Pub Date : 2023-05-18DOI: 10.1109/eIT57321.2023.10187239
T. E. Dettling, Byron DeVries, C. Trefftz
Calculating Voronoi diagrams quickly is useful across a range of fields and application areas. However, existing divide-and-conquer methods decompose into squares while boundaries between Voronoi diagram regions are often not perfectly horizontal or vertical. In this paper we introduce a novel method of dividing Approximate Voronoi Diagram spaces into triangles stored by quadtree data structures. While our implementation stores the resulting Voronoi diagram in a data structure, rather than setting each approximated point to its closest region, we provide a comparison of the decomposition time alone.
{"title":"Calculating an Approximate Voronoi Diagram using QuadTrees and Triangles","authors":"T. E. Dettling, Byron DeVries, C. Trefftz","doi":"10.1109/eIT57321.2023.10187239","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187239","url":null,"abstract":"Calculating Voronoi diagrams quickly is useful across a range of fields and application areas. However, existing divide-and-conquer methods decompose into squares while boundaries between Voronoi diagram regions are often not perfectly horizontal or vertical. In this paper we introduce a novel method of dividing Approximate Voronoi Diagram spaces into triangles stored by quadtree data structures. While our implementation stores the resulting Voronoi diagram in a data structure, rather than setting each approximated point to its closest region, we provide a comparison of the decomposition time alone.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130647381","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187259
Nicholas Ferry, Kishwar Ahmed, S. Tasnim
Recent nanotechnology advances have catalyzed sev-eral different types of engineered nanomaterials (ENMs). The nanomaterial classification interprets to identifying any particle that is smaller than hundred nanometer. Protein corona (PC) is an agglomeration of proteins that form on an ENM in organic fluids. Machine learning techniques can be useful to predict the PC formation and interaction within an ENM. In this paper, we develop a random forest model for PC formation prediction on ENMs. Further, we leverage the deep neural network (DNN) technique to accurately and efficiently predict PC formation. We also present an architecture optimization of the trained DNN model to create practically instantaneous inferences. We preform simulation study to show effectiveness of our proposed model. Experiments show that the DNN model can achieve 83.81% accuracy in PC classification on ENMs, while can significantly improve the classification performance.
{"title":"Protein Corona Formation Prediction on Engineered Nanomaterials","authors":"Nicholas Ferry, Kishwar Ahmed, S. Tasnim","doi":"10.1109/eIT57321.2023.10187259","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187259","url":null,"abstract":"Recent nanotechnology advances have catalyzed sev-eral different types of engineered nanomaterials (ENMs). The nanomaterial classification interprets to identifying any particle that is smaller than hundred nanometer. Protein corona (PC) is an agglomeration of proteins that form on an ENM in organic fluids. Machine learning techniques can be useful to predict the PC formation and interaction within an ENM. In this paper, we develop a random forest model for PC formation prediction on ENMs. Further, we leverage the deep neural network (DNN) technique to accurately and efficiently predict PC formation. We also present an architecture optimization of the trained DNN model to create practically instantaneous inferences. We preform simulation study to show effectiveness of our proposed model. Experiments show that the DNN model can achieve 83.81% accuracy in PC classification on ENMs, while can significantly improve the classification performance.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134151338","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187355
M. Chowdhury, Nafiz Rifat, M. Ahsan, Shadman Latif, Rahul Gomes, Md Saifur Rahman
The AI revolution has brought significant changes to society. AI-powered systems can analyze enormous amounts of data to optimize processes, improve accuracy, and cut costs. Nevertheless, addressing potential hazards and ethical issues related to AI enabled technologies, such as bias and job displacement, is essential. This paper presented an example of an AI revolution threatening cyber security, the ChatGPT. ChatGPT, a chatbot, can generate essays or code on demand. However, ChatGPT's security system can be circumvented or deceived to generate malicious content. Moreover, these AI enabled tools to have design issues, e.g., accuracy issues. As a result, ChatGPT can be accused of violating the confidentiality of information (privacy invasion), producing inaccurate information, and potentially facilitating attack tool generation that can compromise the availability principle of the CIA triad. This paper presents ChatGPT as a threat against the CIA triad principle by focusing on violating these principles.
