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Proceedings of the 2024 6th International Conference on Mechatronics Systems and Control engineering

作者： Ye Zhou Nengxin Mou College of Computer Science and Software Engineering Hohai University Nanjing Jiangsu China

ISBN: (纸本)9798400710346

This paper presents a novel 2D obstacle avoidance model tailored for field search and rescue robots. Leveraging advanced deep reinforcement learning techniques, the proposed model aims to enhance the robots’ navigation capabilities in dynamic and unpredictable environments. We evaluated four distinct reinforcement learning models—Deep Q-Learning (DQL), Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Deep Q-Network (DQN)—to identify the most effective approach for real-time obstacle avoidance. Experimental results demon- strata that the DQL model outperforms the others in terms of success rate, average reward, and stability, making it particularly suitable for complex field operations. This research contributes to the development of robust and efficient navigation strategies for autonomous search and rescue missions, with potential implications for broader applications in robotics.

关键词： 2D Obstacle Avoidance

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MFM: Multimodal Sentiment Analysis Based on Modal Focusing Model

MFM: Multimodal Sentiment Analysis Based on Modal Focusing M...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuangyang Sun Guoyan Xu Sijun Lu College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Multimodal sentiment analysis integrates various modalities of information to collectively inform decision-making processes. Previous studies often treat different modal features equally or emphasize textual information as the primary consideration. However, when the modalities in the sample contain different sentiment information, these methods may not be able to effectively deal with this situation. To solve this problem, we propose a multimodal sentiment analysis model focusing on each modality (MFM). In this paper, we separately integrate each modality as a primary modality interacting with other secondary modal information so that each modality can play a leading role. In addition, we use shared mask in modal interaction to capture important information in the secondary modality related to the primary modality, and improve the effectiveness of the information interaction process. The model is evaluated against baseline models using the MOSI and MOSEI multimodal sentiment analysis datasets. The experimental results show that the model achieves better performance, thereby validating its effectiveness in multimodal sentiment analysis tasks.

关键词： Sentiment analysis Analytical models Attention mechanisms Decision making Focusing Interference Vectors Cybernetics

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Pedestrian Physical Education Training Over Visualization Tool

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computers, Materials & Continua 2022年第11期73卷 2389-2405页

作者： Tamara al Shloul Israr Akhter Suliman A.Alsuhibany Yazeed Yasin Ghadi Ahmad Jalal Jeongmin Park Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

E-learning approaches are one of the most important learning platforms for the learner through electronic *** study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,transports,communication,emergency services,management systems and education sectors.E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and *** of them are currently working on this domain to fulfill the requirements of industry and the *** this paper,we proposed a method for pedestrian behavior mining of aerial data,using deep flow feature,graph mining technique,and convocational neural *** input data,the state-of-the-art crowd activity University of Minnesota(UMN)dataset is adopted,which contains the aerial indoor and outdoor view of the pedestrian,for simplification of extra information and computational cost reduction the pre-processing is *** flow features are extracted to find more accurate ***,to deal with repetition in features data and features mining the graph mining algorithm is applied,while Convolution Neural Network(CNN)is applied for pedestrian behavior *** proposed method shows 84.50%of mean accuracy and a 15.50%of error ***,the achieved results show more accuracy as compared to state-ofthe-art classification algorithms such as decision tree,artificial neural network(ANN).

关键词： Artificial intelligence behavior mining convolution neural network(CNN) deep flow e-learning environment

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Variable Length Motif Mining Method Based on Suffix Array Index

Variable Length Motif Mining Method Based on Suffix Array In...

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Artificial Intelligence, Human-computer Interaction and Robotics (AIHCIR), International Conference on

作者： Jimin Wang Ying Zhu Jiahao Sun College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798331534035

ISBN: (纸本)9798331534042

Existing time series variable-length motif mining algorithms based on suffix arrays suffer from long running times and are prone to premature termination of matching due to variations in individual characters, which hampers the discovery of longer motifs. To address these issues, we propose a novel time series variable-length motif mining algorithm based on suffix array indexing. This algorithm employs PAA and SAX methods for the symbolic representation of time series data, while the DC3 algorithm is utilized for the rapid construction of suffix arrays. Similarity matching is performed based on edit distance, enabling the extraction of variable-length motifs. This approach facilitates the identification of longer motifs with an edit distance less than specified parameters. Experimental results demonstrate that, compared to representative algorithms, our method significantly enhances mining accuracy, maximizes motif length, and improves time efficiency.

