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检索条件"主题词=detection algorithms"

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Compatibility-Based Recommendation Method for Ceramic Pattern Matching

Compatibility-Based Recommendation Method for Ceramic Patter...

引用

algorithms, Computing and Machine Learning (CACML), Asia Conference on

作者： Jiayu Zhang Bingxian Ma School of Information Science and Engineering University of Jinan Jinan China

ISBN: (数字)9798331543143

ISBN: (纸本)9798331543150

Ceramic culture is an important component of traditional Chinese culture, and its inheritance and innovation hold significant importance. As an essential carrier of ceramic art, ceramic patterns embody rich cultural symbolism and aesthetic value. To address the challenge of quantitatively matching user requirements with decoration combinations in ceramic design, this study proposes a compatibility-based recommendation method for ceramic decoration combinations. The method takes user-provided symbolic meanings and preferred elements as inputs. First, we integrate multi-dimensional information such as decorative elements and cultural symbolism to construct a knowledge graph. Second, we design a compatibility calculation model. The model combines shortest-path-based similarity measurement and overlapping community detection to quantify the compatibility potential between decorative elements. Finally, we propose a recommendation algorithm to recommend element combinations. These combinations satisfy users’ symbolic requirements while maximizing compatibility. Experimental results demonstrate that the method effectively enhances the coordination and cultural relevance of decoration combinations. This study provides an intelligent recommendation tool for ceramic decoration design. It can support the inheritance and innovation of ceramic culture.

关键词： Measurement Technological innovation Machine learning algorithms Knowledge graphs Machine learning Ceramics Cultural differences Global communication detection algorithms Pattern matching

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LabEYE: A System to Support Eyetracking Laboratory Experiment Research

LabEYE: A System to Support Eyetracking Laboratory Experimen...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Xuebai Zhang Yuchao Lu School of Computer and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

This paper proposes a system that allows researchers to manage laboratory experiment research based on eye tracking with complete auxiliary functions of the experimental process, including recruitment and registration, experimental arrangement, preparation of experimental materials, design of areas of interest, experiments and analysis of data. In addition, the system mainly achieves the drawing and analysis of Areas of Interest (AOI). The main AOI module adopts the Faster RCNN object detection algorithm, the text region detection algorithm, the face detection algorithm, and builds the DBSCAN clustering algorithm, thereby enabling automatic drawing of AOIs for different types. It utilizes the OpenCV image processing library to realize manual drawing of AOIs. Experimental results demonstrate that this system is simple and user-friendly, enhancing the efficiency of AOI drawing. Additionally, it offers researchers effective research guidance and management methods, improving their work efficiency.

关键词： Image processing Clustering algorithms Gaze tracking Object detection Manuals Libraries Face detection detection algorithms Consumer electronics Recruitment

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A Novel Super Constellation Design Paradigm

A Novel Super Constellation Design Paradigm

引用

IEEE Conference on Wireless Communications and Networking

作者： Thrassos K. Oikonomou Dimitrios Tyrovolas Sotiris A. Tegos Panagiotis D. Diamantoulakis Panagiotis Sarigiannidis George K. Karagiannidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the rapidly advancing field of sixth generation (6G) wireless networks, where the achievement of ultra-high data rates and energy efficiency is crucial, this study introduces the concept of super constellations as a solution to meet these pressing requirements. However, when higher-order modulations are employed, the system becomes increasingly susceptible to Gaussian phase noise (GPN), resulting in significant performance degradation. To address this issue, we propose a novel modulation scheme, called super amplitude phase shift keying (SAPSK), which is specifically designed to improve resilience against GPN. Furthermore, recognizing the critical need for efficient detection methods in high-order constellations, we present a detection framework that is not only capable of mitigating the detrimental effects of GPN, but also provides a foundation for the development of low-complexity detection algorithms. Specifically, we introduce the generalized polar distance detector (GPD-D), a detection metric that approximates maximum likelihood detection (MLD) in GPN-affected channels while introducing structured decision regions. Building on the GPD-D, SAPSK formulates a hexagonal lattice that provides improved energy efficiency and is further supported by a detection algorithm with Ό (1) complexity, ensuring fast and accurate symbol detection. Finally, the superiority of SAPSK is demonstrated through extensive numerical simulations, which show that SAPSK has significant advantages in terms of symbol error probability (SEP) over other conventional modulation schemes.

关键词： Phase noise Maximum likelihood detection Phase shift keying Wireless networks Symbols Detectors Pressing Energy efficiency detection algorithms Resilience

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Research on Semantic Matching of Thesis Themes Based on Comparative Learning and Adversarial Training

Research on Semantic Matching of Thesis Themes Based on Comp...

