Tsinghua Science and Technology最新文献_第10页

Intelligent Transportation System Performance Analysis of Indoor and Outdoor Internet of Vehicle (IoV) Applications Towards 5G 面向 5G 的室内外车联网 (IoV) 应用的智能交通系统性能分析

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010119

Preeti Rani;Rohit Sharma

{"title":"Intelligent Transportation System Performance Analysis of Indoor and Outdoor Internet of Vehicle (IoV) Applications Towards 5G","authors":"Preeti Rani;Rohit Sharma","doi":"10.26599/TST.2023.9010119","DOIUrl":"https://doi.org/10.26599/TST.2023.9010119","url":null,"abstract":"The Internet of Vehicles (IoVs) has seen rapid development due to advances in advanced communication technologies. The 5-th Generation (5G) systems will be integrated into next-generation vehicles, enabling them to operate more efficiently by cooperating with the environment. The millimeter Wave (mmWave) technology is projected to provide a large bandwidth to meet future needs for more effective data rate communications. A viable approach for transferring raw sensor data among autonomous vehicles would be to use mmWave communication. This paper attracts various research interests in academic, indoor, and outdoor mmWave operations. This paper presents mmWave propagation measurements for indoor and outdoor at 66 GHz frequency for IoVs scenarios. The proposed model examines the equivalent path loss using Free-Space Path Loss (FSPL) based on the transmitter and receiver distances for indoor and outdoor communications of the vehicles. In the indoor scenario, path loss propagation has the lowest penetration loss, but it is ineffective in the outdoor scenario because distance increases as free space path loss increases. The probability of error is increased, concerning the transmitter and receiver distances due to propagation effect, packet collisions, busy receiver, and sensing threshold. The proposed methodology shows a higher packet delivery ratio and average throughput with less delay in the connection during transmission.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1785-1795"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10565999","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment 面向 5G 通信环境中网络设备能效和人工智能自动化的智能计算模型

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2024.9010005

Ashish Bagwari;Jaganathan Logeshwaran;M. Raja;P. Devisivasankari;Jyotshana Bagwari;Vikas Rathi;Asma Mohammed Elbashir Saad

{"title":"Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment","authors":"Ashish Bagwari;Jaganathan Logeshwaran;M. Raja;P. Devisivasankari;Jyotshana Bagwari;Vikas Rathi;Asma Mohammed Elbashir Saad","doi":"10.26599/TST.2024.9010005","DOIUrl":"https://doi.org/10.26599/TST.2024.9010005","url":null,"abstract":"Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1728-1751"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-Stage Submodular Maximization Under Knapsack Problem Knapsack 问题下的两阶段次模态最大化

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010107

Zhicheng Liu;Jing Jin;Donglei Du;Xiaoyan Zhang

引用次数: 0

Approximation Algorithms for Maximization of $k$-Submodular Function Under a Matroid Constraint 矩阵约束条件下 $k$ 次模态函数最大化的近似算法

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010122

Yuezhu Liu;Yunjing Sun;Min Li

引用次数: 0

Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models 人工智能支持未来无线电动汽车的多模型学习和决策模型

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010094

Gajula Ramesh;Anil Kumar Budati;Shayla Islam;Louai A. Maghrabi;Abdullah Al-Atwai

{"title":"Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models","authors":"Gajula Ramesh;Anil Kumar Budati;Shayla Islam;Louai A. Maghrabi;Abdullah Al-Atwai","doi":"10.26599/TST.2023.9010094","DOIUrl":"https://doi.org/10.26599/TST.2023.9010094","url":null,"abstract":"In the contemporary era, driverless vehicles are a reality due to the proliferation of distributed technologies, sensing technologies, and Machine to Machine (M2M) communications. However, the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy efficient. From existing methods, it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy efficiency. However, the models focus on different aspects separately. There is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence (AI) on autonomous driving and energy efficiency. Towards this end, we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision making. It focuses on both safe driving in highway scenarios and energy efficiency. The deep learning based framework is realized with many models used for localization, path planning at high level, path planning at low level, reinforcement learning, transfer learning, power control, and speed control. With reinforcement learning, state-action-feedback play important role in decision making. Our simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1776-1784"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimal Pricing and Ordering Policy with Trapezoidal-Type Demand Under Partial Backlogged Shortages 部分积压短缺情况下梯形需求的最优定价和订购政策