{"title":"ChatGPT: A Threat Against the CIA Triad of Cyber Security","authors":"M. Chowdhury, Nafiz Rifat, M. Ahsan, Shadman Latif, Rahul Gomes, Md Saifur Rahman","doi":"10.1109/eIT57321.2023.10187355","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187355","url":null,"abstract":"The AI revolution has brought significant changes to society. AI-powered systems can analyze enormous amounts of data to optimize processes, improve accuracy, and cut costs. Nevertheless, addressing potential hazards and ethical issues related to AI enabled technologies, such as bias and job displacement, is essential. This paper presented an example of an AI revolution threatening cyber security, the ChatGPT. ChatGPT, a chatbot, can generate essays or code on demand. However, ChatGPT's security system can be circumvented or deceived to generate malicious content. Moreover, these AI enabled tools to have design issues, e.g., accuracy issues. As a result, ChatGPT can be accused of violating the confidentiality of information (privacy invasion), producing inaccurate information, and potentially facilitating attack tool generation that can compromise the availability principle of the CIA triad. This paper presents ChatGPT as a threat against the CIA triad principle by focusing on violating these principles.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114226214","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187225
Ben-Li Wang, N. Houshangi
In this paper, an ORB-SLAM algorithm using RGB-D camera collects data for visualization. The collected point cloud data is first processed using a pass-through filter. Statistical filters are used to remove sparse outliers. Thereafter, downsampling is performed using a voxel filter. Increase the sampling amount appropriately according to the actual situation. After mentioned data processing, Octomap is used to visualize the information in Rviz with services provided in Robot Operating System (ROS) platform to build a three-dimensional map. This visualization approach can be applied to preliminary map construction for robot autonomous navigation and 3D modeling of 3D scanner.
{"title":"Octree 3D Visualization Mapping based on Camera Information","authors":"Ben-Li Wang, N. Houshangi","doi":"10.1109/eIT57321.2023.10187225","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187225","url":null,"abstract":"In this paper, an ORB-SLAM algorithm using RGB-D camera collects data for visualization. The collected point cloud data is first processed using a pass-through filter. Statistical filters are used to remove sparse outliers. Thereafter, downsampling is performed using a voxel filter. Increase the sampling amount appropriately according to the actual situation. After mentioned data processing, Octomap is used to visualize the information in Rviz with services provided in Robot Operating System (ROS) platform to build a three-dimensional map. This visualization approach can be applied to preliminary map construction for robot autonomous navigation and 3D modeling of 3D scanner.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127873856","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187309
Jack Li
Hands-on practice is an important fact to students' success, especially to the students who study in the field of Electrical Engineering and Electrical Engineering Technology (EET). Electronics systems have become so complex nowadays because of the high-density integrated circuits are widely used that it is hard for students to grasp the information about the systems in one course, especially microcontroller systems. In order to help students do more hands-on practice and understand a microprocessor system deeply, linking several courses together by using the same microcontroller was proposed in this paper. From the students' feedback, the setup really helps students do hands-on work on microprocesses with more confidence. The setup also helps the program reduce lab maintenance cost.
{"title":"Link Multiple Courses to Enhance Students' Hands-on Practice on Microprocessor Systems","authors":"Jack Li","doi":"10.1109/eIT57321.2023.10187309","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187309","url":null,"abstract":"Hands-on practice is an important fact to students' success, especially to the students who study in the field of Electrical Engineering and Electrical Engineering Technology (EET). Electronics systems have become so complex nowadays because of the high-density integrated circuits are widely used that it is hard for students to grasp the information about the systems in one course, especially microcontroller systems. In order to help students do more hands-on practice and understand a microprocessor system deeply, linking several courses together by using the same microcontroller was proposed in this paper. From the students' feedback, the setup really helps students do hands-on work on microprocesses with more confidence. The setup also helps the program reduce lab maintenance cost.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123973561","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187220
David Arnold, J. Saniie
Principal Component Analysis (PCA) is a versatile Unsupervised Learning (UL) technique for reducing the dimensionality of datasets. As a result, PCA is widely used in consumer and research applications as a preprocessing tool for identifying important features prior to further analysis. In instances where on-site personnel or developers do not have the expertise to apply UL techniques, third party processors are frequently retained. However, the release of client or proprietary data poses a substantial security risk. This risk increases the regulatory and contractual burden on analysts when interacting with sensitive or classified information. Homomorphic Encryption (HE) cryptosystems are a novel family of encryption algorithms that permit approximate addition and multiplication on encrypted data. When applied to UL models, such as PCA, experts may apply their expertise while maintaining data privacy. In order to evaluate the potential application of Homomorphic Encryption, we implemented Principal Component Analysis using the Microsoft SEAL HE libraries. The resulting implementation was applied to the MNIST Handwritten dataset for feature reduction and image reconstruction. Based on our results, HE considerably increased the time required to process the dataset. However, the HE algorithm is still viable for non-real-time applications as it had an average pixel error of near-zero for all image reconstructions.
