检索结果-内蒙古大学图书馆

2024 Small Satellites Systems and Services Symposium, 4S 2024

作者： Juang, Jyh-Ching Lee, Pei-Jun Department of Electrical Engineering National Cheng Kung University Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

ISBN: (纸本)9781510688940

The paper describes the development of a remote sensing payload for CubeSat applications. The inclusion of artificial intelligence into remote sensing payload has been pursued in the IRIS (Intelligent Remote sensing and Internet Satellite) and Lilium CubeSat programs. The paper discusses the development of a series of intelligent remote sensing payloads for CubeSats to address the limitations on computational resources, power, and communication bandwidth. From 2018 to 2021, under the IRIS program, an optical remote sensing payload that contains a high focal-length telescope, a CMOS detector, and the data operation board was developed. An edge intelligence for landslide detection and cloud segmentation is investigated by using MobileNet V2 network. The design has been implemented and tested by using an airborne platform. The design is further refined in the Lilium program with enhanced resolution and frame rate. Functions including auto-focus, automatic white balance, and gain control are also realized in the payload. To account for low-illumination scenario, a zero DCE (zero reference deep curve estimation) is utilized. Further, a light-weight U-net is implemented in the FPGA to achieve cloud detection and segmentation and a YOLO-SPD is implemented for object detection. This self-developed payload has been prototyped and is under tests. The payload will be integrated into the 6U Lilium-2 CubeSat and is planned to be launched in 2025. © 2025 The Authors.

关键词： Proximity sensors

来源：评论

学校读者我要写书评

暂无评论

Efficient Scene Reconstruction for Unmanned Aerial Vehicles 5

Efficient Scene Reconstruction for Unmanned Aerial Vehicles

引用

5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Bond, Yeana L. Ledwell, Spencer Osornio, Esmeralda Cruz, Alberto C. California State University Bakersfield Department of Computer & Electrical Engineering & Computer Science Bakersfield93311 United States

ISBN: (纸本)9798350358018

There is demand for small, cost-effective Unmanned Aerial Vehicles (U A V s) in industrial applications. These machines are expected to perform autonomous decision-making tasks. Recent work shows promise for systems based on images or video. We are motivated to study Structure From Motion (SFM), a three-dimensional (3D) reconstruction technique from two-dimensional images. It uses low-cost cameras rather than expensive LIDAR such as laser scanning sensors. The goal of this work is to seek an efficient way to improve accuracy and performance of SFM on small UAVs that lack the computation needed to perform costly algorithms such as neural networks. A major step of SFM is to estimate the homography between two views of the same scene by matching pairs of distinct features in an image (also known as geometric verification). Accurate estimation of pairs leads to better 3D models. We study feature-based homography estimation with various Random Sample Consensus (RANSAC) algorithms. Specifically, pre-sampling is one of the most common strategies utilized to improve the original RANSAC algorithm. Inspired by the work of others, we implement a new pre-sampling RANSAC method. Our adaptation, Blur and Contrast RANSAC (BC-RANSAC), has state-of-the-art performance while reducing run-time by a factor of 6.78. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

A Novel Topology of a Three-Phase Five-Level Reduced Switch Reduced Component Multilevel Inverter 3

A Novel Topology of a Three-Phase Five-Level Reduced Switch ...

引用

3rd IEEE International Conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Mohanty, Sarthak Parida, Jyotiranjan Parida, Shubhranshu Mohan Rout, Pravat Kumar Samanta, Indu Sekhar Department of Electrical Engineering Bhubaneswar India Department of Electrical and Electronics Engineering Bhubaneswar India Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350304732

This paper introduces a novel three-phase Reduced Switch Reduced Component (RSRC) Multilevel Inverter (MLI) topology to reduce switching losses, cost-effectiveness, and overall performance within power electronic systems. The suggested topology addresses the challenges of complexity and high cost associated with traditional MLIs by significantly minimizing the number of necessary components. Our design process, in-depth analysis, and comprehensive evaluation center around two key performance indicators: voltage waveform quality and total harmonic distortion (THD). To illustrate the superiority of the new design, we draw comparative analyses between our proposed topology and prevailing MLI topologies, underlining the novel advantages our design offers. Particularly designed for compact applications, our topology is relevant to e-mobility loads. Simulation results show that applying an assortment of Pulse Width Modulation (PWM) techniques makes a notable reduction in THD achievable, thus solidifying the proposed topology as a feasible and economically-sound solution for future prototyping. Future work will explore applying optimization methods to regulate duty cycle signals and introducing advanced modulation techniques for achieving further THD reduction. © 2023 IEEE.

关键词： Multilevel inverters (MLIs) Pulse Width Modulation (PWM) Reduced Switch Reduced Component (RSRC) Three-phase RSRC MLI topology Total Harmonic Distortion (THD)

来源：评论

学校读者我要写书评

暂无评论

Movie Sense—Hybrid Movie Recommendation System with Sentimental Analysis 3rd

Movie Sense—Hybrid Movie Recommendation System with Sentime...

