Pub Date : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082586
Hongliang Ni, Longlong Hou, B. Hou, Xudong Zhao, Zhilong Chen
China’s water resources are becoming increasingly tense. In order to support the sustainable development of limited water resources, it is necessary to improve the research on water resource allocation. Based on the domestic and foreign research on the optimal allocation of agricultural water resources, combined with the actual needs of the city, the optimal POS algorithm is used to optimize the allocation of urban water resources, and an optimal water resource allocation model is created. This paper describes the optimal POS algorithm, and on this basis, the optimal POS algorithm is applied to the characteristics and complexity of optimal water resources decision-making. In this paper, the POS optimization algorithm is used to solve the problem. Obtain the results of urban water resource allocation with different guarantee rates in 2024 and 2030, conduct rational analysis in combination with the future water supply situation, and give corresponding suggestions. Experimental research shows that by 2027, the city’s industrial water recycling rate will increase from 34% to over 60%. By 2030, the city’s industrial water recycling rate will increase to over 82%.
{"title":"Optimal Allocation of Regional Defense Resources Based on POS Optimization Algorithm","authors":"Hongliang Ni, Longlong Hou, B. Hou, Xudong Zhao, Zhilong Chen","doi":"10.1109/ITNEC56291.2023.10082586","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082586","url":null,"abstract":"China’s water resources are becoming increasingly tense. In order to support the sustainable development of limited water resources, it is necessary to improve the research on water resource allocation. Based on the domestic and foreign research on the optimal allocation of agricultural water resources, combined with the actual needs of the city, the optimal POS algorithm is used to optimize the allocation of urban water resources, and an optimal water resource allocation model is created. This paper describes the optimal POS algorithm, and on this basis, the optimal POS algorithm is applied to the characteristics and complexity of optimal water resources decision-making. In this paper, the POS optimization algorithm is used to solve the problem. Obtain the results of urban water resource allocation with different guarantee rates in 2024 and 2030, conduct rational analysis in combination with the future water supply situation, and give corresponding suggestions. Experimental research shows that by 2027, the city’s industrial water recycling rate will increase from 34% to over 60%. By 2030, the city’s industrial water recycling rate will increase to over 82%.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117332454","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 : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082025
Bin Luo, Yuanzhong Shu, Yunfeng Nie, Dongyue Chang, Yuhan Pan, Hui Shi
Segmentation of skin lesions from dermoscopic images is very important for clinical diagnosis and treatment planning. In order to segment skin lesions quickly and effectively, the segmentation network I-UNeXt was proposed in this paper. I-UNeXt is to add the Inception module to UNeXt. Compared with UNeXt's original ordinary convolution module, the Inception module added enhances the feature extraction capability of UNeXt by using different convolution kernels to extract information of different scales. At the same time, dilated convolution is introduced into the original Inception module, which reduces the amount of computation in the module while maintaining the original receptive field of convolution. We used the ISIC2017 dataset to train and test the segmentation performance of I-UNeXt. The experimental results show that F1-score, IOU and DICE are 81.95%, 71.10% and 82.46%, respectively. The overall performance of the network is better than that of other most advanced networks. Experiments show that the I-UNeXt network proposed in this paper can effectively segment the skin lesions and provide help for the diagnosis of modern skin diseases.
