Pub Date : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952767
Jeong-Gu Kang, K. Chung
DASH is an effective way to improve the Quality of Experience (QoE) in video streaming. However, most of the existing schemes depend on heuristic algorithms, and the learning-based methods that have recently started to appear also have a problem in that their performance deteriorates in a specific environment. In this paper, we propose an adaptive streaming scheme that utilizes online reinforcement learning. The proposed scheme adapts to changes in the client's environment by upgrading the ABR model while performing video streaming when QoE degradation is confirmed. In order to adapt the ABR model to the changing network environment, the neural network model is trained with the state-of-the-art reinforcement learning algorithm. The performance of the proposed scheme is evaluated through simulation-based experiments under various network conditions. Through the experimental results, it is confirmed that the proposed scheme shows better performance than the existing schemes.
{"title":"Online Reinforcement Learning Based HTTP Adaptive Streaming Scheme","authors":"Jeong-Gu Kang, K. Chung","doi":"10.1109/ICTC55196.2022.9952767","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952767","url":null,"abstract":"DASH is an effective way to improve the Quality of Experience (QoE) in video streaming. However, most of the existing schemes depend on heuristic algorithms, and the learning-based methods that have recently started to appear also have a problem in that their performance deteriorates in a specific environment. In this paper, we propose an adaptive streaming scheme that utilizes online reinforcement learning. The proposed scheme adapts to changes in the client's environment by upgrading the ABR model while performing video streaming when QoE degradation is confirmed. In order to adapt the ABR model to the changing network environment, the neural network model is trained with the state-of-the-art reinforcement learning algorithm. The performance of the proposed scheme is evaluated through simulation-based experiments under various network conditions. Through the experimental results, it is confirmed that the proposed scheme shows better performance than the existing schemes.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124282465","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952438
Woochang Jeong, Chanik Park
Recently, blockchain technology has emerged as an important technology for executing smart contracts and storing consensus data reliably in decentralized manner. On the other hand, it is required that each node has to maintain the consensus ledger in its local storage. Due to limited storage capacity, most blockchain platforms typically adopt the techniques of checkpointing and pruning the consensus ledger database. However, in case of sensitive data such as financial, healthcare or identity information, there may be some regulations on data maintenance. Thus, we have to keep those data until a specified time interval to meet the regulation compliance. In this paper, we propose a general and robust blockchain storage system, BSS, exploiting large-scale external storage services such as Amazon S3, which stores the entire blockchain consensus ledger from the genesis block. It is general in the sense that BSS is designed to be compatible with any blockchain platform. It is robust in the sense that BSS supports the f-tolerant write operation, which tolerates the malicious behavior of blockchain nodes and external storage service. We show that the BSS meets three security properties: safety, liveness, and external validity.
{"title":"A General and Robust Blockchain Storage System based on External Storage Service","authors":"Woochang Jeong, Chanik Park","doi":"10.1109/ICTC55196.2022.9952438","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952438","url":null,"abstract":"Recently, blockchain technology has emerged as an important technology for executing smart contracts and storing consensus data reliably in decentralized manner. On the other hand, it is required that each node has to maintain the consensus ledger in its local storage. Due to limited storage capacity, most blockchain platforms typically adopt the techniques of checkpointing and pruning the consensus ledger database. However, in case of sensitive data such as financial, healthcare or identity information, there may be some regulations on data maintenance. Thus, we have to keep those data until a specified time interval to meet the regulation compliance. In this paper, we propose a general and robust blockchain storage system, BSS, exploiting large-scale external storage services such as Amazon S3, which stores the entire blockchain consensus ledger from the genesis block. It is general in the sense that BSS is designed to be compatible with any blockchain platform. It is robust in the sense that BSS supports the f-tolerant write operation, which tolerates the malicious behavior of blockchain nodes and external storage service. We show that the BSS meets three security properties: safety, liveness, and external validity.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117209649","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952797
Huu-Anh-Duc Cap, Trong-Hop Do, D. Lakew, Sungrae Cho
Time series forecasting is currently a very popular field of study. Easily find a variety of time series data in medicine, weather forecasting, biology, supply chain management, stock price forecasting, and more. With the proliferation of data and computing power in recent years, deep learning has become the first choice for building time series predictive models. While traditional Machine Learning models - such as autoregression (AR), Exponential smoothing, or autoregressive integrated moving average (ARIMA) - perform manual conversion of the original raw data set into a set of attributes, and the optimization of the parameter must also be based on feature selection, the Deep Learning model only learns the features directly from the data alone. As a result, it speeds up the data preparation process and can fully learn more complex data patterns. In this paper, we designed LSTM deep learning network using Automated Machine Learning (AutoML) method to predict time series data which is the heart rate data. The results of this model can be applied to the field of medicine and health care.
