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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

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Development of an Integrated Expert System for Distribution Network Diagnostics Based on Artificial Intelligence Technology

Development of an Integrated Expert System for Distribution ...

引用

2023 international Russian Smart Industry conference, SmartIndustryCon 2023

作者： Galiev, Ilgiz F. Garifullin, Marsel Sh. Alekseev, Ilya P. Gizatullin, Ainaz R. Makletsov, Alexander M. Kazan State Power Engineering University Dept. of Electrical Power Systems and Grids Kazan Russia

ISBN: (纸本)9781665464291

The paper proposes implementation of the concept of integrated expert diagnostic system (IESD) of distribution network equipment based on AI technology. Complexes and modules of IESD interact with subsystems of retrospective, supplemented and updated offline information (database) and online monitoring and diagnostics of main electrical equipment. The objects of study in this work are the operating main equipment of 110/6(10) kV substation and adjacent 0.4÷6(10) kV distribution network. IESD consists of the following computational complexes: executive, which provides the system with offline and online data input;intelligent, which consists of computing and analytical modules;knowledge base (KB) - Expert system that performs requests for additional data to correct calculations in the calculation modules. The aim of the work is to integrate existing and additional subsystems of online monitoring into a unified expert diagnostic system that allows for real objects: to adequately assess the condition of the main equipment and monitor its remaining life;to evaluate the distribution network state, optimize the current mode for reliability, voltage levels and power losses;to monitor the development of equipment defects and use predictive analysis models for planning of repairs and maintenance. The significance of the work lies in the development of mathematical models of operational and predictive assessment of the state of power transformers and network equipment, as well as in the formation of key components of information and analytical support for decision-making on its operation. Scientific novelty consists in the development of methods and algorithms using combined methods and learning models. © 2023 IEEE.

关键词： Expert systems

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Peer evaluation in the context of PBL, a cross-cutting and cohesive approach for university degrees

Peer evaluation in the context of PBL, a cross-cutting and c...

引用

2021 international conference of Innovation, Learning and Cooperation, CINAIC 2021

作者： Manchado-Pérez, Eduardo Diago-Ferrer, Laura Sierra-Pérez, Jorge Sein-Echaluce, María Luisa Department of Design and Manufacturing Engineering School of Engineering and Architecture University of Zaragoza Zaragoza Spain

Two of the main objectives pursued by teaching innovation are the acquisition of transversal competences and the adaptation and improvement of the evaluation and grading systems. Regarding the first, it is already evident that, along with academic knowledge, developing other capacities such as teamwork, leadership or autonomous learning is a complementary but fundamental question. These skills make it possible to apply academic knowledge to its full potential, and in a modern society, access the labour market with guarantees. The evaluation and grading systems considered as element of the learning process should help and guide the student to identify their strengths and weaknesses, also in these matters. In doing so, the two elements contribute to notable successes in environments such as PBL. To do this, peer review brings unquestionable benefits, but it also entails risks and difficulties. The pandemic year also forced to chase new alternatives. In this work we present two solutions adopted in the Design Workshop I subject, of the Degree in Industrial Design engineering and Product Development at the University of Zaragoza. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Product design

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Classification of Dry Beans into Genetic Varieties Using Deep Learning-Based Convolutional Neural Networks (CNNs) 5th

Classification of Dry Beans into Genetic Varieties Using Dee...

引用

5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Fahim, Sajid Faysal Chowdhury, Mehrab Roy, Abin Islam, Md Safayet Simla, Sanjida Hossain, Md Belayet Al-Imran, Md Department of Computer Science and Engineering East West University Dhaka1212 Bangladesh

ISBN: (纸本)9789819725496

Efficient crop management, breeding programs, and the maintenance of sustainable agriculture are all dependent on the critical task of identifying and classifying genetic variations in crops. The study entailed collecting and preprocessing an extensive dataset comprising morphological, physical, and genetic attributes of different dry bean samples. The primary goal was to develop a robust classification model that could discern the genetic variations among various dry bean varieties. To ensure consistency, a preliminary phase of data preprocessing was executed, including color normalization and data augmentation. The findings demonstrated the effectiveness of the proposed deep learning-based method, achieving high accuracy and reliable classification of dry beans, including Seker, Barbunya, Bombay, Cali, Dermosan, Horoz, and Sira into their genetic variations. To accomplish this goal, supervised deep learning techniques have been subjected to rigorous training using one distinct dataset: one containing 70% training data and 30% testing data. These processes were implemented to acquire knowledge and classify the intricate patterns that are inherent in the given data. The classification of Custom CNN is incredibly accurate at 99.85%, while the Xception and MobileNet accuracy in classifying dry bean samples is slightly lower at 82%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Real-Time Automated Detection of Errors in the Product Labels Using Image Processing and OCR 1

Real-Time Automated Detection of Errors in the Product Label...

