检索结果-内蒙古大学图书馆

A New Approach to Determine Feasible Operating Region of Unbalanced Distribution Networks with Distributed Photovoltaics

引用

IEEE Transactions on Power Delivery 2025年第3期40卷 1493-1504页

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai India Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

Amidst rising distributed generation and its potential role in grid management, this article presents a new realistic approach to determine the operational space and flexibility potential of an unbalanced active distribution network. The feasible operating region of an active distribution network is constituted by its range of active-reactive power exchanges with the bulk power system without breaching network constraints. This study explores the potential of highly penetrated single- and three-phase distributed photovoltaics and voltage-sensitive loads to determine the feasible operating region at different operating points. The cumulative response of Volt-Var controlled distributed photovoltaics and voltage-sensitive loads determines the power flow at the bulk power system-active distribution network interconnection, ensuring feasibility with respect to voltage magnitude and unbalance constraints. Further, the correspondence of feasible operating region with the tap-settings of substation transformer enables a novel feature to traverse throughout the available operating region and facilitate bulk power system-active distribution network power coordination. Additionally, a nodal sensitivity-based adjustable Volt-Var control scheme is proposed for distributed photovoltaics, improving the feasible operating region. The proposed strategy is validated on bulk power systemactive distribution network integrated test system. Bulk power system: IEEE-9 bus system-Active distribution network: (highly unbalanced IEEE-13 bus distribution system (medium voltage) + modified CIGRE low voltage system). © 1986-2012 IEEE.

关键词： Power distribution networks

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

引用

HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Utility for Gas/Odor Sensor Signature Analysis 2

Deep Learning Utility for Gas/Odor Sensor Signature Analysis

引用

2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Raut, Yashasvi Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra India

ISBN: (纸本)9798331505745

Gas/odor sensors are integral components of sensor systems used in diverse applications such as food quality control, environmental monitoring, medical diagnostics, odor profiling, industrial safety, agriculture, explosives detection, wine tasting, pharmaceutical quality control, and toxic gas detection. Traditional approaches in this domain of gas/odor sensing technologies face challenges related to accuracy, cross-sensitivity, and drifted real-time performance. The integration of deep learning techniques with gas sensors has shown promise in overcoming these limitations. Moreover, this amalgamation of gas sensing and deep learning strategies enables accurate identification of gases/odors, handling complex patterns, and leading optimal consumption of resources. In this paper, an overview of advancements in deep learning-enabled gas sensing has been presented that focused on methodologies, sensor types, and applications. This review also highlights the state-of-the-art techniques and identifies gaps for future directions for research. © 2025 IEEE.

关键词： Damage detection

来源：评论

学校读者我要写书评

暂无评论

Neuroadaptive Containment control for Nonlinear Multiagent systems With Input Saturation: An Event-Triggered Communication Approach

引用

IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

Toward AI-Augmented Formal Verification: A Preliminary Investigation of ENGRU and Its Challenges

引用

IEEE Access 2025年 13卷 84357-84379页

作者： Dechsupa, Chanon Panboonyuen, Teerapong Vatanawood, Wiwat Padungweang, Praisan So-In, Chakchai Khon Kaen University College of Computing Department of Computer Science Khon Kaen400002 Thailand Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok10300 Thailand

State-space graphs and automata serve as fundamental tools for modeling and analyzing the behavior of computational systems. Recurrent neural networks (RNNs) and language models are deeply intertwined, as RNNS provide the foundational architecture that enables language models to process sequential data, capture contextual dependencies, and improve natural language processing tasks. Both RNNs and state-space graphs help evaluate discrete-time systems within this formal framework. However, the basic question of their equivalence remains an open challenge regarding the models governing sentence structure in natural language and the formal model in automata theory. In this paper, we present ENGRU (Enhanced Gated Recurrent Units), a novel deep learning-based approach for formal verification. ENGRU integrates concepts of model checking techniques, Colored Petri Nets (CPNs), and sequential learning to analyze systems at a high level of abstraction. CPN models undergo state-space enumeration via a model-checking tool, generating a state-space graph and an automaton from inherent state transition patterns. These graphs are transformed into sequential representations as sub-paths, enabling ENGRU to learn the execution paths and predict system behaviors. ENGRU effectively predicts goal states within discrete-time models by the competency of gated recurrent mechanisms, encouraging early bug detection and allowing predictive state-space exploration. Experimental results demonstrate that ENGRU’s ability achieves high accuracy and efficiency in goal state predictions. © 2013 IEEE.

关键词： Model checking

来源：评论

学校读者我要写书评

暂无评论

Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing

引用

Annals of Data science 2025年第1期12卷 223-252页

作者： Shamsinejad, Elham Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approach is founded on the principle of K-anonymity and propels the field forward by critically evaluating various anonymization methods and algorithms, benchmarking their performance with respect to time and space complexities. A distinctive formula for optimized cluster determination in the K-means algorithm is presented, along with a novel tuple expiration time strategy for the efficient purging of clusters. The integration of these components into Spark’s RDD and MLlib modules results in a significant decrease in execution time and data loss rates, even with increasing data volumes. The paper’s notable contributions are its methodological advancements that offer a robust, scalable solution for data anonymization, safeguarding user privacy without sacrificing data utility or processing efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

A Review of Anonymization Algorithms and Methods in Big Data

引用

Annals of Data science 2025年第1期12卷 253-279页

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

引用

Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

引用

Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

A Review on the Use of Smart Agriculture systems to Protect Crops 3

A Review on the Use of Smart Agriculture Systems to Protect ...

引用

3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Kumar, Gulshan Gourshettiwar, Palash Suman, Saurabh Kumar, Shravan Kumar, Dhiraj Raushan, Rahul Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331509675

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：