Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201283
Yi He, Yi Tao
In the coming years, the Underwater Internet of Things is expected to bridge different technologies for sensing the ocean, allowing it to become an intelligent network of interconnected underwater objects with self-learning and intelligent computing capabilities. The key technology of the underwater network is underwater acoustic communication. In order to ensure the performance of the physical layer, channel equalization is usually adopted, the cognitive equalization algorithm is proposed based on the deep Q-network (DQN) to improve the selection of equalizer structure parameters and recursive algorithm parameters. First, the multi-scale time-varying underwater acoustic (UWA) channel model generates a certain number of UWA channels as the training set, and the cognitive equalizer can adaptively select the optimal number of taps and step length according to the channel impulse response (CIR) and signal-to-noise ratio (SNR) conditions of different UWA channels. Simulation results show that compared with the classical adaptive equalization algorithm, the trained cognitive equalizer not only has better generalization performance, but also can significantly reduce the bit error rate (BER) and shorten the channel equalization time, improving the equalization performance.
{"title":"Deep Reinforcement Learning Based Cognitive Equalization Algorithm Research in Underwater Communication","authors":"Yi He, Yi Tao","doi":"10.1109/CCAI57533.2023.10201283","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201283","url":null,"abstract":"In the coming years, the Underwater Internet of Things is expected to bridge different technologies for sensing the ocean, allowing it to become an intelligent network of interconnected underwater objects with self-learning and intelligent computing capabilities. The key technology of the underwater network is underwater acoustic communication. In order to ensure the performance of the physical layer, channel equalization is usually adopted, the cognitive equalization algorithm is proposed based on the deep Q-network (DQN) to improve the selection of equalizer structure parameters and recursive algorithm parameters. First, the multi-scale time-varying underwater acoustic (UWA) channel model generates a certain number of UWA channels as the training set, and the cognitive equalizer can adaptively select the optimal number of taps and step length according to the channel impulse response (CIR) and signal-to-noise ratio (SNR) conditions of different UWA channels. Simulation results show that compared with the classical adaptive equalization algorithm, the trained cognitive equalizer not only has better generalization performance, but also can significantly reduce the bit error rate (BER) and shorten the channel equalization time, improving the equalization performance.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123047346","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}
Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201293
Shuai Li, Linze Wang, Shengzhe Xu, D. Gao
Needle insertion is a minimally invasive treatment technique. During the surgery, the insertion path needs to be planned in advance to manipulate the flexible needle to avoid nerves and organs. In order to predict the insertion trajectory, a force-visual perception prediction model based on the BP neural network is established. Through the force analysis of the flexible needle, the displacement L of the needle holder and the reaction force Fr, insertion force F and torque M on the needle holder are used as the force visual perception model. Input to predict the trajectory of the needle tip during insertion. Through experiments on three different types of flexible needles, data are collected to train the model. The lowest mean absolute error (MAE) of the model is 0.7490, the correlation coefficient R is between 0.99962 and 0.99996, and the accuracy is high. The force visual perception model provides a feasible prediction of the needle tip trajectory. The results show that the displacement of the needle tip in the X and Y directions predicted by the model is basically consistent with the experimental results, and the insertion trajectory can be predicted more accurately.