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010040

Chunming Xu;Mingfei Bai;Chenchen Wu;Qiyue Wang;Yiwei Wang

引用次数: 0

IIN-FFD: Intra-Inter Network for Face Forgery Detection IIN-FFD：用于人脸伪造检测的内部网络

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2024.9010022

Qihua Zhou;Zhili Zhou;Zhipeng Bao;Weina Niu;Yuling Liu

{"title":"IIN-FFD: Intra-Inter Network for Face Forgery Detection","authors":"Qihua Zhou;Zhili Zhou;Zhipeng Bao;Weina Niu;Yuling Liu","doi":"10.26599/TST.2024.9010022","DOIUrl":"https://doi.org/10.26599/TST.2024.9010022","url":null,"abstract":"Since different kinds of face forgeries leave similar forgery traces in videos, learning the common features from different kinds of forged faces would achieve promising generalization ability of forgery detection. Therefore, to accurately detect known forgeries while ensuring high generalization ability of detecting unknown forgeries, we propose an intra-inter network (IIN) for face forgery detection (FFD) in videos with continual learning. The proposed IIN mainly consists of three modules, i.e., intra-module, inter-module, and forged trace masking module (FTMM). Specifically, the intra-module is trained for each kind of face forgeries by supervised learning to extract special features, while the inter-module is trained by self-supervised learning to extract the common features. As a result, the common and special features of the different forgeries are decoupled by the two feature learning modules, and then the decoupled common features can be utlized to achieve high generalization ability for FFD. Moreover, the FTMM is deployed for contrastive learning to further improve detection accuracy. The experimental results on FaceForensic++ dataset demonstrate that the proposed IIN outperforms the state-of-the-arts in FFD. Also, the generalization ability of the IIN verified on DFDC and Celeb-DF datasets demonstrates that the proposed IIN significantly improves the generalization ability for FFD.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1839-1850"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approximation and Heuristic Algorithms for the Priority Facility Location Problem with Outliers 有异常值的优先设施定位问题的近似和启发式算法

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010152

Hang Luo;Lu Han;Tianping Shuai;Fengmin Wang

引用次数: 0

Adaptive Model Compression for Steel Plate Surface Defect Detection: An Expert Knowledge and Working Condition-Based Approach 用于钢板表面缺陷检测的自适应模型压缩：基于专家知识和工作条件的方法

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2024.9010039

Maojie Sun;Fang Dong;Zhaowu Huang;Junzhou Luo

{"title":"Adaptive Model Compression for Steel Plate Surface Defect Detection: An Expert Knowledge and Working Condition-Based Approach","authors":"Maojie Sun;Fang Dong;Zhaowu Huang;Junzhou Luo","doi":"10.26599/TST.2024.9010039","DOIUrl":"https://doi.org/10.26599/TST.2024.9010039","url":null,"abstract":"The steel plate is one of the main products in steel industries, and its surface quality directly affects the final product performance. How to detect surface defects of steel plates in real time during the production process is a challenging problem. The single or fixed model compression method cannot be directly applied to the detection of steel surface defects, because it is difficult to consider the diversity of production tasks, the uncertainty caused by environmental factors, such as communication networks, and the influence of process and working conditions in steel plate production. In this paper, we propose an adaptive model compression method for steel surface defect online detection based on expert knowledge and working conditions. First, we establish an expert system to give lightweight model parameters based on the correlation between defect types and manufacturing processes. Then, lightweight model parameters are adaptively adjusted according to working conditions, which improves detection accuracy while ensuring real-time performance. The experimental results show that compared with the detection method of constant lightweight parameter model, the proposed method makes the total detection time cut down by 23.1%, and the deadline satisfaction ratio increased by 36.5%, while upgrading the accuracy by 4.2% and reducing the false detection rate by 4.3%.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1851-1871"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10565998","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integral Attack on the Full FUTURE Block Cipher 对全 FUTURE 区块密码的积分攻击

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-04 DOI: 10.26599/TST.2024.9010007

Zeyu Xu;Jiamin Cui;Kai Hu;Meiqin Wang

{"title":"Integral Attack on the Full FUTURE Block Cipher","authors":"Zeyu Xu;Jiamin Cui;Kai Hu;Meiqin Wang","doi":"10.26599/TST.2024.9010007","DOIUrl":"https://doi.org/10.26599/TST.2024.9010007","url":null,"abstract":"FUTURE is a recently proposed lightweight block cipher that achieved a remarkable hardware performance due to careful design decisions. FUTURE is an Advanced Encryption Standard (AES)-like Substitution-Permutation Network (SPN) with 10 rounds, whose round function consists of four components, i.e., SubCell, MixColumn, ShiftRow, and AddRoundKey. Unlike AES, it is a 64-bit-size block cipher with a 128-bit secret key, and the state can be arranged into 16 cells. Therefore, the operations of FUTURE including its S-box is defined over \u0000<tex>$boldsymbol{F}_{2}^{4}$</tex>\u0000. The previous studies have shown that the integral properties of 4-bit S-boxes are usually weaker than larger-size S-boxes, thus the number of rounds of FUTURE, i.e., 10 rounds only, might be too aggressive to provide enough resistance against integral cryptanalysis. In this paper, we mount the integral cryptanalysis on FUTURE. With state-of-the-art detection techniques, we identify several integral distinguishers of 7 rounds of FUTURE. By extending this 7-round distinguisher by 3 forward rounds, we manage to recover all the 128 bits secret keys from the full FUTURE cipher without the full codebook for the first time. To further achieve better time complexity, we also present a key recovery attack on full FUTURE with full codebook. Both attacks have better time complexity than existing results.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"161-170"},"PeriodicalIF":6.6,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0