关键词： Human computer interaction Accuracy Time series analysis Arrays Data mining Artificial intelligence Robots Indexing

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Intelligent Sign Language Recognition System for E-Learning Context

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computers, Materials & Continua 2022年第9期72卷 5327-5343页

作者： Muhammad Jamil Hussain Ahmad Shaoor Suliman A.Alsuhibany Yazeed Yasin Ghadi Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-si237Gyeonggi-doKorea

In this research work,an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and *** feature set has the ability to increase the overall performance of machine learning algorithms in an efficient *** hand gesture recognition based on these features has been implemented for usage in *** feature set used hand landmarks,which were generated using media-pipe(MediaPipe)and open computer vision(openCV)on each frame of the incoming *** overall algorithm has been tested on two well-known ASLalphabet(American Sign Language)and ISL-HS(Irish Sign Language)sign language *** machine learning classifiers including random forest,decision tree,and naïve Bayesian have been used to classify hand gestures using this unique feature set and their respective results have been *** the random forest classifier performed better,it has been selected as the base classifier for the proposed *** showed 96.7%accuracy with ISL-HS and 93.7%accuracy with ASL-alphabet dataset using the extracted features.

关键词： Decision tree feature extraction hand gesture recognition landmarks machine learning palm detection

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Deadline-aware Load Allocation for Coded Computation over Heterogeneous Clusters

Deadline-aware Load Allocation for Coded Computation over He...

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Big Data & Artificial Intelligence & software engineering (ICBASE), International Conference on

作者： Shiying Cheng Yuxuan Lin Bin Tang College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In large-scale distributed systems, the performance of computation tasks is often significantly degraded by straggling nodes. Recently, coded computation has emerged as a promising approach to mitigate the effect of stragglers. However, the performance of coded computation is still significantly affected by node workloads, particularly in heterogeneous clusters. Some load allocation schemes have been proposed to minimize the expected computation latency, but for time-sensitive tasks with strict deadlines, they often result in high failure probabilities. In this paper, we examine the fundamental matrix-vector multiplication tasks and focus on the deadline-aware load allocation problem under two typical runtime models, aiming to minimize the task failure probability. We firstly propose a simple yet effective normal approximation method based on the central limit theorem to approximate the failure probability, transforming the problem into a non-convex multivariate optimization, and then present an efficient iterative load allocation algorithm. Extensive simulations demonstrate the effectiveness of our proposed scheme.

关键词： Runtime Computational modeling Simulation Clustering algorithms Iterative algorithms Encoding Resource management Approximation methods Optimization Load modeling

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Enhancing Gene Cluster Identification and Classification in Bacterial Genomes Through Synonym Replacement and Deep Learning

Enhancing Gene Cluster Identification and Classification in ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhihua Du Ningyu Zhong Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Natural products, abundant in biological activities, are synthesized by bacteria during secondary metabolism, yielding a plethora of antibiotics, anticancer drugs, and other potential therapeutics. These compounds are directed by biosynthetic gene clusters (BGCs), which encode the necessary enzymes and regulatory elements for their production. With the rapid advancement of sequencing technology, the number of fully sequenced bacterial genomes has exponentially increased, providing researchers with an unprecedented opportunity to discover more BGCs. However, current identification methods are significantly constrained by their reliance on database quantity and predominantly utilize local features inherent in the BGC sequences. In this study, we introduce Biosynthetic Gene Clusters Catcher: GCN-BERT(BGCCGB), a deep learning model leveraging natural language processing for the identification of BGCs in bacterial genomes and classification of BGCs product types. Our approach represents BGCs as sequences represented by protein family (Pfam) identifiers and performs data augmentation on existing BGC data by introducing a synonym replacement method. Local and global features in BGCs sequences are then integrated by BERT and its self-attention mechanism, in conjunction with Graph Convolutional Networks (GCN). The experiment results indicate that BGCCGB effectively learns the features of BGCs, localizes BGCs in bacterial genomes, and predicts the product types of BGCs. Notably, BGCCGB demonstrates a significant performance improvement over previous methods, with an average precision 6.9% in BGC identification. These enhancements underscore the potential of BGCCGB in advancing the discovery and understanding of natural products synthesized by bacteria.

