检索结果-内蒙古大学图书馆

33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Work in Progress: Congestion Control in mmWave Fluctuating Scenarios in 5G-A/6G 10

Work in Progress: Congestion Control in mmWave Fluctuating S...

引用

10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Sandoval, Jorge Ignacio Cespedes, Sandra University of Chile Department of Electrical Engineering Santiago Chile Concordia University Dept. of Computer Science and Software Eng Montreal Canada

ISBN: (纸本)9798350373011

The development of the next generation of cellular networks, 5G-Advanced and 6G (5G-A/6G), offers higher speeds, sub-millisecond latency, and providing wider coverage, which are key to meeting the requirements of new and demanding applications. As such, there are several challenges in managing and leveraging this opportunity to ensure that channel conditions do not impact the application due to mobility and obstructions. 5G introduces the use of millimeter wave (mmWave), and the commercial deployment of this technology brings to light the issues related to propagation and mobility in high-frequency bands, and their impact on applications has begun to be studied by exploring the response of the congestion control algorithms (CCAs) in representative scenarios. However, the highly variable channel conditions in mmWave require that they be defined as fluctuating bandwidth scenarios. It is necessary to determine how the transport layer can take advantage of the maximum available capacity and not affect the applications, and whether the network promises are fulfilled. This work aims to design and evaluate a CCA capable of efficiently adapting to the fluctuating bandwidth of extreme condition scenarios of 5G-A/6G mobile networks and to compare it with the state-of-the-art algorithms. As a first result, a set of simulation-based evaluation using 5GLENA was discussed. A combination of CCAs and different radio link control (RLC) buffer sizes allows us to highlight the importance of defining appropriate metrics and including convergence times as part of the evaluation performance of the algorithms. Algorithms such as HighSpeed and New Reno are among the slowest to converge, while BBR is the fastest. The analysis and characterization of the algorithms will allow us to define the requirements for designing an Machine Learning (ML) based algorithm with optimal performance and better convergence times. © 2024 IEEE.

关键词： Radio links

来源：评论

学校读者我要写书评

暂无评论

Acoustic data acquisition and integration for semantic organization of sentimental data and analysis in a PWSN

引用

Multimedia Tools and Applications 2024年 1-23页

作者： Das, Sushovan Mondal, Uttam Kr. Dept. of CSE College of Engineering and Mgmt WB Kolaghat721171 India Dept. of Computer Science Vidyasagar University WB Midnapur721102 India

In an acoustic pervasive wireless sensor network (PWSN), the BASE station plays a vital role in gathering and integrating acoustic sensor data from various nodes, including end and router devices tracking time-driven events. The semantic BASE station is crucial in the IoT landscape as it consolidates data from these networks, enabling thorough sentiment analysis of acoustic signals and yielding insights across domains. A semantic processor at the BASE station is essential for an energy-efficient and intelligent PWSN, managing data collection, integration, signal feature extraction, and publication for model training and sentiment analysis. This paper introduces a novel approach to designing a semantic BASE station, focusing on ontology generation, evaluation, and updates to bolster pervasive wireless sensors in capturing and depicting events and time through an ontological framework. The study addresses challenges in efficiently gathering, integrating, and processing acoustic data from pervasive nodes, proposing a semantic processor at the BASE station to enhance feature extraction and metadata publication. The semantic organization of feature-extracted labeled metadata enables the analysis of comprehensive machine learning (ML) applications such as sentiment analysis, type detection, and environment detection by generating confusion matrix. Evaluation includes performance metrics (NEEN, LSNS, BDAS) as well as accuracy, precision, sensitivity, and specificity for sentimental data analysis to validate the proposed technique’s efficacy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

A Synthesizable Thyristor-Like Leakage-Based True Random Number Generator

A Synthesizable Thyristor-Like Leakage-Based True Random Num...

引用

2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Kim, Seohyun Kim, Jang Hyun Lee, Jongmin Ajou University Dept. of Intelligent Semiconductor Engineering Suwon Korea Republic of Ajou University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9783982674100

As the demand for random data in cryptographic systems continues to rise, the importance of True Random Number Generators (TRNGs) becomes increasingly crucial for securing cryptographic applications. However, designing a TRNG that is reliable, secure, and cost-effective presents a significant challenge in hardware security. In this paper, we propose a synthesizable TRNG design based on a thyristor-like leakage-based (TL) structure, optimized for secure applications with small area and cost-efficiency. Our design has been validated using a 65-nm CMOS process, achieving a throughput of 0.397-Mbps within a compact area of 14.4-μm2, offering considerable cost savings while maintaining high randomness and area-throughput trade-off of 27.57 Gbps/mm2. Moreover, this TRNG can be synthesized as a standard cell through a semi-custom design flow, significantly reducing design costs and enabling design automation, which streamlines the process and reduces the time and effort required compared to traditional full-custom TRNGs. Additionally, as it is library characterized, the number of TL TRNG cells can be freely adjusted to meet specific application requirements, offering flexibility in both performance and scalability. To assess its randomness, the NIST statistical test suite was applied, and the proposed TL TRNG successfully passed all applicable tests, demonstrating its randomness. © 2025 EDAA.

