's Special Issue aims to set a milestone in this rapidly growing subject area with archive articles in the journal to reflect the current state of the art in the research and/or current practices, as well as a set of survey, review, and visionary research papers that summarize the results so far, analyze the challenges ahead, and set a roadmap for the future directions. All submissions will be rigorously reviewed according to the standard of internationally top-ranked journals fairly and scientifically according to the criteria of (a) the scientific and technological soundness, (b) the maturity of the research work, (c) the relevance to the theme of the Special Issue, (d) the timeliness of the work, (e) the significance of the contribution, and (f) the presentation quality.

The following topics are especially welcome, but submissions are not limited to them.

Deep learning techniques for legal intelligence, such as legal text classification, argument mining, judgment prediction, and others.
Deep learning techniques for natural language processing, such as information retrieval, text summarization, sentiment analysis, and others.
Deep learning techniques for software engineering, including software development, software testing, software maintenance, and others.
Deep learning techniques for healthcare and medical systems, such as precision medicine, drug discovery, molecular modeling, smart diagnostics, medical imaging, and others.
Deep learning techniques for social media analysis, such as real-time violence detection, dis/misinformation, hate speech recognition, country reputation monitoring, and others
Deep learning techniques for academic data mining, such as information extraction from scientific text, innovation measurement, citation analysis, and others.
Deep generative techniques and applications for education, entertainment, finance, materials science, and others.
The application of deep learning techniques in other special domains such as cybersecurity, business intelligence, Internet of Things, precious agriculture, smart cities, etc.
Data quality evaluation, assurance, and improvement for deep learning in various applications.
Responsibility, fairness, ethics, bias, trustworthiness, transparency, accountability, safety, and privacy in deep learning applications.

Prof. Dr. Junhua Ding
Dr. Haihua Chen
Dr. Yunhe Feng
Dr. Tozammel Hossain
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
deep learning
reinforcement Learning
large language models

Published Papers (14 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

20 pages, 7762 KiB

Open AccessArticle

Applying Swin Architecture to Diverse Sign Language Datasets

by Yulia Kumar, Kuan Huang, Chin-Chien Lin, Annaliese Watson, J. Jenny Li, Patricia Morreale and Justin Delgado

Electronics 2024, 13(8), 1509; https://doi.org/10.3390/electronics13081509 - 16 Apr 2024

Viewed by 386

Abstract

In an era where artificial intelligence (AI) bridges crucial communication gaps, this study extends AI’s utility to American and Taiwan Sign Language (ASL and TSL) communities through advanced models like the hierarchical vision transformer with shifted windows (Swin). This research evaluates Swin’s adaptability across sign languages, aiming for a universal platform for the unvoiced. Utilizing deep learning and transformer technologies, it has developed prototypes for ASL-to-English translation, supported by an educational framework to facilitate learning and comprehension, with the intention to include more languages in the future. This study highlights the efficacy of the Swin model, along with other models such as the vision transformer with deformable attention (DAT), ResNet-50, and VGG-16, in ASL recognition. The Swin model’s accuracy across various datasets underscore its potential. Additionally, this research explores the challenges of balancing accuracy with the need for real-time, portable language recognition capabilities and introduces the use of cutting-edge transformer models like Swin, DAT, and video Swin transformers for diverse datasets in sign language recognition. This study explores the integration of multimodality and large language models (LLMs) to promote global inclusivity. Future efforts will focus on enhancing these models and expanding their linguistic reach, with an emphasis on real-time translation applications and educational frameworks. These achievements not only advance the technology of sign language recognition but also provide more effective communication tools for the deaf and hard-of-hearing community. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

16 pages, 403 KiB

Open AccessArticle

Automatic Speech Recognition of Vietnamese for a New Large-Scale Corpus

by Linh Thi Thuc Tran, Han-Gyu Kim, Hoang Minh La and Su Van Pham

Electronics 2024, 13(5), 977; https://doi.org/10.3390/electronics13050977 - 04 Mar 2024

Viewed by 865

Abstract

Vietnamese is an under-resourced language. The requirement for a large-scale and high-quality Vietnamese speech corpus increases on demand. We introduce a new large-scale Vietnamese speech corpus with 100.5 h collected from various audio sources in the Internet. The raw collected audio was processed to obtain clean speech. Transcription of the clean speech was made manually. The new corpus was analyzed in terms of gender, topic and regional dialect. Results shows that the new corpus has good diversity of genders, topics and regional dialects. We also evaluated the new corpus using state-of-the-art automatic speech recognition models like LAS and Speech-Transformer for multiple scenarios. This is the first time that these models have been applied to Vietnamese speech recognition and obtained reasonable results. Simulation results showed that the new corpus would be a good dataset for the Vietnamese ASR tasks because it reflected correctly difficulties in recognizing speech from different dialects and topic domains. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

