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Research on automatic quantitative quality inspection method for internal geometric accuracy of remote sensing images

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Ge, Huibin Li, Ying Wang, Boyu Geng, Yu Ba, Xiaojuan Northwestern Polytechnical University School of Computer Science Shaanxi China Piesat Information Technology Co. Ltd. Beijing China Beijing University of Civil Engineering and Architecture School of Geomatics and Urban Spatial Information Beijing China

The geometric accuracy of remote sensing images is very important to the mapping accuracy of land surveying and mapping industries, and its internal geometric accuracy affects the accuracy of length and area measurement. Therefore, it is of great significance to automatically screen out remote sensing images with acceptable accuracy. We propose a quantitative quality inspection algorithm for internal geometric accuracy based on automatic image matching to construct a triangulation network, which uses the automatic matching checkpoints to construct a triangulation network, characterizes the internal error between two points of the line segment by calculating the relative error of each line segment that composes each triangle, and finally counts the middle error of the relative accuracy of all line segments in the triangulation network to characterize the internal geometric accuracy of the image. Taking the remote sensing images of plains, hills, and mountains as experimental data, the experimental results show that the quality inspection results of the proposed method are consistent with those of the manual quality inspection method, and the deviation of the quality inspection accuracy is less than 0.3 pixels. The method indicates that it has nearly achieved the objectivity of manual quality inspection. With its characteristics of quantification, automation, and batch processing, this method holds significant practical importance for the internal geometric accuracy quality inspection of massive remote sensing images. In addition, the error results are rendered by color grading using segmentation thresholds, which can quickly and intuitively identify the internal geometric accuracy quality of different areas of the image. © The Authors.

关键词： Triangulation

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Trajectory Prediction and Multi-Risk-Factors Based Dynamic Collision Prediction Method between Construction Vehicles and Workers in Construction Sites

SSRN

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SSRN 2024年

作者： Gan, Wenxia Gu, Kedi Yang, Ruqin Geng, Jing Wang, Huini Qiu, Canzhi Hu, Xiaodi School of Civil Engineering and Architecture Wuhan Institute of Technology Hubei Wuhan430074 China Hubei Land Surveying and Mapping Institute Hubei Wuhan430074 China School of Computer Science & Technology Beijing Institute of Technology Beijing100081 China

Predicting the trajectory of construction vehicles and workers at construction sites is crucial for preventing collision accidents and enhancing safety. However, current prediction methods based on trajectory prediction often focus on the trajectory of workers or construction vehicles and failing to fully exploit critical risk factors, i.e., proximity, velocity, and hazardous zones etc. Therefore, this paper proposes a collision prediction method for construction vehicles and workers by combining trajectory prediction techniques in computer vision with a multiple risk factors-based collision risk assessment. The effectiveness of the method are evaluated across different construction site scenarios. The transformer-based trajectory prediction model demonstrates superior accuracy compared to classical models, such as social-GAN and social-LSTM, which the average displacement error is 0.53m construction site. The research findings demonstrate that proposed collision prediction method can be used to accurately predicting collision risk level of workers on construction sites. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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A Clustering-BIST Design for Inter-Layer Vias in 3D ICs Based on Walking Pattern Approach

TechRxiv

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TechRxiv 2023年

作者： Menbari, Ahmad Rahimi, Hemin Jahanirad, Hadi Ziener, Daniel The Department of Electrical Engineering University of Kurdistan Sanandaj Iran The Department of Computer Architecture and Embedded Systems Ilmenau University of Technology Ilmenau Germany