{"title":"Building a Time-Series Forecast Model with Automated Machine Learning for Heart Rate Forecasting Problem","authors":"Huu-Anh-Duc Cap, Trong-Hop Do, D. Lakew, Sungrae Cho","doi":"10.1109/ICTC55196.2022.9952797","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952797","url":null,"abstract":"Time series forecasting is currently a very popular field of study. Easily find a variety of time series data in medicine, weather forecasting, biology, supply chain management, stock price forecasting, and more. With the proliferation of data and computing power in recent years, deep learning has become the first choice for building time series predictive models. While traditional Machine Learning models - such as autoregression (AR), Exponential smoothing, or autoregressive integrated moving average (ARIMA) - perform manual conversion of the original raw data set into a set of attributes, and the optimization of the parameter must also be based on feature selection, the Deep Learning model only learns the features directly from the data alone. As a result, it speeds up the data preparation process and can fully learn more complex data patterns. In this paper, we designed LSTM deep learning network using Automated Machine Learning (AutoML) method to predict time series data which is the heart rate data. The results of this model can be applied to the field of medicine and health care.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124992566","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952490
Jonghoon Lee, Hyunjin Kim, Chulhee Park, Youngsoo Kim, Jong-Geun Park
The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.
{"title":"AI-based Network Security Enhancement for 5G Industrial Internet of Things Environments","authors":"Jonghoon Lee, Hyunjin Kim, Chulhee Park, Youngsoo Kim, Jong-Geun Park","doi":"10.1109/ICTC55196.2022.9952490","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952490","url":null,"abstract":"The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123562813","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952992
Gyeongyeon Hwang, Hakyoung Yoon, Yewon Ji, Sang Jun Lee
Recently, as the importance of early diagnosis and treatment of cancer has increased, many studies have been introduced to analyze medical images using deep learning. In medical image analysis task, the lesions segmentation methods uses a Fully Convolutional Network (FCN) architecture such as U-Net to predict the lesion area and play an auxiliary role in medical care. So many researchers are working on improving the performance of architectures. But, there are some challenges in that data is imbalanced and the size and shape of lesions are irregular. To solve these problems, we improved the segmentation performance by using a two-stage cascaded method. In stage 1, coarse region of interest (RoI) was extracted using ResUNet, In stage 2, we use Atrous Spatial Pyramid Pooling (ASPP) to extract features to contain a lot of spatial information using various receptive fields from a pretrained DenseNet-161 backbone. In addition, we introduce the RBCA module that combines Reverse, Boundary, and Channel Attention to capture various sizes and shapes of lesions. The performance of the proposed model shows high performance compared to various architectures using the KiTS19 dataset including kidney and tumor.
{"title":"RBCA-Net: Reverse Boundary Channel Attention Network for Kidney Tumor Segmentation in CT images","authors":"Gyeongyeon Hwang, Hakyoung Yoon, Yewon Ji, Sang Jun Lee","doi":"10.1109/ICTC55196.2022.9952992","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952992","url":null,"abstract":"Recently, as the importance of early diagnosis and treatment of cancer has increased, many studies have been introduced to analyze medical images using deep learning. In medical image analysis task, the lesions segmentation methods uses a Fully Convolutional Network (FCN) architecture such as U-Net to predict the lesion area and play an auxiliary role in medical care. So many researchers are working on improving the performance of architectures. But, there are some challenges in that data is imbalanced and the size and shape of lesions are irregular. To solve these problems, we improved the segmentation performance by using a two-stage cascaded method. In stage 1, coarse region of interest (RoI) was extracted using ResUNet, In stage 2, we use Atrous Spatial Pyramid Pooling (ASPP) to extract features to contain a lot of spatial information using various receptive fields from a pretrained DenseNet-161 backbone. In addition, we introduce the RBCA module that combines Reverse, Boundary, and Channel Attention to capture various sizes and shapes of lesions. The performance of the proposed model shows high performance compared to various architectures using the KiTS19 dataset including kidney and tumor.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372476","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952607
Sang-Su Kim, Heesoo Jung, Seung-Jae Lee, Jin-ho Park, Sung-Hwan Yu, Jun-Hui Go
Recently, interest in the use of drones has increased, and drones are being actively introduced in various fields. We are trying to develop and use drones in various fields such as national defense, logistics, life safety, facility safety management, forest protection and monitoring, but there are still restrictions on the use of drones. The biggest limitation is the use of drones on land such as mountains and rivers. For example, if the police are searching for a missing person in an area with mountains, bushes, and a large river, multiple police personnel must visually check the drone footage on site every day. And then there's the problem of finding a missing person or lost article of a missing person and having to re-search where it was found. Therefore, the visualization technology proposed in this paper is a technology that visualizes real-time spatial mapping of drone images taken in real time onto a web-based 2D map. In cooperation with the missing person search AI inference function, the AI analysis result video is mapped on a web-based 2D map in real time. AI analysis results are visualized in real time on a web map using spatial information among the meta information in the video.
