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检索条件"任意字段=11th International Conference on Tools and Algorithms for the Construction and Analysis of Systems"

共 1634 条记录，以下是271-280 订阅

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Improving the Usage of Industrialized Building systems in the South African construction Industry: Principal Component analysis Approach 21

Improving the Usage of Industrialized Building Systems in th...

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11th international conference on Information Communication and Management, ICICM 2021

作者： Oikelomen Ayorinde, Emmanuel Ngcobo, Ntebo Kasenge, Mathe University of Johannesburg South Africa

ISBN: (纸本)9781450390194

the construction industry (CI) is the main driver of sustainable development (SD) and economic expansion in society. Industrialized building systems (IBS) promote productivity and improves quality on construction sites. the purpose of this study is to investigate ways of improving the use of IBS in the SACI. For this study, data was collected using primary and secondary sources of data collection. the study adopted a quantitative research approach with data gathered from the respondents within SACI. the findings revealed that the most significant ways of improving IBS use in SACI are government incentives towards IBS supporting projects, specialized training for IBS contractors, and improving the educational curriculum to include IBS methods. construction waste is a by-product of construction demolition and renovation and has caused adverse effects on the environment. therefore, one of the way to mitigate such environmental hazards is to use IBS. this study will contribute greatly to sustainable construction delivery in South Africa, socio-economic growth and will aid fast delivery of construction projects in terms of cost, time, and quality. Also, construction waste hazards causing harm to the environment can be reduced with the correct implementation of IBS in society. CCS CONCEPTS •Applied engineering •medium relevance © 2021 Owner/Author.

关键词： Economic growth construction industry Sustainability Industrialized building systems

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Advances in Automatic Software Verification: SV-COMP 2020 26th

Advances in Automatic Software Verification: SV-COMP 2020

引用

26th international conference on tools and algorithms for construction and analysis of systems (TACAS)

作者： Beyer, Dirk Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9783030452360;9783030452377

this report describes the 2020 Competition on Software Verification (SV-COMP), the 9th edition of a series of comparative evaluations of fully automatic software verifiers for C and Java programs. the competition provides a snapshot of the current state of the art in the area, and has a strong focus on replicability of its results. the competition was based on 11 052 verification tasks for C programs and 416 verification tasks for Java programs. Each verification task consisted of a program and a property (reachability, memory safety, overflows, termination). SV-COMP 2020 had 28 participating verification systems from 11 countries.

关键词： Formal Verification Program analysis Competition

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Multiple object tracking for robust quantitative analysis of passenger motion while boarding and alighting a metropolitan train 11

Multiple object tracking for robust quantitative analysis of...

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11th international conference of Pattern Recognition systems, ICPRS 2021

作者： Gómez Meza, José Sebastián Delpiano, José Velastin, Sergio A. Fernández, Rodrigo Awad, Sebastián Seriani Department of Engineering and Applied Sciences Universidad de los Andes Santiago Chile School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Leganés28911 Spain

ISBN: (纸本)9781839534300

To achieve significant improvements in public transport it is necessary to develop an autonomous system that locates and counts passengers in real time in scenarios with a high level of occlusion, providing tools to efficiently solve problems such as reduction and stabilization in travel times, greater fluency, better control of fleets and less congestion. A deep learning method based in transfer learning is used to accomplish this: You Only Look Once (YOLO) version 3 and Faster RCNN Inception version 2 architectures are fine tuned using PAMELA-UANDES dataset, which contains annotated images of the boarding and alighting of passengers on a subway platform from a superior perspective. the locations given by the detector are passed through a multiple object tracking system implemented based on a Markov decision process that associates subjects in consecutive frames and assigns identities considering overlaps between past detections and predicted positions using a Kalman filter. © 2021 Institution of Engineering and Technology. All rights reserved.

关键词： Object detection

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Microcontroller-Based Low Latency Audio System to Study Cortical Auditory Evoked Potentials: Applications with Intraoperative Language Mapping

Microcontroller-Based Low Latency Audio System to Study Cort...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Israt Tasnim Priscella Asman Chandra Prakash Swamy Sudhakar Tummala Sujit Prabhu Nuri Firat Ince Department of Biomedical Engineering University of Houston Houston TX USA Department of Neuro-Oncology UT MD Anderson Cancer Center Houston TX USA

Language processing in the brain comprises complex neurophysiology involving multiple regions and multimodal functions like cognition, memory, speech, etc. Recent studies show language processing is not restricted within anatomically established cortical language regions, and there is considerable inter-person variability. Clinically intraoperative functional language mapping is critical for studying patient-specific language processing and identifying the eloquent cortex to retain language functionalities during resection surgeries. Cortical auditory evoked potential (CAEP) induced by different auditory stimuli can provide a profound understanding of language processing. In this study, we present a low-cost, low-latency microcontroller-based audio system to induce and analyze CAEP. the system is established using off the shelf components; a 32bit Teensy (3.2) microcontroller, Teensy (3) audio shield etc. which makes it easily replicable. the Teensy can generate digital trigger locked to auditory stimulus onset to interface the system with biosignal amplifiers to record and study neural data simultaneously. the presented hardware tools enable the analysis of neural signals aligned with the audio stimuli with sub-millisecond temporal precision. Implementation of the system can provide a flexible platform to observe and analyze real-time cortical CAEP in the clinical settings. the system also provides great customization opportunities to study not only CAEP but also complex language processing tasks.

