检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Machida, Manabu Osada, Keita Kagawa, Keiichiro Department of Informatics Faculty of Engineering Kindai University Higashi-Hiroshima739-2116 Japan Graduate School of Integrated Science and Technology Shizuoka University Hamamatsu432-8011 Japan Research Institute of Electronics Shizuoka University Hamamatsu432-8011 Japan

The stripe illumination lies between the illumination in the spatial-frequency domain and the point illumination. Although the stripe illumination has a periodic structure as the illumination in the spatial-frequency domain, light from the stripe illumination can reach deep regions in biological tissue since it can be regarded as an array of point illuminations. For a pair of a source and a detector, the shape of light paths which connect the source and detector is called the banana shape. First, we investigate the depth of the banana. In the case of the zero boundary condition, we found that the depth of the center of the banana is about 0.2dSD for typical optical parameters, where dSD is the distance between the source and detector on the boundary. In general, the depth depends on the absorption and diffusion coefficients, and the ratio of refractive indices on the boundary. Next, we perform diffuse optical tomography for the stripe illumination against forward data taken by Monte Carlo simulation. We consider an impulse illumination of the shape of a stripe. By this time-resolved measurement, the absorption coefficient of a target is reconstructed. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

引用

Electrical and Computer engineering Researches (ICECER), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Tahir Abbas Karamath Ateeq College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Science TIMES Institute Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah UAE

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems.

关键词： Renewable energy sources Accuracy Load forecasting Machine learning Predictive models Power system stability Data models Forecasting Sustainable development Load modeling

来源：评论

学校读者我要写书评

暂无评论

Measurement of Synthetic-Dimension Band Structures in Silicon Coupled Ring Resonators

Measurement of Synthetic-Dimension Band Structures in Silico...

引用

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Ruming Zhang Liucun Li Mikiya Kamata Toshihiko Baba Tomoki Ozawa Yasutomo Ota Satoshi Iwamoto Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Department of Electrical and Computer Engineering Yokohama National University Yokohama Japan Advanced Institute for Materials Research Tohoku University Sendai Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan

We fabricated integrated silicon coupled-ring resonators and measured photonic band structures for synthetic frequency lattices with different configurations. A phase difference between two modulation signals introduc... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Band structures Phase modulation Resonant frequency Measurement by laser beam Ring lasers Silicon Frequency measurement

来源：评论

学校读者我要写书评

暂无评论

Towards Wearable Sensing Enabled Healthcare Framework for Elderly Patients

Towards Wearable Sensing Enabled Healthcare Framework for El...

引用

IEEE International Conference on Communications (ICC)

作者： Ali Hassan Sodhro Mohammad S. Obaidat Andrei Gurtov Noman Zahid Sandeep Pirbhulal Lei Wang Kuei-Fang Hsiao Computer and Information Science Department Linkoping University College of Computing and Informatics University of Sharjah UAEKASIT University of JordanAmman Jordan and University of Science and Technology Beijing China Computer and Information Science Department Linkoping University Linkoping Sweden Departement of Electrical Engineering Sukkur IBA University Sukkur Sindh Pakistan Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Ming-Chuan University Taiwan

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

The pervasive and smart healthcare is important for elderly patients which has revolutionized the medical world and caught the attention from industry and academia with the help of portable sensor-enabled devices. Tiny size and resource-constrained nature restricts them to perform several tasks at a time. Thus, energy drain, limited battery lifetime, and high packet loss ratio (PLR) are the key challenges to be tackled carefully for ubiquitous healthcare. Energy efficiency, reliability and longer battery cycle are the vital ingredients for wearable devices to empower cost-effective and pervasive medical environment. Thus, this research work has three key contributions. First, a novel transmission power control driven energy efficient algorithm (EEA) is proposed to enhance energy, battery lifetime and reliability while monitoring the health status of elderly patients. Proposed EEA and conventional constant transmission power control (TPC) are evaluated by adopting real-time datasets of static (i.e., wheelchair sitting) and dynamic (i.e., wheelchair moving) body postures of elderly patients. Second, smart healthcare framework is proposed. Third, performance metrics such as, energy drain, battery lifetime and reliability are introduced and calculated by considering average and threshold RSSI and TPC values. Finally, it is observed through experimental analysis that the proposed EEA enhances energy efficiency with acceptable PLR than the constant TPC during data transmission.