27 pages, 7266 KiB

Open AccessArticle

ATCNet: A Novel Approach for Predicting Highway Visibility Using Attention-Enhanced Transformer–Capsule Networks

by Wen Li, Xuekun Yang, Guowu Yuan and Dan Xu

Electronics 2024, 13(5), 920; https://doi.org/10.3390/electronics13050920 - 28 Feb 2024

Viewed by 535

Abstract

Meteorological disasters on highways can significantly reduce road traffic efficiency. Low visibility caused by dense fog is a severe meteorological disaster that greatly increases the incidence of traffic accidents on highways. Accurately predicting highway visibility and taking timely countermeasures can mitigate the impact of meteorological disasters and enhance traffic safety. This paper introduces the ATCNet model for highway visibility prediction. In ATCNet, we integrate Transformer, Capsule Networks (CapsNet), and self-attention mechanisms to leverage their respective complementary strengths. The Transformer component effectively captures the temporal characteristics of the data, while the Capsule Network efficiently decodes the spatial correlations and hierarchical structures among multidimensional meteorological elements. The self-attention mechanism, serving as the final decision-refining step, ensures that all key temporal and spatial hierarchical information is fully considered, significantly enhancing the accuracy and reliability of the predictions. This integrated approach is crucial in understanding highway visibility prediction tasks influenced by temporal variations and spatial complexities. Additionally, this study provides a self-collected publicly available dataset, WD13VIS, for meteorological research related to highway traffic in high-altitude mountain areas. This study evaluates the model’s performance in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE). Experimental results show that our ATCNet reduces the MSE and MAE by 1.21% and 3.7% on the WD13VIS dataset compared to the latest time series prediction model architecture. On the comparative dataset WDVigoVis, our ATCNet reduces the MSE and MAE by 2.05% and 5.4%, respectively. Our model’s predictions are accurate and effective, and our model shows significant progress compared to competing models, demonstrating strong universality. This model has been integrated into practical systems and has achieved positive results. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

26 pages, 8539 KiB

Open AccessArticle

Multi-Spatio-Temporal Convolutional Neural Network for Short-Term Metro Passenger Flow Prediction

by Ye Lu, Changjiang Zheng, Shukang Zheng, Junze Ma, Zhilong Wu, Fei Wu and Yang Shen

Electronics 2024, 13(1), 181; https://doi.org/10.3390/electronics13010181 - 30 Dec 2023

Viewed by 920

Abstract

Accurate short-term prediction of metro passenger flow can offer significant assistance in optimizing train schedules, reducing congestion during peak times, and improving the service level of the metro system. Currently, most models do not fully utilize the high-resolution data aggregated by automatic fare collection systems. Therefore, we propose a model, called MST-GRT, that aggregates multi-time granularity data and considers multi-graph structures. Firstly, we analyze the correlation between metro passenger flow sequences at different time granularities and establish a principle for extracting the spatiotemporal correlation of data at different time granularities using the multi-graph neural network. Subsequently, we use residual blocks to construct a deep convolutional neural network to aggregate the data of different time granularities from small to large, obtaining multi-channel feature maps of multi-time granularity. To process the multi-channel feature maps, we use 2D dilated causal convolution to reconstruct the TCN (Temporal Convolutional Network) to compress the channel number of the feature maps and extract the time dependency of the data, and finally output the results through a fully connected layer. The experimental results demonstrate that our model outperforms the baseline models on the Hangzhou Metro smart-card data set. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

18 pages, 9034 KiB

Open AccessArticle

Small Foreign Object Detection in Automated Sugar Dispensing Processes Based on Lightweight Deep Learning Networks

by Jiaqi Lu, Soo-Hong Lee, In-Woo Kim, Won-Joong Kim and Min-Soo Lee

Electronics 2023, 12(22), 4621; https://doi.org/10.3390/electronics12224621 - 12 Nov 2023