{"title":"I-UNeXt: A Skin Lesion Segmentation Network Based on Inception and UNeXt","authors":"Bin Luo, Yuanzhong Shu, Yunfeng Nie, Dongyue Chang, Yuhan Pan, Hui Shi","doi":"10.1109/ITNEC56291.2023.10082025","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082025","url":null,"abstract":"Segmentation of skin lesions from dermoscopic images is very important for clinical diagnosis and treatment planning. In order to segment skin lesions quickly and effectively, the segmentation network I-UNeXt was proposed in this paper. I-UNeXt is to add the Inception module to UNeXt. Compared with UNeXt's original ordinary convolution module, the Inception module added enhances the feature extraction capability of UNeXt by using different convolution kernels to extract information of different scales. At the same time, dilated convolution is introduced into the original Inception module, which reduces the amount of computation in the module while maintaining the original receptive field of convolution. We used the ISIC2017 dataset to train and test the segmentation performance of I-UNeXt. The experimental results show that F1-score, IOU and DICE are 81.95%, 71.10% and 82.46%, respectively. The overall performance of the network is better than that of other most advanced networks. Experiments show that the I-UNeXt network proposed in this paper can effectively segment the skin lesions and provide help for the diagnosis of modern skin diseases.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129570066","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 : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082248
Keyi Ni, Jing Chen, Jian Wang, Bo-Lan Liu, Ting Lei
Multi-agent reinforcement learning(MARL) methods have become an important approach to solving the decision making problems of agents. As the environment’s complexity increases, the attention model can effectively solve the problem of information redundancy. However, the introduction of attention models in reinforcement learning may also lead to over-focusing and neglecting other potentially useful information. Moreover, the presence of attention would slow the convergence in the early stages of training. To address the above problem, we propose a divided attention reinforcement learning approach: (i) the involvement of an attention regularization term to make agents more divergent in their focus on different directions; (ii) the use of a layer normalization network structure and the use of a Pre-Layer Normalization(Pre-LN) network structure for the attention optimization in the initialization phase of training. It allows the agents to have a more stable and smooth gradient descent in the early stages of learning. Our approach has been tested in several multi-agent environment tasks. Compared to other related multi-agent methods, our method obtains higher final rewards and training efficiency.
{"title":"Multi-agent Reinforcement Learning with Multi-head Attention","authors":"Keyi Ni, Jing Chen, Jian Wang, Bo-Lan Liu, Ting Lei","doi":"10.1109/ITNEC56291.2023.10082248","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082248","url":null,"abstract":"Multi-agent reinforcement learning(MARL) methods have become an important approach to solving the decision making problems of agents. As the environment’s complexity increases, the attention model can effectively solve the problem of information redundancy. However, the introduction of attention models in reinforcement learning may also lead to over-focusing and neglecting other potentially useful information. Moreover, the presence of attention would slow the convergence in the early stages of training. To address the above problem, we propose a divided attention reinforcement learning approach: (i) the involvement of an attention regularization term to make agents more divergent in their focus on different directions; (ii) the use of a layer normalization network structure and the use of a Pre-Layer Normalization(Pre-LN) network structure for the attention optimization in the initialization phase of training. It allows the agents to have a more stable and smooth gradient descent in the early stages of learning. Our approach has been tested in several multi-agent environment tasks. Compared to other related multi-agent methods, our method obtains higher final rewards and training efficiency.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129607298","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 : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082482
Feng Sai, Xixuan Wang, Xiangtao Yu, Peipei Yan, Wei Ma
Energy security is related to national security, and power security is the core of energy security. With the process of intelligent transformation of power, the production network gradually moves from being closed to interconnection. Power production and operation are highly dependent on the power monitoring system and dispatching data network. Once an external attack breaks through The safety protection system will directly threaten the safe and stable operation of the power system, so higher requirements are put forward for the detection of abnormal flow in the power system. This paper designs an intrusion detection algorithm based on the normal flow threshold model based on the deep machine learning algorithm, and conducts a comparison test through the flow characteristic value, and finally verifies the accuracy and reliability of the abnormal flow detection algorithm proposed in this paper for modern power networks in different test environments.