关键词： Medical services Wireless communication Reliability Wireless sensor networks Batteries Wheelchairs Senior citizens

来源：评论

学校读者我要写书评

暂无评论

Optimizing FSO systems for 6G network using particle swarm optimization

引用

Journal of Optical Communications 2024年

作者： Allaoua, Oumaima Djellab, Hanane Maamri, Fouzia Belhocine, Yacine Saidi, Riad Boumehrez, Farouk Laboratory of Mathematics Informatics and Systems Laboratory of Signals and Smart Systems Department of Electronics and Telecommunications Larbi Tebessi University Tebessa Algeria Laboratory of Signals and Smart Systems LTI Laboratory of Guelma Department of Electrical Engineering University Larbi Tebessi Tebessa Algeria Laboratory of SATIT Department of Industrial Engineering University of Abbes Laghrour Khenchela Algeria Laboratory of Mathematics Informatics and Systems Laboratory of Signals and Smart Systems Department of Electronics and Telecommunications Echahid Larbi Tebessi University Tebessa Algeria Department of Commun Base Science and Technology Mustapha Ben Boulaid Batna 2 University Batna Algeria Laboratory of LTI of Guelma Department of Industrial Engineering University of Abbes Laghrour Khenchela Algeria

Free Space Optics (FSO) is an innovative optical communication technology that leverages light propagation in free space for direct line-of-sight interactions. This technology is characterized by several benefits, including secure transmission, high bandwidth, and rapid data transfer rates, making it a promising candidate for integration into emerging 6G networks. However, the efficiency of FSO systems is adversely affected by various weather conditions, such as rain, temperature fluctuations, snow, and fog, as well as atmospheric disturbances. This paper presents a Particle Swarm Optimization (PSO)-based approach to optimize the divergence angle of FSO systems. PSO is a type of artificial intelligence that excels at managing complex settings and adapting to changes in the environment, thus making FSO systems more robust against such challenges. By optimizing this critical parameter, the proposed method significantly improves the reliability and efficiency of data transmission in FSO systems, supporting the advanced performance demands of future 6G networks. This optimization ensures FSO systems can achieve faster and more reliable connectivity, essential for the next generation of wireless communication. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

引用

Healthcare Analytics 2023年 4卷 100247-100247页

作者： T R, Mahesh V, Vinoth Kumar V, Dhilip Kumar Geman, Oana Margala, Martin Guduri, Manisha Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India School of Computer Science Engineering & Information Systems (SCORE) Vellore Institute of Technology (VIT) 632014 India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 600062 India Department of Computers Electronics and Automation Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava 720229 Romania School of Computing and Informatics University of Louisiana at Lafayette United States

Breast cancer is one of the most common causes of death among women, and early diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used machine-learning breast cancer prediction and diagnosis methods. We use synthetic minority over-sampling to handle imbalanced data in the breast cancer diagnosis dataset obtained from the Wisconsin Machine Learning Repository. We use a variety of machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Classification and Regression Tree (CART), Naive Bayes (NB), and well-known ensembles methods like Majority-Voting, eXtreme Gradient Boosting algorithm (XGBoost), and Random Forest (RF) for the breast cancer classification. The findings show that the Majority-Voting ensemble method, built on the top three classifiers (LR, SVM, and CART), outperforms all other individual classifiers and offers the highest accuracy of 99.3%. © 2023 The Author(s)

关键词： Breast cancer Class-balancing Cross-validation Diagnostic analytics Ensemble method Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Cancer Image Segmentation

A Novel Approach for Cancer Image Segmentation

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Panagiotis N. Smyrlis Konstantinos Vogklis Odysseas Tsakai Alexandros Tzallas Nikolaos Giannakeas Markos G. Tsipouras Electrical & Computer Engineering University of Western Macedonia Kozani Greece Department of Tourism Ionian University Corfu Greece Q Base R&D Science & Technology Park of Epirus Arta Greece Informatics & Telecommunications University of Ioannina Arta Greece

In this study, we present a novel approach to address the task of identifying nuclei in a diverse set of microscopy scan images containing slides from several distinct cancerous tissue types. Our approach is based on YOLOv5 algorithm. We thoroughly discuss the crucial training specifics that contributed to our outcomes. Additionally, we propose an intuitive strategy for combining multiple segmentation results to further improve the accuracy of nucleus identification. By employing this lightweight model, we successfully achieved results better or close to the state-of-the-art performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pre-trained Molecular Language Models with Random Functional Group Masking

arXiv

引用

arXiv 2024年

作者： Peng, Tianhao Li, Yuchen Li, Xuhong Bian, Jiang Xie, Zeke Sui, Ning Mumtaz, Shahid Xu, Yanwu Kong, Linghe Xiong, Haoyi Baidu Research Baidu Inc Haidian Beijing100085 China School of Computer Science and Engineering Beihang University Haidian Beijing100091 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Minhang200240 China Guangdong Guangzhou511455 China Department of Molecular and Structural Biochemistry North Carolina State University RaleighNC27695 United States Department of Computer Science Nottingham Trent University Nottingham United Kingdom Department of Applied Informatics Silesian University of Technology Gliwice Poland School of Future Technology South China University of Technology Guangdong Guangzhou510641 China