{"title":"Research on Force Visual Perception of Bevel-Tip Needle Based on BP Neural Network","authors":"Shuai Li, Linze Wang, Shengzhe Xu, D. Gao","doi":"10.1109/CCAI57533.2023.10201293","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201293","url":null,"abstract":"Needle insertion is a minimally invasive treatment technique. During the surgery, the insertion path needs to be planned in advance to manipulate the flexible needle to avoid nerves and organs. In order to predict the insertion trajectory, a force-visual perception prediction model based on the BP neural network is established. Through the force analysis of the flexible needle, the displacement L of the needle holder and the reaction force Fr, insertion force F and torque M on the needle holder are used as the force visual perception model. Input to predict the trajectory of the needle tip during insertion. Through experiments on three different types of flexible needles, data are collected to train the model. The lowest mean absolute error (MAE) of the model is 0.7490, the correlation coefficient R is between 0.99962 and 0.99996, and the accuracy is high. The force visual perception model provides a feasible prediction of the needle tip trajectory. The results show that the displacement of the needle tip in the X and Y directions predicted by the model is basically consistent with the experimental results, and the insertion trajectory can be predicted more accurately.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133553159","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}
Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201245
Daiki Nakada, Tomomi Ogawa
Chewing well is known to be beneficial for human health. However, a simple method to measure the number of chews for health guidance has not been established. In this paper, we propose a simple method to measure the number of chews using a photographic device such as a smart phone. When a video of chewing during eating is filmed, the brightness of the chewer's face changes as the jaw moves up and down due to chewing. When the values are graphed, the change in brightness results in a waveform shape that is easy to understand. Since the number of chews can be estimated from the number of waves in the waveform, the number of chews is measured using a neural network that counts the number of waves. To compensate for the small amount of data, we use a large amount of pseudowaveforms, such as sine waves. Then, a learning model that determines the number of repetitions is created, and a large amount of pseudo-waveform data is used for pre-training. The parameters of the trained model are determining by transfer learning so that the model can be applied to a small amount of data. As a result of learning the video waveform data, we were able to measure 96.6% of the data within ± 2 by moving all the parameters of the trained model.
{"title":"Chewing Detection Using Brightness Changes in Video based on Deep Learning","authors":"Daiki Nakada, Tomomi Ogawa","doi":"10.1109/CCAI57533.2023.10201245","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201245","url":null,"abstract":"Chewing well is known to be beneficial for human health. However, a simple method to measure the number of chews for health guidance has not been established. In this paper, we propose a simple method to measure the number of chews using a photographic device such as a smart phone. When a video of chewing during eating is filmed, the brightness of the chewer's face changes as the jaw moves up and down due to chewing. When the values are graphed, the change in brightness results in a waveform shape that is easy to understand. Since the number of chews can be estimated from the number of waves in the waveform, the number of chews is measured using a neural network that counts the number of waves. To compensate for the small amount of data, we use a large amount of pseudowaveforms, such as sine waves. Then, a learning model that determines the number of repetitions is created, and a large amount of pseudo-waveform data is used for pre-training. The parameters of the trained model are determining by transfer learning so that the model can be applied to a small amount of data. As a result of learning the video waveform data, we were able to measure 96.6% of the data within ± 2 by moving all the parameters of the trained model.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127654522","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}
Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201296
Cheng-Fan Li, Linlin Zhao, Zhenguo Zhang
Traffic flow prediction plays an important role in traffic management and urban planning. This task is challenging due to the dependence of the road network and the complexity of information. The existing forecasting methods usually consider the spatio-temporal correlation of traffic flow, which overlook the rich semantic correlation between the nodes of the road network. For example, roads that have similar functional city blocks tend to have similar traffic patterns. To make use of the semantic information contained in road network, we propose a temporal-spatial convolution model based on multi-graph fusion (namely MTGCN). Specifically, we build adjacency graph, similarity graph and reachability graph from the original traffic road network, and fuse them by a learnable parameter-based fusion method. Then, we alternately use causal convolution module and graph convolution module to fully capture the potential temporal dependencies and spatial dependence with semantic correlation in the road network. Experimental results on two real datasets show that our method achieves better performance and consistently outperforms other baselines in short, middle, and long-term forecasting task. From the ablation experiments, we also demonstrate the proposed multi-graph mechanism is effective and can effective encoding the non-Euclidean spatial correlation and semantic attributes in road network.
