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Chinese Automation Congress (CAC)

作者： Hui Zhong Yuanman Miao Liaoning Province Big Data Management and Analysisline Laboratory of Urban Construction School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the process of drawing architectural drawings with AutoCAD software, enterprises will produce a large number of CAD drawings in DWG format. The tables of these CAD drawings contain rich textual information. These text information constitute the key data base of enterprise systematic management drawings. At present, the systematic management of drawing data needs to collect these sheet data, but the sheet data collection mainly depends on manual input, which leads to high error rate of data collection, huge workload, long compilation cycle and low work efficiency. The purpose of this paper is to discuss how to deal with these tables efficiently. Firstly, based on Revit secondary development, the frame recognition algorithm is proposed, which confirms the inner and outer frame by finding the largest rectangle and the second largest rectangle. In addition, the Teigha class library is used to extract text, so as to extract key data, so as to promote the collection, reuse and systematic management of product data. Through the processing of drawing frames and forms, it lays a good foundation for subsequent component identification. The method proposed in this paper has strong versatility and adaptability, and can basically realize the recognition of CAD drawing frame and form, and extract form text, and achieve higher accuracy and efficiency.

关键词： Systematics Accuracy Text recognition Fault tolerant systems Manuals data collection Libraries Software data mining Optimization

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A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks 7th

A Novel Nash-Based Matching Approach for Multirobot Task All...

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7th EAI International Conference on Robotics and Networks, ROSENET 2023

作者： Hamidoğlu, Ali Gül, Ömer Melih Gültekin, Gökhan Koray Kadry, Seifedine Nimer Department of Mathematics Bahcesehir University İstanbul Turkey Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Electrical-Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates

ISBN: (纸本)9783031644948

Efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular MRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same number of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Distributed systems Energy harvesting Game theory Multirobot systems Task allocation

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EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle Detection Network in High Resolution Aerial Imagery

EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Nandini Saini Shubham Gupta Chiranjoy Chattopadhyay Debasis Das Suman Kundu Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The rapid advancement in deep learning-based object detection methods has made them a prevalent choice for real-time applications. Families of object detectors, including one-stage detectors, two-stage detectors, and region-based CNN networks, offer superior performance in accurately detecting objects. Despite their high accuracy, the complex design and black-box functionality of these models are not directly transferable in aerial imagery. Also, raise questions among users regarding the transparency of the algorithm in locating objects. Consequently, to demystify the decision process of these models, there is a need for Explainable AI (XAI) tools. XAI enables an understanding of the significance of each pixel in an image, shedding light on the contributions that lead to the model’s final output. In this context, this work will present an efficient, explainable, multi-scale vehicle detection network from high resolution aerial imagery, named as EVDNet. The EVDNet model has trained with two publicly available aerial image benchmark dataset DOTA and VEDAI. To enhance interpretability, we leverage XAI method using GradCam. The experimental results not only showcase the effectiveness and performance of the EVDNet model but also provide valuable insights into the object detection process. This research contributes to bridging the gap between complex object detection models and user understanding, offering a more transparent and interpretable approach to high-resolution aerial imagery analysis.

关键词： Visualization Accuracy Image resolution Explainable AI Error analysis Vehicle detection Object detection

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Analysis of Multi-Class Convolution Neural Network on Garbage Classification System

Analysis of Multi-Class Convolution Neural Network on Garbag...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Vikas K R Ullas P Sri Sai Teja M S Kumar Swamy S Ashwini Kodipalli Trupthi Rao Department of Computer Science and Engineering Global Academy of Technology Bangalore Karnataka India Department of Artificial Intelligence and Data Science and Engineering Global Academy of Technology Bangalore Karnataka India

The buildup of solid waste in metropolitan areas is a major worry that, if not effectively handled, might lead to environmental contamination and be dangerous to human health. To manage a range of waste products, it's crucial to have an advanced/intelligent waste management system. The act of separating garbage into its many components is one of the most crucial parts of waste management, and it is often carried out manually by hand-picking. To simplify this, we suggest a customized architecture for multiclass Convolutional Neural Networks (CNNs). The design includes elements that are individual-specific, enabling the model to adjust to the traits of each class. Extensive evaluation has shown that our CNN architecture, utilising a personalised approach, outperforms traditional CNN models like ResNet50 and VGG16 in multiclass classification situations. The effectiveness and resilience of our suggested architecture are demonstrated by experimental results on our dataset, opening the path for increased classification accuracy of 71.15%.

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A Comprehensive Evaluation of Face Mask Identification System using Modified Deep Learning Methodology 10

A Comprehensive Evaluation of Face Mask Identification Syste...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Lakshman, Bijee Vanitha, R. Vasuki, R. Uparkar, Satyajit S. Arun, S. Thiagarajan, R. Women's Christian College PG Department of Data Science College Road Chennai India Jerusalem College of Engineering Department of Cyber Security Chennai India Dhanalakshmi Srinivasan University Department of Cse School of Engineering and Technology Trichy India Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Jerusalem College of Engineering Department of ECE Chennai India Prathyusha Engineering College Department of It Chennai India

ISBN: (纸本)9798350353068

Recent years have seen an explosion in the prevalence, infectiousness, and potential harm posed to humanity by the corona virus family of viruses. When one person breathes out the virus, it lands on surfaces and other people breathe them in, causing the sickness to spread from person to person. As a result, safeguarding ourselves and those close to us has assumed paramount importance. There are measures we may do to protect ourselves, including as keeping our distance from others, wearing a mask, washing our hands often (every two hours), and using hand sanitizer. Masks worn by the general public have recently grown ubiquitous across the globe. The situation in India is particularly severe because of the country's dense population packed into a relatively limited landmass. Using a cross-validation strategy involving the conventional learning model known as Convolutional Neural Network (CNN), this paper presents a method called Modified Learning based Mask Identification Rule (MLMIR) for detecting whether or not a face mask is worn in offices or other places with a high volume of employees. The model's performance using live video streaming was astonishing once it was trained with a dataset made from real-world data. Using a number of additional parameters and a large number of people placed at different heights and angles inside the picture, we further evaluate the model's accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Skin Lesions for Identifying of Skin Cancer Melanoma using Customized CNN Model with Transfer Learning

Analysis of Skin Lesions for Identifying of Skin Cancer Mela...