Recent advancements in computational chemistry have leveraged the power of transformer-based language models, such as MoLFormer, pre-trained using a vast amount of simplified molecular-input line-entry system (SMILES) sequences, to understand and predict molecular properties and activities, a critical step in fields like drug discovery and materials science. To further improve performance, researchers have introduced graph neural networks with graph-based molecular representations, such as GEM, incorporating the topology, geometry, 2D or even 3D structures of molecules into pre-training. While most of molecular graphs in existing studies were automatically converted from SMILES sequences, it is to assume that transformer-based language models might be able to implicitly learn structure-aware representations from SMILES sequences. In this paper, we propose MLM-FG – a SMILES-based Molecular Language Model, which randomly masking SMILES subsequences corresponding to specific molecular Functional Groups to incorporate structure information of atoms during the pre-training phase. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities. Extensive experimental evaluations across 11 benchmark classification and regression tasks in the chemical domain demonstrate the robustness and superiority of MLM-FG. Our findings reveal that MLM-FG outperforms existing pre-training models, either based on SMILES or graphs, in 9 out of the 11 downstream tasks, ranking as a close second in the remaining ones. Remarkably, MLM-FG also surpasses 3D-graph-based models, which explicitly incorporate molecular structures into their inputs, highlighting its exceptional capacity for representation learning even without explicit 3D structural information. These results indicate that MLM-FG effectively captures the nuanced language of molecules, offering promising implications for computational chemistry and related disci

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

DRL-Based Orchestration of Multi-User MISO Systems with Stacked Intelligent Metasurfaces

DRL-Based Orchestration of Multi-User MISO Systems with Stac...

引用

IEEE International Conference on Communications (ICC)

作者： Hao Liu Jiancheng An Derrick Wing Kwan Ng George C. Alexandropoulos Lu Gan School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Electrical Engineering and Telecommunications University of New South Wales Sydney NSW Australia Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Stacked intelligent metasurfaces (SIM) represents an advanced signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light. Its multi-layer structure exhibits customizable increased computational capability compared to conventional single-layer reconfigurable intelligent surfaces and metasurface lenses. In this paper, we deploy SIM to improve the performance of multi-user multiple-input single-output (MISO) wireless systems with low complexity transmit radio frequency (RF) chains. In particular, an optimization formulation for the joint design of the SIM phase shifts and the transmit power allocation is presented, which is efficiently solved via a customized deep reinforcement learning (DRL) approach that continuously observes pre-designed states of the SIM-parametrized smart wireless environment. The presented performance evaluation results showcase the proposed method's capability to effectively learn from the wireless environment while outperforming conventional precoding schemes under low transmit power conditions. Finally, a whitening process is presented to further augment the robustness of the proposed scheme.

关键词： Wireless networks Simulation MISO communication Signal processing Metasurfaces Robustness Resource management

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Wireless Sensor Networks for Mobile Sensor Nodes Considering Goodput and Depletion Metrics

Performance Evaluation of Wireless Sensor Networks for Mobil...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Leonard Barolli Tao Yang Gjergji Mino Arjan Durresi Fatos Xhafa Makoto Takizawa Department of Information and Communication Engineering Fukuoka Institute of Technology Fukuoka Japan Comput. Technol. Solution Salem NH USA Department of Computer and Information Science Indiana University-Purdue University Indianapolis Indianapolis IN USA Department of Languages and Informatics Systems Technical University of Catalonia Barcelona Spain Department of Computers and Information Science Seikei University Musashino Tokyo Japan

Sensor networks are a sensing, computing and communication infrastructure that are able to observe and respond to phenomena in the natural environment and in our physical and cyber infrastructure. In this paper, we propose a sensor network with mobile and static sensor nodes set-up for performing tasks like sensing a phenomenon or monitoring a region. We investigate how the sensor network performs in the case when the sensor nodes move. We compare the simulation results for two cases: the sensor network with stationary sensor nodes and multi mobile sensor nodes. The simulation results have shown that for the multi mobile sensors, the good put is unstable. The good put of stationary sensors is better than multi mobile sensors, but the consumed energy of multi mobile sensors is better than stationary sensors.

关键词： Mobile communication Robot sensing systems Wireless sensor networks Simulation Mobile computing Receivers Lattices

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：