{"title":"MTGCN: Multi-graph Fusion Based Temporal-Spatial Convolution for Traffic Flow Forecasting","authors":"Cheng-Fan Li, Linlin Zhao, Zhenguo Zhang","doi":"10.1109/CCAI57533.2023.10201296","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201296","url":null,"abstract":"Traffic flow prediction plays an important role in traffic management and urban planning. This task is challenging due to the dependence of the road network and the complexity of information. The existing forecasting methods usually consider the spatio-temporal correlation of traffic flow, which overlook the rich semantic correlation between the nodes of the road network. For example, roads that have similar functional city blocks tend to have similar traffic patterns. To make use of the semantic information contained in road network, we propose a temporal-spatial convolution model based on multi-graph fusion (namely MTGCN). Specifically, we build adjacency graph, similarity graph and reachability graph from the original traffic road network, and fuse them by a learnable parameter-based fusion method. Then, we alternately use causal convolution module and graph convolution module to fully capture the potential temporal dependencies and spatial dependence with semantic correlation in the road network. Experimental results on two real datasets show that our method achieves better performance and consistently outperforms other baselines in short, middle, and long-term forecasting task. From the ablation experiments, we also demonstrate the proposed multi-graph mechanism is effective and can effective encoding the non-Euclidean spatial correlation and semantic attributes in road network.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116149541","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}
Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201278
Shaoqi He, Liangang Xiao, Shibo Gao, Yichuan Zhu
weak orthogonal matching pursue algorithm cannot obtain high-precision reconstructed signals in the measurement process. Thus, this study proposes an improved SWOMP algorithm called DHP-SWOMP, which is based on partial Hadamard matrix, to overcome the aforementioned shortcoming. First, Dice coefficient matching is introduced to effectively distinguish the atomic correlation and ensure the selection of the best atom for overcoming the similar atom selection in traditional SWOMP algorithm. Then, the sampling partial Hadamard matrix is proposed as the measurement matrix to overcome the issue of failing to obtain high-precision reconstructed signals when Gaussian matrix is used in SWOMP algorithm. The random independence of the matrix is used to improve the reconstruction accuracy of the algorithm. Simulation results show that the proposed algorithm improves the signal-to-noise ratio by 53.97%, shortens the reconstruction time by 87.60%, reduces the mean square error by 15.46%, and have smaller recovery residual and higher signal reconstruction rate than SWOMP algorithm based on Gaussian matrix.
{"title":"Improving the Reconstruction Efficiency of Dice- Stagewise Weak Orthogonal Matching Pursuit","authors":"Shaoqi He, Liangang Xiao, Shibo Gao, Yichuan Zhu","doi":"10.1109/CCAI57533.2023.10201278","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201278","url":null,"abstract":"weak orthogonal matching pursue algorithm cannot obtain high-precision reconstructed signals in the measurement process. Thus, this study proposes an improved SWOMP algorithm called DHP-SWOMP, which is based on partial Hadamard matrix, to overcome the aforementioned shortcoming. First, Dice coefficient matching is introduced to effectively distinguish the atomic correlation and ensure the selection of the best atom for overcoming the similar atom selection in traditional SWOMP algorithm. Then, the sampling partial Hadamard matrix is proposed as the measurement matrix to overcome the issue of failing to obtain high-precision reconstructed signals when Gaussian matrix is used in SWOMP algorithm. The random independence of the matrix is used to improve the reconstruction accuracy of the algorithm. Simulation results show that the proposed algorithm improves the signal-to-noise ratio by 53.97%, shortens the reconstruction time by 87.60%, reduces the mean square error by 15.46%, and have smaller recovery residual and higher signal reconstruction rate than SWOMP algorithm based on Gaussian matrix.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"42 5-7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116504587","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}
Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201244
Md. Abrar Hamim, Jeba Tahseen, Kazi Md. Istiyak Hossain, N. Akter, Umme Fatema Tuj Asha
Finding rotten fruits and vegetables has been important, especially in the agricultural industry. Computer vision has significant applications in the automation of damaged, freshness detection of fruits and vegetables. In recent decades, the farming sector has discovered computer machine vision and image processing technology to be more and more beneficial, particularly for implementations in quality control by identifying rotten and freshness. Farmers cannot contribute effectively between fresh and rotten fruits, vegetables because this is mainly done by people. People tire out after performing the same task for several days, whereas robots do not. By identifying weaknesses in agricultural product, the study suggested a technique for minimizing human effort and worktime. Vegetables and fruits with defects might affect healthy fruits if they are not identified in time. As an outcome, we put up a methodology to stop rottenness from spreading. The suggested model detects between fresh and decaying fruits and vegetables depending on the input fruit and vegetable photos. In this work, we used six different types of fruits and vegetables like carrot, potato, calabash, cucumber, eggplant, and cauliflower, as well as fruits likes mango, banana, star fruit, jackfruit, guava, and papaya. This study discusses multiple image processing methods for rottenness categorization of fruits and vegetables. A Convolutional Neural Network (CNN), KNN, and SVM are used to gather the features from the data fruit and vegetable photos. On Google and Kaggle datasets, the efficiency of the suggested model is evaluated, and CNN model shows the greatest accuracy which is 95 percent.
