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2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)最新文献

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Design of Gearbox Monitoring System Based on Edge Computing 基于边缘计算的齿轮箱监控系统设计
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00074
Daixing Lu, Guoyao Gao, Ye Shen, Zhichao Tong
{"title":"Design of Gearbox Monitoring System Based on Edge Computing","authors":"Daixing Lu, Guoyao Gao, Ye Shen, Zhichao Tong","doi":"10.1109/AIAM57466.2022.00074","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00074","url":null,"abstract":"With the advent of Industry 4.0 and the Industrial Internet, the Internet of Things (IoT) development of applications is booming, the mining machinery working environment is extremely harsh, therefore, the requirements of the performance, quality, durability, reliability of its gearbox is pretty high, to meet these requirements, real-time monitoring is becoming a demanded task. For this purpose, a gearbox monitoring system based on edge computing is established. In the paper at hand, a novel Jacobi-type data parallel processing method is proposed, with which, the efficiency and life of the gearbox are calculated through the edge service APP. Traditional methods by solely utilizing cloud computing cannot effectively accomplish this task. Using cloud-edge collaboration technology, the Web application in scenarios such as intelligent mining is designed, which can grasp the operating status of equipment in the entire mining area, unify scheduling and orchestration of computing resources, update the monitoring model on edge computing nodes, and process and generate effective data of machinery and equipment in real-time at the edge computing device. It reduces the operation and maintenance cost, solves the problem of monitoring data congestion caused by insufficient data bandwidth, and ensures a stable and safe operation of mining machinery.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131180723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Steel Surface Defect Algorithm Based on Deep Residual Network 基于深度残差网络的钢材表面缺陷算法研究
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00068
Ge Jin, R. Hong, Xiaochuan Lin, Yanghe Liu
{"title":"Research on Steel Surface Defect Algorithm Based on Deep Residual Network","authors":"Ge Jin, R. Hong, Xiaochuan Lin, Yanghe Liu","doi":"10.1109/AIAM57466.2022.00068","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00068","url":null,"abstract":"This paper aims at the problems of hot-rolled-steel quality in industrial manufacturing and the difficulty of manual identification, low efficiency, and health hazards. We propose an end-to-end recognition method based on deep residual network to realize the automatic classification of hot-rolled steel surface defects. This method can effectively improve the production efficiency of hot-rolled steel. For the problem of insufficient negative sample data sets in the industrial field, we use varieties of data enhancement strategies to expand the original data, which solves the phenomenon of over-fitting due to insufficient samples during the model training process. The defect features are extracted through the CNN layer. Moreover, the residual structure is introduced to solve the problem of gradient disappearance and degradation when the network layer is deepened. The experimental results indicate that the accuracy of the ResNet-50 network model on the hot-rolled steel defect test sets can reach 93.34%, which is higher than the accuracy of the traditional network model. It also demonstrates this method has high reliability in the identification of defects that often occur in hot-rolled steel processing, including Rolled-in Scale, Crazing, Inclusion, Patches, Pitted Surface, and Scratches. The method proposed in this paper can meet the demand for industrial identification in the production process.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116836210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Emotion Recognition Based on Deep Learning Mixed Modalities 基于深度学习混合模式的情绪识别研究
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00032
Boyan Mi, Jiangdong Lu, Fen Zheng
{"title":"Research on Emotion Recognition Based on Deep Learning Mixed Modalities","authors":"Boyan Mi, Jiangdong Lu, Fen Zheng","doi":"10.1109/AIAM57466.2022.00032","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00032","url":null,"abstract":"With the rapid development of artificial intelligence and machine learning in recent years, emotion recognition has gradually become an important research topic. Emotion recognition in one direction has a good research foundation after long-term development, and from multiple directions, more effective information can be extracted, thereby improving the accuracy of emotion recognition. This paper analyzes from the perspective of emotional recognition of physiological signals such as brainwave signals and facial emotion recognition, respectively, preprocessing, feature extraction, SVM feature classification, LSTM combined with convolutional neural network emotion recognition for the acquired signals. And the accuracy of mixed-modal emotion recognition is compared. Compared with single facial expression emotion recognition, mixed-modal emotion recognition extracts more feature information and has a higher accuracy.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113956798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Power Load Forecasting Based on PSO-LSTM 基于PSO-LSTM的电力负荷预测研究