{"title":"A Study of real-Time 4K drone images visualization to rescue for missing people base on web","authors":"Sang-Su Kim, Heesoo Jung, Seung-Jae Lee, Jin-ho Park, Sung-Hwan Yu, Jun-Hui Go","doi":"10.1109/ICTC55196.2022.9952607","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952607","url":null,"abstract":"Recently, interest in the use of drones has increased, and drones are being actively introduced in various fields. We are trying to develop and use drones in various fields such as national defense, logistics, life safety, facility safety management, forest protection and monitoring, but there are still restrictions on the use of drones. The biggest limitation is the use of drones on land such as mountains and rivers. For example, if the police are searching for a missing person in an area with mountains, bushes, and a large river, multiple police personnel must visually check the drone footage on site every day. And then there's the problem of finding a missing person or lost article of a missing person and having to re-search where it was found. Therefore, the visualization technology proposed in this paper is a technology that visualizes real-time spatial mapping of drone images taken in real time onto a web-based 2D map. In cooperation with the missing person search AI inference function, the AI analysis result video is mapped on a web-based 2D map in real time. AI analysis results are visualized in real time on a web map using spatial information among the meta information in the video.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125473935","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952402
Jinyoung Ha, Jun Heo
In this paper, we propose a construction method of Grover's algorithm to solve the N-Queen problem. Quantum permutation state was designed and applied to the initialization and amplitude amplification process in Grover's algorithm. An oracle-level quantum circuit was constructed using Boolean algebraic expressions. We reduced the number of iterations of the Grover's algorithm by decreasing the number of superposed inputs in the initialize step using quantum permutation state. We show that our algorithm has less time complexity compared to previous study that solved the N -Queen problem using Grover's algorithm with W state as a input.
{"title":"Reducing Iterations of Grover Search Algorithm for N-Queen Problem U sing Quantum Permutation States","authors":"Jinyoung Ha, Jun Heo","doi":"10.1109/ICTC55196.2022.9952402","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952402","url":null,"abstract":"In this paper, we propose a construction method of Grover's algorithm to solve the N-Queen problem. Quantum permutation state was designed and applied to the initialization and amplitude amplification process in Grover's algorithm. An oracle-level quantum circuit was constructed using Boolean algebraic expressions. We reduced the number of iterations of the Grover's algorithm by decreasing the number of superposed inputs in the initialize step using quantum permutation state. We show that our algorithm has less time complexity compared to previous study that solved the N -Queen problem using Grover's algorithm with W state as a input.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115109260","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952633
Anichur Rahman, K. Hasan, Seong-Ho Jeong
Software-Defined Networking (SDN) can be a good option to support Industry 4.0 (4IR) and 5G wireless networks. SDN can also be a secure networking solution that improves the security, capability, and programmability in the networks. In this paper, we present and analyze an SDN-based security architecture for 4IR with 5G. SDN is used for increasing the level of security and reliability of the network by suitably dividing the whole network into data, control, and applications planes. The SDN control layer plays a beneficial role in 4IR with 5G scenarios by managing the data flow properly. We also evaluate the performance of the proposed architecture in terms of key parameters such as data transmission rate and response time.