{"title":"Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using Deep Learning Deployment in Effective Application","authors":"Md. Abrar Hamim, Jeba Tahseen, Kazi Md. Istiyak Hossain, N. Akter, Umme Fatema Tuj Asha","doi":"10.1109/CCAI57533.2023.10201244","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201244","url":null,"abstract":"Finding rotten fruits and vegetables has been important, especially in the agricultural industry. Computer vision has significant applications in the automation of damaged, freshness detection of fruits and vegetables. In recent decades, the farming sector has discovered computer machine vision and image processing technology to be more and more beneficial, particularly for implementations in quality control by identifying rotten and freshness. Farmers cannot contribute effectively between fresh and rotten fruits, vegetables because this is mainly done by people. People tire out after performing the same task for several days, whereas robots do not. By identifying weaknesses in agricultural product, the study suggested a technique for minimizing human effort and worktime. Vegetables and fruits with defects might affect healthy fruits if they are not identified in time. As an outcome, we put up a methodology to stop rottenness from spreading. The suggested model detects between fresh and decaying fruits and vegetables depending on the input fruit and vegetable photos. In this work, we used six different types of fruits and vegetables like carrot, potato, calabash, cucumber, eggplant, and cauliflower, as well as fruits likes mango, banana, star fruit, jackfruit, guava, and papaya. This study discusses multiple image processing methods for rottenness categorization of fruits and vegetables. A Convolutional Neural Network (CNN), KNN, and SVM are used to gather the features from the data fruit and vegetable photos. On Google and Kaggle datasets, the efficiency of the suggested model is evaluated, and CNN model shows the greatest accuracy which is 95 percent.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508003","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}
Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201328
Deheng Li, Qingyun Meng, Yi Liu, Wanyi Zhu
The emergence of the fifth-generation (5G) technology will accelerate the digitization of the economy and society, but the public network-based 5G core network (5GC) cannot meet the usage needs of vertical industries, while the cloud-native lightweight 5GC can be customized according to the usage scenarios, with the characteristics of low cost, customizability, simple deployment and operation and maintenance, which is conducive to the 5G technology in vertical industries. This paper proposes a solution for lightweight 5GC solution on cloud native technology and verifies its feasibility.
{"title":"Research on Lightweight 5G Core Network on Cloud Native Technology","authors":"Deheng Li, Qingyun Meng, Yi Liu, Wanyi Zhu","doi":"10.1109/CCAI57533.2023.10201328","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201328","url":null,"abstract":"The emergence of the fifth-generation (5G) technology will accelerate the digitization of the economy and society, but the public network-based 5G core network (5GC) cannot meet the usage needs of vertical industries, while the cloud-native lightweight 5GC can be customized according to the usage scenarios, with the characteristics of low cost, customizability, simple deployment and operation and maintenance, which is conducive to the 5G technology in vertical industries. This paper proposes a solution for lightweight 5GC solution on cloud native technology and verifies its feasibility.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114425955","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}
Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201272
W. Luo, Jinyu Xue
There exist some issues such as occlusions, variable human body poses, complex backgrounds in the human pose images, so there are still challenges in the task of human body pose estimation. By adding a new attention mechanism module and reweighting the last feature maps by the original HRNet, We propose an improved HRNet model. The ability of the model is enhanced to learn spatial and semantic information. The experiments on the COCO dataset and MPII dataset show that our model could detect some key points that are missed or detected incorrectly by the original network, and the accuracy is also increased.