In the realm of 3D monolithic integrated circuits, inter-layer vias are prone to defects during fabrication, assembly, and operation that necessitates robust Built-In Self-Test solutions. This paper introduces an innovative BIST design aimed at the detection and localization of different types of faults in irregularly placed ILVs using a walking pattern methodology. In the proposed BIST methodology, the ILVs are clustered based on the fault occurrence probability distribution. Then, an effective detection approach is developed for all stuck-at faults, bridging faults, and almost all multiple faults in a single cluster. This approach allows designers to adjust different parameters such as fault coverage percentage, fault localization, and test time in alignment with specific requisites. The proposed BIST method reduces test configurations to two and test time to just 20 cycles by dividing ILVs into more clusters, overcoming a key limitation of recent approach. This methodology also demonstrates improved efficiency in terms of power consumption, area, and hardware utilization, particularly for large-scale benchmarks. For instance, when considering LU32PEENG and the scenario where ILVs are divided into 64 clusters, the power, area, and hardware overhead are 0.82% and 1.03%, and 1.14%, respectively. © 2023, CC BY.

关键词： Built-in self test

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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A computer-aided speech analytics approach for pronunciation feedback using deep feature clustering

A computer-aided speech analytics approach for pronunciation...

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作者： Nazir, Faria Majeed, Muhammad Nadeem Ghazanfar, Mustansar Ali Maqsood, Muazzam Department of Software Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Data Science University of the Punjab Lahore Pakistan School of Architecture Computing and Engineering University of East London London United Kingdom Department of Computer Science COMSATS University Islamabad Attock Campus Pakistan

Nowadays, the demand for language learning is increasing because people need to communicate with other people belonging to different regions for their business deals, study, etc. During language learning, a lot of pronunciation mistakes occur due to unfamiliarity with a new language and differences in accent. In this paper, we perform speech mistakes analysis using deep feature-based clustering. We proposed two novel methods for speech analysis, one to deal with phonemic errors (confusing phonemes) and the other to deal with the prosodic errors (partially changed pronunciation variation of phones). For accurate and efficient language learning, it is important to learn both phonemic as well as prosodic error corrections. In our first method, we perform speech analysis by combining deep CNN features and clustering algorithm to detect the phonemic errors. We classify the phonemes using K-nearest neighbor, Naïve Bayes, and support vector machine (SVM). We perform experiments on the six most frequently mispronounced confusing pairs of Arabic to handle phonemic errors and achieve an accuracy of 94%. In our second method, we proposed the unsupervised phone variation model (PVM) to detect prosodic errors. In PVM, each phone is extended to represent the different types of pronunciation variation of that phone with different proficiency levels. We use an Arabic dataset of 28 individual phones for speech analysis and provide feedback based on the variation of each phone and achieves an accuracy of 97%. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Error correction

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yuanchao Xu Ruyi Qian Yida Wang Qirun Huo College of Information Engineering Capital Normal University Beijing China SKL of Computer Architecture Institute of Computing Technology CAS Beijing China

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time.

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The Role of Artificial Intelligence in Asset Management of Enterprise Systems

The Role of Artificial Intelligence in Asset Management of E...

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2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Adebayo, Abimbola Sharif, Mhd Saeed Elmedany, Wael School of Architecture Computing and Engineering Computer Science and Dt Uel University way LondonE16 2RD United Kingdom University of Bahrain College of Information Technology Sakhir Bahrain

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Initially, when artificial intelligence is mentioned in conjunction with the phrase 'asset management', what springs to mind is how AI is used for investment assets and financial portfolios. However, even more than the management of investment portfolios is the use of AI in asset management. Information asset management plays a key role in cyber security and AI can also aid in the efficient running of this sub-process. The aim of this paper is to highlight the various ways in which AI can help with the management of information assets and contribute to the strengthening of security postures of enterprise systems. It also proposes a method for the identification of rogue assets in an asset list using the isolation forest algorithm. Results of the research showed that in the detection of outlier assets based on chosen attributes, with an accuracy of 90%, the algorithm accurately identified assets with anomalies indicating its usefulness in identifying rogue assets in the asset management process. © 2022 IEEE.