引用

3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sudhane, G. Sai Rao, G. Venkateswara Vani, V. Karthik, N. Department of Electrical and Electronics Engineering Puducherry India Department of Computer Science and Engineering National Institute of Technology Puducherry Puducherry Karaikal609609 India

ISBN: (纸本)9789819783540

Post-pandemic, with the advent of many OTT platforms, there are a number of different movies and web series available which makes it difficult for the users to find a suitable movie to watch. So the movie recommendation system helps the users by recommending some movies based on their past search history, their interests and also considering the movie genre, actors, director and movie ratings. Our system uses a hybrid filtering technique which makes the system suitable for new users as well by considering the interests of various other users using SVD. Sentimental Analysis is done using a Sentiment Intensity Analyzer (SIA). Since it uses the most accurate methods, it makes the system a robust one. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Green Computing Adoption: Understanding the Role of Individual, Social, and Organizational Factors 19

Green Computing Adoption: Understanding the Role of Individu...

引用

19th International Conference on Evaluation of Novel Approaches to Software engineering, ENASE 2024

作者： Anni, Fahima Akter Islam, Muhammad Rezaul Sadia, Farzana Hasan, Mahady Rokonuzzaman, M. Department of Software Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (纸本)9789897586965

This study investigates the factors influencing the attitudes of software developers and IT professionals towards Green Information Technology (GIT) in Bangladeshi IT/software firms and examines their impact on engagement in green computing practices. Data was collected from 130 participants. A thorough literature review was conducted. The findings highlight key individual factors that influence employees’ attitudes, including awareness, knowledge, and perception of environmental issues. The study demonstrates the connection with GIT attitudes and behavior modification, especially through stated usage of green computing methods. Data analysis confirms 5 out of 8 hypotheses and reveals the complexity of the relationships between the constructs. The report promotes the adoption of Green IT technology by software companies in Bangladesh and highlights the significance of an organized office system integrating Green IT. Limitations include a relatively small sample size and the multidimensional nature of the relationships between the constructs. The findings can assist software companies in addressing customer concerns about the performance and functionality of green computing practices, ultimately promoting sustainable computing practices in the IT sector of Bangladesh. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Green computing

来源：评论

学校读者我要写书评

暂无评论

Illuminating the Interconnected Complexity of Cyberbullying Intentions within the Holistic Social Media Ecosystem 15

Illuminating the Interconnected Complexity of Cyberbullying ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shraya, Bejgum Dopathi, Hari Charan Reddy Pattanaik, Balachandra Anitha, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Wallaga University Department of Electrical and Computer Engineering Africa Nekemte Ethiopia

ISBN: (纸本)9798350370249

The research being presented here looks into the problems related to cyberbullying using data from Twitter and Wikipedia. To try and understand why people behave in this way online, the research included several challenging techniques, including a Support Vector Machine, Random Forest, Gaussian Naive Bayes, and a Voting Classifier that combines Logistic Regression, Random Forest, and Gaussian Naive Bayes. The accuracy of the Voting Classifier was 92.86% after Twitter data analysis. This demonstrates how well it detects and comprehends abusive tendencies. Not to mention, the Wikipedia dataset showed strong performance from the Voting Classifier, which had a success rate of 97.12%. Compared to other methods of research, it performed better. The research shows how machine learning algorithms can recognize and determine the identity of individuals attempting to cause harm to others via the internet. © 2024 IEEE.

关键词： Cyber Harassment Gaussian Naive Bayes Machine Learning Random Forest Support Vector Machine Voting Classifier

来源：评论

学校读者我要写书评

暂无评论

Integrating 4QS Factors to Enhance Learning Quality Using Machine Learning 2

Integrating 4QS Factors to Enhance Learning Quality Using Ma...

引用

2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Kale, Ruchita Tasare, Mohan Dhumale, Yogita Rawat, Manoj Kumar Sawale, Gaurav Prmit&r Department of Computer Science & Engineering Badnera Amravati India Prof. Ram Meghe College of Engineering & Management Department of Electrical Engineering Badnera Amravati India Medicaps University Department of Computer Science & Engineering Indore India

ISBN: (纸本)9798331528713

The ultimate goal of any educational institution is to offer the best educational experience and impart knowledge effectively to students. Identifying students who need extra support and taking appropriate actions to enhance their performance is crucial in achieving this goal. This study explores the application of machine learning techniques to predict students' final grades based on their historical data. The proposed strategy integrates the 4QS Model, which focuses on intelligence, adversity, spirituality, emotional, and stress factors to address essential aspects of the academic environment. By targeting these key factors, the aim is to improve the teaching and learning environment, leading to enhanced student performance and future success. This approach enables educators to identify areas where students may struggle and tailor their teaching methods accordingly, resulting in a more effective and supportive educational experience for both faculty and students. A classification machine learning algorithm was employed to predict student performance. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Vision Transformer Approach for Driver Fatigue Detection from EEG Signal with Correlation-Based Channel Selection