关键词： Deep learning Sequential analysis Protein engineering Enzymes Graph convolutional networks Genomics Production Bacteria Natural language processing Metabolism

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Surtify: A Smart Surface Identification System Based on Multi-dimensional Acoustic Dispersion

Surtify: A Smart Surface Identification System Based on Mult...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Yunshu Wang Baojie Yuan Feihang Dong Shicong Hong Yongpan Zou College of Computer Science and Software engineering Shenzhen University Shenzhen China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

With the continuous advancement of the smart home market, household items have become more intelligent. By identifying the different material attributes of various household tabletops, we can obtain contextual information and engage in further interactions. This paper explores a phenomenon, acoustic dispersion and applies it to surface identification. Integrating mobile deep learning, we propose a method to recognize solid surface materials through tapping and utilize tapping events as triggers for interaction by identifying surface material categories. To achieve the goal of zero training effort and tiny hardware cost for unseen users, we analyze the propagation patterns of different frequency components in tapping sound signals from both temporal and spatial dimensions. Based on this, we propose a time-frequency data augmentation strategy that combines multiple event points to improve the recognition accuracy. The results show that, using the leave-one-user-out cross validation for 24 volunteers trained with simple guidance on tapping intensity and position, our system achieves a recognition accuracy of 98.6% for 11 common material types. For 11 newly recruited volunteers without specific control over force and position, the accuracy also remains at 93.4%. We also conduct a series of cross-environment experiments and user study, all of which indicate the high potential of our system.

关键词： Training Time-frequency analysis Solid modeling Accuracy Force Solids Data augmentation Acoustics Time-domain analysis Dispersion

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DeSAM2: Detection-Driven SAM2 for Hyperspectral Object Tracking

DeSAM2: Detection-Driven SAM2 for Hyperspectral Object Track...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Wangquan He Qi Ren Hongtao Zhang Ziqian Mo Nanying Li Meng Xu Sen Jia College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Hyperspectral object tracking (HOT) captures subtle object features, enabling precise identification and tracking in complex backgrounds. However, the high-dimensional nature of data and rapid object changes in dynamic scenes make accurate and robust tracking challenging. This paper proposes a detection-driven Segment Anything Model 2 (SAM2) for HOT (named DeSAM2), which enhances tracking accuracy and robustness through the collaboration of initial prompt tracking and a detection-based self-prompt auxiliary mechanism. Specifically, The method first employs a key band selection strategy to ensure that the spectral difference between object and background is maximized in complex backgrounds. Then, initial prompt tracking is used to acquire initial position of the object, and this process uses SAM2 as a base scheme to maintain high accuracy tracking. Furthermore, to address missed and false detections in initial tracking results within complex scenes, a detection-based self-prompt auxiliary mechanism is designed to further refine and correct tracking, thereby enhancing the adaptability and continuity of the model in dynamic environments. Experimental results on the datasets provided by the HOT Challenge 2024 indicate that the proposed method demonstrates superior performance compared to other advanced trackers.

关键词： Location awareness Adaptation models Accuracy Collaboration Signal processing Robustness Stability analysis Object tracking Object recognition Hyperspectral imaging

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Design and Implementation of an Agricultural Logistics Control System Based on IoT Techology

Design and Implementation of an Agricultural Logistics Contr...

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Advanced Control Systems and Automation Technologies (ACSAT), International Conference on

作者： Pengyu Sun Zhiquan Zhang College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798331541668

ISBN: (纸本)9798331541675

In today’s society, the agricultural logistics and transportation industry faces numerous challenges in meeting the demands for fresh produce preservation and efficient transport. Insufficient environmental monitoring and information management during transportation lead to increased logistics costs and high produce spoilage rates. A high-quality, efficient logistics control system is essential for the agricultural supply chain. This paper presents a logistics control system based on IoT technology, centered on the ESP32 chip, utilizing ESP-NOW and MQTT protocols for communication, integrating multi-sensor data and RFID technology. A sensor module and host gateway were designed, along with a cloud platform visualization dashboard. Through this system, users can view sensor data, track item dynamics, and effectively monitor and control the agricultural logistics process in real-time, enhancing both the efficiency and quality of agricultural logistics.

关键词： Temperature sensors Protocols Transportation industry Transportation Control systems Real-time systems Information management Environmental monitoring Vehicle dynamics Logistics

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