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00038
Zhicheng Yu, H. Sun, Bining Zhang
{"title":"Research on Power Load Forecasting Based on PSO-LSTM","authors":"Zhicheng Yu, H. Sun, Bining Zhang","doi":"10.1109/AIAM57466.2022.00038","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00038","url":null,"abstract":"In order to improve the prediction accuracy of electricity consumption, the particle swarm optimization algorithm was proposed to find the optimal hyperparameters of long-term and short-term memory (LSTM) neural networks, and the two models are combined to form a power load forecasting model. Aiming at the problem that it is difficult to manually select the LSTM hyperparameters, the PSO algorithm can effectively find the global optimal solution to find the hyperparameters of LSTM model. After continuous training, we find the appropriate hyperparameters and verify them The experimental results show that compared with the traditional LSTM network, the performance and prediction accuracy of the combined pso-lstm combination model have been significantly improved, which has certain academic value and application significance.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115920914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
3D Face Recognition Algorithm Based on Deep Belief Network 基于深度信念网络的三维人脸识别算法
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00048
Lixia Liu
{"title":"3D Face Recognition Algorithm Based on Deep Belief Network","authors":"Lixia Liu","doi":"10.1109/AIAM57466.2022.00048","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00048","url":null,"abstract":"Although the depth learning algorithm reduces the workload of face recognition to a certain extent, the local characteristics of 3D face images is ignored, resulting in low accuracy of 3D face recognition. Therefore, this paper proposed a new 3D face recognition method using LBP algorithm improve depth belief network. Firstly, LBP algorithm and depth belief network are analyzed, and then LBP texture feature vector of 3D face image is obtained, which is used as the input feature of depth belief network to capture the local information of 3D face image. Finally, this paper designed a 3D face image recognition process and realized 3D face recognition based on improved depth belief network. The proposed method is trained on FERET face image database, and the simulation results show that the proposed method has higher 3D face recognition rate and shorter recognition time, compared with the comparison method, which shows that the application effect of the improved depth learning algorithm in 3D face recognition is better.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123808409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Test Scheme Design of Chinese Speech Recognition System 中文语音识别系统的测试方案设计
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00017
Yanfei Liu
{"title":"A Test Scheme Design of Chinese Speech Recognition System","authors":"Yanfei Liu","doi":"10.1109/AIAM57466.2022.00017","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00017","url":null,"abstract":"This paper analyzes the characteristics and classification of the current speech recognition system, the existing test scheme and the existing problems. On this basis, a test scheme is provided for voice detection speech recognition system. By establishing a voice sample library to store test samples, the storage of command words, the selection of combinations and the reproduction of test conditions are realized, and the utilization rate and efficiency of test samples are improved. Combined with the actual application environment of the speech recognition system, the test system has carried out the test environment classification test scheme, carried out the pure environment command word test and the interference environment fault tolerance test, respectively characterized the speech recognition system's ability to recognize itself and adapt to the environment, and directly output the test results. Through experimental verification, this scheme can improve the accuracy of the test results of the speech recognition ability test, and improve the horizontal comparability of the related sound detection systems.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123698672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Implements of Transformer in NLP and DKT NLP和DKT中变压器的实现
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00163
Haotong Gong
{"title":"Implements of Transformer in NLP and DKT","authors":"Haotong Gong","doi":"10.1109/AIAM57466.2022.00163","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00163","url":null,"abstract":"Transformer is a strong model proposed by Google team in 2017. It was a huge improvement that it entirely abandons the mechanism of Recurrent Neural Network (RNN) and Convolutional Neural Network (RNN). As a result, soon it became a popular choice in a diversity of scenarios. A typical implement of Transformer is for handling text-like input sequences, such as Natural Language Process (NLP) and Knowledge Tracing (KT). Although Transformer is a strong model, it still has a number of improvements. Some state-of-the-art Deep-Learning-based models (e.g., BERT in [2], SAINT in [3], etc.) are based on Transformer. In this paper, I give some examples of application of Transformer or Transformer-based models and summarize the pros and cons of Transformer.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115271510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Computer Vision Technology Applied in the Task of Mechanical Parts Inspection 计算机视觉技术在机械零件检测任务中的应用