{"title":"Recognition and detection technology for abnormal flow of rebound type remote control Trojan in power monitoring system","authors":"Feng Sai, Xixuan Wang, Xiangtao Yu, Peipei Yan, Wei Ma","doi":"10.1109/ITNEC56291.2023.10082482","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082482","url":null,"abstract":"Energy security is related to national security, and power security is the core of energy security. With the process of intelligent transformation of power, the production network gradually moves from being closed to interconnection. Power production and operation are highly dependent on the power monitoring system and dispatching data network. Once an external attack breaks through The safety protection system will directly threaten the safe and stable operation of the power system, so higher requirements are put forward for the detection of abnormal flow in the power system. This paper designs an intrusion detection algorithm based on the normal flow threshold model based on the deep machine learning algorithm, and conducts a comparison test through the flow characteristic value, and finally verifies the accuracy and reliability of the abnormal flow detection algorithm proposed in this paper for modern power networks in different test environments.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024984","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 : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082350
Jie Liu, Zian Yang, Ruijie Wang, Shanhui Liu
The remaining useful life (RUL) prediction of rolling bearings can avoid unreasonable maintenance and major safety accidents. Considering the non-stationary characteristics, it is difficult to utilize the deep learning-based method to directly extract degradation features from the bearing vibration signal. Therefore, in this paper, a fusion prediction model AE-BiLSTM is proposed. The AutoEncoder (AE) is used to extract degradation features from the frequency-domain signals, and BiLSTM network is used to predict the bearing RUL. The experimental verification is conducted on the FEMTO-ST bearing dataset. Experimental results illustrate that the proposed AE-BiLSTM network can accurately predict the RUL of roll bearings.
{"title":"Bearing Remaining Useful Life Prediction Based on AE-BiLSTM","authors":"Jie Liu, Zian Yang, Ruijie Wang, Shanhui Liu","doi":"10.1109/ITNEC56291.2023.10082350","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082350","url":null,"abstract":"The remaining useful life (RUL) prediction of rolling bearings can avoid unreasonable maintenance and major safety accidents. Considering the non-stationary characteristics, it is difficult to utilize the deep learning-based method to directly extract degradation features from the bearing vibration signal. Therefore, in this paper, a fusion prediction model AE-BiLSTM is proposed. The AutoEncoder (AE) is used to extract degradation features from the frequency-domain signals, and BiLSTM network is used to predict the bearing RUL. The experimental verification is conducted on the FEMTO-ST bearing dataset. Experimental results illustrate that the proposed AE-BiLSTM network can accurately predict the RUL of roll bearings.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126879662","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 : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082163
Ziqian Dai, Qiuping Li, Yichen Shang, Xin’an Wang
Depression is a mental illness that endangers patients’ physical and mental health and imposes burdens on family and society. More and more people suffer from depression nowadays, which increases medical pressure. Depression can be diagnosed by patients’ voice, facial expression and gait. The current study mostly bases on one modality or a fusion of two. In this paper, we gathered 234 pieces of gait video, interview audio and video, proposed our pipeline and compared the performance between three single modalities and multi-modal fusion. The facial expression has the best performance, audio comes second, and gait comes last. The fusion of modalities can improve performance. This can provide a basis for the choice of modality in automatic screening or auxiliary diagnosis of depression. We also evaluated our model on public data set AVEC 2013, AVEC 2014 and Emotion-gait, which verifies its validity.
{"title":"Depression Detection Based on Facial Expression, Audio and Gait","authors":"Ziqian Dai, Qiuping Li, Yichen Shang, Xin’an Wang","doi":"10.1109/ITNEC56291.2023.10082163","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082163","url":null,"abstract":"Depression is a mental illness that endangers patients’ physical and mental health and imposes burdens on family and society. More and more people suffer from depression nowadays, which increases medical pressure. Depression can be diagnosed by patients’ voice, facial expression and gait. The current study mostly bases on one modality or a fusion of two. In this paper, we gathered 234 pieces of gait video, interview audio and video, proposed our pipeline and compared the performance between three single modalities and multi-modal fusion. The facial expression has the best performance, audio comes second, and gait comes last. The fusion of modalities can improve performance. This can provide a basis for the choice of modality in automatic screening or auxiliary diagnosis of depression. We also evaluated our model on public data set AVEC 2013, AVEC 2014 and Emotion-gait, which verifies its validity.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123945826","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 : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082656
Peng He, Lin Li, Junzhen Zhu, Lei Yan
The influence of different charge parameters on the gun interior ballistic performance is of great significance for the propellant shape optimization and charge structure design. In this paper, a gun interior ballistic model is built, and a numerical algorithm based on the fourth-order Runge-Kutta method is designed to solve the interior ballistic parameters. By adjusting the charge parameters the influences of the propellant power, charge quantity, burning rate coefficient, pressure index on the internal ballistic time, maximum chamber pressure, muzzle velocity of the projectile, and gas temperature are calculated. Results show that the variation of charge parameters can not only improve the muzzle velocity and the power of the gun but also affect the maximum chamber pressure and gas temperature. Additionally, To improve the burning rate coefficient, the propellant power, charge quantity, and pressure index should be controlled within a reasonable range.