Cited by 1 | Viewed by 1353

Abstract

This study addresses the challenges that conventional network models face in detecting small foreign objects on industrial production lines, exemplified by scenarios where a single piece of iron filing occupies approximately 0.002% of the image area. To tackle this, we introduce an enhanced YOLOv8-MeY model for detecting foreign objects on the surface of sugar bags. Firstly, the introduction of a 160 × 160-scale small object detection layer and integration of the Global Attention Mechanism (GAM) attention module into the feature fusion network (Neck) increased the network’s focus on small objects. This enhancement improved the network’s feature extraction and fusion capabilities, which ultimately increased the accuracy of small object detection. Secondly, the model employs the lightweight network GhostNet, replacing YOLOv8’s principal feature extraction network, DarkNet53. This adaptation not only diminishes the quantity of network parameters but also augments feature extraction capabilities. Furthermore, we substituted the Bottleneck in the C2f of the YOLOv8 model with the Spatial and Channel Reconstruction Convolution (SCConv) module, which, by mitigating the spatial and channel redundancy inherent in standard convolutions, reduced computational demands while elevating the performance of the convolutional network model. The model has been effectively applied to the automated sugar dispensing process in food factories, exhibiting exemplary performance. In detecting diverse foreign objects like 2 mm iron filings, 7 mm wires, staples, and cockroaches, the YOLOv8-MeY model surpasses the Faster R-CNN model and the contemporaneous YoloV8n model of equivalent parameter scale across six metrics: precision, recall, mAP@0.5, parameters, GFLOPs, and model size. Through 400 manual placement tests involving four types of foreign objects, our statistical results reveal that the model achieves a recognition rate of up to 92.25%. Ultimately, we have successfully deployed this model in automated sugar bag dispensing scenarios. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

18 pages, 41585 KiB

Open AccessArticle

Multi-Agent Multi-Target Pursuit with Dynamic Target Allocation and Actor Network Optimization

by Baoqiang Han, Lin Shi, Xueyuan Wang and Lihua Zhuang

Electronics 2023, 12(22), 4613; https://doi.org/10.3390/electronics12224613 - 11 Nov 2023

Viewed by 856

Abstract

In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-agent systems using multi-agent deep reinforcement learning algorithms. Multi-agent multi-target pursuit has faced new challenges in practical applications, where pursuers need to plan collision-free paths and appropriate multi-target allocation strategies to determine which target to track at the current time for each pursuer. We design three feasible multi-target allocation strategies from different perspectives. We compare our allocation strategies in the multi-agent multi-target pursuit environment that models collision risk and verify the superiority of the allocation strategy marked as POLICY3, considering the overall perspective of agents and targets. We also find that there is a significant gap in the tracking policies learned by agents when using the multi-agent reinforcement learning algorithm MATD3. We propose an improved algorithm, DAO-MATD3, based on dynamic actor network optimization. The simulation results show that the proposed POLICY3-DAO-MATD3 method effectively improves the efficiency of completing multi-agent multi-target pursuit tasks. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

14 pages, 4056 KiB

Open AccessArticle

Using a Deep Neural Network with Small Datasets to Predict the Initial Production of Tight Oil Horizontal Wells

by Yuxi Yang, Chengqian Tan, Youyou Cheng, Xiang Luo and Xiangliang Qiu

Electronics 2023, 12(22), 4570; https://doi.org/10.3390/electronics12224570 - 08 Nov 2023

Cited by 1 | Viewed by 774

Abstract

Due to its abundant reserves, tight oil has emerged as a significant substitute for conventional petroleum resources. It has become one of the focal points of exploration and research, and a new hot spot in global unconventional oil and gas exploration and development. This has led to a significant increase in the demand for forecasting the production capacity of tight oil horizontal wells. The deep neural network (DNN), as a mature model, has demonstrated significant advantages in many fields. However, due to the confidentiality and uniqueness of oilfield data, acquiring large datasets has become a challenge. Traditional methods using small datasets for training DNN models result in low accuracy and overfitting issues, which hinders the development of neural networks in the petroleum industry. This study aims to predict the initial production capacity of tight oil horizontal wells by using a small dataset of 650 data points through a DNN model. The research results indicate that pre-trained and fine-tuned DNNs outperform shallow neural networks, supporting vector machines, and DNN trained with traditional methods in terms of better generalization performance. Their accuracy reached 91.3%, demonstrating that it is reasonable to use a small dataset with pre-trained and fine-tuned DNN models. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

17 pages, 5127 KiB

Open AccessArticle

Deep Learning Neural Network-Based Detection of Wafer Marking Character Recognition in Complex Backgrounds

by Yufan Zhao, Jun Xie and Peiyu He

Electronics 2023, 12(20), 4293; https://doi.org/10.3390/electronics12204293 - 17 Oct 2023