A Novel Vision Transformer Approach for Driver Fatigue Detec...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Khan, Robiul Roy, Dipon Ali, Md. Wajed Refat, Kawsar Ahmed Naziullah, Shekh Pabna University of Science and Technology Department of Electrical and Electronic Engineering Pabna Bangladesh Daffodil International University Department of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Driver fatigue poses a critical threat to road safety, necessitating the development of robust detection methods to minimize traffic accidents and societal burdens. Deep neural networks have recently been effectively applied to Electroencephalography (EEG)-based driving fatigue detection. Nevertheless, most of the existing models, particularly those relying on extensive pooling, often struggle to capture long-range dependencies within images. To address this issue, we propose a Correlation-based Channel Selection (CCS) with a Vision Transformer (ViT) approach for driver fatigue detection using an EEG. Our methodology integrates a pioneering Channel Selection (CS) block to extract discriminative channels via CCS. This mechanism systematically identifies the most informative EEG channels crucial for fatigue detection. Subsequently, we leverage Continuous Wavelet Transform (CWT) to convert the selected EEG channels into a time-frequency spectral image. Finally, the resulting time-frequency spectral images, encompassing both temporal and spectral information, are concatenated and fed into the Vision Transformer (ViT) model to classify them as either normal or fatigued. The proposed model is evaluated on a publicly available EEG dataset containing recordings from twelve subjects. The model achieved superior accuracy: 95.83% for the effective selected combined subject and 99.925% for the average accuracy of each subject, demonstrating its potential for robust driver fatigue detection. © 2024 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Sizing electrolyzer capacity in conjunction with an off-grid photovoltaic system for the highest hydrogen production

引用

Energy Harvesting and Systems 2023年第2期10卷 331-348页

作者： Hassan, Qusay Abbas, Majid K Tabar, Vahid Sohrabi Tohidi, Sajjad Abdulrahman, Imad Saeed Salman, Hayder M. Department of Mechanical Engineering University of Diyala Baqubah Iraq Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran College of Technical Engineering Al-Farahidi University Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq

The electrolysis of renewable energy to produce hydrogen has become a strategy for supporting a decarbonized economy. However, it is typically not cost-effective compared to conventional carbon-emitting methods. Due to the predicted intermediate of low-and zero-marginal-cost renewable energy sources, the ability of electrolysis to connect with electricity pricing offers a novel way to cost reduction. Moreover, renewables, particularly photovoltaics, have a deflationary effect on the value of the grid when they are deployed. This study investigates solar electrolysis configurations employing photovoltaic cells to feed a proton exchange membrane water electrolyzer for hydrogen production. Using experimental meteorological data at 1-min precision, the system has been evaluated in Baghdad, the capital of Iraq. Positioned at the yearly optimum tilt angle for the selected site, the solar array is rated at 12 kWp. Temperature effects on solar module energy loss are taken into account. Several electrolyzers with capacities ranging from 2 to 14 kW in terms of hydrogen production were examined to determine the efficacy and efficiency of renewable sources. MATLAB was utilized for the simulation procedure, with a 2021-2035 project lifespan in mind. The results suggest that a variety of potentially cost-competitive options exist for systems with market configurations that closely approximate wholesale renewable hydrogen. At 4313 h of operation per year, the planned photovoltaic array generated 18,892 kWh of energy. The achieved hydrogen production cost ranges between & amp;dollar;5.39/kg and & amp;dollar;3.23/kg, with an ideal electrolyzer capacity of 8 kW matching a 12 kWp photovoltaic array capable of producing 450 kg/year of hydrogen at a cost of & amp;dollar;3.23/kg. & amp;copy;2022 Walter de Gruyter GmbH, Berlin/Boston. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Electrolysis

来源：评论

学校读者我要写书评

暂无评论

Forecasting EV Charging Demand: A Graph Convolutional Neural Network-Based Approach 4

Forecasting EV Charging Demand: A Graph Convolutional Neural...

引用

4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Fahim, Shahriar Rahman Atat, Rachad Kececi, Cihat Takiddin, Abdulrahman Ismail, Muhammad Davis, Katherine R. Serpedin, Erchin Texas A&M University Electrical & Computer Engineering Department College StationTX77843 United States Texas A&M University at Qatar Electrical & Computer Engineering Department Doha23874 Qatar Florida State University Electrical & Computer Engineering Department TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States

ISBN: (纸本)9798350306262

Electric vehicles (EVs) are expected to revolutionize the global transportation sector by promoting sustainability and eco-friendliness. The continuous proliferation of EVs requires an expansion of the existing charging infrastructure to meet the corresponding increase in electricity demand. Such an expansion requires an accurate forecasting of charging demand in both space and time domains for a well-planned allocation of charging stations (CSs). This paper proposes a Graph Convolutional Neural Networks (GCNN) based approach combined with a Long Short-Term Memory (LSTM) to predict the future charging demands of EVs. The proposed architecture fuses the benefits of both GCNN and LSTM to extract the underlying spatio-temporal features from the collected dataset. The training dataset reflects the coupling between the power and transportation systems, and thereby it helps the proposed deep learning architecture to capture the spatio-temporal patterns of the inter-connected environment. A comparative analysis is conducted with other state-of-the-art EV charging load prediction models to assess the prediction performance of the proposed load forecasting strategy. © 2024 IEEE.

关键词： Forecasting

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：