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1st IEEE international conference on Ambient Intelligence, knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Naveen Kumar, H.N. Kiran Kotiyal, Arnav Jagadeesh, B. Mahadevaswamy Prasad M S, Guru Vidyavardhaka College of Engineering Department of Ece Mysuru India Department of Cse Dehradun India

ISBN: (纸本)9798350316469

In the realm of product manufacturing and distribution, ensuring accurate and compliant product labeling is critical for both regulatory compliance and consumer safety. This research introduces a novel methodology for the automated identification of inaccuracies in product labels through the integration of image processing and Optical Character Recognition (OCR). Image processing techniques are used to recognize and analyze visual components including barcodes, logos, and product images, while OCR accurately transcribes text from the label images. The system uses a rule-based framework to compare the retrieved data to reference template in order to improve error detection. Erroneous or missing information, irregular formatting, or disparities in the visual representation of the label all constitute deviations from the intended content and should set up alarms for further investigation. The methodology has been tested and validated on a custom dataset of product labels from a variety of sectors. The proposed model is implemented in real-time, and its recognition accuracy is found to be 78%. The system's error-detection performance is impressive, making it a viable option for producers and shippers looking to standardize their quality assurance procedures and reduce their exposure to legal liability. The proposed approach not only improves accuracy but also greatly decreases the manual work typically involved with label verification, hence boosting overall operational efficiency in consumer goods production and delivery. © 2023 IEEE.

关键词： Image Processing Optical Character Recognition (OCR) Quality Inspection Rule-based Framework

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Next Generation Design For Testability, Debug and Reliability Using Formal Techniques

Next Generation Design For Testability, Debug and Reliabilit...

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IEEE international Test conference (ITC)

作者： Huhn, Sebastian Drechsler, Rolf Univ Bremen Bremen Germany DFKI GmbH Cyber Phys Syst D-28359 Bremen Germany

ISBN: (数字)9781665462709

ISBN: (纸本)9781665462709

The integration of Design for Testability measures is strictly required when designing complex Integrated Circuits (ICs) to ensure that a good testability prevails in the resulting design. By this, a high-quality manufacturing test can be performed, giving a certain level of confidence that no defects have occurred during the manufacturing process, which potentially tamper with the functional behavior's correctness. However, a high-quality test implies large test data volume and high test application time, yielding high test costs. This effect is even more amplified when testing ICs for safety-critical applications like automotive systems or avionics, enforcing a zero-defect policy. Analogously, specific structures for the Design for Debug and Diagnosis are introduced since similar problems exist when debugging complex systems. Finally, the Design for Reliability is becoming increasingly important in applications like avionics since the introduced system has typically to deal with harsh environmental conditions and, hence, the IC has to exhibit a specific level of robustness to withstand. This paper proposes novel contributions to, in the end, pave the way for the next generation of IC, which can be successfully and reliably integrated even in safety-critical applications. In particular, this paper combines formal techniques, such as the Boolean satisfiability problem and bounded model checking, to propose (I) a novel test access mechanism with embedded compression including an optimization-based retargeting framework, (II) a new hybrid compression architecture to address compression aborts and (II) an effective fault detection mechanism for single transient faults. The proposed measures are evaluated by considering industrial-relevant benchmark candidates, demonstrating their effectiveness and showing that stateof-the-art techniques are outperformed.

关键词： Integrated circuits manufacturing processes Aerospace electronics Model checking Reliability engineering Design for testability Robustness

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Image Analysis for Historical knowledge Discovery and Preservation

Image Analysis for Historical Knowledge Discovery and Preser...

引用

2025 international conference on Visual Analytics and data Visualization, ICVADV 2025

作者： Sakthivel, K. Ashwin, J. Poongodi, K. Oviya, S. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

Digitized archival collections offer rich sources for cultural heritage research. Current systems, though, fail to effectively integrate heterogeneous data forms like images, textual descriptions, and metadata. This study offers a sophisticated framework that blends image recognition, natural language processing (NLP), and knowledge graph methods to facilitate access, enable discovery, and reveal concealed historical patterns. The system comprises several modules such as a data input module, preprocessing pipeline, image recognition, NLP analysis, knowledge graph generation, semantic search engine, and an interactive user interface. Testing the system on a large historical archive dataset proved high accuracy, efficiency, and usability for all modules. Major takeaways are 93.4% accuracy in object detection for images, 94.8% accuracy for named entity recognition, and 95.5% search accuracy in semantic search feature. © 2025 IEEE.

关键词： Usability engineering

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CellFormer: A DaViT Transformer-Based Method for Nuclei Segmentation

CellFormer: A DaViT Transformer-Based Method for Nuclei Segm...