Viewed by 930

Abstract

Wafer characters are used to record the transfer of important information in industrial production and inspection. Wafer character recognition is usually used in the traditional template matching method. However, the accuracy and robustness of the template matching method for detecting complex images are low, which affects production efficiency. An improved model based on YOLO v7-Tiny is proposed for wafer character recognition in complex backgrounds to enhance detection accuracy. In order to improve the robustness of the detection system, the images required for model training and testing are augmented by brightness, rotation, blurring, and cropping. Several improvements were adopted in the improved YOLO model, including an optimized spatial channel attention model (CBAM-L) for better feature extraction capability, improved neck structure based on BiFPN to enhance the feature fusion capability, and the addition of angle parameter to adapt to tilted character detection. The experimental results showed that the model had a value of 99.44% for mAP@0.5 and an F1 score of 0.97. In addition, the proposed model with very few parameters was suitable for embedded industrial devices with small memory, which was crucial for reducing the hardware cost. The results showed that the comprehensive performance of the improved model was better than several existing state-of-the-art detection models. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

16 pages, 4373 KiB

Open AccessEditor’s ChoiceArticle

Computer Vision Algorithms for 3D Object Recognition and Orientation: A Bibliometric Study

by Youssef Yahia, Júlio Castro Lopes and Rui Pedro Lopes

Electronics 2023, 12(20), 4218; https://doi.org/10.3390/electronics12204218 - 12 Oct 2023

Cited by 1 | Viewed by 1259

Abstract

This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

13 pages, 431 KiB

Open AccessArticle

A Multi-Faceted Exploration Incorporating Question Difficulty in Knowledge Tracing for English Proficiency Assessment

by Jinsung Kim, Seonmin Koo and Heuiseok Lim

Electronics 2023, 12(19), 4171; https://doi.org/10.3390/electronics12194171 - 08 Oct 2023

Viewed by 842

Abstract

Knowledge tracing (KT) aims to trace a learner’s understanding or achievement of knowledge based on learning history. The surge in online learning systems has intensified the necessity for automated measurement of students’ knowledge states. In particular, in the case of learning in the English proficiency assessment field, such as TOEIC, it is required to model the knowledge states by reflecting on the difficulty of questions. However, previous KT approaches often overly complexify their model structures solely to accommodate difficulty or consider it only for a secondary purpose such as data augmentation, hindering the adaptability of potent and general-purpose models such as Transformers to other cognitive components. Addressing this, we investigate the integration of question difficulty within KT with a potent general-purpose model for application in English proficiency assessment. We conducted empirical studies with three approaches to embed difficulty effectively: (i) reconstructing input features by incorporating difficulty, (ii) predicting difficulty with a multi-task learning objective, and (iii) enhancing the model’s output representations from (i) and (ii). Experiments validate that direct inclusion of difficulty in input features, paired with enriched output representations, consistently amplifies KT performance, underscoring the significance of holistic consideration of difficulty in the KT domain. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

19 pages, 5943 KiB

Open AccessArticle

A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment

by Wang Li, Chaozhu Hu and Youxi Luo

Electronics 2023, 12(18), 3960; https://doi.org/10.3390/electronics12183960 - 20 Sep 2023

Viewed by 1442

Abstract

Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this paper introduces a novel quantitative investment approach. For the first time, we fully consider two dimensions of news, including headlines and contents, and further explore their combined impact on modeling stock price. Our approach initially employs fundamental analysis to screen valuable stocks. Subsequently, we built technical factors based on historical trading data. We then integrated news headlines and content summarized through language models to extract semantic information and representations. Lastly, we constructed a deep neural model to capture global features by combining technical factors with semantic representations, enabling stock prediction and trading decisions. Empirical results conducted on over 4000 stocks from the Chinese stock market demonstrated that incorporating news content enriched semantic information and enhanced objectivity in sentiment analysis. Our proposed method achieved an annualized return rate of 32.06% with a maximum drawdown rate of 5.14%. It significantly outperformed the CSI 300 index, indicating its applicability to guiding investors in making more effective investment strategies and realizing considerable returns. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

12 pages, 398 KiB

Open AccessArticle

Long-Term Prediction Model for NOx Emission Based on LSTM–Transformer

by Youlin Guo and Zhizhong Mao

Electronics 2023, 12(18), 3929; https://doi.org/10.3390/electronics12183929 - 18 Sep 2023