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Xu Zhang Zengzhen Shao Zhuangzhuang Li Shuai Han Yajing Sun Department of Information Science and Engineering Shandong Normal University Jinan China Department of Data Science and Computer Science Shandong Women's College Jinan China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

At the doctoral level in the field of computer science, the domain of literature plagiarism detection encompasses numerous plagiarism detection algorithms, but relatively fewer have focused on methods specifically designed for topic-based plagiarism detection. To address this research gap, this paper introduces a text semantic matching algorithm based on contrastive learning and adversarial training, which we have named “Comparative Adversarial Training (CAT).” Specifically, during the fine-tuning process, we generate affirmative and negative sentences using paper titles and keywords and optimize the model's performance through contrastive learning. Simultaneously, we generate adversarial samples by perturbing the word embedding matrix of the model and then employ these adversarial samples for adversarial training. Through experiments conducted on our proprietary internal dataset, the fine-tuned BERT model exhibited an average performance improvement of 3.71% compared to the baseline BERT model. Similarly, the fine-tuned RoBERTa model demonstrated a performance enhancement of 3.18% relative to the RoBERTa baseline model. Based on these experimental results, our algorithm's effectiveness has been thoroughly validated.

关键词： Training Computer science Control engineering Plagiarism Semantics Medical services detection algorithms

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IoT-Enhanced Disease Prediction: A Machine Learning Approach to Climate-Health Interactions

IoT-Enhanced Disease Prediction: A Machine Learning Approach...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： K. Vanitha Priyanka G Vijayakumar N Vikram K Department of Electronics and Communication Engineering K S Rangasamy College of Technology Namakkal India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The intersection of climate change and human health has increased the need for new solutions to predict and mitigate infectious diseases. This study explores an Internet of Things-enabled, machine learning-based framework to model and analyze the interactions between climate change and health outcomes. IoT sensors gather real-time environmental data, which is integrated with weather patterns and disease history. Advanced machine-learning techniques are then applied to detect and evaluate potential disease outbreaks in specific regions. This approach demonstrates the potential, accuracy, and potential of early warning, providing a valuable tool for governments to deploy health services promptly. This research advances understanding of the health-security equation and provides ideas and technologies to improve global health in the face of climate change.

关键词： Climate change Diseases detection algorithms Internet of Medical Things Data analysis Risk management Monitoring Machine learning

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Real-Time Gaze Tracking for Online Examination

Real-Time Gaze Tracking for Online Examination

引用

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Preyanshu Bisht Suresh Kumar Dept. of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The exponential growth in online education has increased the demand for automated systems to ensure academic integrity during online examinations. A real-time proctoring system addresses this need by monitoring a student's eye gaze and head movements during an exam. This system leverages Dlib's pre-trained frontal face detector and 68-point facial landmark predictor to detect facial features and track the direction of eye movement and head position. By analyzing these metrics, the system can flag behaviors associated with potential cheating, such as looking away from the screen for extended periods. This paper discusses the design and implementation of the proctoring system, details the detection algorithms, and evaluates its effectiveness for real-time monitoring.

关键词： Measurement Tracking Education Gaze tracking Detectors Real-time systems detection algorithms Monitoring Facial features Faces

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Application of Deep Neural Network for NOMA Systems in 5G Communications

Application of Deep Neural Network for NOMA Systems in 5G Co...

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International Electrical Engineering Congress (iEECON)

作者： Kosin Chaiprathum Samphan Phrompichai Sakol Udomsiri Department of Electrical Engineering Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Currently, multi-channel access techniques may not be able to effectively handle massive data situations. For 5G communication systems, non-orthogonal multiple access (NOMA) can improve channel efficiency to support more users. However, in the transmitter, the complete channel state information (CSI) is required, while in the receiver, the computational complexity is high, which is the main disadvantage of NOMA system. The use of deep neural network (DNN) as a deep learning (DL) technique is an excellent solution to deal with various problems. In this work, DNN predicts parameter values to perform channel estimation in NOMA systems. From the results, the performance in terms of symbol error rate (SER) can be improved higher than standard estimation approach.