关键词： Audio systems Source coding Surgery Neural engineering Real-time systems Hardware Low latency communication

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Fire Detection System Based On YOLOv5

Fire Detection System Based On YOLOv5

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IEEE international conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Zetong Huang Fengling Wu Wei Jin Jiaxing Luo QiKun Shi Runrun Liu College of Computer Science Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Fire is a disaster with great harm in real life, and traditional fire detection methods have many shortcomings. In recent years, the development of computer vision and deep learning has provided new solutions for fire detection. this paper proposes a fire monitoring method based on the YOLOv5 target detection algorithm, which uses monitoring equipment to achieve real-time fire detection. the research content includes the construction of self-built data sets, YOLOv5 model training and optimization, detection system design and performance verification. By embedding the CBAM module in the C3 layer of the YOLOv5s backbone network, the channel and spatial processing of the feature map is performed, the feature refinement is achieved, and the construction and visualization of the detection system are completed. the experimental results show that the detection accuracy of flame targets is 0.745, and the detection accuracy of smoke targets is 0.582; on the test set, the average detection accuracy of flames and smoke is 0.823 and 0.673 respectively, and the overall average detection rate is 0.748. the fire detection system based on YOLOv5 implemented in this work has a significant effect on the current fire prevention and control, and it meets the actual application scenarios to the greatest extent.

关键词： YOLO Deep learning Training Accuracy Fires Feature extraction Real-time systems Security Monitoring System analysis and design

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A Simple, Efficient and Complete Software Pipeline for Constructing an RGB-D Camera Using RGB and ToF Depth Cameras

A Simple, Efficient and Complete Software Pipeline for Const...

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Mechatronics and Robotics Engineering (ICMRE), international conference on

作者： Sunil Kumar Sagar Dhatrak Aayush Shah Meet Gandhi Amit Gupta Mohammad Imran Einfochips (An Arrow Company) Noida India Einfochips (An Arrow Company) Pune India Einfochips (An Arrow Company) Ahmedabad India

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Robotics and computer vision applications have utilized consumer-grade RGB-D cameras because of their low cost and usability for various applications. these cameras capture RGB images with per-pixel depth information. the construction of RGB-D cameras using RGB and ToF depth sensors introduces significant challenges due to differences in resolutions and fields of view (FOV), leading to misalignments between RGB and depth data streams. this work proposes a simple, efficient and complete pipeline for constructing an RGB-D camera using RGB and ToF depth sensors, addressing the challenges of aligning data streams with differing resolutions and fields of view (FoV). the effectiveness of the pipeline is validated using experimental comparisons with existing RGB-D camera systems, including Intel RealSense and Vzense cameras. the registered depth and point clouds generated by the proposed approach were benchmarked against these systems using various datasets. Different performance metrics have been used to assess the effectiveness of these cameras, such as a number of holes in the registered depth image, object boundaries, color mixing at object edges and alignment of RGB and depth images. the performance of these cameras was evaluated under no-light conditions. Furthermore, quantitative analysis has been performed in terms of missing pixel (%) and repeatability to evaluate the performance of all cameras. the results demonstrate that the proposed pipeline achieves good alignment accuracy, improves depth data usability, and offers a practical solution for applications like 3D reconstruction and object detection.

关键词： Computer vision Accuracy three-dimensional displays Statistical analysis Pipelines Robot vision systems Cameras Sensors Usability Streams

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Gaze Detection and Text Extraction from Images using CNN with DBSCAN-based Filter Design

Gaze Detection and Text Extraction from Images using CNN wit...

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international conference on Advanced Computer Control, ICACC

作者： Jithin Jose Ajeesh Ramanujan Dept. Of Computer Science And Engineering College Of Engineering Trivandrum Trivandrum India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

Gaze detection and text extraction are pivotal technologies in the domain of human-computer interaction and computer vision, enabling applications such as assistive technologies, user interface optimization, and automated content analysis. this paper presents a novel method that identifies the user’s gaze direction and extracts the necessary portion of text, which constitutes the user’s region of interest, using a Convolutional Neural Network (CNN) integrated with a spatial attention mechanism. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based filter is incorporated into the system to remove textual noise from the extracted image, thereby improving the model’s accuracy and reducing computation time. the proposed method demonstrates reduced dependency on the CNN compared to previous approaches, resulting in enhanced real-time operation capability and lower latency. Computational optimization and real-time performance were thoroughly evaluated, showing that our system not only achieves high accuracy in gaze detection and text extraction but also maintains efficient processing speeds suitable for dynamic applications. this advancement offers significant improvements for applications in assistive technology, educational tools, and interactive media, where precise and efficient gaze detection and text extraction are critical.