{"title":"Study on Charge Parameter Effects to Gun Interior Ballistic Performance","authors":"Peng He, Lin Li, Junzhen Zhu, Lei Yan","doi":"10.1109/ITNEC56291.2023.10082656","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082656","url":null,"abstract":"The influence of different charge parameters on the gun interior ballistic performance is of great significance for the propellant shape optimization and charge structure design. In this paper, a gun interior ballistic model is built, and a numerical algorithm based on the fourth-order Runge-Kutta method is designed to solve the interior ballistic parameters. By adjusting the charge parameters the influences of the propellant power, charge quantity, burning rate coefficient, pressure index on the internal ballistic time, maximum chamber pressure, muzzle velocity of the projectile, and gas temperature are calculated. Results show that the variation of charge parameters can not only improve the muzzle velocity and the power of the gun but also affect the maximum chamber pressure and gas temperature. Additionally, To improve the burning rate coefficient, the propellant power, charge quantity, and pressure index should be controlled within a reasonable range.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"523 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123207288","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}
As an emerging technology, intelligent reflecting surface (IRS) has attached attention of academia because it can customize a favorable wireless propagation environment. However, for the traditional passive structure, IRS can merely alter the phase of the incident signal, which limits the maximum attainable beamforming gain. In order to give full play to the potential of IRS, in this paper, we employ an active IRS, which is able to alter the phase and amplify the amplitude of the incident signals simultaneously because there exists an additional power supply. So as to improve the performance of active IRS-assisted communication system, the reflection matrix of the IRS and the beamforming vector of the base station (BS) are jointly considered and optimized to minimize the transmit power of the BS. Design the algorithm of power resource allocation usually be converted into an optimization problem, and must meet the maximum power margin of active IRS and satisfy the quality of service (QoS) requirements of users. Aiming at solving the nonconvex problem, we designed a high calculation efficiency algorithm on the strength of the internal approximation (IA) method and bilinear transformation. The algorithm guarantees convergence to the local optimal solution of the problem under consideration. Simulation indicates that this scheme is effective compared with the two blank control schemes. In addition, the results indicate that compared with passive IRS, deploying active IRS has great advantages for boosting the performance of communication systems, especially in the presence of strong direct links.
{"title":"Active IRS -Assisted Resource Allocation for MISO System","authors":"Xin Liu, Han Liu, Zhenghao Li, Donglan Liu, Haotong Zhang, Rui Wang, Honglei Yao, Yuqing Shao","doi":"10.1109/ITNEC56291.2023.10082383","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082383","url":null,"abstract":"As an emerging technology, intelligent reflecting surface (IRS) has attached attention of academia because it can customize a favorable wireless propagation environment. However, for the traditional passive structure, IRS can merely alter the phase of the incident signal, which limits the maximum attainable beamforming gain. In order to give full play to the potential of IRS, in this paper, we employ an active IRS, which is able to alter the phase and amplify the amplitude of the incident signals simultaneously because there exists an additional power supply. So as to improve the performance of active IRS-assisted communication system, the reflection matrix of the IRS and the beamforming vector of the base station (BS) are jointly considered and optimized to minimize the transmit power of the BS. Design the algorithm of power resource allocation usually be converted into an optimization problem, and must meet the maximum power margin of active IRS and satisfy the quality of service (QoS) requirements of users. Aiming at solving the nonconvex problem, we designed a high calculation efficiency algorithm on the strength of the internal approximation (IA) method and bilinear transformation. The algorithm guarantees convergence to the local optimal solution of the problem under consideration. Simulation indicates that this scheme is effective compared with the two blank control schemes. In addition, the results indicate that compared with passive IRS, deploying active IRS has great advantages for boosting the performance of communication systems, especially in the presence of strong direct links.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116278792","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 : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082625
Hang Yu, Yujie Wang, Jiajia Song