{"title":"Human Pose Estimation Based on Improved HRNet Model","authors":"W. Luo, Jinyu Xue","doi":"10.1109/CCAI57533.2023.10201272","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201272","url":null,"abstract":"There exist some issues such as occlusions, variable human body poses, complex backgrounds in the human pose images, so there are still challenges in the task of human body pose estimation. By adding a new attention mechanism module and reweighting the last feature maps by the original HRNet, We propose an improved HRNet model. The ability of the model is enhanced to learn spatial and semantic information. The experiments on the COCO dataset and MPII dataset show that our model could detect some key points that are missed or detected incorrectly by the original network, and the accuracy is also increased.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"404 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858561","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}
Modern exascale supercomputers require more efficient I/O service than traditional single-shared filesystems can provide to support applications with varying I/O loads. Although current supercomputers can offer multiple storage resources for meeting different job I/O requirements, mainstream job schedulers need the ability to allocate hardware based on job I/O characteristics automatically. Job schedulers must first predict the I/O characteristics of the high-performance computing job to enable this ability. However, the traditional I/O feature prediction method uses I/O performance metrics collected after the job starts. The I/O channels are generally built for the job at the beginning, meaning the job schedulers must predict I/O characteristics before the job starts. This paper proposes an I/O characteristics prediction method for supercomputer jobs using only job description information, which can be collected before the jobs start and contain text and numerical data. We solved the problem of integrating different data types and selected the most suitable model through model selectors during the data training process. The method achieves 80.2% accuracy and 88.6% precision through validation using more than 40 days of job records on the new generation Tianhe supercomputer.
{"title":"IOScout: an I/O Characteristics Prediction Method for the Supercomputer Jobs","authors":"Yuqi Li, Li-Quan Xiao, Jinghua Feng, Jian Zhang, Gang Zheng, Yuan Yuan","doi":"10.1109/CCAI57533.2023.10201270","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201270","url":null,"abstract":"Modern exascale supercomputers require more efficient I/O service than traditional single-shared filesystems can provide to support applications with varying I/O loads. Although current supercomputers can offer multiple storage resources for meeting different job I/O requirements, mainstream job schedulers need the ability to allocate hardware based on job I/O characteristics automatically. Job schedulers must first predict the I/O characteristics of the high-performance computing job to enable this ability. However, the traditional I/O feature prediction method uses I/O performance metrics collected after the job starts. The I/O channels are generally built for the job at the beginning, meaning the job schedulers must predict I/O characteristics before the job starts. This paper proposes an I/O characteristics prediction method for supercomputer jobs using only job description information, which can be collected before the jobs start and contain text and numerical data. We solved the problem of integrating different data types and selected the most suitable model through model selectors during the data training process. The method achieves 80.2% accuracy and 88.6% precision through validation using more than 40 days of job records on the new generation Tianhe supercomputer.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359085","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}
Pub Date : 2023-05-26DOI: 10.1109/CCAI57533.2023.10201280
Wenxiao Xu, Zhijie Chen, Jie Jin, Jiangjie Huang, Yuhong Sheng
At present, online teaching resources are mainly produced by some software like Rain classroom, or recording video in professional rooms. Both methods have their own advantages and disadvantages, and put into school level, there are still a large part of schools can not provide students with sufficient and excellent online teaching resources. In order to realize an easier way to digitize teaching resources, this paper uses Raspberry Pi as the core processor, camera module and Python-OpenCV library as the basis, and combines Yolov4-tiny algorithm to realize the digitization of teaching resources, which provides a more economical and convenient way to utilize teaching resources. The digital method could promote the circulation of teaching resources and raise the frequency of information exchange inside and outside the school.
{"title":"Research on the Digital Method of Teaching Resources Based on Raspberry Pi","authors":"Wenxiao Xu, Zhijie Chen, Jie Jin, Jiangjie Huang, Yuhong Sheng","doi":"10.1109/CCAI57533.2023.10201280","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201280","url":null,"abstract":"At present, online teaching resources are mainly produced by some software like Rain classroom, or recording video in professional rooms. Both methods have their own advantages and disadvantages, and put into school level, there are still a large part of schools can not provide students with sufficient and excellent online teaching resources. In order to realize an easier way to digitize teaching resources, this paper uses Raspberry Pi as the core processor, camera module and Python-OpenCV library as the basis, and combines Yolov4-tiny algorithm to realize the digitization of teaching resources, which provides a more economical and convenient way to utilize teaching resources. The digital method could promote the circulation of teaching resources and raise the frequency of information exchange inside and outside the school.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125430065","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}
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