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2023 RIVF international conference on Computing and Communication Technologies, RIVF 2023

作者： Pham, Viet-Tien Vu, Ngoc-Tu Pham, Van-Truong Tran, Thi-Thao School of Electrical and Electronic Engineering Hanoi University of Science and Technology Department of Automation Engineering Viet Nam

ISBN: (纸本)9798350315844

Automated analysis of microscopic pictures in computer-aided clinical support systems has difficulty iden- tifying cell nuclei inside nuclei. Models based on the U- Net network, its derivatives or transformers, and their combinations have been proposed to segment cell nuclei, with positive results. However, these techniques omitted a number of details vital to simulating the regional appearance of the cell nucleus or missing a high-level representation of features for location dependence during decoding. For overcoming these limitations, the CellFormer network has been recommended in this research. With CellFormer, an encoder-decoder architecture, both the encoder and the decoder branches may mimic a long-range knowledge of semantics. The encoder is a compact architecture built on transformers that describe global semantic linkages at different data levels. An AAMC module that we suggested along with the design to learn advanced features to enhance the feature representation is used by the decoder, which is an extended network topology, to convey data from the decoder. In order to improve the boundaries of nuclear items on the global map for precise segmentation, an MSFB block with a novel bypass connection mechanism has been introduced. Through cell segmentation datasets such as GlaS and data Science Bowl 2018, we assess the performance of this suggested architecture. The findings were far better than we had anticipated in the area of biomedical image segmentation. © 2023 IEEE.

关键词： Semantics

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Fusing Deep Learning and Ensemble Techniques for Comic Character Emotion Recognition 4

Fusing Deep Learning and Ensemble Techniques for Comic Chara...

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4th international conference on data Analytics for Business and Industry, ICDABI 2023

作者： Sharma, Rishabh Kukreja, Vinay Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350369786

Emotions play an essential part in human communication and are tightly entwined with visual storytelling techniques such as comic panel visuals. This research studies the use of Convolutional Neural Networks (CNN) in conjunction with the Random Forest (RF) algorithm for the purpose of accurately classifying the range of emotions that may be found in comic book panels. A carefully selected dataset of 10,000 comic panel images was collected, and it included a spectrum of emotions such as happiness, sadness, rage, surprise, and disgust. The dataset was comprised of carefully picked comic panel images. The hybrid CNN-RF model achieved a great overall accuracy rate of 94.6% after making use of careful preprocessing approaches and carrying out feature extraction. The hybrid technique that was given in this work not only improves the classification of emotions that are depicted in comics, but it also broadens its potential applications in the fields of human-computer interaction, sentiment analysis, and content recommendation systems. The findings highlight the potential for combining art and technology as a tool to convey the many emotional facets that are present in visual narratives. This research presents a vital contribution to the current body of knowledge on emotions within the framework of storytelling, signifying a notable improvement in the field of emotion classification, and it does so by focusing on how emotions are evoked through the medium of narrative. © 2023 IEEE.

关键词： Convolutional neural networks

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KSRR: knowledge Centric Semantically Driven Recipe Recommendation Framework Encompassing Collective Intelligence 15th

KSRR: Knowledge Centric Semantically Driven Recipe Recommend...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Shah, Haiya Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9783031889912

In the era of Web 3.0, there is a pressing need for a knowledge-centric model to recommend recipes, as the current structure of the worldwide Web lacks specialized recommendation frameworks for recipe recommendations, even though standard search engines offer some level of recipe recommendation. This paper aims to address this gap by proposing a dedicated recommendation channel. The model utilizes user historical activity on food apps as one of the criteria for personalization, leveraging the SimRank algorithm and Grey Wolf optimization to generate a comprehensive termset. Additionally, the model incorporates semantic similarity-based reasoning, employing techniques such as SimRank, Morisita overlap index, and normalized information distance. The Grey Wolf optimization framework further refines the solution set. The topic network comprises recipe ebooks, food blogs, and crawl labels from food blogs, with the inclusion of scientific ebooks to enrich the knowledge base. A restriction classifier is employed to control the features of the data center, ensuring computational feasibility and reducing complexity. Furthermore, a lightweight decision tree classifier demonstrates strong classification performance, characterized by high precision, a lower false discovery rate (FDR), and notable expressive percentage values. Extensive evaluations reveal that the proposed framework outperforms baseline models, establishing it as the leading model for recipe recommendations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Search engines

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Detection of Defective Steel Surface with Image Segmentation

Detection of Defective Steel Surface with Image Segmentation

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2022 international conference on Sustainable Computing and data Communication systems, ICSCDS 2022

作者： Gupta, Chirag Chauhan, Gaurav Aiyar, Hari Dev S Delhi Technological University Department of Mechanical Engineering Delhi India

ISBN: (纸本)9781665478847

The detection of defects is an important part of steel manufacturing. Conventional ways of detecting defects in steel include ultrasonic testing, magnetic powder testing, osmosis testing, eddy current testing and X-ray testing. In this study, we used deep learning and computer vision to identify surface defects on steel. The study used U-Net, Mask R-CNN and Xception-V1which are deep neural network models used to detect specific parts of an image or image segmentation. The Xception- V1model performed well with complex defects whereas Mask-RCNN gave better results in rare defects. U-Net performed well with every type of defect and was a versatile model. The accuracy of U-Net model came out to be 80.21%. Images with no defects resulted in higher percentage of false predictions. It can be concluded that the existing models, while are capable of identifying different defects, they are not very good at predicting if there is no defect in this particular case. © 2022 IEEE.

关键词： Image segmentation

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