With the growth of society, people have higher and higher requirements for the quality of life, and the Internet has become an indispensable part of our daily life. Ants are very typical, common, convenient, creative and convenient. This paper mainly introduces the methods based on the Internet of Things and ant colony computing. By analyzing the research status of ant algorithm at home and abroad and relevant literature, we draw conclusions, and propose improvement plans to improve the theoretical system in this field, further optimize the image recognition application of medical equipments and medicine materials in the Internet of Things environment. Then, according to the system functions to be achieved in this paper, we determine the objective function, design indicators and parameters, so as to extract features. Finally, we get the optimal solution of the feature vector, and then send the data to the background database to obtain the recognition results, And verify the model in the experiment This paper designs a simple, low-cost, efficient and high-precision recognition system based on the ant algorithm to test. Through the image recognition experiment for medical equipments and drug materials in the Internet of Things environment, the image recognition system based on the ant algorithm achieves the feature extraction time within 20 seconds, while driving the system recognition time to reach 26 seconds, with a feature matching rate of more than 82%, which can fully meet the user’s image recognition needs, The scheme not only saves resources, but also has high practical value.
{"title":"Ant Algorithm Based on Internet of Things in Image Recognition System","authors":"Hang Yu, Yujie Wang, Jiajia Song","doi":"10.1109/ITNEC56291.2023.10082625","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082625","url":null,"abstract":"With the growth of society, people have higher and higher requirements for the quality of life, and the Internet has become an indispensable part of our daily life. Ants are very typical, common, convenient, creative and convenient. This paper mainly introduces the methods based on the Internet of Things and ant colony computing. By analyzing the research status of ant algorithm at home and abroad and relevant literature, we draw conclusions, and propose improvement plans to improve the theoretical system in this field, further optimize the image recognition application of medical equipments and medicine materials in the Internet of Things environment. Then, according to the system functions to be achieved in this paper, we determine the objective function, design indicators and parameters, so as to extract features. Finally, we get the optimal solution of the feature vector, and then send the data to the background database to obtain the recognition results, And verify the model in the experiment This paper designs a simple, low-cost, efficient and high-precision recognition system based on the ant algorithm to test. Through the image recognition experiment for medical equipments and drug materials in the Internet of Things environment, the image recognition system based on the ant algorithm achieves the feature extraction time within 20 seconds, while driving the system recognition time to reach 26 seconds, with a feature matching rate of more than 82%, which can fully meet the user’s image recognition needs, The scheme not only saves resources, but also has high practical value.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121563080","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 : 2023-02-24DOI: 10.1109/ITNEC56291.2023.10082152
Xujun Wu, Daguo Qin
With the explosive growth of remote sensing image data, multi-source data fusion processing has become the development trend of remote sensing image interpretation. Aiming at some problems existing in multi-source remote sensing image fusion, a multi-level and hybrid fusion processing framework based on edge computing and deep learning is designed, and the technical requirements of data association, feature extraction, edge computing and decision generation are analyzed, in order to provide theoretical basis for subsequent construction.
{"title":"Design of Multi-source Remote Sensing Image Fusion Framework","authors":"Xujun Wu, Daguo Qin","doi":"10.1109/ITNEC56291.2023.10082152","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082152","url":null,"abstract":"With the explosive growth of remote sensing image data, multi-source data fusion processing has become the development trend of remote sensing image interpretation. Aiming at some problems existing in multi-source remote sensing image fusion, a multi-level and hybrid fusion processing framework based on edge computing and deep learning is designed, and the technical requirements of data association, feature extraction, edge computing and decision generation are analyzed, in order to provide theoretical basis for subsequent construction.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114830790","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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