关键词： Deep learning Training NOMA 5G mobile communication Transmitters Symbols Artificial neural networks detection algorithms Computational complexity Standards

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detection of malicious nodes in drone ad-hoc network based on supervised learning and clustering algorithms

Detection of malicious nodes in drone ad-hoc network based o...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shanshan Sun Zuchao Ma Liang Liu Hang Gao Jianfei Peng College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Multi-drone swarm has been widely used in disaster monitoring, mapping and remote sensing, national defense military and other fields, and has become a research hotspot in recent years. Due to the openness of its operating environment, attackers can invade the control system to capture drone, and then carry out data attacks such as tamper attack, drop attack and replay attack in drone ad-hoc network, which causes a great threat to the security of drone network. Existing malicious nodes detection algorithms are not efficient when applied to drone ad-hoc network, for the following reasons: (1) The malicious node detection algorithms based on reputation usually adopt a static threshold to determine whether a node is malicious, which is inefficient in dynamic drone network. (2) Mutual cooperation based malicious node detection algorithms rely on the high meeting probability of nodes. In order to solve the above problems, we propose a Malicious Drones detection Algorithm(MDA) based on supervised learning and clustering algorithms. The ground station calculates the reputation value of each routing path according to the received packets from different source nodes, and then evaluates the reputation value of drones with linear regression algorithm. Finally, gaussian clustering algorithm is used to cluster drones and find out malicious drones. Experiments were conducted in indoor and outdoor drone network. The experimental results indicate that the accuracy of MDA outperforms the existing methods by 10% 20%. And in the case of fewer malicious nodes, the accuracy can reach more than 90%, and the error rate is less than 10%.

关键词： Heuristic algorithms Linear regression Supervised learning Clustering algorithms Ad hoc networks detection algorithms Drones

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Analysis of Spatiotemporal Distribution Characteristics of Charging Demand Based on Electric Taxi Trajectory Data

Analysis of Spatiotemporal Distribution Characteristics of C...

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Jiamei Wang Pinxi Wang Ailing Huang School of Traffic and Transportation Beijing Jiaotong University Beijing China State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Under the background of ‘Dual carbon’ goals and taxi electrification, issues such as the mismatch between charging supply and demand in time and space have emerged. In this study, Beijing electric taxis are selected as the study subject. The long-stay detection algorithm is designed based on trajectory data to identify charging behavior. The Natural Breaks method is employed to divide grid functional attributes, which is beneficial for accurately analyzing the spatiotemporal distribution characteristics of charging demand. The results show that charging demand is concentrated after the morning and evening peaks and during meal breaks both on weekends and weekdays. The starting SOC for charging is mostly between 20% and 60%, while the termination SOC is concentrated between 70% and 100%. In different functional areas, charging demand exhibits distinct spatiotemporal patterns. On weekends, demand is more scattered, with noticeable early morning charging. On weekdays, a clear double-peak pattern is observed. In office and transport areas, peaks occur during commuting hours and lunch break. In mixed-use areas, charging demand peaks around 10:00 and 20:00. Commercial areas show scattered and fluctuating charging patterns. In living and residential areas, charging demand is concentrated at night and before the morning peak. Scenic areas experience charging peaks after park entry and closure. The findings will offer valuable insights for optimizing the layout of charging infrastructure.

关键词： Supply and demand Control engineering Layout Electrification Spatiotemporal phenomena Trajectory detection algorithms Public transportation Dispersion

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NL-COMM: Enhanced Video Streaming via Advanced Non-Linear Processing

NL-COMM: Enhanced Video Streaming via Advanced Non-Linear Pr...

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IEEE Conference on Wireless Communications and Networking

作者： Marcin Filo George N. Katsaros Chathura Jayawardena Konstantinos Nikitopoulos Wireless Systems Lab 5G & 6G Innovation Centre Institute for Communication Systems University of Surrey Guildford UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With video streaming now accounting for the majority of internet traffic, wireless networks face increasing demands, especially in densely populated areas where limited spectral resources are shared among many devices. While multi-user (MU)-MIMO technology aims to improve spectral efficiency by enabling concurrent transmissions over the same frequency and time resources, traditional linear processing methods fall short of fully utilizing available channel capacity. These methods require a substantial number of antennas and RF chains, to support a much smaller number of MIMO streams, leading to increased power consumption and operational costs, even when the supported streams are of low rate. In this demo, we present NL-COMM, an advanced non-linear MIMO processing framework, demonstrated for the first time with commercial off-the-shelf (COTS) user equipment (UEs) in a fully 3GPP-compliant environment. In addition, also for the first time, the audience will compare and assess the quality of live, over-the-air video transmission from four concurrently transmitting UE devices, alternating between current state-of-the-art MIMO detection algorithms and NL-COMM. Key gains of NL-COMM include improved stream quality, halving the number of required base station antennas without compromising stream quality compared to linear approaches, as well as achieving antenna overloading factors of 400%.

关键词： Radio frequency Time-frequency analysis Power demand Spectral efficiency Wireless networks Streaming media detection algorithms Streams Antennas Faces

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