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00008
Tianrui Liu, Xumin Zhou
{"title":"A Computer Vision Technology Applied in the Task of Mechanical Parts Inspection","authors":"Tianrui Liu, Xumin Zhou","doi":"10.1109/AIAM57466.2022.00008","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00008","url":null,"abstract":"In this paper, based on the computer vision detection technology, the parts detection system is studied and discussed. This system mainly uses manual image segmentation and other methods, which can fully avoid the defects of image segmentation, improve the accuracy of image detection and ensure the detection speed. The system uses CCD or CMOS digital camera to capture the image of parts on the assembly line, which is roughly divided into three parts. First of all, it is necessary to take the template image that fully meets the quality requirements of the parts, and carry out preprocessing such as smooth filtering for the image taken. Second, during the test, the image of the component to be tested is taken on the assembly line, and the position of the detection target image and the standard template image is registered through the image registration algorithm. Finally, the image features of each partition area of the image to be detected are extracted and compared with the features of each partition area of the standard template image, so as to detect the processing quality of the assembly line parts, whether there is a component assembly error, error prompt and alarm, so as to achieve intelligent detection Purpose.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129898742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Object Detection System Based on Faster R-CNN 基于更快R-CNN的目标检测系统
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00027
Jiangdong Lu, Dongfang Li, M. Wang, Boyan Mi, Penglong Wang, Zhuocheng Dai, Fen Zheng
{"title":"Object Detection System Based on Faster R-CNN","authors":"Jiangdong Lu, Dongfang Li, M. Wang, Boyan Mi, Penglong Wang, Zhuocheng Dai, Fen Zheng","doi":"10.1109/AIAM57466.2022.00027","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00027","url":null,"abstract":"Aiming at the low efficiency of image target detection in cloud computing mode, a target detection system suitable for edge devices is designed. First, the system selects Faster R-CNN in the deep learning algorithm as the target detection and recognition model, and trims the network feature extraction layer through the residual module. Second, a proposal region extraction sub-network with adjustable anchor boxes is used to obtain proposal regions more quickly by setting a convolutional sliding window of reasonable size. Finally, a complete target detection system is built using hardware such as Raspberry Pi development board and Intel neural computing stick. The experimental results on the KITTI dataset show that the system achieves good detection results, and achieves a faster recognition speed without reducing the target detection accuracy, which can meet the real-time requirements of offline work.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124562593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Research on the Prediction Method of Vehicle Moment of Inertia Based on BP Neural Network and Big Data 基于BP神经网络和大数据的汽车惯性矩预测方法研究
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) Pub Date : 2022-10-01 DOI: 10.1109/AIAM57466.2022.00051
Liguang Wu, X. Li, Guang-Ye Li
{"title":"Research on the Prediction Method of Vehicle Moment of Inertia Based on BP Neural Network and Big Data","authors":"Liguang Wu, X. Li, Guang-Ye Li","doi":"10.1109/AIAM57466.2022.00051","DOIUrl":"https://doi.org/10.1109/AIAM57466.2022.00051","url":null,"abstract":"With the development of the production technology of the automobile industry, the requirements for parameter accuracy in various motion states are becoming higher and higher in the process of automobile design. In order to improve the input accuracy of the moment of inertia value in the vehicle simulation, and then make the vehicle simulation get more accurate results, this paper proposes a vehicle moment of inertia prediction method based on BP neural network. Through the selection of the indicators affecting the moment of inertia, the sample data is determined, and the sample data is divided into training set and test set to train and verify the BP neural network prediction model. The results show that the accuracy of the moment of inertia predicted by the neural network is significantly higher than that calculated by the traditional empirical formula, and can be used in the process of automobile development. This paper uses big data and neural network to predict vehicle simulation input parameters, so as to obtain more accurate vehicle simulation results, which can be applied to other aspects of the vehicle simulation field.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126757931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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