主成分分析（PCA）是一种通用的无监督学习（UL）技术，可用于降低数据集的维度。因此，PCA 被广泛应用于消费和研究领域，作为一种预处理工具，用于在进一步分析前识别重要特征。在现场人员或开发人员不具备应用 UL 技术的专业知识的情况下，通常会使用第三方处理器。然而，泄露客户或专有数据会带来巨大的安全风险。这种风险增加了分析师在处理敏感或机密信息时的监管和合同负担。同态加密（HE）密码系统是一种新型加密算法，允许对加密数据进行近似加法和乘法运算。当应用于 UL 模型（如 PCA）时，专家们可以应用他们的专业知识，同时维护数据隐私。为了评估同态加密的潜在应用，我们使用微软 SEAL HE 库实施了主成分分析。由此产生的实现应用于 MNIST 手写数据集，用于特征还原和图像重建。根据我们的结果，HE 大大增加了处理数据集所需的时间。不过，HE 算法在非实时应用中仍然是可行的，因为它在所有图像重建中的平均像素误差几乎为零。
{"title":"Evaluation of Homomorphic Encryption for Privacy in Principal Component Analysis","authors":"David Arnold, J. Saniie","doi":"10.1109/eIT57321.2023.10187220","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187220","url":null,"abstract":"Principal Component Analysis (PCA) is a versatile Unsupervised Learning (UL) technique for reducing the dimensionality of datasets. As a result, PCA is widely used in consumer and research applications as a preprocessing tool for identifying important features prior to further analysis. In instances where on-site personnel or developers do not have the expertise to apply UL techniques, third party processors are frequently retained. However, the release of client or proprietary data poses a substantial security risk. This risk increases the regulatory and contractual burden on analysts when interacting with sensitive or classified information. Homomorphic Encryption (HE) cryptosystems are a novel family of encryption algorithms that permit approximate addition and multiplication on encrypted data. When applied to UL models, such as PCA, experts may apply their expertise while maintaining data privacy. In order to evaluate the potential application of Homomorphic Encryption, we implemented Principal Component Analysis using the Microsoft SEAL HE libraries. The resulting implementation was applied to the MNIST Handwritten dataset for feature reduction and image reconstruction. Based on our results, HE considerably increased the time required to process the dataset. However, the HE algorithm is still viable for non-real-time applications as it had an average pixel error of near-zero for all image reconstructions.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126349251","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187217
Jingwen Zhang, Ge Shi, Song Chen, Mao Zheng
In this paper, we report our experiments on using the Numerai data set to build a financial machine learning model. Numerai [1] is an AI-run, crowd-sourced hedge fund company based in San Francisco. It hosts the Numerai Tournament, which claimes to be the hardest data science tournament in the world [1]. We trained our model and participated in Numerai tournament for four months. The results were promising.
{"title":"Building a Stock Machine Learning Model using Numerai Dataset","authors":"Jingwen Zhang, Ge Shi, Song Chen, Mao Zheng","doi":"10.1109/eIT57321.2023.10187217","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187217","url":null,"abstract":"In this paper, we report our experiments on using the Numerai data set to build a financial machine learning model. Numerai [1] is an AI-run, crowd-sourced hedge fund company based in San Francisco. It hosts the Numerai Tournament, which claimes to be the hardest data science tournament in the world [1]. We trained our model and participated in Numerai tournament for four months. The results were promising.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131769431","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187374
T. T. Khoei, N. Kaabouch
Smart grid is an innovative technology that offers efficiency, low carbon emissions, and high energy storage. However, this promising technology has several shortcomings, including limited security. In this network, Intrusion Detection System (IDS) is one of the likely targeted systems that has limited security and is prone to several cyber vulnerabilities. To address a such challenge, several studies have been proposed to detect, classify, and mitigate these attacks using Artificial Intelligence (AI) techniques, although the proposed techniques in the literature suffer from high misdetection and false alarm rates. Additionally, limited data availability motivated the researchers to use another type of AI method, namely reinforcement learning to detect and classify attacks. In this paper, we propose a deep reinforcement learning-based technique, namely Q learning and capsule network as a deep learning model to detect attacks for IDS on smart grid networks. The benchmark of CICDDOs 2019 is selected to evaluate the model in terms of accuracy, detection, misdetection, false alarm rates, training time, and prediction time. We also investigate the performance of the proposed model based on discount values of 0.001 and 0.9. The experiments demonstrate that the proposed model has acceptable results, and the model with the lower discount values provides better results.