关键词： Asset management

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Achieving concurrency in cloud-orchestrated Internet of Things for resource sharing through multiple concurrent access

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Computational Intelligence 2024年第1期40卷 e12630-e12630页

作者： Xu, Xuedong Sun, Wei Vivekananda, G.N. Shankar, Achyut Department of Mechanical Engineering School of Mechanical and Electrical Engineering Changchun Institute of Technology Changchun China School of Architecture and Design Changchun Institute of Technology Changchun China Department of CSE Madanapalle Institute of Technology & Science India Department of computer science and Engineering ASET Amity University Noida India

Cloud-orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud-assisted communication helps to achieve better resource sharing features with fault tolerance ability. A multi-objective resource allocation and sharing (RAS) for balancing MCA in cloud-orchestrated IoT is presented in this article. The RAS constraints are modeled through linear programming (LP) as an optimization approach. The constraints are resolved using genetic representations (GR) for reducing the unserviced requests and failed resource allocations. Conventional genetic stages are inherited by the LP model to solve resource allocation and access issues reducing latency. The combined LP and GR jointly resolve resource allocation and MCA stagnation in cloud network. A fair outcome of LP-GR is estimation using the metrics response latency, resource utilization, request handled, and average latency. © 2020 Wiley Periodicals LLC.

关键词： Linear programming

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Integrating AEC Domain-Specific Multidisciplinary Knowledge for Informed and Interactive Feedback in Early Design Stages 1

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19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022

作者： Reisinger, Julia Rasoulzadeh, Shervin Kovacs, Balint Istvan Ferschin, Peter Vasylevska, Khrystyna Hensel, Michael Kovacic, Iva Wimmer, Michael Department for Integrated Planning and Industrial Building TU Wien Vienna Austria Department for Digital Architecture and Planning TU Wien Vienna Austria Research Unit of Computer Graphics TU Wien Vienna Austria Institute of Visual Computing & Human-Centered Technology TU Wien Vienna Austria

ISBN: (数字)9783031325151

ISBN: (纸本)9783031325144

In the context of digitalization in the industry, a variety of technologies has been developed for system integration and enhanced team collaboration in the architecture, Engineering and Construction (AEC) industry. Multidisciplinary design requirements are characterized by a high degree of complexity. Early design methods often rely on implicit or experiential design knowledge, whereas contemporary digital design tools mostly reflect domain-specific silo thinking with time-consuming iterative design processes. Yet, the early design stages hold the greatest potential for design optimization. This paper presents a framework of a multidisciplinary computational integration platform for early design stages that enables integration of AEC domain-specific methods from architecture, engineering, mathematics and computer science. The platform couples a semantic integrative mixed reality sketching application to a shape inference machine-learning based algorithm to link methods for different computation, simulation and digital fabrication tasks. A proof of concept of the proposed framework is presented for the use case of a freeform geometry wall. Future research will explore the potential of the framework to be extended to larger building projects with the aim to connect the method into BIM-processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Iterative methods

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F3D: Accelerating 3D Convolutional Neural Networks in Frequency Space Using ReRAM 21

F3D: Accelerating 3D Convolutional Neural Networks in Freque...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Bosheng Liu Zhuoshen Jiang Wu Jigang Xiaoming Chen Yinhe Han Peng Liu School of Computer Science and Technology Guangdong University of Technology China and State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology Guangdong University of Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781665432740

3D convolutional neural networks (CNNs) are widely deployed in video analysis. Fast algorithms such as fast Fourier transforms (FFTs) are gaining popularity in reducing computation complexity for their superior capability of replacing convolutions with simpler element-wise multiplications. Conventional frequency-domain dedicated accelerators employ memory hierarchy organization for high throughput but at the expensive costs of a significant amount of data movements and energy consumptions. This paper presents F3D, a processing-in-memory frequency-domain accelerator using resistive random access memory (ReRAM). F3D supports frequency-domain complex number multiplications directly in ReRAM-based crossbar architecture. We alleviate the overheads of redundant data movements in ReRAM-based complex number multiplications by data reuse and the inherent symmetry of inputs in the frequency space. Evaluation results demonstrate that F3D outperforms state-of-the-art accelerators with significant improvements in performance and energy efficiency.

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