关键词： Human computer interaction Computer vision Accuracy Content management Noise Assistive technologies Media Real-time systems Convolutional neural networks Optimization

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Design and Simulation of Standalone Solar Agri-PV System in Bangladesh: A Case Study

Design and Simulation of Standalone Solar Agri-PV System in ...

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IEEE Region 10 Humanitarian Technology conference (R10-HTC)

作者： Raihan Chowdhury Abu Shufian Saima Nusrat Nur Mohammad Institute of Energy Technology Chittagong University of Engineering and Technology Chattogram Bangladesh Dept. of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Dept. of Electrical and Electronic Engineering International Islamic University Chittagong Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

One of the biggest socioeconomic concerns of our day is finding ways to replace fossil fuels with Agri-photovoltaic (Agri-PV)-based renewable energy sources so that we can meet the combined world's energy needs and land inadequacy. the photovoltaic (PV) simulation tools are essential for predicting the solar system's electricity production. In view of this problem, this paper presents an assessment of a 300 W stand-alone solar PV system (SAPV) using the PV syst software. this study utilized simulation software to create a reliable and consistent SAPV system and to forecast the system's output. Also, LER analysis will be performed with respect to PV and non-PV systems. the total amount of electrical energy produced by the PV array that supplies the load is determined, along with the various types of power losses. the results of the simulation show the total amount of energy produced by the PV array, the total quantity of unused energy, the proportion of energy transferred to the load, system losses, performance ratio, and the project's economic viability. Using the same method described in this paper to model and design SAPV s for other places will help this paper be useful.

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An Enhancing Timeseries Anomaly Detection Using LSTM and Bi-LSTM Architectures

An Enhancing Timeseries Anomaly Detection Using LSTM and Bi-...

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international conference on Wireless Networks and Mobile Communications (WINCOM)

作者： Yousra Fadili Yassine El Yamani Jihad Kilani Najib El Kamoun Youssef Baddi Faycal Bensalah STIC Lab FSJ Chouaib Doukkali University El Jadida Morocco LERSEM Lab Chouaib Doukkali University El Jadida Morocco

ISBN: (数字)9798350377866

ISBN: (纸本)9798350377873

Anomaly detection in time series data plays a critical role in various domains, including cybersecurity, industrial monitoring, and financial fraud detection. In recent years, deep learning models have emerged as promising tools for addressing the challenges associated with anomaly detection in sequential data. this study investigates the effectiveness of advanced deep learning models in detecting anomalies within real-world time series datasets. Utilizing a diverse dataset, which encompasses a diverse range of time series data, we evaluate the performance of these models across multiple domains. Our experimental results demonstrate the high accuracy and robustness of the evaluated models in detecting anomalies. these models showcase the ability to capture complex temporal patterns and discern anomalies effectively, thereby showcasing their potential for real-world applications. Additionally, we discuss future research directions, emphasizing the exploration of alternative approaches for real-time anomaly detection and the optimization of models for deployment in production environments.

关键词： Deep learning Accuracy Wireless networks Time series analysis Production Real-time systems Data models

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Convolutional Neural Network (CNN) for Building Energy Efficiency analysis, Prediction, and Real-time Adjustment Strategies

Convolutional Neural Network (CNN) for Building Energy Effic...

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Information Technology: IoT and Smart City (ITIoTSC), international conference on

作者： Bin Liu Yujian Cao Yichao Gan Qianhang Huang Tong Zeng Jing Zhang Kangjie Cao China Construction Seventh Engineering Division. Corp. LTD Zhengzhou China Hong Kong Baptist University Hong Kong China School of Information Engineering Minzu University of China Haidian Beijing China

ISBN: (数字)9798350328349

ISBN: (纸本)9798350328356

In the era of sustainable development, building energy efficiency stands out as a pivotal concern, given the significant energy consumption attributed to buildings globally. this paper introduces a novel approach, harnessing the power of Convolutional Neural Networks (CNN) to analyze and predict energy inefficiencies using thermal imaging data. By training our model on a diverse dataset of thermal images from various buildings, we demonstrate the potential of CNNs in not only identifying energy inefficiencies but also suggesting real-time adjustment strategies. Preliminary results indicate a promising avenue for the integration of deep learning in the domain of sustainable architecture and building management.

关键词： Training Smart cities Architecture Buildings Energy efficiency Real-time systems thermal analysis

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