{"title":"ACapsule Q-Learning Based Reinforcement Model for Intrusion Detection System on Smart Grid","authors":"T. T. Khoei, N. Kaabouch","doi":"10.1109/eIT57321.2023.10187374","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187374","url":null,"abstract":"Smart grid is an innovative technology that offers efficiency, low carbon emissions, and high energy storage. However, this promising technology has several shortcomings, including limited security. In this network, Intrusion Detection System (IDS) is one of the likely targeted systems that has limited security and is prone to several cyber vulnerabilities. To address a such challenge, several studies have been proposed to detect, classify, and mitigate these attacks using Artificial Intelligence (AI) techniques, although the proposed techniques in the literature suffer from high misdetection and false alarm rates. Additionally, limited data availability motivated the researchers to use another type of AI method, namely reinforcement learning to detect and classify attacks. In this paper, we propose a deep reinforcement learning-based technique, namely Q learning and capsule network as a deep learning model to detect attacks for IDS on smart grid networks. The benchmark of CICDDOs 2019 is selected to evaluate the model in terms of accuracy, detection, misdetection, false alarm rates, training time, and prediction time. We also investigate the performance of the proposed model based on discount values of 0.001 and 0.9. The experiments demonstrate that the proposed model has acceptable results, and the model with the lower discount values provides better results.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130606181","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187231
Zeeshan Abbas, K. Chong
Security has now become an essential part of communication on both sender and receiver's side. It is challenging to deliver comprehensive security against invaders since a lot of algorithms are expounded and can effortlessly get significant data by performing different hacking methods. High security-based algorithms like AES are nowadays used in various systems, as they present a high performance and is quite faster than most of stream-based encryption algorithms. Regardless of how reliable the technique is, the cipher-text always creates a doubt in brain when someone notices it, which makes the data prone to interception. Here arises another method of security to eliminate that suspicion, hence, we incorporate the confidential information into another format, such as an image, sound, video, or text and then transmit through the network. A novel security system based on three steps of hybrid architecture has been proposed in this paper by combining steganography and cryptography with codebooks. Two different keys are used to encrypt and then hide that highly secured encrypted message into the cover image. The message is encrypted first with AES algorithm and then placed at random positions in the cover image using codebook and KSA algorithm. AES encrypted message is mapped onto values of predefined codebooks then hide those encrypted message bits in LSBs of the cover image. The selected parameters are then passed on to the decryption side to recover the original message. Based on the achieved experimental results, it has outperformed the previous methodologies and without having the secret keys it is unfeasible to extract the message in a readable format.
{"title":"A Novel Approach Towards Fusion of Steganography and Cryptography for Enhanced Data Security using RGB Image","authors":"Zeeshan Abbas, K. Chong","doi":"10.1109/eIT57321.2023.10187231","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187231","url":null,"abstract":"Security has now become an essential part of communication on both sender and receiver's side. It is challenging to deliver comprehensive security against invaders since a lot of algorithms are expounded and can effortlessly get significant data by performing different hacking methods. High security-based algorithms like AES are nowadays used in various systems, as they present a high performance and is quite faster than most of stream-based encryption algorithms. Regardless of how reliable the technique is, the cipher-text always creates a doubt in brain when someone notices it, which makes the data prone to interception. Here arises another method of security to eliminate that suspicion, hence, we incorporate the confidential information into another format, such as an image, sound, video, or text and then transmit through the network. A novel security system based on three steps of hybrid architecture has been proposed in this paper by combining steganography and cryptography with codebooks. Two different keys are used to encrypt and then hide that highly secured encrypted message into the cover image. The message is encrypted first with AES algorithm and then placed at random positions in the cover image using codebook and KSA algorithm. AES encrypted message is mapped onto values of predefined codebooks then hide those encrypted message bits in LSBs of the cover image. The selected parameters are then passed on to the decryption side to recover the original message. Based on the achieved experimental results, it has outperformed the previous methodologies and without having the secret keys it is unfeasible to extract the message in a readable format.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132950872","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 : 2023-05-18DOI: 10.1109/eIT57321.2023.10187222
Lila Areephanthu, B. Abegaz
This paper focuses on the verification of an object detection and avoidance method for autonomous vehicles to maneuver their way safely and efficiently. The model predictive control approach is explored and compared with other control approaches based on variables such as safe detection distance and safe operating time. The results indicate that the proposed approach could be promising for autonomous vehicles that often comprise various types of sensors and components and could otherwise be difficult to test and verify with traditional methods.
{"title":"Verification of a Predictive Method for Obstacle Detection and Safe Operation of Autonomous Vehicles","authors":"Lila Areephanthu, B. Abegaz","doi":"10.1109/eIT57321.2023.10187222","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187222","url":null,"abstract":"This paper focuses on the verification of an object detection and avoidance method for autonomous vehicles to maneuver their way safely and efficiently. The model predictive control approach is explored and compared with other control approaches based on variables such as safe detection distance and safe operating time. The results indicate that the proposed approach could be promising for autonomous vehicles that often comprise various types of sensors and components and could otherwise be difficult to test and verify with traditional methods.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134501586","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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