关键词： Random number generation

来源：评论

学校读者我要写书评

暂无评论

Facial Emotional Recognition Using Convolutional Neural Network 2

Facial Emotional Recognition Using Convolutional Neural Netw...

引用

2nd International Conference for Innovation in Technology, INOCON 2023

作者： Raj, Deepak Wassay, Md. Abdul Chandigarh University Dept. of Computer Science Mohali India Chandigarh University Dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798350320923

The face of a person is his identity. Most of his emotions, issues can be derived from his face. Face is the window to the soul which was said by famous French doctor Duchenne de Boulogne. He used several techniques to support his theory like giving shocks of electric impulses to understand how a person reacts to muscular contractions. He also tried to induce some of the expressions of bizarre looking. The ultimate aim is to analyze how much muscles contribute to emotion. He successfully derived lots of human emotions which is hidden. After 200 years this field is still active as it requires more experiments to extract invisible truths of human truths. Lots of Emotion Recognition which is automatic have been seen in the field of Marketing and Advertising. It is also seen in the field of Medical, Law and order...etc. The main fundamental question arrives whether it is good to enter into our personal space. This a question which has lots of dimension. Those who are against this practice is claiming that this is a violation of human rights by which we can use the stored data to harm the society in future. Even though it is a major concern if we can rectify the above the scope in this area is phenomenal. Lots of researchers are still working on this area because of mainly one reason that is its wide application in the area of Medical science. It can be used by a doctor for diagnosing a person with certain psychological disorders, children with Autism, person with Parkinson's disease. It can be widely used to diagnose children with Autism so that particular child can be motivated at early stage leads to his success in future and thus provide a good citizen to the society. In our research we are using several datasets such as FER2013, CK+...etc. These datasets have been given to a model which includes Deep Convolutional Neural Network. Here input image is given to a model using camera. The particular image has to done preprocessing to extract fine features in the model. A

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson’s Disease Through Drawn Image Patterns 2

Exploring the Efficacy of Modified Transfer Learning in Iden...

引用

2nd International Conference on Information and Communication Technology, ICICT 2024

作者： Daiyan, Nabil Haque, Md Rakibul Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331508227

Parkinson’s disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (CNNs), transfer learning, and attention mechanisms to improve model performance and resilience against overfitting. To enhance the diversity and richness of both spiral and wave categories, the training dataset undergoes augmentation to increase the number of images. The proposed architecture comprises three phases: utilizing pre-trained CNNs, incorporating custom convolutional layers, and ensemble voting. Employing hard voting further enhances performance by aggregating predictions from multiple models. Experimental results show promising accuracy rates. For spiral images, weighted average precision, recall, and F1-score are 90%, and for wave images, they are 96.67%. After combining the predictions through ensemble hard voting, the overall accuracy is 93.3%. These findings underscore the potential of machine learning in early PD diagnosis, offering a non-invasive and cost-effective solution to improve patient outcomes. ©2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs 14

Cost-balanced Adaptive Replica Server Cache Management under...

引用

14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (纸本)9798331531935

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache. © 2025 IEEE.

关键词： Cache memory

来源：评论

学校读者我要写书评

暂无评论

Graph Neural Network Guided Music Mashup Generation

Graph Neural Network Guided Music Mashup Generation

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mashups integrate elements from different songs to create surprising and engaging listening experiences. Typically, a mashup combines the vocal track of a base song with the instrumental tracks of complementary songs. Automating the production of mashups has been an area of research for decades. Traditional approaches utilize rule-based methods, such as matching tempo and harmonic similarity, to select optimal segments for mashup generation. More recent techniques leverage neural networks to classify segment compatibility. However, both approaches primarily focus on layering segments that are generally compatible, without ensuring their detailed integration and alignment with the vocal track. In contrast, we introduce a novel approach using Graph Neural Networks (GNNs) that learns to rearrange instrumental segments to better align with the base vocal track, resulting in more surprising and accurate music blends for mashup generation. Additionally, we conducted subjective listening tests to evaluate our generated mashups against a baseline model using the same song pairs and the original base songs, assessing our model's performance. Generated mashups used for evaluation can be found in https://***/***/. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Classifying white blood cells using combining different convolutional neural networks

引用

Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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：