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Global Conference for Advancement in Technology (GCAT)

作者： Ns Sinchana K Jeevitha S Kumar Swamy Ashwini Kodipalli Trupthi Rao Department of Computer Science and Engineering Global Academy of Technology Bangalore Karnataka India Department of Artificial Intelligence and Data Science and Engineering Global Academy of Technology Bangalore Karnataka India

Melanoma is a type of cancer known to be the most dangerous and is called the life threatening disease. Every year, there is rise in number of people being diagnosed with melanoma which is more frequent among the males than the females. This type of skin cancer has an uncontrollable growth of cells that can grow really faster, if not treated it can spread all over the body. Hence this disease needs to be detected at the early stages so that the patients can be treated properly. Machine learning approaches were applied to detect skin cancer but the drawbacks were that the performance to identify the cancer was limited. Therefore, we constructed a model by utilizing a Convolution Neural Network of the most advanced deep learning where we conduct investigations for discovering the melanoma skin cancer on the basis of the skin lesion. In this research, using transfer learning techniques ResNet and VGG based models have been brought together for melanoma skin detection. The procedures were applied to train these models using optimizers like RMSprop, Adam, Stochastic Gradient Descent (SGD) and findings were presented with the support of accuracy, confusion matrix, sensitivity, specificity and precision.

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Congestion aware clustered WSN based on an improved ant colony algorithm

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Measurement: Sensors 2024年 34卷

作者： Anto Pravin, R. Asha Shiny, X.S. Baby Vennila, V. Selvaraju, P. Uma Mageswari, R. Satish kumar, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Information Technology CMR Engineering College Hyderabad India Department of Information Technology SSM College of Engineering Tamil Nadu India Department of Artificial Intelligence and Data Science Excel Engineering College Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electrical and Electronics Engineering AMET University Tamil Nadu India

Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method. © 2024 The Authors

关键词： Ant colony optimization

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Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

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IEEE Conference on Wireless Communications and Networking

作者： Houze Feng Jingjing Wang Jianrui Chen Xiangwang Hou Jiacheng Wang Geng Sun Dusit Niyato School of Cyber Science and Technology Beihang University Beijing China Department of Electronic Engineering Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments.

关键词： Simulation Stability criteria Autonomous aerial vehicles Throughput Routing Routing protocols Vectors Delays Vehicle dynamics AODV

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MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation

arXiv

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arXiv 2025年

作者： Sun, Guokai Zhuang, Yuan Zhang, Shuo Feng, Xiaoyu Liu, Zhenguang Zhang, Liguo College of Computer Science and Technology Harbin Engineering University Heilongjiang China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China

Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety of tools have been developed to identify vulnerability in smart contract. However, they fail to overcome two challenges at the same time when faced with smart contract bytecode: (i) strong interference caused by enormous non-relevant instructions;(ii) missing semantics of bytecode due to incomplete data and control flow dependencies. In this paper, we propose a multi-teacher based bytecode vulnerability detection method, namely Multi-Teacher Vulnerability Hunter (MTVHunter), which delivers effective denoising and missing semantic to bytecode under multi-teacher guidance. Specifically, we first propose an instruction denoising teacher to eliminate noise interference by abstract vulnerability pattern and further reflect in contract embeddings. Secondly, we design a novel semantic complementary teacher with neuron distillation, which effectively extracts necessary semantic from source code to replenish the bytecode. Particularly, the proposed neuron distillation accelerate this semantic filling by turning the knowledge transition into a regression task. We conduct experiments on 229,178 real-world smart contracts that concerns four types of common vulnerabilities. Extensive experiments show MTVHunter achieves significantly performance gains over state-of-the-art approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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A Centralized Blockchain Architecture with Optimum Sharing

A Centralized Blockchain Architecture with Optimum Sharing

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International Conference on Communication and Electronics Systems (ICCES)

作者： Viswanathan Ramasamy S. Padmapriya G. Kavitha D. Lekha Department of Computer Science and Engineering KL Univrsity Gundoore Andhra Pradesh India Department of Artificial Intelligence and Data Science Velammal Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India

The monolithic network architectures are used in Bitcoin and other big cryptos. Different hierarchical ways to protect the decentralized blockchain have been discussed to improve its usability. However, no debate in the literature on how an appropriate shard size can be calculated to optimize the output and how deceptive or unreliable nodes can affect the optimum shard size range. This study includes an exchange and authentication scheme for hierarchical blockchain architecture in order to fix these issues. The hierarchy splits the network nodes into many disjoint fragments, with most transfers unregulated between these shards. Taking into account the performance and precision of the transaction validation in the case of malicious or defective nodes, the optimum shard size is calculated. It ensures that most of the good shards are available with the right scale, according to the highest acceptable threshold of defective nodes. It also offers a method to distinguish damaged shards by overlapping a tiny proportion of purchases across all shields and discarding all transactions. The theoretical efficiency analysis and the influence of network sharing on performance were based on experimental findings conducted on Amazon EC-2 nodes.

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