Cited by 1 | Viewed by 924

Abstract

Excessive nitrogen oxide (NOx) emissions result in growing environmental problems and increasingly stringent emission standards. This requires a precise control for NOx emissions. A prerequisite for precise control is accurate NOx emission detection. However, the NOx measurement sensors currently in use have serious lag problems in measurement due to the harsh operating environment and other problems. To address this issue, we need to make long-term prediction for NOx emissions. In this paper, we propose a long-term prediction model based on LSTM–Transformer. First, the model uses self-attention to capture long-term trend. Second, long short-term memory network (LSTM) is used to capture short-term trends and as secondary position encoding to provide positional information. We construct them using a parallel structure. In long-term prediction, experimental results on two real datasets with different sampling intervals show that the proposed prediction model performs better than the currently popular methods, with 28.2% and 19.1% relative average improvements on the two datasets, respectively. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

13 pages, 4023 KiB

Open AccessArticle

Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost

by Tingxia Li, Tiankai Li, Rina Su, Jile Xin and Ding Han

Electronics 2023, 12(16), 3506; https://doi.org/10.3390/electronics12163506 - 18 Aug 2023

Viewed by 857

Abstract

Aiming at the problem of time-consuming, labor-intensive, and low-accuracy monitoring of goat motion behavior (lying, standing, walking, and running) while relying on the three-axis acceleration sensor and taking the acceleration data obtained from the goat back collection point as the research object, a method based on social learning (SL) is proposed using the Whale Optimization Algorithm (WOA) and XGBoost for goat motion behavior recognition. In this method, the XGBoost parameters are optimized by the WOA combined with social learning strategies to improve the classification and recognition accuracy. The results show that the recognition rate of lying behavior was as high as 97.14%, and the average recognition rate of the four movement behaviors was 94.42%, meeting the requirements of goat motion behavior recognition. Compared with the conventional XGBoost algorithm, the average recognition rate was increased by 3.41% and the recognition accuracy was improved. The results of this study can provide a reference for goat health assessment and intelligent disease warning. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Figure 1

18 pages, 7036 KiB

Open AccessArticle

Deconvolutional Neural Network for Generating Spray Trajectory of Shoe Soles

by Jing Li, Yuming Wang, Lijun Li, Chao Xiong and Hongdi Zhou

Electronics 2023, 12(16), 3470; https://doi.org/10.3390/electronics12163470 - 16 Aug 2023

Viewed by 858

Abstract

The footwear industry is moving towards automation and intellectualization. To overcome the drawbacks of the high-cost and low-efficiency traditional manual shoe sole gluing process, automatic methods were utilized for generating spray trajectories. Currently, most of the reported automatic methods for generating spray trajectories mainly rely on the outer contour bias method. However, the glue is only applied to the area near the edge/contour of shoe soles and the fixed offset distance in the outer contour bias method cannot adapt to the immense amount of shoe styles with high precision and achieve applicability for irregular and unique sole designs. An intuitive yet logical approach to fulfill the requirements is to utilize the deconvolutional neural network for generating shoe sole spray trajectories. In this work, we treated the glue trajectory prediction as an image-to-image prediction and established a novel deconvolutional neural network to generate shoe sole spray trajectories. The as-proposed deconvolutional neural network for generating spray trajectory offered significant advantages over the existing bias-based methods, including: (1) based on the novel deconvolutional neural network, the proposed method for generating shoe sole spray trajectories exhibits greater applicability to irregular shoe soles, which improves the spray accuracy without compromising the spray efficiency; (2) we discard all the pooling layers, which only consist of convolutional and deconvolutional layers, to preserve more spatial information and achieve higher spray accuracy through end-to-end mapping from shoe sole images to shoe sole spray trajectories, resulting in an improved spray accuracy without sacrificing spray efficiency. The Dice similarity coefficient and Hausdorff distance were used as the evaluation metrics to assess the performance of our approach. Our proposed method showed an ultra-high accuracy and precision with a Dice similarity coefficient over 99.25% and a Hausdorff distance less than 1.2 mm, which are ~10% higher than the spray accuracy of other reported traditional methods. Our findings would bring significant improvements to the field of automatic shoe sole spray trajectory generation, which has the potential to promote the utilization of intelligent technologies in the footwear industry. Full article

(This article belongs to the Special Issue Applications of Deep Learning Techniques)

► Show Figures

Journal Menu

Journal Browser

Applications of Deep Learning Techniques

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (14 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI