检索结果-内蒙古大学图书馆

5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mehra, Devansh Vamsi, A. Sri Kavin, Prabhu Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350383522

CBDC-as-a-problem looks like a mania of interest from central banks and financial institutions all over the world. It may describe CBDC technology and CBDC technology development, the positive aspects of CBDC and its future perspectives as follows: The CBDC is linked to the existing banking systems in the past which was based on the past findings of the blockchain, DLT, etc., that possessed the qualities the previous ones did not have - security, trust, and transparency. Innovation, design and CBDC safety that falls below this paper are discussed in this section below. The paper proceeds to show several paths that central banks are taking to CBDC development given the regulation and changing dynamic of the financial world. However, the studies do not end there;information on the potential of CBDC for financial stability, monetary policy formulation trajectories, transaction privacy, and the role of banks is included. Given that in field of finance the CBDCs are becoming more and more important, this research paper shall ensure to provide consistent background to policymakers, financial institutions and society at large on CBDC and its structural implications. This guarantees that everyone is in the know so it will be possible for them to come up with compliant plans and decisions informed by the knowledge in this changing landscape that simply does not stop evolving. © 2024 IEEE.

关键词： CBDC DLT

来源：评论

学校读者我要写书评

暂无评论

The Bigger Picture: Scene Understanding for Recognizing Accidents within Mixed-Traffic Scenario using Deep Learning 13

The Bigger Picture: Scene Understanding for Recognizing Acci...

引用

13th IEEE International Conference on Control System, computing and engineering, ICCSCE 2023

作者： Tee, Fu Swee Theng, Lau Bee Shiong, Brian Loh Chung Tsun, Mark Tee Kit Computing and Science Swinburne University of Technology Sarawak Faculty of Engineering Kuching Malaysia

ISBN: (纸本)9798350323184

Detecting road traffic accidents in a timely manner is crucial for reducing traffic fatalities. Computer vision techniques are increasingly being used for road traffic accident recognition because of their ability to automatically analyze traffic scenes without needing manual human interpretation. To date, Deep learning has made significant paces in addressing time-series problems and is increasingly being utilized for road traffic accident detection. However, detecting accidents in a mixed traffic flow environment is challenging due to difficulties in detecting smaller-sized road users and annotating unsafe trajectories, particularly in scenarios where larger-sized road users overshadow smaller-sized road users. Therefore, it may be more effective to consider the entire traffic scene as a whole, instead of focusing on individual objects. This paper proposes a Deep Learning model that analyzes the entire traffic scene using a recurrent neural network, ConvLSTM2D, for road traffic accident detection in mixed traffic flow environments. The model is evaluated on 139 videos of a testing dataset, achieving an accuracy of 84%. Future work will focus on further analyzing potential accident features across video frames to enhance the model's performance. © 2023 IEEE.

关键词： Accidents

来源：评论

学校读者我要写书评

暂无评论

To Jack or Not to Jack: Inside the Minds of Pipe Jacking Operators

To Jack or Not to Jack: Inside the Minds of Pipe Jacking Ope...

引用

ASEAN Australian engineering Congress, AAEC 2022

作者： Yeo, Lit Yen Choo, Chung Siung Lee, Sue Han Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Limited available land is leading to the emerging demand for trenchless methods for the installation of buried pipelines. Pipe jacking is one such method. The prediction of jacking forces is crucial in assessing the performance of the pipe jacking process, thus many efforts have been made to predict the jacking forces. Current approaches are based on the negotiated geological conditions, either through empirical or mechanistic approaches. However, such methods are only useful during the planning and design stages. During the construction stage, the microtunnel boring machine (mTBM) operators base their decisions on the operation parameters. This is further complicated when pipe jacking is performed through highly weathered lithology, i.e. ‘soft rocks’. There continues to be a gap in this domain of modelling jacking forces for soft rocks based on operation parameters. In the current study, deep learning techniques were applied to pipe jacking field-measured operation parameters obtained from the Kuching Wastewater Management System. The deep learning models helped to relate the time series input operation parameters to the jacking forces. Subsequently, a study on different moving average periods towards the input features was applied to understand the changes in the prediction performance of the jacking forces using the deep learning model. From the study, the results showed that the cumulative parameters which are the drive length, the cumulative days elapsed, and the cumulative lubricant used have the highest moving average periods. This suggested that along the pipe jacking process, these features would have a significant impact on the jacking forces. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithology

来源：评论

学校读者我要写书评

暂无评论

Development of Integrated Structure of Hydrogen Production Using Photovoltaic Panels and Detailed Electrical Model for Electrolyser 7

Development of Integrated Structure of Hydrogen Production U...

引用

7th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2023

作者： Chieh, Nelson Jin Wei Afrouzi, Hadi Nabipour Tavalaei, Jalal Swinburne University of Technology Sarawak Faculty of Engineering Computing and Science Kuching Malaysia

ISBN: (纸本)9798350305517

Sunlight power is a type of green energy that can be harvested by using photovoltaic (PV) panels to convert solar radiation to electrical power for fulfilling the power demand. However, there are limitations on renewable energy as it relies on weather conditions in which a confined portion of power will be generated during the night or cloudy periods. This is where hydrogen comes in and act as an energy career that stores the electrical power generated from the photovoltaic panel and can be converted back to electric power through electrochemical processes. The aim of this article is to analyse the feasibility of implementing PV panels and electrolyser for hydrogen generation. Simulation of solar system for power generation and detailed electrolyser with PEM cell stack is modelled for hydrogen production. Performance evaluation is then fulfilled to the developed simulation model of hydrogen production system for identifying the efficiencies of the developed PV system and PEM electrolyser as well as the solar to hydrogen's turnover of the developed hydrogen production system to find out the feasibility of system. © 2023 IEEE.

关键词： Solar panels

来源：评论

学校读者我要写书评

暂无评论

UAV-Based Lane-Changing Reduction Using Traffic Video Analyser

UAV-Based Lane-Changing Reduction Using Traffic Video Analys...

引用

ASEAN Australian engineering Congress, AAEC 2022

作者： Bong, Jit Boon Chong, Elizabeth Eu Mee Choo, Chung Siung Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Multi-lane roundabouts allow drivers to change lanes by merging and diverging among circulating lanes, yielding weaving in the roundabout. Weaving yields traffic conflicts as crossing traffic slows down the circulating flow. Speed reduction creates significant gaps that reduce capacity. Thus, weaving is crucial due to its significant effect on roundabout capacity. However, little information is available on weaving in multi-lane roundabouts with inscribed circle diameters (ICD) of more than 100 m. A possible reason for the lack of weaving data is the difficulty of collecting lane change data due to the traffic flow complexity, which involves several lane changes and the required resources. Typical data collection methods include using ground-level recorders and surveyors. Ground-level recorders could only provide limited data points due to view coverage constraints. Increasing the workforce and equipment can overcome this limitation, but it may prolong the post-recording analysis due to more views for analysis. Conversely, Unmanned Aerial Vehicles (UAVs) can capture complex traffic interaction data in a single session. Extraction of UAV-based recording by traffic video analyser could provide a wide range of outputs in a shorter time than manual analysis. This paper proposes a novel methodology to extract UAV-based lane change data using a traffic video analyser. The fieldwork involves flying a UAV at a multi-lane roundabout with an ICD of 151 m in Kuching, Malaysia. Turning movement and lane change were obtained using a traffic video analyser. Virtual gates are specified at 10 m intervals to get lane change location. A lane change can be detected when a vehicle crosses two consecutive virtual gates over two adjacent circulating lanes. Weaving occurs when two lane change swap paths along circulating lanes. The findings show that 86% of lane change happens in the weaving section. This paper demonstrated that using UAV and traffic video analyser is feasible to investig

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

Development of Smart HVAC Thermostat with AI Using Passive Infrared Sensors for Energy Saving

Development of Smart HVAC Thermostat with AI Using Passive I...

引用

ASEAN Australian engineering Congress, AAEC 2022

作者： Wong, Basil T. Sim, Rui Li Faculty of Engineering Computing and Science Swinburne University of Technology Kuching Sarawak Malaysia

ISBN: (纸本)9789819955466

Global warming is causing climate change, disrupting the national economy, and affecting people's living habits. Numerous papers point out that human activity shows that large amounts of waste heat or energy are being continuously released into the climate system. This heat is thought to be responsible for the current global warming. In particular, there is strong evidence that HVAC systems consume more than 50% of total electricity consumption. Therefore, today's social products are environmentally friendly and energy efficient to reduce heat and energy waste. In addition, homeowners who use programmable thermostats correctly can save a lot of electricity. The reduction in energy consumption is due to the automatic use of energy-saving settings, i.e., shutting down the HVAC system when not needed. However, many programmable thermostats have user interfaces that are difficult to navigate and program. To overcome the shortcomings of programmed thermostats, smart thermostats can recognize occupancy and create schedules automatically without human involvement. The project aims to develop a system for sensing room occupants through passive infrared sensors. It then adjusts the thermostat temperature and fan speed accordingly to ensure thermal comfort. To some extent, energy savings can also be achieved through intelligent automatic control settings. Results show that using flow simulations, fluid motion in a room can be assessed, a database developed, and a smart thermostat can be used to automatically configure HVAC systems to reduce energy consumption while maintaining acceptable comfort levels for most indoor occupants. The results showed that the average energy consumption of an HVAC system controlled by a smart thermostat was 22.36% lower than that of a manually programmed thermostat. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Cross-Selling Health Insurance Classification

Deep Learning for Cross-Selling Health Insurance Classificat...

引用

2024 International Conference on Green Energy, computing and Sustainable Technology, GECOST 2024

作者： Chu, Jasmin Z. K. Than, Joel C. M. Jo, Hudyjaya Siswoyo Swinburne University of Technology Sarawak Faculty of Engineering Sarawak Kuching Malaysia Computing and Science Swinburne University of Technology Sarawak Faculty of Engineering Sarawak Kuching Malaysia

ISBN: (纸本)9798350357905

In the ever-evolving landscape of insurance services, the strategic cross-selling of health insurance to existing customers stands as a pivotal endeavor. This study delves into this domain, aiming to harness the potential of machine learning and deep learning methodologies to identify prospects for health insurance adoption within the existing customer base, irrespective of their current insurance holdings. To achieve this goal, this study conducts various experiments employing different machine learning and deep learning algorithms, such as TabPFN, Graph Convolutional Network (GCN), LightGBM, Hist Gradient Boosting and XGBoost. Data preprocessing involves filtering out empty data and normalizing the remaining data. The dataset is then subjected to various optimization methods, including Arithmetic Optimization Algorithm (AOA), Gradient-Based Optimization (GBO), and Sine Cosine Algorithm (SCA), to enhance the model's performance, ensuring the development of a reliable and trustworthy predictive model for identifying customers likely to purchase health insurance. Notably, AOA consistently demonstrates strong performance in LightGBM with an increment of 9.34%, XGBoost with an increment of 8.66%, and TabPFN with an increment of 10.48%, establishing it as the optimal method for enhancing model performance across different models. Moreover, an additional discovery in this study emphasizes TabPFN's notable superiority over other algorithms, showcasing a substantial improvement in accuracy from 52.62% to 63.10%. This substantial enhancement underscores TabPFN's exceptional efficacy specifically in managing tabular data. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A two-dimensional double layer-averaged model of hyperconcentrated turbidity currents with non-Newtonian rheology

引用

International Journal of Sediment Research 2023年第6期38卷 794-810页

作者： Yining Sun Ji Li Zhixian Cao Alistair George Liam Borthwick State Key Laboratory of Water Resources Engineering and Management Wuhan University Zienkiewicz Centre for Computational Engineering Faculty of Science and EngineeringSwansea University Institute for Infrastructure and Environment University of Edinburgh School of Engineering Computing and MathematicsUniversity of Plymouth

Hyperconcentrated turbidity currents typically display non-Newtonian characteristics that influence sediment transport and morphological evolution in alluvial ***,hydro-sedimentmorphological processes involving hyperconcentrated turbidity currents are poorly understood,with little known about the effect of the non-Newtonian *** current paper extends a recent twodimensional double layer-averaged model to incorporate non-Newtonian constitutive *** extended model is benchmarked against experimental and numerical data for cases including subaerial mud flow,subaqueous debris flow,and reservoir turbidity *** computational results agree well with observations for the subaerial mud flow and independent numerical simulations of subaqueous debris *** between the non-Newtonian and Newtonian model results become more pronounced in terms of propagation distance and sediment transport rate as sediment concentration *** model is then applied to turbidity currents in the Guxian Reservoir planned for middle Yellow River,China,which connects to a tributary featuring hyperconcentrated sediment-laden *** non-Newtonian model predicts slower propagation of turbidity currents and more significant bed aggradation at the confluence between the tributary Wuding River and the Yellow River in the reservoir than its Newtonian *** difference in model performance could be of considerable importance when optimizing reservoir operation schemes.

关键词： Double layer-averaged model Non-Newtonian rheology Mud flow Reservoir turbidity current Yellow River

来源：评论

学校读者我要写书评

暂无评论

Boosting EEG-Based Emotion Recognition with Kolmogorov-Arnold Networks 5

Boosting EEG-Based Emotion Recognition with Kolmogorov-Arnol...

引用

5th International Conference on Advanced Research in computing, ICARC 2025

作者： Nimishan, Sivaraj Thuseethan, Selvarajah Ragel, Roshan G. Vasanthapriyan, Shanmuganathan University of Peradeniya Faculty of Engineering Sri Lanka Charles Darwin University Faculty of Science and Technology Australia Sabaragamuwa University of Sri Lanka Faculty of Computing Sri Lanka

ISBN: (纸本)9798331530983

As biomedical signal processing continues to advance, the need for reliable and deployable solutions for identifying human emotional states grows. Electroencephalography (EEG) is a promising modality for emotion recognition;however, existing systems often face challenges related to accuracy and efficiency. In this work, the use of the Kolmogorov-Arnold Network (KAN) for EEG-based emotion recognition is studied. KANs are a novel type of neural network that utilize edge-based activation functions, learning complex patterns with fewer parameters than traditional Multi-Layer Perceptrons (MLP). In addition, to enhance the quality of EEG signals, wavelet denoising is utilized. The proposed KAN model is evaluated on two publicly available datasets: GAMEEMO and LUMED. The results demonstrate that KAN significantly outperforms MLP in both accuracy and F1 score. On the GAMEEMO dataset, KAN achieved an average accuracy of 80.79% and an F1 score of 78.63%, while on the LUMED dataset, it reached an accuracy of 90.95% and an F1 score of 89.53%. This is a significant improvement of over 4% in both metrics compared to MLP. These results highlight the potential of KANs as an efficient and effective approach for EEG-based emotion recognition. © 2025 IEEE.

关键词： Signal denoising

来源：评论

学校读者我要写书评

暂无评论

FAIRMod: Detecting & Restricting Harmful Content using Machine Learning 2

FAIRMod: Detecting & Restricting Harmful Content using Machi...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jadon, Aryan Singh Vanusha, D. Delsi Robinsha, S. Faculty of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies School of Computing Tamil Nadu Kattankulathur603 203 India

ISBN: (纸本)9798350365269

The Content Restriction System (CRS) harnesses the power of machine learning to mitigate the proliferation of harmful content in digital spaces. The current systems are mostly made by private social media giants and there is a lack of a wholesome all-round CRS for all. This can be useful to remove bad comments in the comment section, removing it from the reach of children as well. It detects profanity, harmful and violent behavior. We are creating this using Long Short Term Memory (LSTM) and the algorithm is modified such that it detects the comment as quickly as possible. This is an improved version of this algorithm that can give us faster results. For this we have implemented several filters before sending a text object for analysis minimizing the need for analyzing all the data objects. After these filters are applied, we send the data objects for analysis and even this stage takes very less time. This algorithm is meant to work for large datasets with over a million data points. In the future, we can use it as a scanner tool to check the client's database for such harmful content and mark it up for them. FAIRMod will increase the overall health of the internet and its accessibility. Our goal is to achieve 90% accuracy while having faster detection speed. © 2024 IEEE.

关键词： Application programming interfaces (API)

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：