{"title":"An Enhanced Security Architecture for Industry 4.0 Applications based on Software-Defined Networking","authors":"Anichur Rahman, K. Hasan, Seong-Ho Jeong","doi":"10.1109/ICTC55196.2022.9952633","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952633","url":null,"abstract":"Software-Defined Networking (SDN) can be a good option to support Industry 4.0 (4IR) and 5G wireless networks. SDN can also be a secure networking solution that improves the security, capability, and programmability in the networks. In this paper, we present and analyze an SDN-based security architecture for 4IR with 5G. SDN is used for increasing the level of security and reliability of the network by suitably dividing the whole network into data, control, and applications planes. The SDN control layer plays a beneficial role in 4IR with 5G scenarios by managing the data flow properly. We also evaluate the performance of the proposed architecture in terms of key parameters such as data transmission rate and response time.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116006885","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952818
Yeomin Jeong, Junbeom Hur
In a Progressive Web App (PWA), a kind of application software of the web, a service worker (SW) plays a key role as a one of the fundamental components to enhance the user's browsing experiences. For this purpose, the SW supports several features such as push notification, offline access, background code execution, etc. Since the SW provides prolific capabilities, it has been the main target to abuse by malicious attackers to deliver diverse attacks through the web applications such as crypto-currency mining, history sniffing, phishing. In this paper, we introduce the SW's functionalities and vulnerabilities, and discuss the existing attack methodologies and their implications.
在渐进式Web应用程序(Progressive Web App, PWA)中，service worker (SW)作为增强用户浏览体验的基础组件之一，发挥着关键作用。为此，软件支持推送通知、脱机访问、后台代码执行等功能。由于SW提供了丰富的功能，它一直是恶意攻击者滥用的主要目标，通过web应用程序提供各种攻击，如加密货币挖掘，历史嗅探，网络钓鱼。在本文中，我们介绍了软件的功能和漏洞，讨论了现有的攻击方法及其影响。
{"title":"A Survey on Vulnerabilities of Service Workers","authors":"Yeomin Jeong, Junbeom Hur","doi":"10.1109/ICTC55196.2022.9952818","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952818","url":null,"abstract":"In a Progressive Web App (PWA), a kind of application software of the web, a service worker (SW) plays a key role as a one of the fundamental components to enhance the user's browsing experiences. For this purpose, the SW supports several features such as push notification, offline access, background code execution, etc. Since the SW provides prolific capabilities, it has been the main target to abuse by malicious attackers to deliver diverse attacks through the web applications such as crypto-currency mining, history sniffing, phishing. In this paper, we introduce the SW's functionalities and vulnerabilities, and discuss the existing attack methodologies and their implications.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122322310","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 : 2022-10-19DOI: 10.1109/ICTC55196.2022.9952410
Xun Lu, Yong Kyu Kim, Seong-min Lee, Chengjun Jin, Seong-Cheol Byeon, Tasadduq Hussain, Muzahir Ali, Seok-min Kim
The performance of nanophotonic devices was very sensitive and nonlinear to the structural design parameters. In this manuscript, two examples of multi-objective optimizations using the response surface method and Kriging surrogate model with the disability function for the designing of nanophotonic devices were introduced. Although reasonable optimum design parameters could be obtained using performance expectation models after the proper selection of key design factors and ranges of design factors, a machine learning method with big data could be a powerful solution for the extensive parametric analysis and optimization in the design of nanophotonic devices.
{"title":"Design of Nanophotonic Devices using Multi Objective Optimization Method","authors":"Xun Lu, Yong Kyu Kim, Seong-min Lee, Chengjun Jin, Seong-Cheol Byeon, Tasadduq Hussain, Muzahir Ali, Seok-min Kim","doi":"10.1109/ICTC55196.2022.9952410","DOIUrl":"https://doi.org/10.1109/ICTC55196.2022.9952410","url":null,"abstract":"The performance of nanophotonic devices was very sensitive and nonlinear to the structural design parameters. In this manuscript, two examples of multi-objective optimizations using the response surface method and Kriging surrogate model with the disability function for the designing of nanophotonic devices were introduced. Although reasonable optimum design parameters could be obtained using performance expectation models after the proper selection of key design factors and ranges of design factors, a machine learning method with big data could be a powerful solution for the extensive parametric analysis and optimization in the design of nanophotonic devices.","PeriodicalId":441404,"journal":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","volume":"615 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122941718","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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